diff --git a/.github/workflows/api-model-runtime-tests.yml b/.github/workflows/api-model-runtime-tests.yml new file mode 100644 index 00000000000000..a263d033e3925a --- /dev/null +++ b/.github/workflows/api-model-runtime-tests.yml @@ -0,0 +1,58 @@ +name: Run Pytest + +on: + pull_request: + branches: + - main + push: + branches: + - deploy/dev + - feat/model-runtime + +jobs: + test: + runs-on: ubuntu-latest + + env: + OPENAI_API_KEY: sk-IamNotARealKeyJustForMockTestKawaiiiiiiiiii + AZURE_OPENAI_API_BASE: https://difyai-openai.openai.azure.com + AZURE_OPENAI_API_KEY: xxxxb1707exxxxxxxxxxaaxxxxxf94 + ANTHROPIC_API_KEY: sk-ant-api11-IamNotARealKeyJustForMockTestKawaiiiiiiiiii-NotBaka-ASkksz + CHATGLM_API_BASE: http://a.abc.com:11451 + XINFERENCE_SERVER_URL: http://a.abc.com:11451 + XINFERENCE_GENERATION_MODEL_UID: generate + XINFERENCE_CHAT_MODEL_UID: chat + XINFERENCE_EMBEDDINGS_MODEL_UID: embedding + XINFERENCE_RERANK_MODEL_UID: rerank + GOOGLE_API_KEY: abcdefghijklmnopqrstuvwxyz + HUGGINGFACE_API_KEY: hf-awuwuwuwuwuwuwuwuwuwuwuwuwuwuwuwuwu + HUGGINGFACE_TEXT_GEN_ENDPOINT_URL: a + HUGGINGFACE_TEXT2TEXT_GEN_ENDPOINT_URL: b + HUGGINGFACE_EMBEDDINGS_ENDPOINT_URL: c + MOCK_SWITCH: true + + + steps: + - name: Checkout code + uses: actions/checkout@v2 + + - name: Set up Python + uses: actions/setup-python@v2 + with: + python-version: '3.10' + + - name: Cache pip dependencies + uses: actions/cache@v2 + with: + path: ~/.cache/pip + key: ${{ runner.os }}-pip-${{ hashFiles('api/requirements.txt') }} + restore-keys: ${{ runner.os }}-pip- + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install pytest + pip install -r api/requirements.txt + + - name: Run pytest + run: pytest api/tests/integration_tests/model_runtime/anthropic api/tests/integration_tests/model_runtime/azure_openai api/tests/integration_tests/model_runtime/openai api/tests/integration_tests/model_runtime/chatglm api/tests/integration_tests/model_runtime/google api/tests/integration_tests/model_runtime/xinference api/tests/integration_tests/model_runtime/huggingface_hub/test_llm.py diff --git a/.github/workflows/api-unit-tests.yml b/.github/workflows/api-unit-tests.yml deleted file mode 100644 index 6e795c953f9104..00000000000000 --- a/.github/workflows/api-unit-tests.yml +++ /dev/null @@ -1,38 +0,0 @@ -name: Run Pytest - -on: - pull_request: - branches: - - main - push: - branches: - - deploy/dev - -jobs: - test: - runs-on: ubuntu-latest - - steps: - - name: Checkout code - uses: actions/checkout@v2 - - - name: Set up Python - uses: actions/setup-python@v2 - with: - python-version: '3.10' - - - name: Cache pip dependencies - uses: actions/cache@v2 - with: - path: ~/.cache/pip - key: ${{ runner.os }}-pip-${{ hashFiles('api/requirements.txt') }} - restore-keys: ${{ runner.os }}-pip- - - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install pytest - pip install -r api/requirements.txt - - - name: Run pytest - run: pytest api/tests/unit_tests diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 9321d2807fbdd1..9db7fbfe9c1ef7 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -55,6 +55,11 @@ Did you have an issue, like a merge conflict, or don't know how to open a pull r Stuck somewhere? Have any questions? Join the [Discord Community Server](https://discord.gg/j3XRWSPBf7). We are here to help! + +### Provider Integrations +If you see a model provider not yet supported by Dify that you'd like to use, follow these [steps](api/core/model_runtime/README.md) to submit a PR. + + ### i18n (Internationalization) Support We are looking for contributors to help with translations in other languages. If you are interested in helping, please join the [Discord Community Server](https://discord.gg/AhzKf7dNgk) and let us know. diff --git a/api/.vscode/launch.json b/api/.vscode/launch.json index e3c1f797c61601..cb718f9f60abcf 100644 --- a/api/.vscode/launch.json +++ b/api/.vscode/launch.json @@ -4,6 +4,21 @@ // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 "version": "0.2.0", "configurations": [ + { + "name": "Python: Celery", + "type": "python", + "request": "launch", + "module": "celery", + "justMyCode": true, + "args": ["-A", "app.celery", "worker", "-P", "gevent", "-c", "1", "--loglevel", "info", "-Q", "dataset,generation,mail"], + "envFile": "${workspaceFolder}/.env", + "env": { + "FLASK_APP": "app.py", + "FLASK_DEBUG": "1", + "GEVENT_SUPPORT": "True" + }, + "console": "integratedTerminal" + }, { "name": "Python: Flask", "type": "python", diff --git a/api/Dockerfile b/api/Dockerfile index 12ce05ac03b981..c18ce73dfa507d 100644 --- a/api/Dockerfile +++ b/api/Dockerfile @@ -34,9 +34,6 @@ RUN apt-get update \ COPY --from=base /pkg /usr/local COPY . /app/api/ -RUN python -c "from transformers import GPT2TokenizerFast; GPT2TokenizerFast.from_pretrained('gpt2')" -ENV TRANSFORMERS_OFFLINE true - COPY docker/entrypoint.sh /entrypoint.sh RUN chmod +x /entrypoint.sh diff --git a/api/app.py b/api/app.py index 487cd45fb4f0fe..4e5fd1f24bd368 100644 --- a/api/app.py +++ b/api/app.py @@ -6,9 +6,12 @@ if not os.environ.get("DEBUG") or os.environ.get("DEBUG").lower() != 'true': from gevent import monkey monkey.patch_all() - if os.environ.get("VECTOR_STORE") == 'milvus': - import grpc.experimental.gevent - grpc.experimental.gevent.init_gevent() + # if os.environ.get("VECTOR_STORE") == 'milvus': + import grpc.experimental.gevent + grpc.experimental.gevent.init_gevent() + + import langchain + langchain.verbose = True import time import logging @@ -18,9 +21,8 @@ from flask import Flask, request, Response from flask_cors import CORS -from core.model_providers.providers import hosted from extensions import ext_celery, ext_sentry, ext_redis, ext_login, ext_migrate, \ - ext_database, ext_storage, ext_mail, ext_code_based_extension + ext_database, ext_storage, ext_mail, ext_code_based_extension, ext_hosting_provider from extensions.ext_database import db from extensions.ext_login import login_manager @@ -79,8 +81,6 @@ def create_app(test_config=None) -> Flask: register_blueprints(app) register_commands(app) - hosted.init_app(app) - return app @@ -95,6 +95,7 @@ def initialize_extensions(app): ext_celery.init_app(app) ext_login.init_app(app) ext_mail.init_app(app) + ext_hosting_provider.init_app(app) ext_sentry.init_app(app) @@ -105,13 +106,18 @@ def load_user_from_request(request_from_flask_login): if request.blueprint == 'console': # Check if the user_id contains a dot, indicating the old format auth_header = request.headers.get('Authorization', '') - if ' ' not in auth_header: - raise Unauthorized('Invalid Authorization header format. Expected \'Bearer \' format.') - auth_scheme, auth_token = auth_header.split(None, 1) - auth_scheme = auth_scheme.lower() - if auth_scheme != 'bearer': - raise Unauthorized('Invalid Authorization header format. Expected \'Bearer \' format.') - + if not auth_header: + auth_token = request.args.get('_token') + if not auth_token: + raise Unauthorized('Invalid Authorization token.') + else: + if ' ' not in auth_header: + raise Unauthorized('Invalid Authorization header format. Expected \'Bearer \' format.') + auth_scheme, auth_token = auth_header.split(None, 1) + auth_scheme = auth_scheme.lower() + if auth_scheme != 'bearer': + raise Unauthorized('Invalid Authorization header format. Expected \'Bearer \' format.') + decoded = PassportService().verify(auth_token) user_id = decoded.get('user_id') diff --git a/api/commands.py b/api/commands.py index deaa2e1675b7bc..9e4681b42f530b 100644 --- a/api/commands.py +++ b/api/commands.py @@ -12,16 +12,12 @@ from qdrant_client.http.models import TextIndexParams, TextIndexType, TokenizerType from tqdm import tqdm from flask import current_app, Flask -from langchain.embeddings import OpenAIEmbeddings from werkzeug.exceptions import NotFound from core.embedding.cached_embedding import CacheEmbedding from core.index.index import IndexBuilder -from core.model_providers.model_factory import ModelFactory -from core.model_providers.models.embedding.openai_embedding import OpenAIEmbedding -from core.model_providers.models.entity.model_params import ModelType -from core.model_providers.providers.hosted import hosted_model_providers -from core.model_providers.providers.openai_provider import OpenAIProvider +from core.model_manager import ModelManager +from core.model_runtime.entities.model_entities import ModelType from libs.password import password_pattern, valid_password, hash_password from libs.helper import email as email_validate from extensions.ext_database import db @@ -327,6 +323,8 @@ def create_qdrant_indexes(): except NotFound: break + model_manager = ModelManager() + page += 1 for dataset in datasets: if dataset.index_struct_dict: @@ -334,19 +332,23 @@ def create_qdrant_indexes(): try: click.echo('Create dataset qdrant index: {}'.format(dataset.id)) try: - embedding_model = ModelFactory.get_embedding_model( + embedding_model = model_manager.get_model_instance( tenant_id=dataset.tenant_id, - model_provider_name=dataset.embedding_model_provider, - model_name=dataset.embedding_model + provider=dataset.embedding_model_provider, + model_type=ModelType.TEXT_EMBEDDING, + model=dataset.embedding_model + ) except Exception: try: - embedding_model = ModelFactory.get_embedding_model( - tenant_id=dataset.tenant_id + embedding_model = model_manager.get_default_model_instance( + tenant_id=dataset.tenant_id, + model_type=ModelType.TEXT_EMBEDDING, ) - dataset.embedding_model = embedding_model.name - dataset.embedding_model_provider = embedding_model.model_provider.provider_name + dataset.embedding_model = embedding_model.model + dataset.embedding_model_provider = embedding_model.provider except Exception: + provider = Provider( id='provider_id', tenant_id=dataset.tenant_id, diff --git a/api/config.py b/api/config.py index 2f0ec1cdb74eb1..df8d24de373068 100644 --- a/api/config.py +++ b/api/config.py @@ -87,7 +87,7 @@ def __init__(self): # ------------------------ # General Configurations. # ------------------------ - self.CURRENT_VERSION = "0.3.34" + self.CURRENT_VERSION = "0.4.0" self.COMMIT_SHA = get_env('COMMIT_SHA') self.EDITION = "SELF_HOSTED" self.DEPLOY_ENV = get_env('DEPLOY_ENV') diff --git a/api/controllers/console/__init__.py b/api/controllers/console/__init__.py index 6fa896c3e82190..95a9fe3a089949 100644 --- a/api/controllers/console/__init__.py +++ b/api/controllers/console/__init__.py @@ -18,7 +18,7 @@ from .datasets import datasets, datasets_document, datasets_segments, file, hit_testing, data_source # Import workspace controllers -from .workspace import workspace, members, providers, model_providers, account, tool_providers, models +from .workspace import workspace, members, model_providers, account, tool_providers, models # Import explore controllers from .explore import installed_app, recommended_app, completion, conversation, message, parameter, saved_message, audio diff --git a/api/controllers/console/app/app.py b/api/controllers/console/app/app.py index ff41c929ac7894..c5d24f04997755 100644 --- a/api/controllers/console/app/app.py +++ b/api/controllers/console/app/app.py @@ -4,6 +4,10 @@ from datetime import datetime from flask_login import current_user + +from core.model_manager import ModelManager +from core.model_runtime.entities.model_entities import ModelType +from core.provider_manager import ProviderManager from libs.login import login_required from flask_restful import Resource, reqparse, marshal_with, abort, inputs from werkzeug.exceptions import Forbidden @@ -13,9 +17,7 @@ from controllers.console.app.error import AppNotFoundError, ProviderNotInitializeError from controllers.console.setup import setup_required from controllers.console.wraps import account_initialization_required, cloud_edition_billing_resource_check -from core.model_providers.error import ProviderTokenNotInitError, LLMBadRequestError -from core.model_providers.model_factory import ModelFactory -from core.model_providers.model_provider_factory import ModelProviderFactory +from core.errors.error import ProviderTokenNotInitError, LLMBadRequestError from events.app_event import app_was_created, app_was_deleted from fields.app_fields import app_pagination_fields, app_detail_fields, template_list_fields, \ app_detail_fields_with_site @@ -73,39 +75,41 @@ def post(self): raise Forbidden() try: - default_model = ModelFactory.get_text_generation_model( - tenant_id=current_user.current_tenant_id + provider_manager = ProviderManager() + default_model_entity = provider_manager.get_default_model( + tenant_id=current_user.current_tenant_id, + model_type=ModelType.LLM ) except (ProviderTokenNotInitError, LLMBadRequestError): - default_model = None + default_model_entity = None except Exception as e: logging.exception(e) - default_model = None + default_model_entity = None if args['model_config'] is not None: # validate config model_config_dict = args['model_config'] # get model provider - model_provider = ModelProviderFactory.get_preferred_model_provider( - current_user.current_tenant_id, - model_config_dict["model"]["provider"] + model_manager = ModelManager() + model_instance = model_manager.get_default_model_instance( + tenant_id=current_user.current_tenant_id, + model_type=ModelType.LLM ) - if not model_provider: - if not default_model: - raise ProviderNotInitializeError( - f"No Default System Reasoning Model available. Please configure " - f"in the Settings -> Model Provider.") - else: - model_config_dict["model"]["provider"] = default_model.model_provider.provider_name - model_config_dict["model"]["name"] = default_model.name + if not model_instance: + raise ProviderNotInitializeError( + f"No Default System Reasoning Model available. Please configure " + f"in the Settings -> Model Provider.") + else: + model_config_dict["model"]["provider"] = model_instance.provider + model_config_dict["model"]["name"] = model_instance.model model_configuration = AppModelConfigService.validate_configuration( tenant_id=current_user.current_tenant_id, account=current_user, config=model_config_dict, - mode=args['mode'] + app_mode=args['mode'] ) app = App( @@ -129,21 +133,27 @@ def post(self): app_model_config = AppModelConfig(**model_config_template['model_config']) # get model provider - model_provider = ModelProviderFactory.get_preferred_model_provider( - current_user.current_tenant_id, - app_model_config.model_dict["provider"] - ) - - if not model_provider: - if not default_model: - raise ProviderNotInitializeError( - f"No Default System Reasoning Model available. Please configure " - f"in the Settings -> Model Provider.") - else: - model_dict = app_model_config.model_dict - model_dict['provider'] = default_model.model_provider.provider_name - model_dict['name'] = default_model.name - app_model_config.model = json.dumps(model_dict) + model_manager = ModelManager() + + try: + model_instance = model_manager.get_default_model_instance( + tenant_id=current_user.current_tenant_id, + model_type=ModelType.LLM + ) + except ProviderTokenNotInitError: + raise ProviderNotInitializeError( + f"No Default System Reasoning Model available. Please configure " + f"in the Settings -> Model Provider.") + + if not model_instance: + raise ProviderNotInitializeError( + f"No Default System Reasoning Model available. Please configure " + f"in the Settings -> Model Provider.") + else: + model_dict = app_model_config.model_dict + model_dict['provider'] = model_instance.provider + model_dict['name'] = model_instance.model + app_model_config.model = json.dumps(model_dict) app.name = args['name'] app.mode = args['mode'] diff --git a/api/controllers/console/app/audio.py b/api/controllers/console/app/audio.py index 628b586d5a7da7..7829374238249d 100644 --- a/api/controllers/console/app/audio.py +++ b/api/controllers/console/app/audio.py @@ -2,6 +2,8 @@ import logging from flask import request + +from core.model_runtime.errors.invoke import InvokeError from libs.login import login_required from werkzeug.exceptions import InternalServerError @@ -14,8 +16,7 @@ UnsupportedAudioTypeError, ProviderNotSupportSpeechToTextError from controllers.console.setup import setup_required from controllers.console.wraps import account_initialization_required -from core.model_providers.error import LLMBadRequestError, LLMAPIUnavailableError, LLMAuthorizationError, LLMAPIConnectionError, \ - LLMRateLimitError, ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError +from core.errors.error import ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError from flask_restful import Resource from services.audio_service import AudioService from services.errors.audio import NoAudioUploadedServiceError, AudioTooLargeServiceError, \ @@ -56,8 +57,7 @@ def post(self, app_id): raise ProviderQuotaExceededError() except ModelCurrentlyNotSupportError: raise ProviderModelCurrentlyNotSupportError() - except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, - LLMRateLimitError, LLMAuthorizationError) as e: + except InvokeError as e: raise CompletionRequestError(str(e)) except ValueError as e: raise e diff --git a/api/controllers/console/app/completion.py b/api/controllers/console/app/completion.py index b62c751a0dbdb5..b1deb100da03ba 100644 --- a/api/controllers/console/app/completion.py +++ b/api/controllers/console/app/completion.py @@ -5,6 +5,10 @@ import flask_login from flask import Response, stream_with_context + +from core.application_queue_manager import ApplicationQueueManager +from core.entities.application_entities import InvokeFrom +from core.model_runtime.errors.invoke import InvokeError from libs.login import login_required from werkzeug.exceptions import InternalServerError, NotFound @@ -16,9 +20,7 @@ ProviderModelCurrentlyNotSupportError from controllers.console.setup import setup_required from controllers.console.wraps import account_initialization_required -from core.conversation_message_task import PubHandler -from core.model_providers.error import LLMBadRequestError, LLMAPIUnavailableError, LLMAuthorizationError, LLMAPIConnectionError, \ - LLMRateLimitError, ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError +from core.errors.error import ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError from libs.helper import uuid_value from flask_restful import Resource, reqparse @@ -56,7 +58,7 @@ def post(self, app_id): app_model=app_model, user=account, args=args, - from_source='console', + invoke_from=InvokeFrom.DEBUGGER, streaming=streaming, is_model_config_override=True ) @@ -75,8 +77,7 @@ def post(self, app_id): raise ProviderQuotaExceededError() except ModelCurrentlyNotSupportError: raise ProviderModelCurrentlyNotSupportError() - except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, - LLMRateLimitError, LLMAuthorizationError) as e: + except InvokeError as e: raise CompletionRequestError(str(e)) except ValueError as e: raise e @@ -97,7 +98,7 @@ def post(self, app_id, task_id): account = flask_login.current_user - PubHandler.stop(account, task_id) + ApplicationQueueManager.set_stop_flag(task_id, InvokeFrom.DEBUGGER, account.id) return {'result': 'success'}, 200 @@ -132,7 +133,7 @@ def post(self, app_id): app_model=app_model, user=account, args=args, - from_source='console', + invoke_from=InvokeFrom.DEBUGGER, streaming=streaming, is_model_config_override=True ) @@ -151,8 +152,7 @@ def post(self, app_id): raise ProviderQuotaExceededError() except ModelCurrentlyNotSupportError: raise ProviderModelCurrentlyNotSupportError() - except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, - LLMRateLimitError, LLMAuthorizationError) as e: + except InvokeError as e: raise CompletionRequestError(str(e)) except ValueError as e: raise e @@ -182,9 +182,8 @@ def generate() -> Generator: yield "data: " + json.dumps(api.handle_error(ProviderQuotaExceededError()).get_json()) + "\n\n" except ModelCurrentlyNotSupportError: yield "data: " + json.dumps(api.handle_error(ProviderModelCurrentlyNotSupportError()).get_json()) + "\n\n" - except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, - LLMRateLimitError, LLMAuthorizationError) as e: - yield "data: " + json.dumps(api.handle_error(CompletionRequestError(str(e))).get_json()) + "\n\n" + except InvokeError as e: + yield "data: " + json.dumps(api.handle_error(CompletionRequestError(e.description)).get_json()) + "\n\n" except ValueError as e: yield "data: " + json.dumps(api.handle_error(e).get_json()) + "\n\n" except Exception: @@ -207,7 +206,7 @@ def post(self, app_id, task_id): account = flask_login.current_user - PubHandler.stop(account, task_id) + ApplicationQueueManager.set_stop_flag(task_id, InvokeFrom.DEBUGGER, account.id) return {'result': 'success'}, 200 diff --git a/api/controllers/console/app/generator.py b/api/controllers/console/app/generator.py index f454426ab4b29c..90cb0a836756cf 100644 --- a/api/controllers/console/app/generator.py +++ b/api/controllers/console/app/generator.py @@ -1,4 +1,6 @@ from flask_login import current_user + +from core.model_runtime.errors.invoke import InvokeError from libs.login import login_required from flask_restful import Resource, reqparse @@ -8,8 +10,7 @@ from controllers.console.setup import setup_required from controllers.console.wraps import account_initialization_required from core.generator.llm_generator import LLMGenerator -from core.model_providers.error import ProviderTokenNotInitError, QuotaExceededError, LLMBadRequestError, LLMAPIConnectionError, \ - LLMAPIUnavailableError, LLMRateLimitError, LLMAuthorizationError, ModelCurrentlyNotSupportError +from core.errors.error import ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError class RuleGenerateApi(Resource): @@ -36,8 +37,7 @@ def post(self): raise ProviderQuotaExceededError() except ModelCurrentlyNotSupportError: raise ProviderModelCurrentlyNotSupportError() - except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, - LLMRateLimitError, LLMAuthorizationError) as e: + except InvokeError as e: raise CompletionRequestError(str(e)) return rules diff --git a/api/controllers/console/app/message.py b/api/controllers/console/app/message.py index b26287fd4bd27d..fb13f4f68e565b 100644 --- a/api/controllers/console/app/message.py +++ b/api/controllers/console/app/message.py @@ -14,8 +14,9 @@ AppMoreLikeThisDisabledError, ProviderQuotaExceededError, ProviderModelCurrentlyNotSupportError from controllers.console.setup import setup_required from controllers.console.wraps import account_initialization_required, cloud_edition_billing_resource_check -from core.model_providers.error import LLMRateLimitError, LLMBadRequestError, LLMAuthorizationError, LLMAPIConnectionError, \ - ProviderTokenNotInitError, LLMAPIUnavailableError, QuotaExceededError, ModelCurrentlyNotSupportError +from core.entities.application_entities import InvokeFrom +from core.errors.error import ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError +from core.model_runtime.errors.invoke import InvokeError from libs.login import login_required from fields.conversation_fields import message_detail_fields, annotation_fields from libs.helper import uuid_value @@ -208,7 +209,13 @@ def get(self, app_id, message_id): app_model = _get_app(app_id, 'completion') try: - response = CompletionService.generate_more_like_this(app_model, current_user, message_id, streaming) + response = CompletionService.generate_more_like_this( + app_model=app_model, + user=current_user, + message_id=message_id, + invoke_from=InvokeFrom.DEBUGGER, + streaming=streaming + ) return compact_response(response) except MessageNotExistsError: raise NotFound("Message Not Exists.") @@ -220,8 +227,7 @@ def get(self, app_id, message_id): raise ProviderQuotaExceededError() except ModelCurrentlyNotSupportError: raise ProviderModelCurrentlyNotSupportError() - except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, - LLMRateLimitError, LLMAuthorizationError) as e: + except InvokeError as e: raise CompletionRequestError(str(e)) except ValueError as e: raise e @@ -249,8 +255,7 @@ def generate() -> Generator: except ModelCurrentlyNotSupportError: yield "data: " + json.dumps( api.handle_error(ProviderModelCurrentlyNotSupportError()).get_json()) + "\n\n" - except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, - LLMRateLimitError, LLMAuthorizationError) as e: + except InvokeError as e: yield "data: " + json.dumps(api.handle_error(CompletionRequestError(str(e))).get_json()) + "\n\n" except ValueError as e: yield "data: " + json.dumps(api.handle_error(e).get_json()) + "\n\n" @@ -290,8 +295,7 @@ def get(self, app_id, message_id): raise ProviderQuotaExceededError() except ModelCurrentlyNotSupportError: raise ProviderModelCurrentlyNotSupportError() - except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, - LLMRateLimitError, LLMAuthorizationError) as e: + except InvokeError as e: raise CompletionRequestError(str(e)) except Exception: logging.exception("internal server error.") diff --git a/api/controllers/console/app/model_config.py b/api/controllers/console/app/model_config.py index 5103fddca3fe44..7ed92a65312431 100644 --- a/api/controllers/console/app/model_config.py +++ b/api/controllers/console/app/model_config.py @@ -31,7 +31,7 @@ def post(self, app_id): tenant_id=current_user.current_tenant_id, account=current_user, config=request.json, - mode=app.mode + app_mode=app.mode ) new_app_model_config = AppModelConfig( diff --git a/api/controllers/console/datasets/datasets.py b/api/controllers/console/datasets/datasets.py index b4323f18ec2d23..ac793746601f1b 100644 --- a/api/controllers/console/datasets/datasets.py +++ b/api/controllers/console/datasets/datasets.py @@ -4,6 +4,8 @@ from flask_login import current_user from controllers.console.apikey import api_key_list, api_key_fields +from core.model_runtime.entities.model_entities import ModelType +from core.provider_manager import ProviderManager from libs.login import login_required from flask_restful import Resource, reqparse, marshal, marshal_with from werkzeug.exceptions import NotFound, Forbidden @@ -14,8 +16,7 @@ from controllers.console.setup import setup_required from controllers.console.wraps import account_initialization_required from core.indexing_runner import IndexingRunner -from core.model_providers.error import LLMBadRequestError, ProviderTokenNotInitError -from core.model_providers.models.entity.model_params import ModelType +from core.errors.error import LLMBadRequestError, ProviderTokenNotInitError from fields.app_fields import related_app_list from fields.dataset_fields import dataset_detail_fields, dataset_query_detail_fields from fields.document_fields import document_status_fields @@ -23,7 +24,6 @@ from models.dataset import DocumentSegment, Document from models.model import UploadFile, ApiToken from services.dataset_service import DatasetService, DocumentService -from services.provider_service import ProviderService def _validate_name(name): @@ -55,16 +55,20 @@ def get(self): current_user.current_tenant_id, current_user) # check embedding setting - provider_service = ProviderService() - valid_model_list = provider_service.get_valid_model_list(current_user.current_tenant_id, - ModelType.EMBEDDINGS.value) - # if len(valid_model_list) == 0: - # raise ProviderNotInitializeError( - # f"No Embedding Model available. Please configure a valid provider " - # f"in the Settings -> Model Provider.") + provider_manager = ProviderManager() + configurations = provider_manager.get_configurations( + tenant_id=current_user.current_tenant_id + ) + + embedding_models = configurations.get_models( + model_type=ModelType.TEXT_EMBEDDING, + only_active=True + ) + model_names = [] - for valid_model in valid_model_list: - model_names.append(f"{valid_model['model_name']}:{valid_model['model_provider']['provider_name']}") + for embedding_model in embedding_models: + model_names.append(f"{embedding_model.model}:{embedding_model.provider.provider}") + data = marshal(datasets, dataset_detail_fields) for item in data: if item['indexing_technique'] == 'high_quality': @@ -75,6 +79,7 @@ def get(self): item['embedding_available'] = False else: item['embedding_available'] = True + response = { 'data': data, 'has_more': len(datasets) == limit, @@ -130,13 +135,20 @@ def get(self, dataset_id): raise Forbidden(str(e)) data = marshal(dataset, dataset_detail_fields) # check embedding setting - provider_service = ProviderService() - # get valid model list - valid_model_list = provider_service.get_valid_model_list(current_user.current_tenant_id, - ModelType.EMBEDDINGS.value) + provider_manager = ProviderManager() + configurations = provider_manager.get_configurations( + tenant_id=current_user.current_tenant_id + ) + + embedding_models = configurations.get_models( + model_type=ModelType.TEXT_EMBEDDING, + only_active=True + ) + model_names = [] - for valid_model in valid_model_list: - model_names.append(f"{valid_model['model_name']}:{valid_model['model_provider']['provider_name']}") + for embedding_model in embedding_models: + model_names.append(f"{embedding_model.model}:{embedding_model.provider.provider}") + if data['indexing_technique'] == 'high_quality': item_model = f"{data['embedding_model']}:{data['embedding_model_provider']}" if item_model in model_names: diff --git a/api/controllers/console/datasets/datasets_document.py b/api/controllers/console/datasets/datasets_document.py index 128a76f18ac416..1b501037ab1eba 100644 --- a/api/controllers/console/datasets/datasets_document.py +++ b/api/controllers/console/datasets/datasets_document.py @@ -2,8 +2,12 @@ from datetime import datetime from typing import List -from flask import request, current_app +from flask import request from flask_login import current_user + +from core.model_manager import ModelManager +from core.model_runtime.entities.model_entities import ModelType +from core.model_runtime.errors.invoke import InvokeAuthorizationError from libs.login import login_required from flask_restful import Resource, fields, marshal, marshal_with, reqparse from sqlalchemy import desc, asc @@ -18,9 +22,8 @@ from controllers.console.setup import setup_required from controllers.console.wraps import account_initialization_required, cloud_edition_billing_resource_check from core.indexing_runner import IndexingRunner -from core.model_providers.error import ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError, \ +from core.errors.error import ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError, \ LLMBadRequestError -from core.model_providers.model_factory import ModelFactory from extensions.ext_redis import redis_client from fields.document_fields import document_with_segments_fields, document_fields, \ dataset_and_document_fields, document_status_fields @@ -272,10 +275,12 @@ def post(self): args = parser.parse_args() if args['indexing_technique'] == 'high_quality': try: - ModelFactory.get_embedding_model( - tenant_id=current_user.current_tenant_id + model_manager = ModelManager() + model_manager.get_default_model_instance( + tenant_id=current_user.current_tenant_id, + model_type=ModelType.TEXT_EMBEDDING ) - except LLMBadRequestError: + except InvokeAuthorizationError: raise ProviderNotInitializeError( f"No Embedding Model available. Please configure a valid provider " f"in the Settings -> Model Provider.") diff --git a/api/controllers/console/datasets/datasets_segments.py b/api/controllers/console/datasets/datasets_segments.py index 6051d12999f5fa..24cf2433e7d076 100644 --- a/api/controllers/console/datasets/datasets_segments.py +++ b/api/controllers/console/datasets/datasets_segments.py @@ -12,8 +12,9 @@ from controllers.console.datasets.error import InvalidActionError, NoFileUploadedError, TooManyFilesError from controllers.console.setup import setup_required from controllers.console.wraps import account_initialization_required, cloud_edition_billing_resource_check -from core.model_providers.error import LLMBadRequestError, ProviderTokenNotInitError -from core.model_providers.model_factory import ModelFactory +from core.errors.error import LLMBadRequestError, ProviderTokenNotInitError +from core.model_manager import ModelManager +from core.model_runtime.entities.model_entities import ModelType from libs.login import login_required from extensions.ext_database import db from extensions.ext_redis import redis_client @@ -133,10 +134,12 @@ def patch(self, dataset_id, segment_id, action): if dataset.indexing_technique == 'high_quality': # check embedding model setting try: - ModelFactory.get_embedding_model( + model_manager = ModelManager() + model_manager.get_model_instance( tenant_id=current_user.current_tenant_id, - model_provider_name=dataset.embedding_model_provider, - model_name=dataset.embedding_model + provider=dataset.embedding_model_provider, + model_type=ModelType.TEXT_EMBEDDING, + model=dataset.embedding_model ) except LLMBadRequestError: raise ProviderNotInitializeError( @@ -219,10 +222,12 @@ def post(self, dataset_id, document_id): # check embedding model setting if dataset.indexing_technique == 'high_quality': try: - ModelFactory.get_embedding_model( + model_manager = ModelManager() + model_manager.get_model_instance( tenant_id=current_user.current_tenant_id, - model_provider_name=dataset.embedding_model_provider, - model_name=dataset.embedding_model + provider=dataset.embedding_model_provider, + model_type=ModelType.TEXT_EMBEDDING, + model=dataset.embedding_model ) except LLMBadRequestError: raise ProviderNotInitializeError( @@ -269,10 +274,12 @@ def patch(self, dataset_id, document_id, segment_id): if dataset.indexing_technique == 'high_quality': # check embedding model setting try: - ModelFactory.get_embedding_model( + model_manager = ModelManager() + model_manager.get_model_instance( tenant_id=current_user.current_tenant_id, - model_provider_name=dataset.embedding_model_provider, - model_name=dataset.embedding_model + provider=dataset.embedding_model_provider, + model_type=ModelType.TEXT_EMBEDDING, + model=dataset.embedding_model ) except LLMBadRequestError: raise ProviderNotInitializeError( diff --git a/api/controllers/console/datasets/hit_testing.py b/api/controllers/console/datasets/hit_testing.py index ce4897100d9df9..79c7878717d170 100644 --- a/api/controllers/console/datasets/hit_testing.py +++ b/api/controllers/console/datasets/hit_testing.py @@ -12,7 +12,7 @@ from controllers.console.datasets.error import HighQualityDatasetOnlyError, DatasetNotInitializedError from controllers.console.setup import setup_required from controllers.console.wraps import account_initialization_required -from core.model_providers.error import ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError, \ +from core.errors.error import ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError, \ LLMBadRequestError from fields.hit_testing_fields import hit_testing_record_fields from services.dataset_service import DatasetService diff --git a/api/controllers/console/explore/audio.py b/api/controllers/console/explore/audio.py index 50ddfac98f2cb8..12a5ff518c24be 100644 --- a/api/controllers/console/explore/audio.py +++ b/api/controllers/console/explore/audio.py @@ -11,8 +11,8 @@ NoAudioUploadedError, AudioTooLargeError, \ UnsupportedAudioTypeError, ProviderNotSupportSpeechToTextError from controllers.console.explore.wraps import InstalledAppResource -from core.model_providers.error import LLMBadRequestError, LLMAPIUnavailableError, LLMAuthorizationError, LLMAPIConnectionError, \ - LLMRateLimitError, ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError +from core.errors.error import ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError +from core.model_runtime.errors.invoke import InvokeError from services.audio_service import AudioService from services.errors.audio import NoAudioUploadedServiceError, AudioTooLargeServiceError, \ UnsupportedAudioTypeServiceError, ProviderNotSupportSpeechToTextServiceError @@ -53,8 +53,7 @@ def post(self, installed_app): raise ProviderQuotaExceededError() except ModelCurrentlyNotSupportError: raise ProviderModelCurrentlyNotSupportError() - except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, - LLMRateLimitError, LLMAuthorizationError) as e: + except InvokeError as e: raise CompletionRequestError(str(e)) except ValueError as e: raise e diff --git a/api/controllers/console/explore/completion.py b/api/controllers/console/explore/completion.py index f1527bf63b5dcb..8d554720d41884 100644 --- a/api/controllers/console/explore/completion.py +++ b/api/controllers/console/explore/completion.py @@ -15,9 +15,10 @@ ProviderQuotaExceededError, ProviderModelCurrentlyNotSupportError, CompletionRequestError from controllers.console.explore.error import NotCompletionAppError, NotChatAppError from controllers.console.explore.wraps import InstalledAppResource -from core.conversation_message_task import PubHandler -from core.model_providers.error import LLMBadRequestError, LLMAPIUnavailableError, LLMAuthorizationError, LLMAPIConnectionError, \ - LLMRateLimitError, ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError +from core.application_queue_manager import ApplicationQueueManager +from core.entities.application_entities import InvokeFrom +from core.errors.error import ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError +from core.model_runtime.errors.invoke import InvokeError from extensions.ext_database import db from libs.helper import uuid_value from services.completion_service import CompletionService @@ -50,7 +51,7 @@ def post(self, installed_app): app_model=app_model, user=current_user, args=args, - from_source='console', + invoke_from=InvokeFrom.EXPLORE, streaming=streaming ) @@ -68,8 +69,7 @@ def post(self, installed_app): raise ProviderQuotaExceededError() except ModelCurrentlyNotSupportError: raise ProviderModelCurrentlyNotSupportError() - except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, - LLMRateLimitError, LLMAuthorizationError) as e: + except InvokeError as e: raise CompletionRequestError(str(e)) except ValueError as e: raise e @@ -84,7 +84,7 @@ def post(self, installed_app, task_id): if app_model.mode != 'completion': raise NotCompletionAppError() - PubHandler.stop(current_user, task_id) + ApplicationQueueManager.set_stop_flag(task_id, InvokeFrom.EXPLORE, current_user.id) return {'result': 'success'}, 200 @@ -115,7 +115,7 @@ def post(self, installed_app): app_model=app_model, user=current_user, args=args, - from_source='console', + invoke_from=InvokeFrom.EXPLORE, streaming=streaming ) @@ -133,8 +133,7 @@ def post(self, installed_app): raise ProviderQuotaExceededError() except ModelCurrentlyNotSupportError: raise ProviderModelCurrentlyNotSupportError() - except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, - LLMRateLimitError, LLMAuthorizationError) as e: + except InvokeError as e: raise CompletionRequestError(str(e)) except ValueError as e: raise e @@ -149,7 +148,7 @@ def post(self, installed_app, task_id): if app_model.mode != 'chat': raise NotChatAppError() - PubHandler.stop(current_user, task_id) + ApplicationQueueManager.set_stop_flag(task_id, InvokeFrom.EXPLORE, current_user.id) return {'result': 'success'}, 200 @@ -175,8 +174,7 @@ def generate() -> Generator: yield "data: " + json.dumps(api.handle_error(ProviderQuotaExceededError()).get_json()) + "\n\n" except ModelCurrentlyNotSupportError: yield "data: " + json.dumps(api.handle_error(ProviderModelCurrentlyNotSupportError()).get_json()) + "\n\n" - except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, - LLMRateLimitError, LLMAuthorizationError) as e: + except InvokeError as e: yield "data: " + json.dumps(api.handle_error(CompletionRequestError(str(e))).get_json()) + "\n\n" except ValueError as e: yield "data: " + json.dumps(api.handle_error(e).get_json()) + "\n\n" diff --git a/api/controllers/console/explore/message.py b/api/controllers/console/explore/message.py index 5d07477b5eeb14..13d3696e1266ff 100644 --- a/api/controllers/console/explore/message.py +++ b/api/controllers/console/explore/message.py @@ -5,7 +5,7 @@ from flask import stream_with_context, Response from flask_login import current_user -from flask_restful import reqparse, fields, marshal_with +from flask_restful import reqparse, marshal_with from flask_restful.inputs import int_range from werkzeug.exceptions import NotFound, InternalServerError @@ -13,12 +13,14 @@ from controllers.console import api from controllers.console.app.error import AppMoreLikeThisDisabledError, ProviderNotInitializeError, \ ProviderQuotaExceededError, ProviderModelCurrentlyNotSupportError, CompletionRequestError -from controllers.console.explore.error import NotCompletionAppError, AppSuggestedQuestionsAfterAnswerDisabledError +from controllers.console.explore.error import NotCompletionAppError, AppSuggestedQuestionsAfterAnswerDisabledError, \ + NotChatAppError from controllers.console.explore.wraps import InstalledAppResource -from core.model_providers.error import LLMRateLimitError, LLMBadRequestError, LLMAuthorizationError, LLMAPIConnectionError, \ - ProviderTokenNotInitError, LLMAPIUnavailableError, QuotaExceededError, ModelCurrentlyNotSupportError +from core.entities.application_entities import InvokeFrom +from core.errors.error import ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError +from core.model_runtime.errors.invoke import InvokeError from fields.message_fields import message_infinite_scroll_pagination_fields -from libs.helper import uuid_value, TimestampField +from libs.helper import uuid_value from services.completion_service import CompletionService from services.errors.app import MoreLikeThisDisabledError from services.errors.conversation import ConversationNotExistsError @@ -83,7 +85,13 @@ def get(self, installed_app, message_id): streaming = args['response_mode'] == 'streaming' try: - response = CompletionService.generate_more_like_this(app_model, current_user, message_id, streaming) + response = CompletionService.generate_more_like_this( + app_model=app_model, + user=current_user, + message_id=message_id, + invoke_from=InvokeFrom.EXPLORE, + streaming=streaming + ) return compact_response(response) except MessageNotExistsError: raise NotFound("Message Not Exists.") @@ -95,8 +103,7 @@ def get(self, installed_app, message_id): raise ProviderQuotaExceededError() except ModelCurrentlyNotSupportError: raise ProviderModelCurrentlyNotSupportError() - except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, - LLMRateLimitError, LLMAuthorizationError) as e: + except InvokeError as e: raise CompletionRequestError(str(e)) except ValueError as e: raise e @@ -123,8 +130,7 @@ def generate() -> Generator: yield "data: " + json.dumps(api.handle_error(ProviderQuotaExceededError()).get_json()) + "\n\n" except ModelCurrentlyNotSupportError: yield "data: " + json.dumps(api.handle_error(ProviderModelCurrentlyNotSupportError()).get_json()) + "\n\n" - except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, - LLMRateLimitError, LLMAuthorizationError) as e: + except InvokeError as e: yield "data: " + json.dumps(api.handle_error(CompletionRequestError(str(e))).get_json()) + "\n\n" except ValueError as e: yield "data: " + json.dumps(api.handle_error(e).get_json()) + "\n\n" @@ -162,8 +168,7 @@ def get(self, installed_app, message_id): raise ProviderQuotaExceededError() except ModelCurrentlyNotSupportError: raise ProviderModelCurrentlyNotSupportError() - except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, - LLMRateLimitError, LLMAuthorizationError) as e: + except InvokeError as e: raise CompletionRequestError(str(e)) except Exception: logging.exception("internal server error.") diff --git a/api/controllers/console/universal_chat/audio.py b/api/controllers/console/universal_chat/audio.py index 38bcc25b292b01..ea48b2d82db38b 100644 --- a/api/controllers/console/universal_chat/audio.py +++ b/api/controllers/console/universal_chat/audio.py @@ -11,8 +11,8 @@ NoAudioUploadedError, AudioTooLargeError, \ UnsupportedAudioTypeError, ProviderNotSupportSpeechToTextError from controllers.console.universal_chat.wraps import UniversalChatResource -from core.model_providers.error import LLMBadRequestError, LLMAPIUnavailableError, LLMAuthorizationError, LLMAPIConnectionError, \ - LLMRateLimitError, ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError +from core.errors.error import ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError +from core.model_runtime.errors.invoke import InvokeError from services.audio_service import AudioService from services.errors.audio import NoAudioUploadedServiceError, AudioTooLargeServiceError, \ UnsupportedAudioTypeServiceError, ProviderNotSupportSpeechToTextServiceError @@ -53,8 +53,7 @@ def post(self, universal_app): raise ProviderQuotaExceededError() except ModelCurrentlyNotSupportError: raise ProviderModelCurrentlyNotSupportError() - except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, - LLMRateLimitError, LLMAuthorizationError) as e: + except InvokeError as e: raise CompletionRequestError(str(e)) except ValueError as e: raise e diff --git a/api/controllers/console/universal_chat/chat.py b/api/controllers/console/universal_chat/chat.py index c50625adc072b1..e7257157f7501d 100644 --- a/api/controllers/console/universal_chat/chat.py +++ b/api/controllers/console/universal_chat/chat.py @@ -12,9 +12,10 @@ from controllers.console.app.error import ConversationCompletedError, AppUnavailableError, ProviderNotInitializeError, \ ProviderQuotaExceededError, ProviderModelCurrentlyNotSupportError, CompletionRequestError from controllers.console.universal_chat.wraps import UniversalChatResource -from core.conversation_message_task import PubHandler -from core.model_providers.error import ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError, \ - LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, LLMRateLimitError, LLMAuthorizationError +from core.application_queue_manager import ApplicationQueueManager +from core.entities.application_entities import InvokeFrom +from core.errors.error import ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError +from core.model_runtime.errors.invoke import InvokeError from libs.helper import uuid_value from services.completion_service import CompletionService @@ -68,7 +69,7 @@ def post(self, universal_app): app_model=app_model, user=current_user, args=args, - from_source='console', + invoke_from=InvokeFrom.EXPLORE, streaming=True, is_model_config_override=True, ) @@ -87,8 +88,7 @@ def post(self, universal_app): raise ProviderQuotaExceededError() except ModelCurrentlyNotSupportError: raise ProviderModelCurrentlyNotSupportError() - except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, - LLMRateLimitError, LLMAuthorizationError) as e: + except InvokeError as e: raise CompletionRequestError(str(e)) except ValueError as e: raise e @@ -99,7 +99,7 @@ def post(self, universal_app): class UniversalChatStopApi(UniversalChatResource): def post(self, universal_app, task_id): - PubHandler.stop(current_user, task_id) + ApplicationQueueManager.set_stop_flag(task_id, InvokeFrom.EXPLORE, current_user.id) return {'result': 'success'}, 200 @@ -125,8 +125,7 @@ def generate() -> Generator: yield "data: " + json.dumps(api.handle_error(ProviderQuotaExceededError()).get_json()) + "\n\n" except ModelCurrentlyNotSupportError: yield "data: " + json.dumps(api.handle_error(ProviderModelCurrentlyNotSupportError()).get_json()) + "\n\n" - except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, - LLMRateLimitError, LLMAuthorizationError) as e: + except InvokeError as e: yield "data: " + json.dumps(api.handle_error(CompletionRequestError(str(e))).get_json()) + "\n\n" except ValueError as e: yield "data: " + json.dumps(api.handle_error(e).get_json()) + "\n\n" diff --git a/api/controllers/console/universal_chat/message.py b/api/controllers/console/universal_chat/message.py index 8568d3e9e9015e..38f3712a15ec9a 100644 --- a/api/controllers/console/universal_chat/message.py +++ b/api/controllers/console/universal_chat/message.py @@ -12,8 +12,8 @@ ProviderQuotaExceededError, ProviderModelCurrentlyNotSupportError, CompletionRequestError from controllers.console.explore.error import AppSuggestedQuestionsAfterAnswerDisabledError from controllers.console.universal_chat.wraps import UniversalChatResource -from core.model_providers.error import LLMRateLimitError, LLMBadRequestError, LLMAuthorizationError, LLMAPIConnectionError, \ - ProviderTokenNotInitError, LLMAPIUnavailableError, QuotaExceededError, ModelCurrentlyNotSupportError +from core.errors.error import ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError +from core.model_runtime.errors.invoke import InvokeError from libs.helper import uuid_value, TimestampField from services.errors.conversation import ConversationNotExistsError from services.errors.message import MessageNotExistsError, SuggestedQuestionsAfterAnswerDisabledError @@ -132,8 +132,7 @@ def get(self, universal_app, message_id): raise ProviderQuotaExceededError() except ModelCurrentlyNotSupportError: raise ProviderModelCurrentlyNotSupportError() - except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, - LLMRateLimitError, LLMAuthorizationError) as e: + except InvokeError as e: raise CompletionRequestError(str(e)) except Exception: logging.exception("internal server error.") diff --git a/api/controllers/console/workspace/model_providers.py b/api/controllers/console/workspace/model_providers.py index e92a07b2ad8507..4086fdf049ea61 100644 --- a/api/controllers/console/workspace/model_providers.py +++ b/api/controllers/console/workspace/model_providers.py @@ -1,16 +1,19 @@ +import io + +from flask import send_file from flask_login import current_user -from libs.login import login_required from flask_restful import Resource, reqparse from werkzeug.exceptions import Forbidden from controllers.console import api -from controllers.console.app.error import ProviderNotInitializeError from controllers.console.setup import setup_required from controllers.console.wraps import account_initialization_required -from core.model_providers.error import LLMBadRequestError -from core.model_providers.providers.base import CredentialsValidateFailedError -from services.provider_service import ProviderService +from core.model_runtime.entities.model_entities import ModelType +from core.model_runtime.errors.validate import CredentialsValidateFailedError +from core.model_runtime.utils.encoders import jsonable_encoder +from libs.login import login_required from services.billing_service import BillingService +from services.model_provider_service import ModelProviderService class ModelProviderListApi(Resource): @@ -22,13 +25,36 @@ def get(self): tenant_id = current_user.current_tenant_id parser = reqparse.RequestParser() - parser.add_argument('model_type', type=str, required=False, nullable=True, location='args') + parser.add_argument('model_type', type=str, required=False, nullable=True, + choices=[mt.value for mt in ModelType], location='args') args = parser.parse_args() - provider_service = ProviderService() - provider_list = provider_service.get_provider_list(tenant_id=tenant_id, model_type=args.get('model_type')) + model_provider_service = ModelProviderService() + provider_list = model_provider_service.get_provider_list( + tenant_id=tenant_id, + model_type=args.get('model_type') + ) + + return jsonable_encoder({"data": provider_list}) + + +class ModelProviderCredentialApi(Resource): + + @setup_required + @login_required + @account_initialization_required + def get(self, provider: str): + tenant_id = current_user.current_tenant_id + + model_provider_service = ModelProviderService() + credentials = model_provider_service.get_provider_credentials( + tenant_id=tenant_id, + provider=provider + ) - return provider_list + return { + "credentials": credentials + } class ModelProviderValidateApi(Resource): @@ -36,21 +62,24 @@ class ModelProviderValidateApi(Resource): @setup_required @login_required @account_initialization_required - def post(self, provider_name: str): + def post(self, provider: str): parser = reqparse.RequestParser() - parser.add_argument('config', type=dict, required=True, nullable=False, location='json') + parser.add_argument('credentials', type=dict, required=True, nullable=False, location='json') args = parser.parse_args() - provider_service = ProviderService() + tenant_id = current_user.current_tenant_id + + model_provider_service = ModelProviderService() result = True error = None try: - provider_service.custom_provider_config_validate( - provider_name=provider_name, - config=args['config'] + model_provider_service.provider_credentials_validate( + tenant_id=tenant_id, + provider=provider, + credentials=args['credentials'] ) except CredentialsValidateFailedError as ex: result = False @@ -64,26 +93,26 @@ def post(self, provider_name: str): return response -class ModelProviderUpdateApi(Resource): +class ModelProviderApi(Resource): @setup_required @login_required @account_initialization_required - def post(self, provider_name: str): + def post(self, provider: str): if current_user.current_tenant.current_role not in ['admin', 'owner']: raise Forbidden() parser = reqparse.RequestParser() - parser.add_argument('config', type=dict, required=True, nullable=False, location='json') + parser.add_argument('credentials', type=dict, required=True, nullable=False, location='json') args = parser.parse_args() - provider_service = ProviderService() + model_provider_service = ModelProviderService() try: - provider_service.save_custom_provider_config( + model_provider_service.save_provider_credentials( tenant_id=current_user.current_tenant_id, - provider_name=provider_name, - config=args['config'] + provider=provider, + credentials=args['credentials'] ) except CredentialsValidateFailedError as ex: raise ValueError(str(ex)) @@ -93,109 +122,36 @@ def post(self, provider_name: str): @setup_required @login_required @account_initialization_required - def delete(self, provider_name: str): + def delete(self, provider: str): if current_user.current_tenant.current_role not in ['admin', 'owner']: raise Forbidden() - provider_service = ProviderService() - provider_service.delete_custom_provider( + model_provider_service = ModelProviderService() + model_provider_service.remove_provider_credentials( tenant_id=current_user.current_tenant_id, - provider_name=provider_name + provider=provider ) return {'result': 'success'}, 204 -class ModelProviderModelValidateApi(Resource): - - @setup_required - @login_required - @account_initialization_required - def post(self, provider_name: str): - parser = reqparse.RequestParser() - parser.add_argument('model_name', type=str, required=True, nullable=False, location='json') - parser.add_argument('model_type', type=str, required=True, nullable=False, - choices=['text-generation', 'embeddings', 'speech2text', 'reranking'], location='json') - parser.add_argument('config', type=dict, required=True, nullable=False, location='json') - args = parser.parse_args() - - provider_service = ProviderService() - - result = True - error = None - - try: - provider_service.custom_provider_model_config_validate( - provider_name=provider_name, - model_name=args['model_name'], - model_type=args['model_type'], - config=args['config'] - ) - except CredentialsValidateFailedError as ex: - result = False - error = str(ex) - - response = {'result': 'success' if result else 'error'} - - if not result: - response['error'] = error - - return response - - -class ModelProviderModelUpdateApi(Resource): +class ModelProviderIconApi(Resource): + """ + Get model provider icon + """ @setup_required @login_required @account_initialization_required - def post(self, provider_name: str): - if current_user.current_tenant.current_role not in ['admin', 'owner']: - raise Forbidden() - - parser = reqparse.RequestParser() - parser.add_argument('model_name', type=str, required=True, nullable=False, location='json') - parser.add_argument('model_type', type=str, required=True, nullable=False, - choices=['text-generation', 'embeddings', 'speech2text', 'reranking'], location='json') - parser.add_argument('config', type=dict, required=True, nullable=False, location='json') - args = parser.parse_args() - - provider_service = ProviderService() - - try: - provider_service.add_or_save_custom_provider_model_config( - tenant_id=current_user.current_tenant_id, - provider_name=provider_name, - model_name=args['model_name'], - model_type=args['model_type'], - config=args['config'] - ) - except CredentialsValidateFailedError as ex: - raise ValueError(str(ex)) - - return {'result': 'success'}, 200 - - @setup_required - @login_required - @account_initialization_required - def delete(self, provider_name: str): - if current_user.current_tenant.current_role not in ['admin', 'owner']: - raise Forbidden() - - parser = reqparse.RequestParser() - parser.add_argument('model_name', type=str, required=True, nullable=False, location='args') - parser.add_argument('model_type', type=str, required=True, nullable=False, - choices=['text-generation', 'embeddings', 'speech2text', 'reranking'], location='args') - args = parser.parse_args() - - provider_service = ProviderService() - provider_service.delete_custom_provider_model( - tenant_id=current_user.current_tenant_id, - provider_name=provider_name, - model_name=args['model_name'], - model_type=args['model_type'] + def get(self, provider: str, icon_type: str, lang: str): + model_provider_service = ModelProviderService() + icon, mimetype = model_provider_service.get_model_provider_icon( + provider=provider, + icon_type=icon_type, + lang=lang ) - return {'result': 'success'}, 204 + return send_file(io.BytesIO(icon), mimetype=mimetype) class PreferredProviderTypeUpdateApi(Resource): @@ -203,71 +159,36 @@ class PreferredProviderTypeUpdateApi(Resource): @setup_required @login_required @account_initialization_required - def post(self, provider_name: str): + def post(self, provider: str): if current_user.current_tenant.current_role not in ['admin', 'owner']: raise Forbidden() + tenant_id = current_user.current_tenant_id + parser = reqparse.RequestParser() parser.add_argument('preferred_provider_type', type=str, required=True, nullable=False, choices=['system', 'custom'], location='json') args = parser.parse_args() - provider_service = ProviderService() - provider_service.switch_preferred_provider( - tenant_id=current_user.current_tenant_id, - provider_name=provider_name, + model_provider_service = ModelProviderService() + model_provider_service.switch_preferred_provider( + tenant_id=tenant_id, + provider=provider, preferred_provider_type=args['preferred_provider_type'] ) return {'result': 'success'} -class ModelProviderModelParameterRuleApi(Resource): - - @setup_required - @login_required - @account_initialization_required - def get(self, provider_name: str): - parser = reqparse.RequestParser() - parser.add_argument('model_name', type=str, required=True, nullable=False, location='args') - args = parser.parse_args() - - provider_service = ProviderService() - - try: - parameter_rules = provider_service.get_model_parameter_rules( - tenant_id=current_user.current_tenant_id, - model_provider_name=provider_name, - model_name=args['model_name'], - model_type='text-generation' - ) - except LLMBadRequestError: - raise ProviderNotInitializeError( - f"Current Text Generation Model is invalid. Please switch to the available model.") - - rules = { - k: { - 'enabled': v.enabled, - 'min': v.min, - 'max': v.max, - 'default': v.default, - 'precision': v.precision - } - for k, v in vars(parameter_rules).items() - } - - return rules - - class ModelProviderPaymentCheckoutUrlApi(Resource): @setup_required @login_required @account_initialization_required - def get(self, provider_name: str): - if provider_name != 'anthropic': - raise ValueError(f'provider name {provider_name} is invalid') + def get(self, provider: str): + if provider != 'anthropic': + raise ValueError(f'provider name {provider} is invalid') - data = BillingService.get_model_provider_payment_link(provider_name=provider_name, + data = BillingService.get_model_provider_payment_link(provider_name=provider, tenant_id=current_user.current_tenant_id, account_id=current_user.id) return data @@ -277,11 +198,11 @@ class ModelProviderFreeQuotaSubmitApi(Resource): @setup_required @login_required @account_initialization_required - def post(self, provider_name: str): - provider_service = ProviderService() - result = provider_service.free_quota_submit( + def post(self, provider: str): + model_provider_service = ModelProviderService() + result = model_provider_service.free_quota_submit( tenant_id=current_user.current_tenant_id, - provider_name=provider_name + provider=provider ) return result @@ -291,15 +212,15 @@ class ModelProviderFreeQuotaQualificationVerifyApi(Resource): @setup_required @login_required @account_initialization_required - def get(self, provider_name: str): + def get(self, provider: str): parser = reqparse.RequestParser() parser.add_argument('token', type=str, required=False, nullable=True, location='args') args = parser.parse_args() - provider_service = ProviderService() - result = provider_service.free_quota_qualification_verify( + model_provider_service = ModelProviderService() + result = model_provider_service.free_quota_qualification_verify( tenant_id=current_user.current_tenant_id, - provider_name=provider_name, + provider=provider, token=args['token'] ) @@ -307,19 +228,18 @@ def get(self, provider_name: str): api.add_resource(ModelProviderListApi, '/workspaces/current/model-providers') -api.add_resource(ModelProviderValidateApi, '/workspaces/current/model-providers//validate') -api.add_resource(ModelProviderUpdateApi, '/workspaces/current/model-providers/') -api.add_resource(ModelProviderModelValidateApi, - '/workspaces/current/model-providers//models/validate') -api.add_resource(ModelProviderModelUpdateApi, - '/workspaces/current/model-providers//models') + +api.add_resource(ModelProviderCredentialApi, '/workspaces/current/model-providers//credentials') +api.add_resource(ModelProviderValidateApi, '/workspaces/current/model-providers//credentials/validate') +api.add_resource(ModelProviderApi, '/workspaces/current/model-providers/') +api.add_resource(ModelProviderIconApi, '/workspaces/current/model-providers//' + '/') + api.add_resource(PreferredProviderTypeUpdateApi, - '/workspaces/current/model-providers//preferred-provider-type') -api.add_resource(ModelProviderModelParameterRuleApi, - '/workspaces/current/model-providers//models/parameter-rules') + '/workspaces/current/model-providers//preferred-provider-type') api.add_resource(ModelProviderPaymentCheckoutUrlApi, - '/workspaces/current/model-providers//checkout-url') + '/workspaces/current/model-providers//checkout-url') api.add_resource(ModelProviderFreeQuotaSubmitApi, - '/workspaces/current/model-providers//free-quota-submit') + '/workspaces/current/model-providers//free-quota-submit') api.add_resource(ModelProviderFreeQuotaQualificationVerifyApi, - '/workspaces/current/model-providers//free-quota-qualification-verify') + '/workspaces/current/model-providers//free-quota-qualification-verify') diff --git a/api/controllers/console/workspace/models.py b/api/controllers/console/workspace/models.py index 6df99ebe1bda4f..b5cb97d5478443 100644 --- a/api/controllers/console/workspace/models.py +++ b/api/controllers/console/workspace/models.py @@ -1,16 +1,17 @@ import logging from flask_login import current_user -from libs.login import login_required -from flask_restful import Resource, reqparse +from flask_restful import reqparse, Resource +from werkzeug.exceptions import Forbidden from controllers.console import api from controllers.console.setup import setup_required from controllers.console.wraps import account_initialization_required -from core.model_providers.model_provider_factory import ModelProviderFactory -from core.model_providers.models.entity.model_params import ModelType -from models.provider import ProviderType -from services.provider_service import ProviderService +from core.model_runtime.entities.model_entities import ModelType +from core.model_runtime.errors.validate import CredentialsValidateFailedError +from core.model_runtime.utils.encoders import jsonable_encoder +from libs.login import login_required +from services.model_provider_service import ModelProviderService class DefaultModelApi(Resource): @@ -21,52 +22,20 @@ class DefaultModelApi(Resource): def get(self): parser = reqparse.RequestParser() parser.add_argument('model_type', type=str, required=True, nullable=False, - choices=['text-generation', 'embeddings', 'speech2text', 'reranking'], location='args') + choices=[mt.value for mt in ModelType], location='args') args = parser.parse_args() tenant_id = current_user.current_tenant_id - provider_service = ProviderService() - default_model = provider_service.get_default_model_of_model_type( + model_provider_service = ModelProviderService() + default_model_entity = model_provider_service.get_default_model_of_model_type( tenant_id=tenant_id, model_type=args['model_type'] ) - if not default_model: - return None - - model_provider = ModelProviderFactory.get_preferred_model_provider( - tenant_id, - default_model.provider_name - ) - - if not model_provider: - return { - 'model_name': default_model.model_name, - 'model_type': default_model.model_type, - 'model_provider': { - 'provider_name': default_model.provider_name - } - } - - provider = model_provider.provider - rst = { - 'model_name': default_model.model_name, - 'model_type': default_model.model_type, - 'model_provider': { - 'provider_name': provider.provider_name, - 'provider_type': provider.provider_type - } - } - - model_provider_rules = ModelProviderFactory.get_provider_rule(default_model.provider_name) - if provider.provider_type == ProviderType.SYSTEM.value: - rst['model_provider']['quota_type'] = provider.quota_type - rst['model_provider']['quota_unit'] = model_provider_rules['system_config']['quota_unit'] - rst['model_provider']['quota_limit'] = provider.quota_limit - rst['model_provider']['quota_used'] = provider.quota_used - - return rst + return jsonable_encoder({ + "data": default_model_entity + }) @setup_required @login_required @@ -76,15 +45,26 @@ def post(self): parser.add_argument('model_settings', type=list, required=True, nullable=False, location='json') args = parser.parse_args() - provider_service = ProviderService() + tenant_id = current_user.current_tenant_id + + model_provider_service = ModelProviderService() model_settings = args['model_settings'] for model_setting in model_settings: + if 'model_type' not in model_setting or model_setting['model_type'] not in [mt.value for mt in ModelType]: + raise ValueError('invalid model type') + + if 'provider' not in model_setting: + continue + + if 'model' not in model_setting: + raise ValueError('invalid model') + try: - provider_service.update_default_model_of_model_type( - tenant_id=current_user.current_tenant_id, + model_provider_service.update_default_model_of_model_type( + tenant_id=tenant_id, model_type=model_setting['model_type'], - provider_name=model_setting['provider_name'], - model_name=model_setting['model_name'] + provider=model_setting['provider'], + model=model_setting['model'] ) except Exception: logging.warning(f"{model_setting['model_type']} save error") @@ -92,22 +72,198 @@ def post(self): return {'result': 'success'} -class ValidModelApi(Resource): +class ModelProviderModelApi(Resource): + + @setup_required + @login_required + @account_initialization_required + def get(self, provider): + tenant_id = current_user.current_tenant_id + + model_provider_service = ModelProviderService() + models = model_provider_service.get_models_by_provider( + tenant_id=tenant_id, + provider=provider + ) + + return jsonable_encoder({ + "data": models + }) + + @setup_required + @login_required + @account_initialization_required + def post(self, provider: str): + if current_user.current_tenant.current_role not in ['admin', 'owner']: + raise Forbidden() + + tenant_id = current_user.current_tenant_id + + parser = reqparse.RequestParser() + parser.add_argument('model', type=str, required=True, nullable=False, location='json') + parser.add_argument('model_type', type=str, required=True, nullable=False, + choices=[mt.value for mt in ModelType], location='json') + parser.add_argument('credentials', type=dict, required=True, nullable=False, location='json') + args = parser.parse_args() + + model_provider_service = ModelProviderService() + + try: + model_provider_service.save_model_credentials( + tenant_id=tenant_id, + provider=provider, + model=args['model'], + model_type=args['model_type'], + credentials=args['credentials'] + ) + except CredentialsValidateFailedError as ex: + raise ValueError(str(ex)) + + return {'result': 'success'}, 200 + + @setup_required + @login_required + @account_initialization_required + def delete(self, provider: str): + if current_user.current_tenant.current_role not in ['admin', 'owner']: + raise Forbidden() + + tenant_id = current_user.current_tenant_id + + parser = reqparse.RequestParser() + parser.add_argument('model', type=str, required=True, nullable=False, location='json') + parser.add_argument('model_type', type=str, required=True, nullable=False, + choices=[mt.value for mt in ModelType], location='json') + args = parser.parse_args() + + model_provider_service = ModelProviderService() + model_provider_service.remove_model_credentials( + tenant_id=tenant_id, + provider=provider, + model=args['model'], + model_type=args['model_type'] + ) + + return {'result': 'success'}, 204 + + +class ModelProviderModelCredentialApi(Resource): + + @setup_required + @login_required + @account_initialization_required + def get(self, provider: str): + tenant_id = current_user.current_tenant_id + + parser = reqparse.RequestParser() + parser.add_argument('model', type=str, required=True, nullable=False, location='args') + parser.add_argument('model_type', type=str, required=True, nullable=False, + choices=[mt.value for mt in ModelType], location='args') + args = parser.parse_args() + + model_provider_service = ModelProviderService() + credentials = model_provider_service.get_model_credentials( + tenant_id=tenant_id, + provider=provider, + model_type=args['model_type'], + model=args['model'] + ) + + return { + "credentials": credentials + } + + +class ModelProviderModelValidateApi(Resource): + + @setup_required + @login_required + @account_initialization_required + def post(self, provider: str): + tenant_id = current_user.current_tenant_id + + parser = reqparse.RequestParser() + parser.add_argument('model', type=str, required=True, nullable=False, location='json') + parser.add_argument('model_type', type=str, required=True, nullable=False, + choices=[mt.value for mt in ModelType], location='json') + parser.add_argument('credentials', type=dict, required=True, nullable=False, location='json') + args = parser.parse_args() + + model_provider_service = ModelProviderService() + + result = True + error = None + + try: + model_provider_service.model_credentials_validate( + tenant_id=tenant_id, + provider=provider, + model=args['model'], + model_type=args['model_type'], + credentials=args['credentials'] + ) + except CredentialsValidateFailedError as ex: + result = False + error = str(ex) + + response = {'result': 'success' if result else 'error'} + + if not result: + response['error'] = error + + return response + + +class ModelProviderModelParameterRuleApi(Resource): + + @setup_required + @login_required + @account_initialization_required + def get(self, provider: str): + parser = reqparse.RequestParser() + parser.add_argument('model', type=str, required=True, nullable=False, location='args') + args = parser.parse_args() + + tenant_id = current_user.current_tenant_id + + model_provider_service = ModelProviderService() + parameter_rules = model_provider_service.get_model_parameter_rules( + tenant_id=tenant_id, + provider=provider, + model=args['model'] + ) + + return jsonable_encoder({ + "data": parameter_rules + }) + + +class ModelProviderAvailableModelApi(Resource): @setup_required @login_required @account_initialization_required def get(self, model_type): - ModelType.value_of(model_type) + tenant_id = current_user.current_tenant_id - provider_service = ProviderService() - valid_models = provider_service.get_valid_model_list( - tenant_id=current_user.current_tenant_id, + model_provider_service = ModelProviderService() + models = model_provider_service.get_models_by_model_type( + tenant_id=tenant_id, model_type=model_type ) - return valid_models + return jsonable_encoder({ + "data": models + }) + +api.add_resource(ModelProviderModelApi, '/workspaces/current/model-providers//models') +api.add_resource(ModelProviderModelCredentialApi, + '/workspaces/current/model-providers//models/credentials') +api.add_resource(ModelProviderModelValidateApi, + '/workspaces/current/model-providers//models/credentials/validate') +api.add_resource(ModelProviderModelParameterRuleApi, + '/workspaces/current/model-providers//models/parameter-rules') +api.add_resource(ModelProviderAvailableModelApi, '/workspaces/current/models/model-types/') api.add_resource(DefaultModelApi, '/workspaces/current/default-model') -api.add_resource(ValidModelApi, '/workspaces/current/models/model-type/') diff --git a/api/controllers/console/workspace/providers.py b/api/controllers/console/workspace/providers.py deleted file mode 100644 index 267fb6975fc260..00000000000000 --- a/api/controllers/console/workspace/providers.py +++ /dev/null @@ -1,131 +0,0 @@ -# -*- coding:utf-8 -*- -from flask_login import current_user -from libs.login import login_required -from flask_restful import Resource, reqparse -from werkzeug.exceptions import Forbidden - -from controllers.console import api -from controllers.console.setup import setup_required -from controllers.console.wraps import account_initialization_required -from core.model_providers.providers.base import CredentialsValidateFailedError -from models.provider import ProviderType -from services.provider_service import ProviderService - - -class ProviderListApi(Resource): - - @setup_required - @login_required - @account_initialization_required - def get(self): - tenant_id = current_user.current_tenant_id - - """ - If the type is AZURE_OPENAI, decode and return the four fields of azure_api_type, azure_api_version:, - azure_api_base, azure_api_key as an object, where azure_api_key displays the first 6 bits in plaintext, and the - rest is replaced by * and the last two bits are displayed in plaintext - - If the type is other, decode and return the Token field directly, the field displays the first 6 bits in - plaintext, the rest is replaced by * and the last two bits are displayed in plaintext - """ - - provider_service = ProviderService() - provider_info_list = provider_service.get_provider_list(tenant_id) - - provider_list = [ - { - 'provider_name': p['provider_name'], - 'provider_type': p['provider_type'], - 'is_valid': p['is_valid'], - 'last_used': p['last_used'], - 'is_enabled': p['is_valid'], - **({ - 'quota_type': p['quota_type'], - 'quota_limit': p['quota_limit'], - 'quota_used': p['quota_used'] - } if p['provider_type'] == ProviderType.SYSTEM.value else {}), - 'token': (p['config'] if p['provider_name'] != 'openai' else p['config']['openai_api_key']) - if p['config'] else None - } - for name, provider_info in provider_info_list.items() - for p in provider_info['providers'] - ] - - return provider_list - - -class ProviderTokenApi(Resource): - - @setup_required - @login_required - @account_initialization_required - def post(self, provider): - # The role of the current user in the ta table must be admin or owner - if current_user.current_tenant.current_role not in ['admin', 'owner']: - raise Forbidden() - - parser = reqparse.RequestParser() - parser.add_argument('token', required=True, nullable=False, location='json') - args = parser.parse_args() - - if provider == 'openai': - args['token'] = { - 'openai_api_key': args['token'] - } - - provider_service = ProviderService() - try: - provider_service.save_custom_provider_config( - tenant_id=current_user.current_tenant_id, - provider_name=provider, - config=args['token'] - ) - except CredentialsValidateFailedError as ex: - raise ValueError(str(ex)) - - return {'result': 'success'}, 201 - - -class ProviderTokenValidateApi(Resource): - - @setup_required - @login_required - @account_initialization_required - def post(self, provider): - parser = reqparse.RequestParser() - parser.add_argument('token', required=True, nullable=False, location='json') - args = parser.parse_args() - - provider_service = ProviderService() - - if provider == 'openai': - args['token'] = { - 'openai_api_key': args['token'] - } - - result = True - error = None - - try: - provider_service.custom_provider_config_validate( - provider_name=provider, - config=args['token'] - ) - except CredentialsValidateFailedError as ex: - result = False - error = str(ex) - - response = {'result': 'success' if result else 'error'} - - if not result: - response['error'] = error - - return response - - -api.add_resource(ProviderTokenApi, '/workspaces/current/providers//token', - endpoint='workspaces_current_providers_token') # PUT for updating provider token -api.add_resource(ProviderTokenValidateApi, '/workspaces/current/providers//token-validate', - endpoint='workspaces_current_providers_token_validate') # POST for validating provider token - -api.add_resource(ProviderListApi, '/workspaces/current/providers') # GET for getting providers list diff --git a/api/controllers/console/workspace/workspace.py b/api/controllers/console/workspace/workspace.py index 2288ae21bd5a1d..de07ab62c717c0 100644 --- a/api/controllers/console/workspace/workspace.py +++ b/api/controllers/console/workspace/workspace.py @@ -34,7 +34,6 @@ 'status': fields.String, 'created_at': TimestampField, 'role': fields.String, - 'providers': fields.List(fields.Nested(provider_fields)), 'in_trial': fields.Boolean, 'trial_end_reason': fields.String, 'custom_config': fields.Raw(attribute='custom_config'), diff --git a/api/controllers/service_api/app/audio.py b/api/controllers/service_api/app/audio.py index 4b03de06379a10..87bdadcdd1bc01 100644 --- a/api/controllers/service_api/app/audio.py +++ b/api/controllers/service_api/app/audio.py @@ -9,8 +9,8 @@ ProviderModelCurrentlyNotSupportError, NoAudioUploadedError, AudioTooLargeError, UnsupportedAudioTypeError, \ ProviderNotSupportSpeechToTextError from controllers.service_api.wraps import AppApiResource -from core.model_providers.error import LLMBadRequestError, LLMAuthorizationError, LLMAPIUnavailableError, LLMAPIConnectionError, \ - LLMRateLimitError, ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError +from core.errors.error import ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError +from core.model_runtime.errors.invoke import InvokeError from models.model import App, AppModelConfig from services.audio_service import AudioService from services.errors.audio import NoAudioUploadedServiceError, AudioTooLargeServiceError, \ @@ -49,8 +49,7 @@ def post(self, app_model: App, end_user): raise ProviderQuotaExceededError() except ModelCurrentlyNotSupportError: raise ProviderModelCurrentlyNotSupportError() - except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, - LLMRateLimitError, LLMAuthorizationError) as e: + except InvokeError as e: raise CompletionRequestError(str(e)) except ValueError as e: raise e diff --git a/api/controllers/service_api/app/completion.py b/api/controllers/service_api/app/completion.py index 5cd33dcd577ee2..a98b7c7721a3b7 100644 --- a/api/controllers/service_api/app/completion.py +++ b/api/controllers/service_api/app/completion.py @@ -13,9 +13,10 @@ ConversationCompletedError, CompletionRequestError, ProviderQuotaExceededError, \ ProviderModelCurrentlyNotSupportError from controllers.service_api.wraps import AppApiResource -from core.conversation_message_task import PubHandler -from core.model_providers.error import LLMBadRequestError, LLMAuthorizationError, LLMAPIUnavailableError, LLMAPIConnectionError, \ - LLMRateLimitError, ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError +from core.application_queue_manager import ApplicationQueueManager +from core.entities.application_entities import InvokeFrom +from core.errors.error import ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError +from core.model_runtime.errors.invoke import InvokeError from libs.helper import uuid_value from services.completion_service import CompletionService @@ -47,7 +48,7 @@ def post(self, app_model, end_user): app_model=app_model, user=end_user, args=args, - from_source='api', + invoke_from=InvokeFrom.SERVICE_API, streaming=streaming, ) @@ -65,8 +66,7 @@ def post(self, app_model, end_user): raise ProviderQuotaExceededError() except ModelCurrentlyNotSupportError: raise ProviderModelCurrentlyNotSupportError() - except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, - LLMRateLimitError, LLMAuthorizationError) as e: + except InvokeError as e: raise CompletionRequestError(str(e)) except ValueError as e: raise e @@ -80,7 +80,7 @@ def post(self, app_model, end_user, task_id): if app_model.mode != 'completion': raise AppUnavailableError() - PubHandler.stop(end_user, task_id) + ApplicationQueueManager.set_stop_flag(task_id, InvokeFrom.SERVICE_API, end_user.id) return {'result': 'success'}, 200 @@ -112,7 +112,7 @@ def post(self, app_model, end_user): app_model=app_model, user=end_user, args=args, - from_source='api', + invoke_from=InvokeFrom.SERVICE_API, streaming=streaming ) @@ -130,8 +130,7 @@ def post(self, app_model, end_user): raise ProviderQuotaExceededError() except ModelCurrentlyNotSupportError: raise ProviderModelCurrentlyNotSupportError() - except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, - LLMRateLimitError, LLMAuthorizationError) as e: + except InvokeError as e: raise CompletionRequestError(str(e)) except ValueError as e: raise e @@ -145,7 +144,7 @@ def post(self, app_model, end_user, task_id): if app_model.mode != 'chat': raise NotChatAppError() - PubHandler.stop(end_user, task_id) + ApplicationQueueManager.set_stop_flag(task_id, InvokeFrom.SERVICE_API, end_user.id) return {'result': 'success'}, 200 @@ -171,8 +170,7 @@ def generate() -> Generator: yield "data: " + json.dumps(api.handle_error(ProviderQuotaExceededError()).get_json()) + "\n\n" except ModelCurrentlyNotSupportError: yield "data: " + json.dumps(api.handle_error(ProviderModelCurrentlyNotSupportError()).get_json()) + "\n\n" - except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, - LLMRateLimitError, LLMAuthorizationError) as e: + except InvokeError as e: yield "data: " + json.dumps(api.handle_error(CompletionRequestError(str(e))).get_json()) + "\n\n" except ValueError as e: yield "data: " + json.dumps(api.handle_error(e).get_json()) + "\n\n" diff --git a/api/controllers/service_api/dataset/dataset.py b/api/controllers/service_api/dataset/dataset.py index 17b659cb1a44d0..813f9295574023 100644 --- a/api/controllers/service_api/dataset/dataset.py +++ b/api/controllers/service_api/dataset/dataset.py @@ -4,11 +4,11 @@ from controllers.service_api import api from controllers.service_api.dataset.error import DatasetNameDuplicateError from controllers.service_api.wraps import DatasetApiResource +from core.model_runtime.entities.model_entities import ModelType +from core.provider_manager import ProviderManager from libs.login import current_user -from core.model_providers.models.entity.model_params import ModelType from fields.dataset_fields import dataset_detail_fields from services.dataset_service import DatasetService -from services.provider_service import ProviderService def _validate_name(name): @@ -27,12 +27,20 @@ def get(self, tenant_id): datasets, total = DatasetService.get_datasets(page, limit, provider, tenant_id, current_user) # check embedding setting - provider_service = ProviderService() - valid_model_list = provider_service.get_valid_model_list(current_user.current_tenant_id, - ModelType.EMBEDDINGS.value) + provider_manager = ProviderManager() + configurations = provider_manager.get_configurations( + tenant_id=current_user.current_tenant_id + ) + + embedding_models = configurations.get_models( + model_type=ModelType.TEXT_EMBEDDING, + only_active=True + ) + model_names = [] - for valid_model in valid_model_list: - model_names.append(f"{valid_model['model_name']}:{valid_model['model_provider']['provider_name']}") + for embedding_model in embedding_models: + model_names.append(f"{embedding_model.model}:{embedding_model.provider.provider}") + data = marshal(datasets, dataset_detail_fields) for item in data: if item['indexing_technique'] == 'high_quality': diff --git a/api/controllers/service_api/dataset/document.py b/api/controllers/service_api/dataset/document.py index 6a4057c1f61629..e851799f75a21d 100644 --- a/api/controllers/service_api/dataset/document.py +++ b/api/controllers/service_api/dataset/document.py @@ -13,7 +13,7 @@ NoFileUploadedError, TooManyFilesError from controllers.service_api.wraps import DatasetApiResource, cloud_edition_billing_resource_check from libs.login import current_user -from core.model_providers.error import ProviderTokenNotInitError +from core.errors.error import ProviderTokenNotInitError from extensions.ext_database import db from fields.document_fields import document_fields, document_status_fields from models.dataset import Dataset, Document, DocumentSegment diff --git a/api/controllers/service_api/dataset/segment.py b/api/controllers/service_api/dataset/segment.py index 2cd6da3d13e3da..9940dba1e132e3 100644 --- a/api/controllers/service_api/dataset/segment.py +++ b/api/controllers/service_api/dataset/segment.py @@ -4,8 +4,9 @@ from controllers.service_api import api from controllers.service_api.app.error import ProviderNotInitializeError from controllers.service_api.wraps import DatasetApiResource, cloud_edition_billing_resource_check -from core.model_providers.error import ProviderTokenNotInitError, LLMBadRequestError -from core.model_providers.model_factory import ModelFactory +from core.errors.error import ProviderTokenNotInitError, LLMBadRequestError +from core.model_manager import ModelManager +from core.model_runtime.entities.model_entities import ModelType from extensions.ext_database import db from fields.segment_fields import segment_fields from models.dataset import Dataset, DocumentSegment @@ -35,10 +36,12 @@ def post(self, tenant_id, dataset_id, document_id): # check embedding model setting if dataset.indexing_technique == 'high_quality': try: - ModelFactory.get_embedding_model( + model_manager = ModelManager() + model_manager.get_model_instance( tenant_id=current_user.current_tenant_id, - model_provider_name=dataset.embedding_model_provider, - model_name=dataset.embedding_model + provider=dataset.embedding_model_provider, + model_type=ModelType.TEXT_EMBEDDING, + model=dataset.embedding_model ) except LLMBadRequestError: raise ProviderNotInitializeError( @@ -77,10 +80,12 @@ def get(self, tenant_id, dataset_id, document_id): # check embedding model setting if dataset.indexing_technique == 'high_quality': try: - ModelFactory.get_embedding_model( + model_manager = ModelManager() + model_manager.get_model_instance( tenant_id=current_user.current_tenant_id, - model_provider_name=dataset.embedding_model_provider, - model_name=dataset.embedding_model + provider=dataset.embedding_model_provider, + model_type=ModelType.TEXT_EMBEDDING, + model=dataset.embedding_model ) except LLMBadRequestError: raise ProviderNotInitializeError( @@ -167,10 +172,12 @@ def post(self, tenant_id, dataset_id, document_id, segment_id): if dataset.indexing_technique == 'high_quality': # check embedding model setting try: - ModelFactory.get_embedding_model( + model_manager = ModelManager() + model_manager.get_model_instance( tenant_id=current_user.current_tenant_id, - model_provider_name=dataset.embedding_model_provider, - model_name=dataset.embedding_model + provider=dataset.embedding_model_provider, + model_type=ModelType.TEXT_EMBEDDING, + model=dataset.embedding_model ) except LLMBadRequestError: raise ProviderNotInitializeError( diff --git a/api/controllers/web/audio.py b/api/controllers/web/audio.py index b3272de1c7a963..e8c2fd335edf9a 100644 --- a/api/controllers/web/audio.py +++ b/api/controllers/web/audio.py @@ -10,8 +10,8 @@ ProviderQuotaExceededError, ProviderModelCurrentlyNotSupportError, NoAudioUploadedError, AudioTooLargeError, \ UnsupportedAudioTypeError, ProviderNotSupportSpeechToTextError from controllers.web.wraps import WebApiResource -from core.model_providers.error import LLMBadRequestError, LLMAPIUnavailableError, LLMAuthorizationError, LLMAPIConnectionError, \ - LLMRateLimitError, ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError +from core.errors.error import ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError +from core.model_runtime.errors.invoke import InvokeError from services.audio_service import AudioService from services.errors.audio import NoAudioUploadedServiceError, AudioTooLargeServiceError, \ UnsupportedAudioTypeServiceError, ProviderNotSupportSpeechToTextServiceError @@ -51,8 +51,7 @@ def post(self, app_model: App, end_user): raise ProviderQuotaExceededError() except ModelCurrentlyNotSupportError: raise ProviderModelCurrentlyNotSupportError() - except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, - LLMRateLimitError, LLMAuthorizationError) as e: + except InvokeError as e: raise CompletionRequestError(str(e)) except ValueError as e: raise e diff --git a/api/controllers/web/completion.py b/api/controllers/web/completion.py index b49fec5110b21a..01dd3e1ab01416 100644 --- a/api/controllers/web/completion.py +++ b/api/controllers/web/completion.py @@ -13,9 +13,10 @@ ProviderNotInitializeError, NotChatAppError, NotCompletionAppError, CompletionRequestError, \ ProviderQuotaExceededError, ProviderModelCurrentlyNotSupportError from controllers.web.wraps import WebApiResource -from core.conversation_message_task import PubHandler -from core.model_providers.error import LLMBadRequestError, LLMAPIUnavailableError, LLMAuthorizationError, LLMAPIConnectionError, \ - LLMRateLimitError, ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError +from core.application_queue_manager import ApplicationQueueManager +from core.entities.application_entities import InvokeFrom +from core.errors.error import ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError +from core.model_runtime.errors.invoke import InvokeError from libs.helper import uuid_value from services.completion_service import CompletionService @@ -44,7 +45,7 @@ def post(self, app_model, end_user): app_model=app_model, user=end_user, args=args, - from_source='api', + invoke_from=InvokeFrom.WEB_APP, streaming=streaming ) @@ -62,8 +63,7 @@ def post(self, app_model, end_user): raise ProviderQuotaExceededError() except ModelCurrentlyNotSupportError: raise ProviderModelCurrentlyNotSupportError() - except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, - LLMRateLimitError, LLMAuthorizationError) as e: + except InvokeError as e: raise CompletionRequestError(str(e)) except ValueError as e: raise e @@ -77,7 +77,7 @@ def post(self, app_model, end_user, task_id): if app_model.mode != 'completion': raise NotCompletionAppError() - PubHandler.stop(end_user, task_id) + ApplicationQueueManager.set_stop_flag(task_id, InvokeFrom.WEB_APP, end_user.id) return {'result': 'success'}, 200 @@ -105,7 +105,7 @@ def post(self, app_model, end_user): app_model=app_model, user=end_user, args=args, - from_source='api', + invoke_from=InvokeFrom.WEB_APP, streaming=streaming ) @@ -123,8 +123,7 @@ def post(self, app_model, end_user): raise ProviderQuotaExceededError() except ModelCurrentlyNotSupportError: raise ProviderModelCurrentlyNotSupportError() - except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, - LLMRateLimitError, LLMAuthorizationError) as e: + except InvokeError as e: raise CompletionRequestError(str(e)) except ValueError as e: raise e @@ -138,7 +137,7 @@ def post(self, app_model, end_user, task_id): if app_model.mode != 'chat': raise NotChatAppError() - PubHandler.stop(end_user, task_id) + ApplicationQueueManager.set_stop_flag(task_id, InvokeFrom.WEB_APP, end_user.id) return {'result': 'success'}, 200 @@ -164,8 +163,7 @@ def generate() -> Generator: yield "data: " + json.dumps(api.handle_error(ProviderQuotaExceededError()).get_json()) + "\n\n" except ModelCurrentlyNotSupportError: yield "data: " + json.dumps(api.handle_error(ProviderModelCurrentlyNotSupportError()).get_json()) + "\n\n" - except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, - LLMRateLimitError, LLMAuthorizationError) as e: + except InvokeError as e: yield "data: " + json.dumps(api.handle_error(CompletionRequestError(str(e))).get_json()) + "\n\n" except ValueError as e: yield "data: " + json.dumps(api.handle_error(e).get_json()) + "\n\n" diff --git a/api/controllers/web/message.py b/api/controllers/web/message.py index 80b0741c03a4af..d0789ee10af35a 100644 --- a/api/controllers/web/message.py +++ b/api/controllers/web/message.py @@ -14,8 +14,9 @@ AppMoreLikeThisDisabledError, NotCompletionAppError, AppSuggestedQuestionsAfterAnswerDisabledError, \ ProviderQuotaExceededError, ProviderModelCurrentlyNotSupportError from controllers.web.wraps import WebApiResource -from core.model_providers.error import LLMRateLimitError, LLMBadRequestError, LLMAuthorizationError, LLMAPIConnectionError, \ - ProviderTokenNotInitError, LLMAPIUnavailableError, QuotaExceededError, ModelCurrentlyNotSupportError +from core.entities.application_entities import InvokeFrom +from core.errors.error import ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError +from core.model_runtime.errors.invoke import InvokeError from libs.helper import uuid_value, TimestampField from services.completion_service import CompletionService from services.errors.app import MoreLikeThisDisabledError @@ -117,7 +118,14 @@ def get(self, app_model, end_user, message_id): streaming = args['response_mode'] == 'streaming' try: - response = CompletionService.generate_more_like_this(app_model, end_user, message_id, streaming, 'web_app') + response = CompletionService.generate_more_like_this( + app_model=app_model, + user=end_user, + message_id=message_id, + invoke_from=InvokeFrom.WEB_APP, + streaming=streaming + ) + return compact_response(response) except MessageNotExistsError: raise NotFound("Message Not Exists.") @@ -129,8 +137,7 @@ def get(self, app_model, end_user, message_id): raise ProviderQuotaExceededError() except ModelCurrentlyNotSupportError: raise ProviderModelCurrentlyNotSupportError() - except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, - LLMRateLimitError, LLMAuthorizationError) as e: + except InvokeError as e: raise CompletionRequestError(str(e)) except ValueError as e: raise e @@ -157,8 +164,7 @@ def generate() -> Generator: yield "data: " + json.dumps(api.handle_error(ProviderQuotaExceededError()).get_json()) + "\n\n" except ModelCurrentlyNotSupportError: yield "data: " + json.dumps(api.handle_error(ProviderModelCurrentlyNotSupportError()).get_json()) + "\n\n" - except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, - LLMRateLimitError, LLMAuthorizationError) as e: + except InvokeError as e: yield "data: " + json.dumps(api.handle_error(CompletionRequestError(str(e))).get_json()) + "\n\n" except ValueError as e: yield "data: " + json.dumps(api.handle_error(e).get_json()) + "\n\n" @@ -195,8 +201,7 @@ def get(self, app_model, end_user, message_id): raise ProviderQuotaExceededError() except ModelCurrentlyNotSupportError: raise ProviderModelCurrentlyNotSupportError() - except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, - LLMRateLimitError, LLMAuthorizationError) as e: + except InvokeError as e: raise CompletionRequestError(str(e)) except Exception: logging.exception("internal server error.") diff --git a/api/core/agent/agent/agent_llm_callback.py b/api/core/agent/agent/agent_llm_callback.py new file mode 100644 index 00000000000000..04b9bab141aff6 --- /dev/null +++ b/api/core/agent/agent/agent_llm_callback.py @@ -0,0 +1,101 @@ +import logging +from typing import Optional, List + +from core.callback_handler.agent_loop_gather_callback_handler import AgentLoopGatherCallbackHandler +from core.model_runtime.callbacks.base_callback import Callback +from core.model_runtime.entities.llm_entities import LLMResultChunk, LLMResult +from core.model_runtime.entities.message_entities import PromptMessageTool, PromptMessage +from core.model_runtime.model_providers.__base.ai_model import AIModel + +logger = logging.getLogger(__name__) + + +class AgentLLMCallback(Callback): + + def __init__(self, agent_callback: AgentLoopGatherCallbackHandler) -> None: + self.agent_callback = agent_callback + + def on_before_invoke(self, llm_instance: AIModel, model: str, credentials: dict, + prompt_messages: list[PromptMessage], model_parameters: dict, + tools: Optional[list[PromptMessageTool]] = None, stop: Optional[List[str]] = None, + stream: bool = True, user: Optional[str] = None) -> None: + """ + Before invoke callback + + :param llm_instance: LLM instance + :param model: model name + :param credentials: model credentials + :param prompt_messages: prompt messages + :param model_parameters: model parameters + :param tools: tools for tool calling + :param stop: stop words + :param stream: is stream response + :param user: unique user id + """ + self.agent_callback.on_llm_before_invoke( + prompt_messages=prompt_messages + ) + + def on_new_chunk(self, llm_instance: AIModel, chunk: LLMResultChunk, model: str, credentials: dict, + prompt_messages: list[PromptMessage], model_parameters: dict, + tools: Optional[list[PromptMessageTool]] = None, stop: Optional[List[str]] = None, + stream: bool = True, user: Optional[str] = None): + """ + On new chunk callback + + :param llm_instance: LLM instance + :param chunk: chunk + :param model: model name + :param credentials: model credentials + :param prompt_messages: prompt messages + :param model_parameters: model parameters + :param tools: tools for tool calling + :param stop: stop words + :param stream: is stream response + :param user: unique user id + """ + pass + + def on_after_invoke(self, llm_instance: AIModel, result: LLMResult, model: str, credentials: dict, + prompt_messages: list[PromptMessage], model_parameters: dict, + tools: Optional[list[PromptMessageTool]] = None, stop: Optional[List[str]] = None, + stream: bool = True, user: Optional[str] = None) -> None: + """ + After invoke callback + + :param llm_instance: LLM instance + :param result: result + :param model: model name + :param credentials: model credentials + :param prompt_messages: prompt messages + :param model_parameters: model parameters + :param tools: tools for tool calling + :param stop: stop words + :param stream: is stream response + :param user: unique user id + """ + self.agent_callback.on_llm_after_invoke( + result=result + ) + + def on_invoke_error(self, llm_instance: AIModel, ex: Exception, model: str, credentials: dict, + prompt_messages: list[PromptMessage], model_parameters: dict, + tools: Optional[list[PromptMessageTool]] = None, stop: Optional[List[str]] = None, + stream: bool = True, user: Optional[str] = None) -> None: + """ + Invoke error callback + + :param llm_instance: LLM instance + :param ex: exception + :param model: model name + :param credentials: model credentials + :param prompt_messages: prompt messages + :param model_parameters: model parameters + :param tools: tools for tool calling + :param stop: stop words + :param stream: is stream response + :param user: unique user id + """ + self.agent_callback.on_llm_error( + error=ex + ) diff --git a/api/core/agent/agent/calc_token_mixin.py b/api/core/agent/agent/calc_token_mixin.py index 97d2b7740f2ee9..d8cdf9fe2bbcf9 100644 --- a/api/core/agent/agent/calc_token_mixin.py +++ b/api/core/agent/agent/calc_token_mixin.py @@ -1,28 +1,49 @@ -from typing import List +from typing import List, cast from langchain.schema import BaseMessage -from core.model_providers.models.entity.message import to_prompt_messages -from core.model_providers.models.llm.base import BaseLLM +from core.entities.application_entities import ModelConfigEntity +from core.entities.message_entities import lc_messages_to_prompt_messages +from core.model_runtime.entities.message_entities import PromptMessage +from core.model_runtime.entities.model_entities import ModelPropertyKey +from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel class CalcTokenMixin: - def get_num_tokens_from_messages(self, model_instance: BaseLLM, messages: List[BaseMessage], **kwargs) -> int: - return model_instance.get_num_tokens(to_prompt_messages(messages)) - - def get_message_rest_tokens(self, model_instance: BaseLLM, messages: List[BaseMessage], **kwargs) -> int: + def get_message_rest_tokens(self, model_config: ModelConfigEntity, messages: List[PromptMessage], **kwargs) -> int: """ Got the rest tokens available for the model after excluding messages tokens and completion max tokens - :param llm: + :param model_config: :param messages: :return: """ - llm_max_tokens = model_instance.model_rules.max_tokens.max - completion_max_tokens = model_instance.model_kwargs.max_tokens - used_tokens = self.get_num_tokens_from_messages(model_instance, messages, **kwargs) - rest_tokens = llm_max_tokens - completion_max_tokens - used_tokens + model_type_instance = model_config.provider_model_bundle.model_type_instance + model_type_instance = cast(LargeLanguageModel, model_type_instance) + + model_context_tokens = model_config.model_schema.model_properties.get(ModelPropertyKey.CONTEXT_SIZE) + + max_tokens = 0 + for parameter_rule in model_config.model_schema.parameter_rules: + if (parameter_rule.name == 'max_tokens' + or (parameter_rule.use_template and parameter_rule.use_template == 'max_tokens')): + max_tokens = (model_config.parameters.get(parameter_rule.name) + or model_config.parameters.get(parameter_rule.use_template)) or 0 + + if model_context_tokens is None: + return 0 + + if max_tokens is None: + max_tokens = 0 + + prompt_tokens = model_type_instance.get_num_tokens( + model_config.model, + model_config.credentials, + messages + ) + + rest_tokens = model_context_tokens - max_tokens - prompt_tokens return rest_tokens diff --git a/api/core/agent/agent/multi_dataset_router_agent.py b/api/core/agent/agent/multi_dataset_router_agent.py index 4fb71211270df1..9b622a8689cc3a 100644 --- a/api/core/agent/agent/multi_dataset_router_agent.py +++ b/api/core/agent/agent/multi_dataset_router_agent.py @@ -1,4 +1,3 @@ -import json from typing import Tuple, List, Any, Union, Sequence, Optional, cast from langchain.agents import OpenAIFunctionsAgent, BaseSingleActionAgent @@ -6,13 +5,14 @@ from langchain.callbacks.base import BaseCallbackManager from langchain.callbacks.manager import Callbacks from langchain.prompts.chat import BaseMessagePromptTemplate -from langchain.schema import AgentAction, AgentFinish, SystemMessage, Generation, LLMResult, AIMessage -from langchain.schema.language_model import BaseLanguageModel +from langchain.schema import AgentAction, AgentFinish, SystemMessage, AIMessage from langchain.tools import BaseTool from pydantic import root_validator -from core.model_providers.models.entity.message import to_prompt_messages -from core.model_providers.models.llm.base import BaseLLM +from core.entities.application_entities import ModelConfigEntity +from core.model_manager import ModelInstance +from core.entities.message_entities import lc_messages_to_prompt_messages +from core.model_runtime.entities.message_entities import PromptMessageTool from core.third_party.langchain.llms.fake import FakeLLM @@ -20,7 +20,7 @@ class MultiDatasetRouterAgent(OpenAIFunctionsAgent): """ An Multi Dataset Retrieve Agent driven by Router. """ - model_instance: BaseLLM + model_config: ModelConfigEntity class Config: """Configuration for this pydantic object.""" @@ -81,8 +81,7 @@ def plan( agent_decision.return_values['output'] = '' return agent_decision except Exception as e: - new_exception = self.model_instance.handle_exceptions(e) - raise new_exception + raise e def real_plan( self, @@ -106,16 +105,39 @@ def real_plan( full_inputs = dict(**selected_inputs, agent_scratchpad=agent_scratchpad) prompt = self.prompt.format_prompt(**full_inputs) messages = prompt.to_messages() - prompt_messages = to_prompt_messages(messages) - result = self.model_instance.run( - messages=prompt_messages, - functions=self.functions, + prompt_messages = lc_messages_to_prompt_messages(messages) + + model_instance = ModelInstance( + provider_model_bundle=self.model_config.provider_model_bundle, + model=self.model_config.model, + ) + + tools = [] + for function in self.functions: + tool = PromptMessageTool( + **function + ) + + tools.append(tool) + + result = model_instance.invoke_llm( + prompt_messages=prompt_messages, + tools=tools, + stream=False, + model_parameters={ + 'temperature': 0.2, + 'top_p': 0.3, + 'max_tokens': 1500 + } ) ai_message = AIMessage( - content=result.content, + content=result.message.content or "", additional_kwargs={ - 'function_call': result.function_call + 'function_call': { + 'id': result.message.tool_calls[0].id, + **result.message.tool_calls[0].function.dict() + } if result.message.tool_calls else None } ) @@ -133,7 +155,7 @@ async def aplan( @classmethod def from_llm_and_tools( cls, - model_instance: BaseLLM, + model_config: ModelConfigEntity, tools: Sequence[BaseTool], callback_manager: Optional[BaseCallbackManager] = None, extra_prompt_messages: Optional[List[BaseMessagePromptTemplate]] = None, @@ -147,7 +169,7 @@ def from_llm_and_tools( system_message=system_message, ) return cls( - model_instance=model_instance, + model_config=model_config, llm=FakeLLM(response=''), prompt=prompt, tools=tools, diff --git a/api/core/agent/agent/openai_function_call.py b/api/core/agent/agent/openai_function_call.py index 8931bdc216a6a0..181208eb6a457f 100644 --- a/api/core/agent/agent/openai_function_call.py +++ b/api/core/agent/agent/openai_function_call.py @@ -1,4 +1,4 @@ -from typing import List, Tuple, Any, Union, Sequence, Optional +from typing import List, Tuple, Any, Union, Sequence, Optional, cast from langchain.agents import OpenAIFunctionsAgent, BaseSingleActionAgent from langchain.agents.openai_functions_agent.base import _parse_ai_message, \ @@ -13,18 +13,23 @@ from langchain.tools import BaseTool from pydantic import root_validator +from core.agent.agent.agent_llm_callback import AgentLLMCallback from core.agent.agent.calc_token_mixin import ExceededLLMTokensLimitError, CalcTokenMixin from core.chain.llm_chain import LLMChain -from core.model_providers.models.entity.message import to_prompt_messages -from core.model_providers.models.llm.base import BaseLLM +from core.entities.application_entities import ModelConfigEntity +from core.model_manager import ModelInstance +from core.entities.message_entities import lc_messages_to_prompt_messages +from core.model_runtime.entities.message_entities import PromptMessageTool, PromptMessage +from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel from core.third_party.langchain.llms.fake import FakeLLM class AutoSummarizingOpenAIFunctionCallAgent(OpenAIFunctionsAgent, CalcTokenMixin): moving_summary_buffer: str = "" moving_summary_index: int = 0 - summary_model_instance: BaseLLM = None - model_instance: BaseLLM + summary_model_config: ModelConfigEntity = None + model_config: ModelConfigEntity + agent_llm_callback: Optional[AgentLLMCallback] = None class Config: """Configuration for this pydantic object.""" @@ -38,13 +43,14 @@ def validate_llm(cls, values: dict) -> dict: @classmethod def from_llm_and_tools( cls, - model_instance: BaseLLM, + model_config: ModelConfigEntity, tools: Sequence[BaseTool], callback_manager: Optional[BaseCallbackManager] = None, extra_prompt_messages: Optional[List[BaseMessagePromptTemplate]] = None, system_message: Optional[SystemMessage] = SystemMessage( content="You are a helpful AI assistant." ), + agent_llm_callback: Optional[AgentLLMCallback] = None, **kwargs: Any, ) -> BaseSingleActionAgent: prompt = cls.create_prompt( @@ -52,11 +58,12 @@ def from_llm_and_tools( system_message=system_message, ) return cls( - model_instance=model_instance, + model_config=model_config, llm=FakeLLM(response=''), prompt=prompt, tools=tools, callback_manager=callback_manager, + agent_llm_callback=agent_llm_callback, **kwargs, ) @@ -67,28 +74,49 @@ def should_use_agent(self, query: str): :param query: :return: """ - original_max_tokens = self.model_instance.model_kwargs.max_tokens - self.model_instance.model_kwargs.max_tokens = 40 + original_max_tokens = 0 + for parameter_rule in self.model_config.model_schema.parameter_rules: + if (parameter_rule.name == 'max_tokens' + or (parameter_rule.use_template and parameter_rule.use_template == 'max_tokens')): + original_max_tokens = (self.model_config.parameters.get(parameter_rule.name) + or self.model_config.parameters.get(parameter_rule.use_template)) or 0 + + self.model_config.parameters['max_tokens'] = 40 prompt = self.prompt.format_prompt(input=query, agent_scratchpad=[]) messages = prompt.to_messages() try: - prompt_messages = to_prompt_messages(messages) - result = self.model_instance.run( - messages=prompt_messages, - functions=self.functions, - callbacks=None + prompt_messages = lc_messages_to_prompt_messages(messages) + model_instance = ModelInstance( + provider_model_bundle=self.model_config.provider_model_bundle, + model=self.model_config.model, ) - except Exception as e: - new_exception = self.model_instance.handle_exceptions(e) - raise new_exception - function_call = result.function_call + tools = [] + for function in self.functions: + tool = PromptMessageTool( + **function + ) + + tools.append(tool) + + result = model_instance.invoke_llm( + prompt_messages=prompt_messages, + tools=tools, + stream=False, + model_parameters={ + 'temperature': 0.2, + 'top_p': 0.3, + 'max_tokens': 1500 + } + ) + except Exception as e: + raise e - self.model_instance.model_kwargs.max_tokens = original_max_tokens + self.model_config.parameters['max_tokens'] = original_max_tokens - return True if function_call else False + return True if result.message.tool_calls else False def plan( self, @@ -113,22 +141,46 @@ def plan( prompt = self.prompt.format_prompt(**full_inputs) messages = prompt.to_messages() + prompt_messages = lc_messages_to_prompt_messages(messages) + # summarize messages if rest_tokens < 0 try: - messages = self.summarize_messages_if_needed(messages, functions=self.functions) + prompt_messages = self.summarize_messages_if_needed(prompt_messages, functions=self.functions) except ExceededLLMTokensLimitError as e: return AgentFinish(return_values={"output": str(e)}, log=str(e)) - prompt_messages = to_prompt_messages(messages) - result = self.model_instance.run( - messages=prompt_messages, - functions=self.functions, + model_instance = ModelInstance( + provider_model_bundle=self.model_config.provider_model_bundle, + model=self.model_config.model, + ) + + tools = [] + for function in self.functions: + tool = PromptMessageTool( + **function + ) + + tools.append(tool) + + result = model_instance.invoke_llm( + prompt_messages=prompt_messages, + tools=tools, + stream=False, + callbacks=[self.agent_llm_callback] if self.agent_llm_callback else [], + model_parameters={ + 'temperature': 0.2, + 'top_p': 0.3, + 'max_tokens': 1500 + } ) ai_message = AIMessage( - content=result.content, + content=result.message.content or "", additional_kwargs={ - 'function_call': result.function_call + 'function_call': { + 'id': result.message.tool_calls[0].id, + **result.message.tool_calls[0].function.dict() + } if result.message.tool_calls else None } ) agent_decision = _parse_ai_message(ai_message) @@ -158,9 +210,14 @@ def return_stopped_response( except ValueError: return AgentFinish({"output": "I'm sorry, I don't know how to respond to that."}, "") - def summarize_messages_if_needed(self, messages: List[BaseMessage], **kwargs) -> List[BaseMessage]: + def summarize_messages_if_needed(self, messages: List[PromptMessage], **kwargs) -> List[PromptMessage]: # calculate rest tokens and summarize previous function observation messages if rest_tokens < 0 - rest_tokens = self.get_message_rest_tokens(self.model_instance, messages, **kwargs) + rest_tokens = self.get_message_rest_tokens( + self.model_config, + messages, + **kwargs + ) + rest_tokens = rest_tokens - 20 # to deal with the inaccuracy of rest_tokens if rest_tokens >= 0: return messages @@ -210,19 +267,19 @@ def predict_new_summary( ai_prefix="AI", ) - chain = LLMChain(model_instance=self.summary_model_instance, prompt=SUMMARY_PROMPT) + chain = LLMChain(model_config=self.summary_model_config, prompt=SUMMARY_PROMPT) return chain.predict(summary=existing_summary, new_lines=new_lines) - def get_num_tokens_from_messages(self, model_instance: BaseLLM, messages: List[BaseMessage], **kwargs) -> int: + def get_num_tokens_from_messages(self, model_config: ModelConfigEntity, messages: List[BaseMessage], **kwargs) -> int: """Calculate num tokens for gpt-3.5-turbo and gpt-4 with tiktoken package. Official documentation: https://github.com/openai/openai-cookbook/blob/ main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb""" - if model_instance.model_provider.provider_name == 'azure_openai': - model = model_instance.base_model_name + if model_config.provider == 'azure_openai': + model = model_config.model model = model.replace("gpt-35", "gpt-3.5") else: - model = model_instance.base_model_name + model = model_config.credentials.get("base_model_name") tiktoken_ = _import_tiktoken() try: diff --git a/api/core/agent/agent/output_parser/retirver_dataset_agent.py b/api/core/agent/agent/output_parser/retirver_dataset_agent.py deleted file mode 100644 index 16b4a2ab248786..00000000000000 --- a/api/core/agent/agent/output_parser/retirver_dataset_agent.py +++ /dev/null @@ -1,158 +0,0 @@ -import json -from typing import Tuple, List, Any, Union, Sequence, Optional, cast - -from langchain.agents import OpenAIFunctionsAgent, BaseSingleActionAgent -from langchain.agents.openai_functions_agent.base import _format_intermediate_steps, _parse_ai_message -from langchain.callbacks.base import BaseCallbackManager -from langchain.callbacks.manager import Callbacks -from langchain.prompts.chat import BaseMessagePromptTemplate -from langchain.schema import AgentAction, AgentFinish, SystemMessage, Generation, LLMResult, AIMessage -from langchain.schema.language_model import BaseLanguageModel -from langchain.tools import BaseTool -from pydantic import root_validator - -from core.model_providers.models.entity.message import to_prompt_messages -from core.model_providers.models.llm.base import BaseLLM -from core.third_party.langchain.llms.fake import FakeLLM -from core.tool.dataset_retriever_tool import DatasetRetrieverTool - - -class MultiDatasetRouterAgent(OpenAIFunctionsAgent): - """ - An Multi Dataset Retrieve Agent driven by Router. - """ - model_instance: BaseLLM - - class Config: - """Configuration for this pydantic object.""" - - arbitrary_types_allowed = True - - @root_validator - def validate_llm(cls, values: dict) -> dict: - return values - - def should_use_agent(self, query: str): - """ - return should use agent - - :param query: - :return: - """ - return True - - def plan( - self, - intermediate_steps: List[Tuple[AgentAction, str]], - callbacks: Callbacks = None, - **kwargs: Any, - ) -> Union[AgentAction, AgentFinish]: - """Given input, decided what to do. - - Args: - intermediate_steps: Steps the LLM has taken to date, along with observations - **kwargs: User inputs. - - Returns: - Action specifying what tool to use. - """ - if len(self.tools) == 0: - return AgentFinish(return_values={"output": ''}, log='') - elif len(self.tools) == 1: - tool = next(iter(self.tools)) - tool = cast(DatasetRetrieverTool, tool) - rst = tool.run(tool_input={'query': kwargs['input']}) - # output = '' - # rst_json = json.loads(rst) - # for item in rst_json: - # output += f'{item["content"]}\n' - return AgentFinish(return_values={"output": rst}, log=rst) - - if intermediate_steps: - _, observation = intermediate_steps[-1] - return AgentFinish(return_values={"output": observation}, log=observation) - - try: - agent_decision = self.real_plan(intermediate_steps, callbacks, **kwargs) - if isinstance(agent_decision, AgentAction): - tool_inputs = agent_decision.tool_input - if isinstance(tool_inputs, dict) and 'query' in tool_inputs and 'chat_history' not in kwargs: - tool_inputs['query'] = kwargs['input'] - agent_decision.tool_input = tool_inputs - else: - agent_decision.return_values['output'] = '' - return agent_decision - except Exception as e: - new_exception = self.model_instance.handle_exceptions(e) - raise new_exception - - def real_plan( - self, - intermediate_steps: List[Tuple[AgentAction, str]], - callbacks: Callbacks = None, - **kwargs: Any, - ) -> Union[AgentAction, AgentFinish]: - """Given input, decided what to do. - - Args: - intermediate_steps: Steps the LLM has taken to date, along with observations - **kwargs: User inputs. - - Returns: - Action specifying what tool to use. - """ - agent_scratchpad = _format_intermediate_steps(intermediate_steps) - selected_inputs = { - k: kwargs[k] for k in self.prompt.input_variables if k != "agent_scratchpad" - } - full_inputs = dict(**selected_inputs, agent_scratchpad=agent_scratchpad) - prompt = self.prompt.format_prompt(**full_inputs) - messages = prompt.to_messages() - prompt_messages = to_prompt_messages(messages) - result = self.model_instance.run( - messages=prompt_messages, - functions=self.functions, - ) - - ai_message = AIMessage( - content=result.content, - additional_kwargs={ - 'function_call': result.function_call - } - ) - - agent_decision = _parse_ai_message(ai_message) - return agent_decision - - async def aplan( - self, - intermediate_steps: List[Tuple[AgentAction, str]], - callbacks: Callbacks = None, - **kwargs: Any, - ) -> Union[AgentAction, AgentFinish]: - raise NotImplementedError() - - @classmethod - def from_llm_and_tools( - cls, - model_instance: BaseLLM, - tools: Sequence[BaseTool], - callback_manager: Optional[BaseCallbackManager] = None, - extra_prompt_messages: Optional[List[BaseMessagePromptTemplate]] = None, - system_message: Optional[SystemMessage] = SystemMessage( - content="You are a helpful AI assistant." - ), - **kwargs: Any, - ) -> BaseSingleActionAgent: - prompt = cls.create_prompt( - extra_prompt_messages=extra_prompt_messages, - system_message=system_message, - ) - return cls( - model_instance=model_instance, - llm=FakeLLM(response=''), - prompt=prompt, - tools=tools, - callback_manager=callback_manager, - **kwargs, - ) diff --git a/api/core/agent/agent/structed_multi_dataset_router_agent.py b/api/core/agent/agent/structed_multi_dataset_router_agent.py index 115ed69d17879d..bc35ef0371e29b 100644 --- a/api/core/agent/agent/structed_multi_dataset_router_agent.py +++ b/api/core/agent/agent/structed_multi_dataset_router_agent.py @@ -12,9 +12,7 @@ from langchain.agents.structured_chat.prompt import PREFIX, SUFFIX from core.chain.llm_chain import LLMChain -from core.model_providers.models.entity.model_params import ModelMode -from core.model_providers.models.llm.base import BaseLLM -from core.tool.dataset_retriever_tool import DatasetRetrieverTool +from core.entities.application_entities import ModelConfigEntity FORMAT_INSTRUCTIONS = """Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input). The nouns in the format of "Thought", "Action", "Action Input", "Final Answer" must be expressed in English. @@ -69,10 +67,10 @@ def should_use_agent(self, query: str): return True def plan( - self, - intermediate_steps: List[Tuple[AgentAction, str]], - callbacks: Callbacks = None, - **kwargs: Any, + self, + intermediate_steps: List[Tuple[AgentAction, str]], + callbacks: Callbacks = None, + **kwargs: Any, ) -> Union[AgentAction, AgentFinish]: """Given input, decided what to do. @@ -101,8 +99,7 @@ def plan( try: full_output = self.llm_chain.predict(callbacks=callbacks, **full_inputs) except Exception as e: - new_exception = self.llm_chain.model_instance.handle_exceptions(e) - raise new_exception + raise e try: agent_decision = self.output_parser.parse(full_output) @@ -119,6 +116,7 @@ def plan( except OutputParserException: return AgentFinish({"output": "I'm sorry, the answer of model is invalid, " "I don't know how to respond to that."}, "") + @classmethod def create_prompt( cls, @@ -182,7 +180,7 @@ def create_completion_prompt( return PromptTemplate(template=template, input_variables=input_variables) def _construct_scratchpad( - self, intermediate_steps: List[Tuple[AgentAction, str]] + self, intermediate_steps: List[Tuple[AgentAction, str]] ) -> str: agent_scratchpad = "" for action, observation in intermediate_steps: @@ -193,7 +191,7 @@ def _construct_scratchpad( raise ValueError("agent_scratchpad should be of type string.") if agent_scratchpad: llm_chain = cast(LLMChain, self.llm_chain) - if llm_chain.model_instance.model_mode == ModelMode.CHAT: + if llm_chain.model_config.mode == "chat": return ( f"This was your previous work " f"(but I haven't seen any of it! I only see what " @@ -207,7 +205,7 @@ def _construct_scratchpad( @classmethod def from_llm_and_tools( cls, - model_instance: BaseLLM, + model_config: ModelConfigEntity, tools: Sequence[BaseTool], callback_manager: Optional[BaseCallbackManager] = None, output_parser: Optional[AgentOutputParser] = None, @@ -221,7 +219,7 @@ def from_llm_and_tools( ) -> Agent: """Construct an agent from an LLM and tools.""" cls._validate_tools(tools) - if model_instance.model_mode == ModelMode.CHAT: + if model_config.mode == "chat": prompt = cls.create_prompt( tools, prefix=prefix, @@ -238,10 +236,16 @@ def from_llm_and_tools( format_instructions=format_instructions, input_variables=input_variables ) + llm_chain = LLMChain( - model_instance=model_instance, + model_config=model_config, prompt=prompt, callback_manager=callback_manager, + parameters={ + 'temperature': 0.2, + 'top_p': 0.3, + 'max_tokens': 1500 + } ) tool_names = [tool.name for tool in tools] _output_parser = output_parser diff --git a/api/core/agent/agent/structured_chat.py b/api/core/agent/agent/structured_chat.py index aca6de79b08106..bbce1ca4402cb6 100644 --- a/api/core/agent/agent/structured_chat.py +++ b/api/core/agent/agent/structured_chat.py @@ -13,10 +13,11 @@ from langchain.tools import BaseTool from langchain.agents.structured_chat.prompt import PREFIX, SUFFIX +from core.agent.agent.agent_llm_callback import AgentLLMCallback from core.agent.agent.calc_token_mixin import CalcTokenMixin, ExceededLLMTokensLimitError from core.chain.llm_chain import LLMChain -from core.model_providers.models.entity.model_params import ModelMode -from core.model_providers.models.llm.base import BaseLLM +from core.entities.application_entities import ModelConfigEntity +from core.entities.message_entities import lc_messages_to_prompt_messages FORMAT_INSTRUCTIONS = """Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input). The nouns in the format of "Thought", "Action", "Action Input", "Final Answer" must be expressed in English. @@ -54,7 +55,7 @@ class AutoSummarizingStructuredChatAgent(StructuredChatAgent, CalcTokenMixin): moving_summary_buffer: str = "" moving_summary_index: int = 0 - summary_model_instance: BaseLLM = None + summary_model_config: ModelConfigEntity = None class Config: """Configuration for this pydantic object.""" @@ -82,7 +83,7 @@ def plan( Args: intermediate_steps: Steps the LLM has taken to date, - along with observations + along with observatons callbacks: Callbacks to run. **kwargs: User inputs. @@ -96,15 +97,16 @@ def plan( if prompts: messages = prompts[0].to_messages() - rest_tokens = self.get_message_rest_tokens(self.llm_chain.model_instance, messages) + prompt_messages = lc_messages_to_prompt_messages(messages) + + rest_tokens = self.get_message_rest_tokens(self.llm_chain.model_config, prompt_messages) if rest_tokens < 0: full_inputs = self.summarize_messages(intermediate_steps, **kwargs) try: full_output = self.llm_chain.predict(callbacks=callbacks, **full_inputs) except Exception as e: - new_exception = self.llm_chain.model_instance.handle_exceptions(e) - raise new_exception + raise e try: agent_decision = self.output_parser.parse(full_output) @@ -119,7 +121,7 @@ def plan( "I don't know how to respond to that."}, "") def summarize_messages(self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs): - if len(intermediate_steps) >= 2 and self.summary_model_instance: + if len(intermediate_steps) >= 2 and self.summary_model_config: should_summary_intermediate_steps = intermediate_steps[self.moving_summary_index:-1] should_summary_messages = [AIMessage(content=observation) for _, observation in should_summary_intermediate_steps] @@ -153,7 +155,7 @@ def predict_new_summary( ai_prefix="AI", ) - chain = LLMChain(model_instance=self.summary_model_instance, prompt=SUMMARY_PROMPT) + chain = LLMChain(model_config=self.summary_model_config, prompt=SUMMARY_PROMPT) return chain.predict(summary=existing_summary, new_lines=new_lines) @classmethod @@ -229,7 +231,7 @@ def _construct_scratchpad( raise ValueError("agent_scratchpad should be of type string.") if agent_scratchpad: llm_chain = cast(LLMChain, self.llm_chain) - if llm_chain.model_instance.model_mode == ModelMode.CHAT: + if llm_chain.model_config.mode == "chat": return ( f"This was your previous work " f"(but I haven't seen any of it! I only see what " @@ -243,7 +245,7 @@ def _construct_scratchpad( @classmethod def from_llm_and_tools( cls, - model_instance: BaseLLM, + model_config: ModelConfigEntity, tools: Sequence[BaseTool], callback_manager: Optional[BaseCallbackManager] = None, output_parser: Optional[AgentOutputParser] = None, @@ -253,11 +255,12 @@ def from_llm_and_tools( format_instructions: str = FORMAT_INSTRUCTIONS, input_variables: Optional[List[str]] = None, memory_prompts: Optional[List[BasePromptTemplate]] = None, + agent_llm_callback: Optional[AgentLLMCallback] = None, **kwargs: Any, ) -> Agent: """Construct an agent from an LLM and tools.""" cls._validate_tools(tools) - if model_instance.model_mode == ModelMode.CHAT: + if model_config.mode == "chat": prompt = cls.create_prompt( tools, prefix=prefix, @@ -275,9 +278,15 @@ def from_llm_and_tools( input_variables=input_variables, ) llm_chain = LLMChain( - model_instance=model_instance, + model_config=model_config, prompt=prompt, callback_manager=callback_manager, + agent_llm_callback=agent_llm_callback, + parameters={ + 'temperature': 0.2, + 'top_p': 0.3, + 'max_tokens': 1500 + } ) tool_names = [tool.name for tool in tools] _output_parser = output_parser diff --git a/api/core/agent/agent_executor.py b/api/core/agent/agent_executor.py index 40200c60e44dcc..52cc424ffbf471 100644 --- a/api/core/agent/agent_executor.py +++ b/api/core/agent/agent_executor.py @@ -4,10 +4,10 @@ from langchain.agents import BaseSingleActionAgent, BaseMultiActionAgent from langchain.callbacks.manager import Callbacks -from langchain.memory.chat_memory import BaseChatMemory from langchain.tools import BaseTool from pydantic import BaseModel, Extra +from core.agent.agent.agent_llm_callback import AgentLLMCallback from core.agent.agent.multi_dataset_router_agent import MultiDatasetRouterAgent from core.agent.agent.openai_function_call import AutoSummarizingOpenAIFunctionCallAgent from core.agent.agent.output_parser.structured_chat import StructuredChatOutputParser @@ -15,9 +15,11 @@ from core.agent.agent.structured_chat import AutoSummarizingStructuredChatAgent from langchain.agents import AgentExecutor as LCAgentExecutor +from core.entities.application_entities import ModelConfigEntity +from core.entities.message_entities import prompt_messages_to_lc_messages from core.helper import moderation -from core.model_providers.error import LLMError -from core.model_providers.models.llm.base import BaseLLM +from core.memory.token_buffer_memory import TokenBufferMemory +from core.model_runtime.errors.invoke import InvokeError from core.tool.dataset_multi_retriever_tool import DatasetMultiRetrieverTool from core.tool.dataset_retriever_tool import DatasetRetrieverTool @@ -31,14 +33,15 @@ class PlanningStrategy(str, enum.Enum): class AgentConfiguration(BaseModel): strategy: PlanningStrategy - model_instance: BaseLLM + model_config: ModelConfigEntity tools: list[BaseTool] - summary_model_instance: BaseLLM = None - memory: Optional[BaseChatMemory] = None + summary_model_config: Optional[ModelConfigEntity] = None + memory: Optional[TokenBufferMemory] = None callbacks: Callbacks = None max_iterations: int = 6 max_execution_time: Optional[float] = None early_stopping_method: str = "generate" + agent_llm_callback: Optional[AgentLLMCallback] = None # `generate` will continue to complete the last inference after reaching the iteration limit or request time limit class Config: @@ -62,34 +65,42 @@ def __init__(self, configuration: AgentConfiguration): def _init_agent(self) -> Union[BaseSingleActionAgent, BaseMultiActionAgent]: if self.configuration.strategy == PlanningStrategy.REACT: agent = AutoSummarizingStructuredChatAgent.from_llm_and_tools( - model_instance=self.configuration.model_instance, + model_config=self.configuration.model_config, tools=self.configuration.tools, output_parser=StructuredChatOutputParser(), - summary_model_instance=self.configuration.summary_model_instance - if self.configuration.summary_model_instance else None, + summary_model_config=self.configuration.summary_model_config + if self.configuration.summary_model_config else None, + agent_llm_callback=self.configuration.agent_llm_callback, verbose=True ) elif self.configuration.strategy == PlanningStrategy.FUNCTION_CALL: agent = AutoSummarizingOpenAIFunctionCallAgent.from_llm_and_tools( - model_instance=self.configuration.model_instance, + model_config=self.configuration.model_config, tools=self.configuration.tools, - extra_prompt_messages=self.configuration.memory.buffer if self.configuration.memory else None, # used for read chat histories memory - summary_model_instance=self.configuration.summary_model_instance - if self.configuration.summary_model_instance else None, + extra_prompt_messages=prompt_messages_to_lc_messages(self.configuration.memory.get_history_prompt_messages()) + if self.configuration.memory else None, # used for read chat histories memory + summary_model_config=self.configuration.summary_model_config + if self.configuration.summary_model_config else None, + agent_llm_callback=self.configuration.agent_llm_callback, verbose=True ) elif self.configuration.strategy == PlanningStrategy.ROUTER: - self.configuration.tools = [t for t in self.configuration.tools if isinstance(t, DatasetRetrieverTool) or isinstance(t, DatasetMultiRetrieverTool)] + self.configuration.tools = [t for t in self.configuration.tools + if isinstance(t, DatasetRetrieverTool) + or isinstance(t, DatasetMultiRetrieverTool)] agent = MultiDatasetRouterAgent.from_llm_and_tools( - model_instance=self.configuration.model_instance, + model_config=self.configuration.model_config, tools=self.configuration.tools, - extra_prompt_messages=self.configuration.memory.buffer if self.configuration.memory else None, + extra_prompt_messages=prompt_messages_to_lc_messages(self.configuration.memory.get_history_prompt_messages()) + if self.configuration.memory else None, verbose=True ) elif self.configuration.strategy == PlanningStrategy.REACT_ROUTER: - self.configuration.tools = [t for t in self.configuration.tools if isinstance(t, DatasetRetrieverTool) or isinstance(t, DatasetMultiRetrieverTool)] + self.configuration.tools = [t for t in self.configuration.tools + if isinstance(t, DatasetRetrieverTool) + or isinstance(t, DatasetMultiRetrieverTool)] agent = StructuredMultiDatasetRouterAgent.from_llm_and_tools( - model_instance=self.configuration.model_instance, + model_config=self.configuration.model_config, tools=self.configuration.tools, output_parser=StructuredChatOutputParser(), verbose=True @@ -104,11 +115,11 @@ def should_use_agent(self, query: str) -> bool: def run(self, query: str) -> AgentExecuteResult: moderation_result = moderation.check_moderation( - self.configuration.model_instance.model_provider, + self.configuration.model_config, query ) - if not moderation_result: + if moderation_result: return AgentExecuteResult( output="I apologize for any confusion, but I'm an AI assistant to be helpful, harmless, and honest.", strategy=self.configuration.strategy, @@ -118,7 +129,6 @@ def run(self, query: str) -> AgentExecuteResult: agent_executor = LCAgentExecutor.from_agent_and_tools( agent=self.agent, tools=self.configuration.tools, - memory=self.configuration.memory, max_iterations=self.configuration.max_iterations, max_execution_time=self.configuration.max_execution_time, early_stopping_method=self.configuration.early_stopping_method, @@ -126,8 +136,8 @@ def run(self, query: str) -> AgentExecuteResult: ) try: - output = agent_executor.run(query) - except LLMError as ex: + output = agent_executor.run(input=query) + except InvokeError as ex: raise ex except Exception as ex: logging.exception("agent_executor run failed") diff --git a/api/core/model_providers/models/__init__.py b/api/core/app_runner/__init__.py similarity index 100% rename from api/core/model_providers/models/__init__.py rename to api/core/app_runner/__init__.py diff --git a/api/core/app_runner/agent_app_runner.py b/api/core/app_runner/agent_app_runner.py new file mode 100644 index 00000000000000..02bdae900dedba --- /dev/null +++ b/api/core/app_runner/agent_app_runner.py @@ -0,0 +1,251 @@ +import json +import logging +from typing import cast + +from core.agent.agent.agent_llm_callback import AgentLLMCallback +from core.app_runner.app_runner import AppRunner +from core.callback_handler.agent_loop_gather_callback_handler import AgentLoopGatherCallbackHandler +from core.entities.application_entities import ApplicationGenerateEntity, PromptTemplateEntity, ModelConfigEntity +from core.application_queue_manager import ApplicationQueueManager +from core.features.agent_runner import AgentRunnerFeature +from core.memory.token_buffer_memory import TokenBufferMemory +from core.model_manager import ModelInstance +from core.model_runtime.entities.llm_entities import LLMUsage +from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel +from extensions.ext_database import db +from models.model import Conversation, Message, App, MessageChain, MessageAgentThought + +logger = logging.getLogger(__name__) + + +class AgentApplicationRunner(AppRunner): + """ + Agent Application Runner + """ + + def run(self, application_generate_entity: ApplicationGenerateEntity, + queue_manager: ApplicationQueueManager, + conversation: Conversation, + message: Message) -> None: + """ + Run agent application + :param application_generate_entity: application generate entity + :param queue_manager: application queue manager + :param conversation: conversation + :param message: message + :return: + """ + app_record = db.session.query(App).filter(App.id == application_generate_entity.app_id).first() + if not app_record: + raise ValueError(f"App not found") + + app_orchestration_config = application_generate_entity.app_orchestration_config_entity + + inputs = application_generate_entity.inputs + query = application_generate_entity.query + files = application_generate_entity.files + + # Pre-calculate the number of tokens of the prompt messages, + # and return the rest number of tokens by model context token size limit and max token size limit. + # If the rest number of tokens is not enough, raise exception. + # Include: prompt template, inputs, query(optional), files(optional) + # Not Include: memory, external data, dataset context + self.get_pre_calculate_rest_tokens( + app_record=app_record, + model_config=app_orchestration_config.model_config, + prompt_template_entity=app_orchestration_config.prompt_template, + inputs=inputs, + files=files, + query=query + ) + + memory = None + if application_generate_entity.conversation_id: + # get memory of conversation (read-only) + model_instance = ModelInstance( + provider_model_bundle=app_orchestration_config.model_config.provider_model_bundle, + model=app_orchestration_config.model_config.model + ) + + memory = TokenBufferMemory( + conversation=conversation, + model_instance=model_instance + ) + + # reorganize all inputs and template to prompt messages + # Include: prompt template, inputs, query(optional), files(optional) + # memory(optional) + prompt_messages, stop = self.originze_prompt_messages( + app_record=app_record, + model_config=app_orchestration_config.model_config, + prompt_template_entity=app_orchestration_config.prompt_template, + inputs=inputs, + files=files, + query=query, + context=None, + memory=memory + ) + + # Create MessageChain + message_chain = self._init_message_chain( + message=message, + query=query + ) + + # add agent callback to record agent thoughts + agent_callback = AgentLoopGatherCallbackHandler( + model_config=app_orchestration_config.model_config, + message=message, + queue_manager=queue_manager, + message_chain=message_chain + ) + + # init LLM Callback + agent_llm_callback = AgentLLMCallback( + agent_callback=agent_callback + ) + + agent_runner = AgentRunnerFeature( + tenant_id=application_generate_entity.tenant_id, + app_orchestration_config=app_orchestration_config, + model_config=app_orchestration_config.model_config, + config=app_orchestration_config.agent, + queue_manager=queue_manager, + message=message, + user_id=application_generate_entity.user_id, + agent_llm_callback=agent_llm_callback, + callback=agent_callback, + memory=memory + ) + + # agent run + result = agent_runner.run( + query=query, + invoke_from=application_generate_entity.invoke_from + ) + + if result: + self._save_message_chain( + message_chain=message_chain, + output_text=result + ) + + if (result + and app_orchestration_config.prompt_template.prompt_type == PromptTemplateEntity.PromptType.SIMPLE + and app_orchestration_config.prompt_template.simple_prompt_template + ): + # Direct output if agent result exists and has pre prompt + self.direct_output( + queue_manager=queue_manager, + app_orchestration_config=app_orchestration_config, + prompt_messages=prompt_messages, + stream=application_generate_entity.stream, + text=result, + usage=self._get_usage_of_all_agent_thoughts( + model_config=app_orchestration_config.model_config, + message=message + ) + ) + else: + # As normal LLM run, agent result as context + context = result + + # reorganize all inputs and template to prompt messages + # Include: prompt template, inputs, query(optional), files(optional) + # memory(optional), external data, dataset context(optional) + prompt_messages, stop = self.originze_prompt_messages( + app_record=app_record, + model_config=app_orchestration_config.model_config, + prompt_template_entity=app_orchestration_config.prompt_template, + inputs=inputs, + files=files, + query=query, + context=context, + memory=memory + ) + + # Re-calculate the max tokens if sum(prompt_token + max_tokens) over model token limit + self.recale_llm_max_tokens( + model_config=app_orchestration_config.model_config, + prompt_messages=prompt_messages + ) + + # Invoke model + model_instance = ModelInstance( + provider_model_bundle=app_orchestration_config.model_config.provider_model_bundle, + model=app_orchestration_config.model_config.model + ) + + invoke_result = model_instance.invoke_llm( + prompt_messages=prompt_messages, + model_parameters=app_orchestration_config.model_config.parameters, + stop=stop, + stream=application_generate_entity.stream, + user=application_generate_entity.user_id, + ) + + # handle invoke result + self._handle_invoke_result( + invoke_result=invoke_result, + queue_manager=queue_manager, + stream=application_generate_entity.stream + ) + + def _init_message_chain(self, message: Message, query: str) -> MessageChain: + """ + Init MessageChain + :param message: message + :param query: query + :return: + """ + message_chain = MessageChain( + message_id=message.id, + type="AgentExecutor", + input=json.dumps({ + "input": query + }) + ) + + db.session.add(message_chain) + db.session.commit() + + return message_chain + + def _save_message_chain(self, message_chain: MessageChain, output_text: str) -> None: + """ + Save MessageChain + :param message_chain: message chain + :param output_text: output text + :return: + """ + message_chain.output = json.dumps({ + "output": output_text + }) + db.session.commit() + + def _get_usage_of_all_agent_thoughts(self, model_config: ModelConfigEntity, + message: Message) -> LLMUsage: + """ + Get usage of all agent thoughts + :param model_config: model config + :param message: message + :return: + """ + agent_thoughts = (db.session.query(MessageAgentThought) + .filter(MessageAgentThought.message_id == message.id).all()) + + all_message_tokens = 0 + all_answer_tokens = 0 + for agent_thought in agent_thoughts: + all_message_tokens += agent_thought.message_tokens + all_answer_tokens += agent_thought.answer_tokens + + model_type_instance = model_config.provider_model_bundle.model_type_instance + model_type_instance = cast(LargeLanguageModel, model_type_instance) + + return model_type_instance._calc_response_usage( + model_config.model, + model_config.credentials, + all_message_tokens, + all_answer_tokens + ) diff --git a/api/core/app_runner/app_runner.py b/api/core/app_runner/app_runner.py new file mode 100644 index 00000000000000..4f247aa8dab838 --- /dev/null +++ b/api/core/app_runner/app_runner.py @@ -0,0 +1,267 @@ +import time +from typing import cast, Optional, List, Tuple, Generator, Union + +from core.application_queue_manager import ApplicationQueueManager +from core.entities.application_entities import ModelConfigEntity, PromptTemplateEntity, AppOrchestrationConfigEntity +from core.file.file_obj import FileObj +from core.memory.token_buffer_memory import TokenBufferMemory +from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta, LLMUsage +from core.model_runtime.entities.message_entities import PromptMessage, AssistantPromptMessage +from core.model_runtime.entities.model_entities import ModelPropertyKey +from core.model_runtime.errors.invoke import InvokeBadRequestError +from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel +from core.prompt.prompt_transform import PromptTransform +from models.model import App + + +class AppRunner: + def get_pre_calculate_rest_tokens(self, app_record: App, + model_config: ModelConfigEntity, + prompt_template_entity: PromptTemplateEntity, + inputs: dict[str, str], + files: list[FileObj], + query: Optional[str] = None) -> int: + """ + Get pre calculate rest tokens + :param app_record: app record + :param model_config: model config entity + :param prompt_template_entity: prompt template entity + :param inputs: inputs + :param files: files + :param query: query + :return: + """ + model_type_instance = model_config.provider_model_bundle.model_type_instance + model_type_instance = cast(LargeLanguageModel, model_type_instance) + + model_context_tokens = model_config.model_schema.model_properties.get(ModelPropertyKey.CONTEXT_SIZE) + + max_tokens = 0 + for parameter_rule in model_config.model_schema.parameter_rules: + if (parameter_rule.name == 'max_tokens' + or (parameter_rule.use_template and parameter_rule.use_template == 'max_tokens')): + max_tokens = (model_config.parameters.get(parameter_rule.name) + or model_config.parameters.get(parameter_rule.use_template)) or 0 + + if model_context_tokens is None: + return -1 + + if max_tokens is None: + max_tokens = 0 + + # get prompt messages without memory and context + prompt_messages, stop = self.originze_prompt_messages( + app_record=app_record, + model_config=model_config, + prompt_template_entity=prompt_template_entity, + inputs=inputs, + files=files, + query=query + ) + + prompt_tokens = model_type_instance.get_num_tokens( + model_config.model, + model_config.credentials, + prompt_messages + ) + + rest_tokens = model_context_tokens - max_tokens - prompt_tokens + if rest_tokens < 0: + raise InvokeBadRequestError("Query or prefix prompt is too long, you can reduce the prefix prompt, " + "or shrink the max token, or switch to a llm with a larger token limit size.") + + return rest_tokens + + def recale_llm_max_tokens(self, model_config: ModelConfigEntity, + prompt_messages: List[PromptMessage]): + # recalc max_tokens if sum(prompt_token + max_tokens) over model token limit + model_type_instance = model_config.provider_model_bundle.model_type_instance + model_type_instance = cast(LargeLanguageModel, model_type_instance) + + model_context_tokens = model_config.model_schema.model_properties.get(ModelPropertyKey.CONTEXT_SIZE) + + max_tokens = 0 + for parameter_rule in model_config.model_schema.parameter_rules: + if (parameter_rule.name == 'max_tokens' + or (parameter_rule.use_template and parameter_rule.use_template == 'max_tokens')): + max_tokens = (model_config.parameters.get(parameter_rule.name) + or model_config.parameters.get(parameter_rule.use_template)) or 0 + + if model_context_tokens is None: + return -1 + + if max_tokens is None: + max_tokens = 0 + + prompt_tokens = model_type_instance.get_num_tokens( + model_config.model, + model_config.credentials, + prompt_messages + ) + + if prompt_tokens + max_tokens > model_context_tokens: + max_tokens = max(model_context_tokens - prompt_tokens, 16) + + for parameter_rule in model_config.model_schema.parameter_rules: + if (parameter_rule.name == 'max_tokens' + or (parameter_rule.use_template and parameter_rule.use_template == 'max_tokens')): + model_config.parameters[parameter_rule.name] = max_tokens + + def originze_prompt_messages(self, app_record: App, + model_config: ModelConfigEntity, + prompt_template_entity: PromptTemplateEntity, + inputs: dict[str, str], + files: list[FileObj], + query: Optional[str] = None, + context: Optional[str] = None, + memory: Optional[TokenBufferMemory] = None) \ + -> Tuple[List[PromptMessage], Optional[List[str]]]: + """ + Organize prompt messages + :param context: + :param app_record: app record + :param model_config: model config entity + :param prompt_template_entity: prompt template entity + :param inputs: inputs + :param files: files + :param query: query + :param memory: memory + :return: + """ + prompt_transform = PromptTransform() + + # get prompt without memory and context + if prompt_template_entity.prompt_type == PromptTemplateEntity.PromptType.SIMPLE: + prompt_messages, stop = prompt_transform.get_prompt( + app_mode=app_record.mode, + prompt_template_entity=prompt_template_entity, + inputs=inputs, + query=query if query else '', + files=files, + context=context, + memory=memory, + model_config=model_config + ) + else: + prompt_messages = prompt_transform.get_advanced_prompt( + app_mode=app_record.mode, + prompt_template_entity=prompt_template_entity, + inputs=inputs, + query=query, + files=files, + context=context, + memory=memory, + model_config=model_config + ) + stop = model_config.stop + + return prompt_messages, stop + + def direct_output(self, queue_manager: ApplicationQueueManager, + app_orchestration_config: AppOrchestrationConfigEntity, + prompt_messages: list, + text: str, + stream: bool, + usage: Optional[LLMUsage] = None) -> None: + """ + Direct output + :param queue_manager: application queue manager + :param app_orchestration_config: app orchestration config + :param prompt_messages: prompt messages + :param text: text + :param stream: stream + :param usage: usage + :return: + """ + if stream: + index = 0 + for token in text: + queue_manager.publish_chunk_message(LLMResultChunk( + model=app_orchestration_config.model_config.model, + prompt_messages=prompt_messages, + delta=LLMResultChunkDelta( + index=index, + message=AssistantPromptMessage(content=token) + ) + )) + index += 1 + time.sleep(0.01) + + queue_manager.publish_message_end( + llm_result=LLMResult( + model=app_orchestration_config.model_config.model, + prompt_messages=prompt_messages, + message=AssistantPromptMessage(content=text), + usage=usage if usage else LLMUsage.empty_usage() + ) + ) + + def _handle_invoke_result(self, invoke_result: Union[LLMResult, Generator], + queue_manager: ApplicationQueueManager, + stream: bool) -> None: + """ + Handle invoke result + :param invoke_result: invoke result + :param queue_manager: application queue manager + :param stream: stream + :return: + """ + if not stream: + self._handle_invoke_result_direct( + invoke_result=invoke_result, + queue_manager=queue_manager + ) + else: + self._handle_invoke_result_stream( + invoke_result=invoke_result, + queue_manager=queue_manager + ) + + def _handle_invoke_result_direct(self, invoke_result: LLMResult, + queue_manager: ApplicationQueueManager) -> None: + """ + Handle invoke result direct + :param invoke_result: invoke result + :param queue_manager: application queue manager + :return: + """ + queue_manager.publish_message_end( + llm_result=invoke_result + ) + + def _handle_invoke_result_stream(self, invoke_result: Generator, + queue_manager: ApplicationQueueManager) -> None: + """ + Handle invoke result + :param invoke_result: invoke result + :param queue_manager: application queue manager + :return: + """ + model = None + prompt_messages = [] + text = '' + usage = None + for result in invoke_result: + queue_manager.publish_chunk_message(result) + + text += result.delta.message.content + + if not model: + model = result.model + + if not prompt_messages: + prompt_messages = result.prompt_messages + + if not usage and result.delta.usage: + usage = result.delta.usage + + llm_result = LLMResult( + model=model, + prompt_messages=prompt_messages, + message=AssistantPromptMessage(content=text), + usage=usage + ) + + queue_manager.publish_message_end( + llm_result=llm_result + ) diff --git a/api/core/app_runner/basic_app_runner.py b/api/core/app_runner/basic_app_runner.py new file mode 100644 index 00000000000000..517cd8302adb70 --- /dev/null +++ b/api/core/app_runner/basic_app_runner.py @@ -0,0 +1,363 @@ +import logging +from typing import Tuple, Optional + +from core.app_runner.app_runner import AppRunner +from core.callback_handler.index_tool_callback_handler import DatasetIndexToolCallbackHandler +from core.entities.application_entities import ApplicationGenerateEntity, ModelConfigEntity, \ + AppOrchestrationConfigEntity, InvokeFrom, ExternalDataVariableEntity, DatasetEntity +from core.application_queue_manager import ApplicationQueueManager +from core.features.annotation_reply import AnnotationReplyFeature +from core.features.dataset_retrieval import DatasetRetrievalFeature +from core.features.external_data_fetch import ExternalDataFetchFeature +from core.features.hosting_moderation import HostingModerationFeature +from core.features.moderation import ModerationFeature +from core.memory.token_buffer_memory import TokenBufferMemory +from core.model_manager import ModelInstance +from core.model_runtime.entities.message_entities import PromptMessage +from core.moderation.base import ModerationException +from core.prompt.prompt_transform import AppMode +from extensions.ext_database import db +from models.model import Conversation, Message, App, MessageAnnotation + +logger = logging.getLogger(__name__) + + +class BasicApplicationRunner(AppRunner): + """ + Basic Application Runner + """ + + def run(self, application_generate_entity: ApplicationGenerateEntity, + queue_manager: ApplicationQueueManager, + conversation: Conversation, + message: Message) -> None: + """ + Run application + :param application_generate_entity: application generate entity + :param queue_manager: application queue manager + :param conversation: conversation + :param message: message + :return: + """ + app_record = db.session.query(App).filter(App.id == application_generate_entity.app_id).first() + if not app_record: + raise ValueError(f"App not found") + + app_orchestration_config = application_generate_entity.app_orchestration_config_entity + + inputs = application_generate_entity.inputs + query = application_generate_entity.query + files = application_generate_entity.files + + # Pre-calculate the number of tokens of the prompt messages, + # and return the rest number of tokens by model context token size limit and max token size limit. + # If the rest number of tokens is not enough, raise exception. + # Include: prompt template, inputs, query(optional), files(optional) + # Not Include: memory, external data, dataset context + self.get_pre_calculate_rest_tokens( + app_record=app_record, + model_config=app_orchestration_config.model_config, + prompt_template_entity=app_orchestration_config.prompt_template, + inputs=inputs, + files=files, + query=query + ) + + memory = None + if application_generate_entity.conversation_id: + # get memory of conversation (read-only) + model_instance = ModelInstance( + provider_model_bundle=app_orchestration_config.model_config.provider_model_bundle, + model=app_orchestration_config.model_config.model + ) + + memory = TokenBufferMemory( + conversation=conversation, + model_instance=model_instance + ) + + # organize all inputs and template to prompt messages + # Include: prompt template, inputs, query(optional), files(optional) + # memory(optional) + prompt_messages, stop = self.originze_prompt_messages( + app_record=app_record, + model_config=app_orchestration_config.model_config, + prompt_template_entity=app_orchestration_config.prompt_template, + inputs=inputs, + files=files, + query=query, + memory=memory + ) + + # moderation + try: + # process sensitive_word_avoidance + _, inputs, query = self.moderation_for_inputs( + app_id=app_record.id, + tenant_id=application_generate_entity.tenant_id, + app_orchestration_config_entity=app_orchestration_config, + inputs=inputs, + query=query, + ) + except ModerationException as e: + self.direct_output( + queue_manager=queue_manager, + app_orchestration_config=app_orchestration_config, + prompt_messages=prompt_messages, + text=str(e), + stream=application_generate_entity.stream + ) + return + + if query: + # annotation reply + annotation_reply = self.query_app_annotations_to_reply( + app_record=app_record, + message=message, + query=query, + user_id=application_generate_entity.user_id, + invoke_from=application_generate_entity.invoke_from + ) + + if annotation_reply: + queue_manager.publish_annotation_reply( + message_annotation_id=annotation_reply.id + ) + self.direct_output( + queue_manager=queue_manager, + app_orchestration_config=app_orchestration_config, + prompt_messages=prompt_messages, + text=annotation_reply.content, + stream=application_generate_entity.stream + ) + return + + # fill in variable inputs from external data tools if exists + external_data_tools = app_orchestration_config.external_data_variables + if external_data_tools: + inputs = self.fill_in_inputs_from_external_data_tools( + tenant_id=app_record.tenant_id, + app_id=app_record.id, + external_data_tools=external_data_tools, + inputs=inputs, + query=query + ) + + # get context from datasets + context = None + if app_orchestration_config.dataset: + context = self.retrieve_dataset_context( + tenant_id=app_record.tenant_id, + app_record=app_record, + queue_manager=queue_manager, + model_config=app_orchestration_config.model_config, + show_retrieve_source=app_orchestration_config.show_retrieve_source, + dataset_config=app_orchestration_config.dataset, + message=message, + inputs=inputs, + query=query, + user_id=application_generate_entity.user_id, + invoke_from=application_generate_entity.invoke_from, + memory=memory + ) + + # reorganize all inputs and template to prompt messages + # Include: prompt template, inputs, query(optional), files(optional) + # memory(optional), external data, dataset context(optional) + prompt_messages, stop = self.originze_prompt_messages( + app_record=app_record, + model_config=app_orchestration_config.model_config, + prompt_template_entity=app_orchestration_config.prompt_template, + inputs=inputs, + files=files, + query=query, + context=context, + memory=memory + ) + + # check hosting moderation + hosting_moderation_result = self.check_hosting_moderation( + application_generate_entity=application_generate_entity, + queue_manager=queue_manager, + prompt_messages=prompt_messages + ) + + if hosting_moderation_result: + return + + # Re-calculate the max tokens if sum(prompt_token + max_tokens) over model token limit + self.recale_llm_max_tokens( + model_config=app_orchestration_config.model_config, + prompt_messages=prompt_messages + ) + + # Invoke model + model_instance = ModelInstance( + provider_model_bundle=app_orchestration_config.model_config.provider_model_bundle, + model=app_orchestration_config.model_config.model + ) + + invoke_result = model_instance.invoke_llm( + prompt_messages=prompt_messages, + model_parameters=app_orchestration_config.model_config.parameters, + stop=stop, + stream=application_generate_entity.stream, + user=application_generate_entity.user_id, + ) + + # handle invoke result + self._handle_invoke_result( + invoke_result=invoke_result, + queue_manager=queue_manager, + stream=application_generate_entity.stream + ) + + def moderation_for_inputs(self, app_id: str, + tenant_id: str, + app_orchestration_config_entity: AppOrchestrationConfigEntity, + inputs: dict, + query: str) -> Tuple[bool, dict, str]: + """ + Process sensitive_word_avoidance. + :param app_id: app id + :param tenant_id: tenant id + :param app_orchestration_config_entity: app orchestration config entity + :param inputs: inputs + :param query: query + :return: + """ + moderation_feature = ModerationFeature() + return moderation_feature.check( + app_id=app_id, + tenant_id=tenant_id, + app_orchestration_config_entity=app_orchestration_config_entity, + inputs=inputs, + query=query, + ) + + def query_app_annotations_to_reply(self, app_record: App, + message: Message, + query: str, + user_id: str, + invoke_from: InvokeFrom) -> Optional[MessageAnnotation]: + """ + Query app annotations to reply + :param app_record: app record + :param message: message + :param query: query + :param user_id: user id + :param invoke_from: invoke from + :return: + """ + annotation_reply_feature = AnnotationReplyFeature() + return annotation_reply_feature.query( + app_record=app_record, + message=message, + query=query, + user_id=user_id, + invoke_from=invoke_from + ) + + def fill_in_inputs_from_external_data_tools(self, tenant_id: str, + app_id: str, + external_data_tools: list[ExternalDataVariableEntity], + inputs: dict, + query: str) -> dict: + """ + Fill in variable inputs from external data tools if exists. + + :param tenant_id: workspace id + :param app_id: app id + :param external_data_tools: external data tools configs + :param inputs: the inputs + :param query: the query + :return: the filled inputs + """ + external_data_fetch_feature = ExternalDataFetchFeature() + return external_data_fetch_feature.fetch( + tenant_id=tenant_id, + app_id=app_id, + external_data_tools=external_data_tools, + inputs=inputs, + query=query + ) + + def retrieve_dataset_context(self, tenant_id: str, + app_record: App, + queue_manager: ApplicationQueueManager, + model_config: ModelConfigEntity, + dataset_config: DatasetEntity, + show_retrieve_source: bool, + message: Message, + inputs: dict, + query: str, + user_id: str, + invoke_from: InvokeFrom, + memory: Optional[TokenBufferMemory] = None) -> Optional[str]: + """ + Retrieve dataset context + :param tenant_id: tenant id + :param app_record: app record + :param queue_manager: queue manager + :param model_config: model config + :param dataset_config: dataset config + :param show_retrieve_source: show retrieve source + :param message: message + :param inputs: inputs + :param query: query + :param user_id: user id + :param invoke_from: invoke from + :param memory: memory + :return: + """ + hit_callback = DatasetIndexToolCallbackHandler( + queue_manager, + app_record.id, + message.id, + user_id, + invoke_from + ) + + if (app_record.mode == AppMode.COMPLETION.value and dataset_config + and dataset_config.retrieve_config.query_variable): + query = inputs.get(dataset_config.retrieve_config.query_variable, "") + + dataset_retrieval = DatasetRetrievalFeature() + return dataset_retrieval.retrieve( + tenant_id=tenant_id, + model_config=model_config, + config=dataset_config, + query=query, + invoke_from=invoke_from, + show_retrieve_source=show_retrieve_source, + hit_callback=hit_callback, + memory=memory + ) + + def check_hosting_moderation(self, application_generate_entity: ApplicationGenerateEntity, + queue_manager: ApplicationQueueManager, + prompt_messages: list[PromptMessage]) -> bool: + """ + Check hosting moderation + :param application_generate_entity: application generate entity + :param queue_manager: queue manager + :param prompt_messages: prompt messages + :return: + """ + hosting_moderation_feature = HostingModerationFeature() + moderation_result = hosting_moderation_feature.check( + application_generate_entity=application_generate_entity, + prompt_messages=prompt_messages + ) + + if moderation_result: + self.direct_output( + queue_manager=queue_manager, + app_orchestration_config=application_generate_entity.app_orchestration_config_entity, + prompt_messages=prompt_messages, + text="I apologize for any confusion, " \ + "but I'm an AI assistant to be helpful, harmless, and honest.", + stream=application_generate_entity.stream + ) + + return moderation_result diff --git a/api/core/app_runner/generate_task_pipeline.py b/api/core/app_runner/generate_task_pipeline.py new file mode 100644 index 00000000000000..0281259453c39a --- /dev/null +++ b/api/core/app_runner/generate_task_pipeline.py @@ -0,0 +1,483 @@ +import json +import logging +import time +from typing import Union, Generator, cast, Optional + +from pydantic import BaseModel + +from core.app_runner.moderation_handler import OutputModerationHandler, ModerationRule +from core.entities.application_entities import ApplicationGenerateEntity +from core.application_queue_manager import ApplicationQueueManager +from core.entities.queue_entities import QueueErrorEvent, QueueStopEvent, QueueMessageEndEvent, \ + QueueRetrieverResourcesEvent, QueueAgentThoughtEvent, QueuePingEvent, QueueMessageEvent, QueueMessageReplaceEvent, \ + AnnotationReplyEvent +from core.model_runtime.entities.llm_entities import LLMResult, LLMUsage, LLMResultChunk, LLMResultChunkDelta +from core.model_runtime.entities.message_entities import AssistantPromptMessage, PromptMessageRole, \ + TextPromptMessageContent, PromptMessageContentType, ImagePromptMessageContent, PromptMessage +from core.model_runtime.errors.invoke import InvokeError, InvokeAuthorizationError +from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel +from core.prompt.prompt_template import PromptTemplateParser +from events.message_event import message_was_created +from extensions.ext_database import db +from models.model import Message, Conversation, MessageAgentThought +from services.annotation_service import AppAnnotationService + +logger = logging.getLogger(__name__) + + +class TaskState(BaseModel): + """ + TaskState entity + """ + llm_result: LLMResult + metadata: dict = {} + + +class GenerateTaskPipeline: + """ + GenerateTaskPipeline is a class that generate stream output and state management for Application. + """ + + def __init__(self, application_generate_entity: ApplicationGenerateEntity, + queue_manager: ApplicationQueueManager, + conversation: Conversation, + message: Message) -> None: + """ + Initialize GenerateTaskPipeline. + :param application_generate_entity: application generate entity + :param queue_manager: queue manager + :param conversation: conversation + :param message: message + """ + self._application_generate_entity = application_generate_entity + self._queue_manager = queue_manager + self._conversation = conversation + self._message = message + self._task_state = TaskState( + llm_result=LLMResult( + model=self._application_generate_entity.app_orchestration_config_entity.model_config.model, + prompt_messages=[], + message=AssistantPromptMessage(content=""), + usage=LLMUsage.empty_usage() + ) + ) + self._start_at = time.perf_counter() + self._output_moderation_handler = self._init_output_moderation() + + def process(self, stream: bool) -> Union[dict, Generator]: + """ + Process generate task pipeline. + :return: + """ + if stream: + return self._process_stream_response() + else: + return self._process_blocking_response() + + def _process_blocking_response(self) -> dict: + """ + Process blocking response. + :return: + """ + for queue_message in self._queue_manager.listen(): + event = queue_message.event + + if isinstance(event, QueueErrorEvent): + raise self._handle_error(event) + elif isinstance(event, QueueRetrieverResourcesEvent): + self._task_state.metadata['retriever_resources'] = event.retriever_resources + elif isinstance(event, AnnotationReplyEvent): + annotation = AppAnnotationService.get_annotation_by_id(event.message_annotation_id) + if annotation: + account = annotation.account + self._task_state.metadata['annotation_reply'] = { + 'id': annotation.id, + 'account': { + 'id': annotation.account_id, + 'name': account.name if account else 'Dify user' + } + } + + self._task_state.llm_result.message.content = annotation.content + elif isinstance(event, (QueueStopEvent, QueueMessageEndEvent)): + if isinstance(event, QueueMessageEndEvent): + self._task_state.llm_result = event.llm_result + else: + model_config = self._application_generate_entity.app_orchestration_config_entity.model_config + model = model_config.model + model_type_instance = model_config.provider_model_bundle.model_type_instance + model_type_instance = cast(LargeLanguageModel, model_type_instance) + + # calculate num tokens + prompt_tokens = 0 + if event.stopped_by != QueueStopEvent.StopBy.ANNOTATION_REPLY: + prompt_tokens = model_type_instance.get_num_tokens( + model, + model_config.credentials, + self._task_state.llm_result.prompt_messages + ) + + completion_tokens = 0 + if event.stopped_by == QueueStopEvent.StopBy.USER_MANUAL: + completion_tokens = model_type_instance.get_num_tokens( + model, + model_config.credentials, + [self._task_state.llm_result.message] + ) + + credentials = model_config.credentials + + # transform usage + self._task_state.llm_result.usage = model_type_instance._calc_response_usage( + model, + credentials, + prompt_tokens, + completion_tokens + ) + + # response moderation + if self._output_moderation_handler: + self._output_moderation_handler.stop_thread() + + self._task_state.llm_result.message.content = self._output_moderation_handler.moderation_completion( + completion=self._task_state.llm_result.message.content, + public_event=False + ) + + # Save message + self._save_message(event.llm_result) + + response = { + 'event': 'message', + 'task_id': self._application_generate_entity.task_id, + 'id': self._message.id, + 'mode': self._conversation.mode, + 'answer': event.llm_result.message.content, + 'metadata': {}, + 'created_at': int(self._message.created_at.timestamp()) + } + + if self._conversation.mode == 'chat': + response['conversation_id'] = self._conversation.id + + if self._task_state.metadata: + response['metadata'] = self._task_state.metadata + + return response + else: + continue + + def _process_stream_response(self) -> Generator: + """ + Process stream response. + :return: + """ + for message in self._queue_manager.listen(): + event = message.event + + if isinstance(event, QueueErrorEvent): + raise self._handle_error(event) + elif isinstance(event, (QueueStopEvent, QueueMessageEndEvent)): + if isinstance(event, QueueMessageEndEvent): + self._task_state.llm_result = event.llm_result + else: + model_config = self._application_generate_entity.app_orchestration_config_entity.model_config + model = model_config.model + model_type_instance = model_config.provider_model_bundle.model_type_instance + model_type_instance = cast(LargeLanguageModel, model_type_instance) + + # calculate num tokens + prompt_tokens = 0 + if event.stopped_by != QueueStopEvent.StopBy.ANNOTATION_REPLY: + prompt_tokens = model_type_instance.get_num_tokens( + model, + model_config.credentials, + self._task_state.llm_result.prompt_messages + ) + + completion_tokens = 0 + if event.stopped_by == QueueStopEvent.StopBy.USER_MANUAL: + completion_tokens = model_type_instance.get_num_tokens( + model, + model_config.credentials, + [self._task_state.llm_result.message] + ) + + credentials = model_config.credentials + + # transform usage + self._task_state.llm_result.usage = model_type_instance._calc_response_usage( + model, + credentials, + prompt_tokens, + completion_tokens + ) + + # response moderation + if self._output_moderation_handler: + self._output_moderation_handler.stop_thread() + + self._task_state.llm_result.message.content = self._output_moderation_handler.moderation_completion( + completion=self._task_state.llm_result.message.content, + public_event=False + ) + + self._output_moderation_handler = None + + replace_response = { + 'event': 'message_replace', + 'task_id': self._application_generate_entity.task_id, + 'message_id': self._message.id, + 'answer': self._task_state.llm_result.message.content, + 'created_at': int(self._message.created_at.timestamp()) + } + + if self._conversation.mode == 'chat': + replace_response['conversation_id'] = self._conversation.id + + yield self._yield_response(replace_response) + + # Save message + self._save_message(self._task_state.llm_result) + + response = { + 'event': 'message_end', + 'task_id': self._application_generate_entity.task_id, + 'id': self._message.id, + } + + if self._conversation.mode == 'chat': + response['conversation_id'] = self._conversation.id + + if self._task_state.metadata: + response['metadata'] = self._task_state.metadata + + yield self._yield_response(response) + elif isinstance(event, QueueRetrieverResourcesEvent): + self._task_state.metadata['retriever_resources'] = event.retriever_resources + elif isinstance(event, AnnotationReplyEvent): + annotation = AppAnnotationService.get_annotation_by_id(event.message_annotation_id) + if annotation: + account = annotation.account + self._task_state.metadata['annotation_reply'] = { + 'id': annotation.id, + 'account': { + 'id': annotation.account_id, + 'name': account.name if account else 'Dify user' + } + } + + self._task_state.llm_result.message.content = annotation.content + elif isinstance(event, QueueAgentThoughtEvent): + agent_thought = ( + db.session.query(MessageAgentThought) + .filter(MessageAgentThought.id == event.agent_thought_id) + .first() + ) + + if agent_thought: + response = { + 'event': 'agent_thought', + 'id': agent_thought.id, + 'task_id': self._application_generate_entity.task_id, + 'message_id': self._message.id, + 'position': agent_thought.position, + 'thought': agent_thought.thought, + 'tool': agent_thought.tool, + 'tool_input': agent_thought.tool_input, + 'created_at': int(self._message.created_at.timestamp()) + } + + if self._conversation.mode == 'chat': + response['conversation_id'] = self._conversation.id + + yield self._yield_response(response) + elif isinstance(event, QueueMessageEvent): + chunk = event.chunk + delta_text = chunk.delta.message.content + if delta_text is None: + continue + + if not self._task_state.llm_result.prompt_messages: + self._task_state.llm_result.prompt_messages = chunk.prompt_messages + + if self._output_moderation_handler: + if self._output_moderation_handler.should_direct_output(): + # stop subscribe new token when output moderation should direct output + self._task_state.llm_result.message.content = self._output_moderation_handler.get_final_output() + self._queue_manager.publish_chunk_message(LLMResultChunk( + model=self._task_state.llm_result.model, + prompt_messages=self._task_state.llm_result.prompt_messages, + delta=LLMResultChunkDelta( + index=0, + message=AssistantPromptMessage(content=self._task_state.llm_result.message.content) + ) + )) + self._queue_manager.publish(QueueStopEvent(stopped_by=QueueStopEvent.StopBy.OUTPUT_MODERATION)) + continue + else: + self._output_moderation_handler.append_new_token(delta_text) + + self._task_state.llm_result.message.content += delta_text + response = self._handle_chunk(delta_text) + yield self._yield_response(response) + elif isinstance(event, QueueMessageReplaceEvent): + response = { + 'event': 'message_replace', + 'task_id': self._application_generate_entity.task_id, + 'message_id': self._message.id, + 'answer': event.text, + 'created_at': int(self._message.created_at.timestamp()) + } + + if self._conversation.mode == 'chat': + response['conversation_id'] = self._conversation.id + + yield self._yield_response(response) + elif isinstance(event, QueuePingEvent): + yield "event: ping\n\n" + else: + continue + + def _save_message(self, llm_result: LLMResult) -> None: + """ + Save message. + :param llm_result: llm result + :return: + """ + usage = llm_result.usage + + self._message = db.session.query(Message).filter(Message.id == self._message.id).first() + + self._message.message = self._prompt_messages_to_prompt_for_saving(self._task_state.llm_result.prompt_messages) + self._message.message_tokens = usage.prompt_tokens + self._message.message_unit_price = usage.prompt_unit_price + self._message.message_price_unit = usage.prompt_price_unit + self._message.answer = PromptTemplateParser.remove_template_variables(llm_result.message.content.strip()) \ + if llm_result.message.content else '' + self._message.answer_tokens = usage.completion_tokens + self._message.answer_unit_price = usage.completion_unit_price + self._message.answer_price_unit = usage.completion_price_unit + self._message.provider_response_latency = time.perf_counter() - self._start_at + self._message.total_price = usage.total_price + + db.session.commit() + + message_was_created.send( + self._message, + application_generate_entity=self._application_generate_entity, + conversation=self._conversation, + is_first_message=self._application_generate_entity.conversation_id is None, + extras=self._application_generate_entity.extras + ) + + def _handle_chunk(self, text: str) -> dict: + """ + Handle completed event. + :param text: text + :return: + """ + response = { + 'event': 'message', + 'id': self._message.id, + 'task_id': self._application_generate_entity.task_id, + 'message_id': self._message.id, + 'answer': text, + 'created_at': int(self._message.created_at.timestamp()) + } + + if self._conversation.mode == 'chat': + response['conversation_id'] = self._conversation.id + + return response + + def _handle_error(self, event: QueueErrorEvent) -> Exception: + """ + Handle error event. + :param event: event + :return: + """ + logger.debug("error: %s", event.error) + e = event.error + + if isinstance(e, InvokeAuthorizationError): + return InvokeAuthorizationError('Incorrect API key provided') + elif isinstance(e, InvokeError) or isinstance(e, ValueError): + return e + else: + return Exception(e.description if getattr(e, 'description', None) is not None else str(e)) + + def _yield_response(self, response: dict) -> str: + """ + Yield response. + :param response: response + :return: + """ + return "data: " + json.dumps(response) + "\n\n" + + def _prompt_messages_to_prompt_for_saving(self, prompt_messages: list[PromptMessage]) -> list[dict]: + """ + Prompt messages to prompt for saving. + :param prompt_messages: prompt messages + :return: + """ + prompts = [] + if self._application_generate_entity.app_orchestration_config_entity.model_config.mode == 'chat': + for prompt_message in prompt_messages: + if prompt_message.role == PromptMessageRole.USER: + role = 'user' + elif prompt_message.role == PromptMessageRole.ASSISTANT: + role = 'assistant' + elif prompt_message.role == PromptMessageRole.SYSTEM: + role = 'system' + else: + continue + + text = '' + files = [] + if isinstance(prompt_message.content, list): + for content in prompt_message.content: + if content.type == PromptMessageContentType.TEXT: + content = cast(TextPromptMessageContent, content) + text += content.data + else: + content = cast(ImagePromptMessageContent, content) + files.append({ + "type": 'image', + "data": content.data[:10] + '...[TRUNCATED]...' + content.data[-10:], + "detail": content.detail.value + }) + else: + text = prompt_message.content + + prompts.append({ + "role": role, + "text": text, + "files": files + }) + else: + prompts.append({ + "role": 'user', + "text": prompt_messages[0].content + }) + + return prompts + + def _init_output_moderation(self) -> Optional[OutputModerationHandler]: + """ + Init output moderation. + :return: + """ + app_orchestration_config_entity = self._application_generate_entity.app_orchestration_config_entity + sensitive_word_avoidance = app_orchestration_config_entity.sensitive_word_avoidance + + if sensitive_word_avoidance: + return OutputModerationHandler( + tenant_id=self._application_generate_entity.tenant_id, + app_id=self._application_generate_entity.app_id, + rule=ModerationRule( + type=sensitive_word_avoidance.type, + config=sensitive_word_avoidance.config + ), + on_message_replace_func=self._queue_manager.publish_message_replace + ) diff --git a/api/core/app_runner/moderation_handler.py b/api/core/app_runner/moderation_handler.py new file mode 100644 index 00000000000000..c4f2403e7f59bf --- /dev/null +++ b/api/core/app_runner/moderation_handler.py @@ -0,0 +1,138 @@ +import logging +import threading +import time +from typing import Any, Optional, Dict + +from flask import current_app, Flask +from pydantic import BaseModel + +from core.moderation.base import ModerationAction, ModerationOutputsResult +from core.moderation.factory import ModerationFactory + +logger = logging.getLogger(__name__) + + +class ModerationRule(BaseModel): + type: str + config: Dict[str, Any] + + +class OutputModerationHandler(BaseModel): + DEFAULT_BUFFER_SIZE: int = 300 + + tenant_id: str + app_id: str + + rule: ModerationRule + on_message_replace_func: Any + + thread: Optional[threading.Thread] = None + thread_running: bool = True + buffer: str = '' + is_final_chunk: bool = False + final_output: Optional[str] = None + + class Config: + arbitrary_types_allowed = True + + def should_direct_output(self): + return self.final_output is not None + + def get_final_output(self): + return self.final_output + + def append_new_token(self, token: str): + self.buffer += token + + if not self.thread: + self.thread = self.start_thread() + + def moderation_completion(self, completion: str, public_event: bool = False) -> str: + self.buffer = completion + self.is_final_chunk = True + + result = self.moderation( + tenant_id=self.tenant_id, + app_id=self.app_id, + moderation_buffer=completion + ) + + if not result or not result.flagged: + return completion + + if result.action == ModerationAction.DIRECT_OUTPUT: + final_output = result.preset_response + else: + final_output = result.text + + if public_event: + self.on_message_replace_func(final_output) + + return final_output + + def start_thread(self) -> threading.Thread: + buffer_size = int(current_app.config.get('MODERATION_BUFFER_SIZE', self.DEFAULT_BUFFER_SIZE)) + thread = threading.Thread(target=self.worker, kwargs={ + 'flask_app': current_app._get_current_object(), + 'buffer_size': buffer_size if buffer_size > 0 else self.DEFAULT_BUFFER_SIZE + }) + + thread.start() + + return thread + + def stop_thread(self): + if self.thread and self.thread.is_alive(): + self.thread_running = False + + def worker(self, flask_app: Flask, buffer_size: int): + with flask_app.app_context(): + current_length = 0 + while self.thread_running: + moderation_buffer = self.buffer + buffer_length = len(moderation_buffer) + if not self.is_final_chunk: + chunk_length = buffer_length - current_length + if 0 <= chunk_length < buffer_size: + time.sleep(1) + continue + + current_length = buffer_length + + result = self.moderation( + tenant_id=self.tenant_id, + app_id=self.app_id, + moderation_buffer=moderation_buffer + ) + + if not result or not result.flagged: + continue + + if result.action == ModerationAction.DIRECT_OUTPUT: + final_output = result.preset_response + self.final_output = final_output + else: + final_output = result.text + self.buffer[len(moderation_buffer):] + + # trigger replace event + if self.thread_running: + self.on_message_replace_func(final_output) + + if result.action == ModerationAction.DIRECT_OUTPUT: + break + + def moderation(self, tenant_id: str, app_id: str, moderation_buffer: str) -> Optional[ModerationOutputsResult]: + try: + moderation_factory = ModerationFactory( + name=self.rule.type, + app_id=app_id, + tenant_id=tenant_id, + config=self.rule.config + ) + + result: ModerationOutputsResult = moderation_factory.moderation_for_outputs(moderation_buffer) + return result + except Exception as e: + logger.error("Moderation Output error: %s", e) + + return None diff --git a/api/core/application_manager.py b/api/core/application_manager.py new file mode 100644 index 00000000000000..cad03c710d099d --- /dev/null +++ b/api/core/application_manager.py @@ -0,0 +1,655 @@ +import json +import logging +import threading +import uuid +from typing import cast, Optional, Any, Union, Generator, Tuple + +from flask import Flask, current_app +from pydantic import ValidationError + +from core.app_runner.agent_app_runner import AgentApplicationRunner +from core.app_runner.basic_app_runner import BasicApplicationRunner +from core.app_runner.generate_task_pipeline import GenerateTaskPipeline +from core.entities.application_entities import ApplicationGenerateEntity, AppOrchestrationConfigEntity, \ + ModelConfigEntity, PromptTemplateEntity, AdvancedChatPromptTemplateEntity, \ + AdvancedCompletionPromptTemplateEntity, ExternalDataVariableEntity, DatasetEntity, DatasetRetrieveConfigEntity, \ + AgentEntity, AgentToolEntity, FileUploadEntity, SensitiveWordAvoidanceEntity, InvokeFrom +from core.entities.model_entities import ModelStatus +from core.file.file_obj import FileObj +from core.errors.error import QuotaExceededError, ProviderTokenNotInitError, ModelCurrentlyNotSupportError +from core.model_runtime.entities.message_entities import PromptMessageRole +from core.model_runtime.entities.model_entities import ModelType +from core.model_runtime.errors.invoke import InvokeAuthorizationError, InvokeError +from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel +from core.prompt.prompt_template import PromptTemplateParser +from core.provider_manager import ProviderManager +from core.application_queue_manager import ApplicationQueueManager, ConversationTaskStoppedException +from extensions.ext_database import db +from models.account import Account +from models.model import EndUser, Conversation, Message, MessageFile, App + +logger = logging.getLogger(__name__) + + +class ApplicationManager: + """ + This class is responsible for managing application + """ + + def generate(self, tenant_id: str, + app_id: str, + app_model_config_id: str, + app_model_config_dict: dict, + app_model_config_override: bool, + user: Union[Account, EndUser], + invoke_from: InvokeFrom, + inputs: dict[str, str], + query: Optional[str] = None, + files: Optional[list[FileObj]] = None, + conversation: Optional[Conversation] = None, + stream: bool = False, + extras: Optional[dict[str, Any]] = None) \ + -> Union[dict, Generator]: + """ + Generate App response. + + :param tenant_id: workspace ID + :param app_id: app ID + :param app_model_config_id: app model config id + :param app_model_config_dict: app model config dict + :param app_model_config_override: app model config override + :param user: account or end user + :param invoke_from: invoke from source + :param inputs: inputs + :param query: query + :param files: file obj list + :param conversation: conversation + :param stream: is stream + :param extras: extras + """ + # init task id + task_id = str(uuid.uuid4()) + + # init application generate entity + application_generate_entity = ApplicationGenerateEntity( + task_id=task_id, + tenant_id=tenant_id, + app_id=app_id, + app_model_config_id=app_model_config_id, + app_model_config_dict=app_model_config_dict, + app_orchestration_config_entity=self._convert_from_app_model_config_dict( + tenant_id=tenant_id, + app_model_config_dict=app_model_config_dict + ), + app_model_config_override=app_model_config_override, + conversation_id=conversation.id if conversation else None, + inputs=conversation.inputs if conversation else inputs, + query=query.replace('\x00', '') if query else None, + files=files if files else [], + user_id=user.id, + stream=stream, + invoke_from=invoke_from, + extras=extras + ) + + # init generate records + ( + conversation, + message + ) = self._init_generate_records(application_generate_entity) + + # init queue manager + queue_manager = ApplicationQueueManager( + task_id=application_generate_entity.task_id, + user_id=application_generate_entity.user_id, + invoke_from=application_generate_entity.invoke_from, + conversation_id=conversation.id, + app_mode=conversation.mode, + message_id=message.id + ) + + # new thread + worker_thread = threading.Thread(target=self._generate_worker, kwargs={ + 'flask_app': current_app._get_current_object(), + 'application_generate_entity': application_generate_entity, + 'queue_manager': queue_manager, + 'conversation_id': conversation.id, + 'message_id': message.id, + }) + + worker_thread.start() + + # return response or stream generator + return self._handle_response( + application_generate_entity=application_generate_entity, + queue_manager=queue_manager, + conversation=conversation, + message=message, + stream=stream + ) + + def _generate_worker(self, flask_app: Flask, + application_generate_entity: ApplicationGenerateEntity, + queue_manager: ApplicationQueueManager, + conversation_id: str, + message_id: str) -> None: + """ + Generate worker in a new thread. + :param flask_app: Flask app + :param application_generate_entity: application generate entity + :param queue_manager: queue manager + :param conversation_id: conversation ID + :param message_id: message ID + :return: + """ + with flask_app.app_context(): + try: + # get conversation and message + conversation = self._get_conversation(conversation_id) + message = self._get_message(message_id) + + if application_generate_entity.app_orchestration_config_entity.agent: + # agent app + runner = AgentApplicationRunner() + runner.run( + application_generate_entity=application_generate_entity, + queue_manager=queue_manager, + conversation=conversation, + message=message + ) + else: + # basic app + runner = BasicApplicationRunner() + runner.run( + application_generate_entity=application_generate_entity, + queue_manager=queue_manager, + conversation=conversation, + message=message + ) + except ConversationTaskStoppedException: + pass + except InvokeAuthorizationError: + queue_manager.publish_error(InvokeAuthorizationError('Incorrect API key provided')) + except ValidationError as e: + logger.exception("Validation Error when generating") + queue_manager.publish_error(e) + except (ValueError, InvokeError) as e: + queue_manager.publish_error(e) + except Exception as e: + logger.exception("Unknown Error when generating") + queue_manager.publish_error(e) + finally: + db.session.remove() + + def _handle_response(self, application_generate_entity: ApplicationGenerateEntity, + queue_manager: ApplicationQueueManager, + conversation: Conversation, + message: Message, + stream: bool = False) -> Union[dict, Generator]: + """ + Handle response. + :param application_generate_entity: application generate entity + :param queue_manager: queue manager + :param conversation: conversation + :param message: message + :param stream: is stream + :return: + """ + # init generate task pipeline + generate_task_pipeline = GenerateTaskPipeline( + application_generate_entity=application_generate_entity, + queue_manager=queue_manager, + conversation=conversation, + message=message + ) + + try: + return generate_task_pipeline.process(stream=stream) + except ValueError as e: + if e.args[0] == "I/O operation on closed file.": # ignore this error + raise ConversationTaskStoppedException() + else: + logger.exception(e) + raise e + finally: + db.session.remove() + + def _convert_from_app_model_config_dict(self, tenant_id: str, app_model_config_dict: dict) \ + -> AppOrchestrationConfigEntity: + """ + Convert app model config dict to entity. + :param tenant_id: tenant ID + :param app_model_config_dict: app model config dict + :raises ProviderTokenNotInitError: provider token not init error + :return: app orchestration config entity + """ + properties = {} + + copy_app_model_config_dict = app_model_config_dict.copy() + + provider_manager = ProviderManager() + provider_model_bundle = provider_manager.get_provider_model_bundle( + tenant_id=tenant_id, + provider=copy_app_model_config_dict['model']['provider'], + model_type=ModelType.LLM + ) + + provider_name = provider_model_bundle.configuration.provider.provider + model_name = copy_app_model_config_dict['model']['name'] + + model_type_instance = provider_model_bundle.model_type_instance + model_type_instance = cast(LargeLanguageModel, model_type_instance) + + # check model credentials + model_credentials = provider_model_bundle.configuration.get_current_credentials( + model_type=ModelType.LLM, + model=copy_app_model_config_dict['model']['name'] + ) + + if model_credentials is None: + raise ProviderTokenNotInitError(f"Model {model_name} credentials is not initialized.") + + # check model + provider_model = provider_model_bundle.configuration.get_provider_model( + model=copy_app_model_config_dict['model']['name'], + model_type=ModelType.LLM + ) + + if provider_model is None: + model_name = copy_app_model_config_dict['model']['name'] + raise ValueError(f"Model {model_name} not exist.") + + if provider_model.status == ModelStatus.NO_CONFIGURE: + raise ProviderTokenNotInitError(f"Model {model_name} credentials is not initialized.") + elif provider_model.status == ModelStatus.NO_PERMISSION: + raise ModelCurrentlyNotSupportError(f"Dify Hosted OpenAI {model_name} currently not support.") + elif provider_model.status == ModelStatus.QUOTA_EXCEEDED: + raise QuotaExceededError(f"Model provider {provider_name} quota exceeded.") + + # model config + completion_params = copy_app_model_config_dict['model'].get('completion_params') + stop = [] + if 'stop' in completion_params: + stop = completion_params['stop'] + del completion_params['stop'] + + # get model mode + model_mode = copy_app_model_config_dict['model'].get('mode') + if not model_mode: + mode_enum = model_type_instance.get_model_mode( + model=copy_app_model_config_dict['model']['name'], + credentials=model_credentials + ) + + model_mode = mode_enum.value + + model_schema = model_type_instance.get_model_schema( + copy_app_model_config_dict['model']['name'], + model_credentials + ) + + if not model_schema: + raise ValueError(f"Model {model_name} not exist.") + + properties['model_config'] = ModelConfigEntity( + provider=copy_app_model_config_dict['model']['provider'], + model=copy_app_model_config_dict['model']['name'], + model_schema=model_schema, + mode=model_mode, + provider_model_bundle=provider_model_bundle, + credentials=model_credentials, + parameters=completion_params, + stop=stop, + ) + + # prompt template + prompt_type = PromptTemplateEntity.PromptType.value_of(copy_app_model_config_dict['prompt_type']) + if prompt_type == PromptTemplateEntity.PromptType.SIMPLE: + simple_prompt_template = copy_app_model_config_dict.get("pre_prompt", "") + properties['prompt_template'] = PromptTemplateEntity( + prompt_type=prompt_type, + simple_prompt_template=simple_prompt_template + ) + else: + advanced_chat_prompt_template = None + chat_prompt_config = copy_app_model_config_dict.get("chat_prompt_config", {}) + if chat_prompt_config: + chat_prompt_messages = [] + for message in chat_prompt_config.get("prompt", []): + chat_prompt_messages.append({ + "text": message["text"], + "role": PromptMessageRole.value_of(message["role"]) + }) + + advanced_chat_prompt_template = AdvancedChatPromptTemplateEntity( + messages=chat_prompt_messages + ) + + advanced_completion_prompt_template = None + completion_prompt_config = copy_app_model_config_dict.get("completion_prompt_config", {}) + if completion_prompt_config: + completion_prompt_template_params = { + 'prompt': completion_prompt_config['prompt']['text'], + } + + if 'conversation_histories_role' in completion_prompt_config: + completion_prompt_template_params['role_prefix'] = { + 'user': completion_prompt_config['conversation_histories_role']['user_prefix'], + 'assistant': completion_prompt_config['conversation_histories_role']['assistant_prefix'] + } + + advanced_completion_prompt_template = AdvancedCompletionPromptTemplateEntity( + **completion_prompt_template_params + ) + + properties['prompt_template'] = PromptTemplateEntity( + prompt_type=prompt_type, + advanced_chat_prompt_template=advanced_chat_prompt_template, + advanced_completion_prompt_template=advanced_completion_prompt_template + ) + + # external data variables + properties['external_data_variables'] = [] + external_data_tools = copy_app_model_config_dict.get('external_data_tools', []) + for external_data_tool in external_data_tools: + if 'enabled' not in external_data_tool or not external_data_tool['enabled']: + continue + + properties['external_data_variables'].append( + ExternalDataVariableEntity( + variable=external_data_tool['variable'], + type=external_data_tool['type'], + config=external_data_tool['config'] + ) + ) + + # show retrieve source + show_retrieve_source = False + retriever_resource_dict = copy_app_model_config_dict.get('retriever_resource') + if retriever_resource_dict: + if 'enabled' in retriever_resource_dict and retriever_resource_dict['enabled']: + show_retrieve_source = True + + properties['show_retrieve_source'] = show_retrieve_source + + if 'agent_mode' in copy_app_model_config_dict and copy_app_model_config_dict['agent_mode'] \ + and 'enabled' in copy_app_model_config_dict['agent_mode'] and copy_app_model_config_dict['agent_mode'][ + 'enabled']: + agent_dict = copy_app_model_config_dict.get('agent_mode') + if agent_dict['strategy'] in ['router', 'react_router']: + dataset_ids = [] + for tool in agent_dict.get('tools', []): + key = list(tool.keys())[0] + + if key != 'dataset': + continue + + tool_item = tool[key] + + if "enabled" not in tool_item or not tool_item["enabled"]: + continue + + dataset_id = tool_item['id'] + dataset_ids.append(dataset_id) + + dataset_configs = copy_app_model_config_dict.get('dataset_configs', {'retrieval_model': 'single'}) + query_variable = copy_app_model_config_dict.get('dataset_query_variable') + if dataset_configs['retrieval_model'] == 'single': + properties['dataset'] = DatasetEntity( + dataset_ids=dataset_ids, + retrieve_config=DatasetRetrieveConfigEntity( + query_variable=query_variable, + retrieve_strategy=DatasetRetrieveConfigEntity.RetrieveStrategy.value_of( + dataset_configs['retrieval_model'] + ), + single_strategy=agent_dict['strategy'] + ) + ) + else: + properties['dataset'] = DatasetEntity( + dataset_ids=dataset_ids, + retrieve_config=DatasetRetrieveConfigEntity( + query_variable=query_variable, + retrieve_strategy=DatasetRetrieveConfigEntity.RetrieveStrategy.value_of( + dataset_configs['retrieval_model'] + ), + top_k=dataset_configs.get('top_k'), + score_threshold=dataset_configs.get('score_threshold'), + reranking_model=dataset_configs.get('reranking_model') + ) + ) + else: + if agent_dict['strategy'] == 'react': + strategy = AgentEntity.Strategy.CHAIN_OF_THOUGHT + else: + strategy = AgentEntity.Strategy.FUNCTION_CALLING + + agent_tools = [] + for tool in agent_dict.get('tools', []): + key = list(tool.keys())[0] + tool_item = tool[key] + + agent_tool_properties = { + "tool_id": key + } + + if "enabled" not in tool_item or not tool_item["enabled"]: + continue + + agent_tool_properties["config"] = tool_item + agent_tools.append(AgentToolEntity(**agent_tool_properties)) + + properties['agent'] = AgentEntity( + provider=properties['model_config'].provider, + model=properties['model_config'].model, + strategy=strategy, + tools=agent_tools + ) + + # file upload + file_upload_dict = copy_app_model_config_dict.get('file_upload') + if file_upload_dict: + if 'image' in file_upload_dict and file_upload_dict['image']: + if 'enabled' in file_upload_dict['image'] and file_upload_dict['image']['enabled']: + properties['file_upload'] = FileUploadEntity( + image_config={ + 'number_limits': file_upload_dict['image']['number_limits'], + 'detail': file_upload_dict['image']['detail'], + 'transfer_methods': file_upload_dict['image']['transfer_methods'] + } + ) + + # opening statement + properties['opening_statement'] = copy_app_model_config_dict.get('opening_statement') + + # suggested questions after answer + suggested_questions_after_answer_dict = copy_app_model_config_dict.get('suggested_questions_after_answer') + if suggested_questions_after_answer_dict: + if 'enabled' in suggested_questions_after_answer_dict and suggested_questions_after_answer_dict['enabled']: + properties['suggested_questions_after_answer'] = True + + # more like this + more_like_this_dict = copy_app_model_config_dict.get('more_like_this') + if more_like_this_dict: + if 'enabled' in more_like_this_dict and more_like_this_dict['enabled']: + properties['more_like_this'] = copy_app_model_config_dict.get('opening_statement') + + # speech to text + speech_to_text_dict = copy_app_model_config_dict.get('speech_to_text') + if speech_to_text_dict: + if 'enabled' in speech_to_text_dict and speech_to_text_dict['enabled']: + properties['speech_to_text'] = True + + # sensitive word avoidance + sensitive_word_avoidance_dict = copy_app_model_config_dict.get('sensitive_word_avoidance') + if sensitive_word_avoidance_dict: + if 'enabled' in sensitive_word_avoidance_dict and sensitive_word_avoidance_dict['enabled']: + properties['sensitive_word_avoidance'] = SensitiveWordAvoidanceEntity( + type=sensitive_word_avoidance_dict.get('type'), + config=sensitive_word_avoidance_dict.get('config'), + ) + + return AppOrchestrationConfigEntity(**properties) + + def _init_generate_records(self, application_generate_entity: ApplicationGenerateEntity) \ + -> Tuple[Conversation, Message]: + """ + Initialize generate records + :param application_generate_entity: application generate entity + :return: + """ + app_orchestration_config_entity = application_generate_entity.app_orchestration_config_entity + + model_type_instance = app_orchestration_config_entity.model_config.provider_model_bundle.model_type_instance + model_type_instance = cast(LargeLanguageModel, model_type_instance) + model_schema = model_type_instance.get_model_schema( + model=app_orchestration_config_entity.model_config.model, + credentials=app_orchestration_config_entity.model_config.credentials + ) + + app_record = (db.session.query(App) + .filter(App.id == application_generate_entity.app_id).first()) + + app_mode = app_record.mode + + # get from source + end_user_id = None + account_id = None + if application_generate_entity.invoke_from in [InvokeFrom.WEB_APP, InvokeFrom.SERVICE_API]: + from_source = 'api' + end_user_id = application_generate_entity.user_id + else: + from_source = 'console' + account_id = application_generate_entity.user_id + + override_model_configs = None + if application_generate_entity.app_model_config_override: + override_model_configs = application_generate_entity.app_model_config_dict + + introduction = '' + if app_mode == 'chat': + # get conversation introduction + introduction = self._get_conversation_introduction(application_generate_entity) + + if not application_generate_entity.conversation_id: + conversation = Conversation( + app_id=app_record.id, + app_model_config_id=application_generate_entity.app_model_config_id, + model_provider=app_orchestration_config_entity.model_config.provider, + model_id=app_orchestration_config_entity.model_config.model, + override_model_configs=json.dumps(override_model_configs) if override_model_configs else None, + mode=app_mode, + name='New conversation', + inputs=application_generate_entity.inputs, + introduction=introduction, + system_instruction="", + system_instruction_tokens=0, + status='normal', + from_source=from_source, + from_end_user_id=end_user_id, + from_account_id=account_id, + ) + + db.session.add(conversation) + db.session.commit() + else: + conversation = ( + db.session.query(Conversation) + .filter( + Conversation.id == application_generate_entity.conversation_id, + Conversation.app_id == app_record.id + ).first() + ) + + currency = model_schema.pricing.currency if model_schema.pricing else 'USD' + + message = Message( + app_id=app_record.id, + model_provider=app_orchestration_config_entity.model_config.provider, + model_id=app_orchestration_config_entity.model_config.model, + override_model_configs=json.dumps(override_model_configs) if override_model_configs else None, + conversation_id=conversation.id, + inputs=application_generate_entity.inputs, + query=application_generate_entity.query or "", + message="", + message_tokens=0, + message_unit_price=0, + message_price_unit=0, + answer="", + answer_tokens=0, + answer_unit_price=0, + answer_price_unit=0, + provider_response_latency=0, + total_price=0, + currency=currency, + from_source=from_source, + from_end_user_id=end_user_id, + from_account_id=account_id, + agent_based=app_orchestration_config_entity.agent is not None + ) + + db.session.add(message) + db.session.commit() + + for file in application_generate_entity.files: + message_file = MessageFile( + message_id=message.id, + type=file.type.value, + transfer_method=file.transfer_method.value, + url=file.url, + upload_file_id=file.upload_file_id, + created_by_role=('account' if account_id else 'end_user'), + created_by=account_id or end_user_id, + ) + db.session.add(message_file) + db.session.commit() + + return conversation, message + + def _get_conversation_introduction(self, application_generate_entity: ApplicationGenerateEntity) -> str: + """ + Get conversation introduction + :param application_generate_entity: application generate entity + :return: conversation introduction + """ + app_orchestration_config_entity = application_generate_entity.app_orchestration_config_entity + introduction = app_orchestration_config_entity.opening_statement + + if introduction: + try: + inputs = application_generate_entity.inputs + prompt_template = PromptTemplateParser(template=introduction) + prompt_inputs = {k: inputs[k] for k in prompt_template.variable_keys if k in inputs} + introduction = prompt_template.format(prompt_inputs) + except KeyError: + pass + + return introduction + + def _get_conversation(self, conversation_id: str) -> Conversation: + """ + Get conversation by conversation id + :param conversation_id: conversation id + :return: conversation + """ + conversation = ( + db.session.query(Conversation) + .filter(Conversation.id == conversation_id) + .first() + ) + + return conversation + + def _get_message(self, message_id: str) -> Message: + """ + Get message by message id + :param message_id: message id + :return: message + """ + message = ( + db.session.query(Message) + .filter(Message.id == message_id) + .first() + ) + + return message diff --git a/api/core/application_queue_manager.py b/api/core/application_queue_manager.py new file mode 100644 index 00000000000000..fd549ff39f1424 --- /dev/null +++ b/api/core/application_queue_manager.py @@ -0,0 +1,228 @@ +import queue +import time +from typing import Generator, Any + +from sqlalchemy.orm import DeclarativeMeta + +from core.entities.application_entities import InvokeFrom +from core.entities.queue_entities import QueueStopEvent, AppQueueEvent, QueuePingEvent, QueueErrorEvent, \ + QueueAgentThoughtEvent, QueueMessageEndEvent, QueueRetrieverResourcesEvent, QueueMessageReplaceEvent, \ + QueueMessageEvent, QueueMessage, AnnotationReplyEvent +from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk +from extensions.ext_redis import redis_client +from models.model import MessageAgentThought + + +class ApplicationQueueManager: + def __init__(self, task_id: str, + user_id: str, + invoke_from: InvokeFrom, + conversation_id: str, + app_mode: str, + message_id: str) -> None: + if not user_id: + raise ValueError("user is required") + + self._task_id = task_id + self._user_id = user_id + self._invoke_from = invoke_from + self._conversation_id = str(conversation_id) + self._app_mode = app_mode + self._message_id = str(message_id) + + user_prefix = 'account' if self._invoke_from in [InvokeFrom.EXPLORE, InvokeFrom.DEBUGGER] else 'end-user' + redis_client.setex(ApplicationQueueManager._generate_task_belong_cache_key(self._task_id), 1800, f"{user_prefix}-{self._user_id}") + + q = queue.Queue() + + self._q = q + + def listen(self) -> Generator: + """ + Listen to queue + :return: + """ + # wait for 10 minutes to stop listen + listen_timeout = 600 + start_time = time.time() + last_ping_time = 0 + + while True: + try: + message = self._q.get(timeout=1) + if message is None: + break + + yield message + except queue.Empty: + continue + finally: + elapsed_time = time.time() - start_time + if elapsed_time >= listen_timeout or self._is_stopped(): + # publish two messages to make sure the client can receive the stop signal + # and stop listening after the stop signal processed + self.publish(QueueStopEvent(stopped_by=QueueStopEvent.StopBy.USER_MANUAL)) + self.stop_listen() + + if elapsed_time // 10 > last_ping_time: + self.publish(QueuePingEvent()) + last_ping_time = elapsed_time // 10 + + def stop_listen(self) -> None: + """ + Stop listen to queue + :return: + """ + self._q.put(None) + + def publish_chunk_message(self, chunk: LLMResultChunk) -> None: + """ + Publish chunk message to channel + + :param chunk: chunk + :return: + """ + self.publish(QueueMessageEvent( + chunk=chunk + )) + + def publish_message_replace(self, text: str) -> None: + """ + Publish message replace + :param text: text + :return: + """ + self.publish(QueueMessageReplaceEvent( + text=text + )) + + def publish_retriever_resources(self, retriever_resources: list[dict]) -> None: + """ + Publish retriever resources + :return: + """ + self.publish(QueueRetrieverResourcesEvent(retriever_resources=retriever_resources)) + + def publish_annotation_reply(self, message_annotation_id: str) -> None: + """ + Publish annotation reply + :param message_annotation_id: message annotation id + :return: + """ + self.publish(AnnotationReplyEvent(message_annotation_id=message_annotation_id)) + + def publish_message_end(self, llm_result: LLMResult) -> None: + """ + Publish message end + :param llm_result: llm result + :return: + """ + self.publish(QueueMessageEndEvent(llm_result=llm_result)) + self.stop_listen() + + def publish_agent_thought(self, message_agent_thought: MessageAgentThought) -> None: + """ + Publish agent thought + :param message_agent_thought: message agent thought + :return: + """ + self.publish(QueueAgentThoughtEvent( + agent_thought_id=message_agent_thought.id + )) + + def publish_error(self, e) -> None: + """ + Publish error + :param e: error + :return: + """ + self.publish(QueueErrorEvent( + error=e + )) + self.stop_listen() + + def publish(self, event: AppQueueEvent) -> None: + """ + Publish event to queue + :param event: + :return: + """ + self._check_for_sqlalchemy_models(event.dict()) + + message = QueueMessage( + task_id=self._task_id, + message_id=self._message_id, + conversation_id=self._conversation_id, + app_mode=self._app_mode, + event=event + ) + + self._q.put(message) + + if isinstance(event, QueueStopEvent): + self.stop_listen() + + @classmethod + def set_stop_flag(cls, task_id: str, invoke_from: InvokeFrom, user_id: str) -> None: + """ + Set task stop flag + :return: + """ + result = redis_client.get(cls._generate_task_belong_cache_key(task_id)) + if result is None: + return + + user_prefix = 'account' if invoke_from in [InvokeFrom.EXPLORE, InvokeFrom.DEBUGGER] else 'end-user' + if result != f"{user_prefix}-{user_id}": + return + + stopped_cache_key = cls._generate_stopped_cache_key(task_id) + redis_client.setex(stopped_cache_key, 600, 1) + + def _is_stopped(self) -> bool: + """ + Check if task is stopped + :return: + """ + stopped_cache_key = ApplicationQueueManager._generate_stopped_cache_key(self._task_id) + result = redis_client.get(stopped_cache_key) + if result is not None: + redis_client.delete(stopped_cache_key) + return True + + return False + + @classmethod + def _generate_task_belong_cache_key(cls, task_id: str) -> str: + """ + Generate task belong cache key + :param task_id: task id + :return: + """ + return f"generate_task_belong:{task_id}" + + @classmethod + def _generate_stopped_cache_key(cls, task_id: str) -> str: + """ + Generate stopped cache key + :param task_id: task id + :return: + """ + return f"generate_task_stopped:{task_id}" + + def _check_for_sqlalchemy_models(self, data: Any): + # from entity to dict or list + if isinstance(data, dict): + for key, value in data.items(): + self._check_for_sqlalchemy_models(value) + elif isinstance(data, list): + for item in data: + self._check_for_sqlalchemy_models(item) + else: + if isinstance(data, DeclarativeMeta) or hasattr(data, '_sa_instance_state'): + raise TypeError("Critical Error: Passing SQLAlchemy Model instances " + "that cause thread safety issues is not allowed.") + + +class ConversationTaskStoppedException(Exception): + pass diff --git a/api/core/callback_handler/agent_loop_gather_callback_handler.py b/api/core/callback_handler/agent_loop_gather_callback_handler.py index 218a9e4e07f495..ec2964f2afdc6d 100644 --- a/api/core/callback_handler/agent_loop_gather_callback_handler.py +++ b/api/core/callback_handler/agent_loop_gather_callback_handler.py @@ -2,30 +2,40 @@ import logging import time -from typing import Any, Dict, List, Union, Optional +from typing import Any, Dict, List, Union, Optional, cast from langchain.agents import openai_functions_agent, openai_functions_multi_agent from langchain.callbacks.base import BaseCallbackHandler from langchain.schema import AgentAction, AgentFinish, LLMResult, ChatGeneration, BaseMessage +from core.application_queue_manager import ApplicationQueueManager from core.callback_handler.entity.agent_loop import AgentLoop -from core.conversation_message_task import ConversationMessageTask -from core.model_providers.models.entity.message import PromptMessage -from core.model_providers.models.llm.base import BaseLLM +from core.entities.application_entities import ModelConfigEntity +from core.model_runtime.entities.llm_entities import LLMResult as RuntimeLLMResult +from core.model_runtime.entities.message_entities import UserPromptMessage, AssistantPromptMessage, PromptMessage +from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel +from extensions.ext_database import db +from models.model import MessageChain, MessageAgentThought, Message class AgentLoopGatherCallbackHandler(BaseCallbackHandler): """Callback Handler that prints to std out.""" raise_error: bool = True - def __init__(self, model_instance: BaseLLM, conversation_message_task: ConversationMessageTask) -> None: + def __init__(self, model_config: ModelConfigEntity, + queue_manager: ApplicationQueueManager, + message: Message, + message_chain: MessageChain) -> None: """Initialize callback handler.""" - self.model_instance = model_instance - self.conversation_message_task = conversation_message_task + self.model_config = model_config + self.queue_manager = queue_manager + self.message = message + self.message_chain = message_chain + model_type_instance = self.model_config.provider_model_bundle.model_type_instance + self.model_type_instance = cast(LargeLanguageModel, model_type_instance) self._agent_loops = [] self._current_loop = None self._message_agent_thought = None - self.current_chain = None @property def agent_loops(self) -> List[AgentLoop]: @@ -46,66 +56,61 @@ def ignore_chain(self) -> bool: """Whether to ignore chain callbacks.""" return True - def on_chat_model_start( - self, - serialized: Dict[str, Any], - messages: List[List[BaseMessage]], - **kwargs: Any - ) -> Any: - if not self._current_loop: - # Agent start with a LLM query - self._current_loop = AgentLoop( - position=len(self._agent_loops) + 1, - prompt="\n".join([message.content for message in messages[0]]), - status='llm_started', - started_at=time.perf_counter() - ) - - def on_llm_start( - self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any - ) -> None: - """Print out the prompts.""" - # serialized={'name': 'OpenAI'} - # prompts=['Answer the following questions...\nThought:'] - # kwargs={} + def on_llm_before_invoke(self, prompt_messages: list[PromptMessage]) -> None: if not self._current_loop: # Agent start with a LLM query self._current_loop = AgentLoop( position=len(self._agent_loops) + 1, - prompt=prompts[0], + prompt="\n".join([prompt_message.content for prompt_message in prompt_messages]), status='llm_started', started_at=time.perf_counter() ) - def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None: - """Do nothing.""" - # kwargs={} + def on_llm_after_invoke(self, result: RuntimeLLMResult) -> None: if self._current_loop and self._current_loop.status == 'llm_started': self._current_loop.status = 'llm_end' - if response.llm_output: - self._current_loop.prompt_tokens = response.llm_output['token_usage']['prompt_tokens'] + if result.usage: + self._current_loop.prompt_tokens = result.usage.prompt_tokens else: - self._current_loop.prompt_tokens = self.model_instance.get_num_tokens( - [PromptMessage(content=self._current_loop.prompt)] + self._current_loop.prompt_tokens = self.model_type_instance.get_num_tokens( + model=self.model_config.model, + credentials=self.model_config.credentials, + prompt_messages=[UserPromptMessage(content=self._current_loop.prompt)] ) - completion_generation = response.generations[0][0] - if isinstance(completion_generation, ChatGeneration): - completion_message = completion_generation.message - if 'function_call' in completion_message.additional_kwargs: - self._current_loop.completion \ - = json.dumps({'function_call': completion_message.additional_kwargs['function_call']}) - else: - self._current_loop.completion = response.generations[0][0].text + + completion_message = result.message + if completion_message.tool_calls: + self._current_loop.completion \ + = json.dumps({'function_call': completion_message.tool_calls}) else: - self._current_loop.completion = completion_generation.text + self._current_loop.completion = completion_message.content - if response.llm_output: - self._current_loop.completion_tokens = response.llm_output['token_usage']['completion_tokens'] + if result.usage: + self._current_loop.completion_tokens = result.usage.completion_tokens else: - self._current_loop.completion_tokens = self.model_instance.get_num_tokens( - [PromptMessage(content=self._current_loop.completion)] + self._current_loop.completion_tokens = self.model_type_instance.get_num_tokens( + model=self.model_config.model, + credentials=self.model_config.credentials, + prompt_messages=[AssistantPromptMessage(content=self._current_loop.completion)] ) + def on_chat_model_start( + self, + serialized: Dict[str, Any], + messages: List[List[BaseMessage]], + **kwargs: Any + ) -> Any: + pass + + def on_llm_start( + self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any + ) -> None: + pass + + def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None: + """Do nothing.""" + pass + def on_llm_error( self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any ) -> None: @@ -150,10 +155,7 @@ def on_agent_action( if completion is not None: self._current_loop.completion = completion - self._message_agent_thought = self.conversation_message_task.on_agent_start( - self.current_chain, - self._current_loop - ) + self._message_agent_thought = self._init_agent_thought() def on_tool_end( self, @@ -176,9 +178,7 @@ def on_tool_end( self._current_loop.completed_at = time.perf_counter() self._current_loop.latency = self._current_loop.completed_at - self._current_loop.started_at - self.conversation_message_task.on_agent_end( - self._message_agent_thought, self.model_instance, self._current_loop - ) + self._complete_agent_thought(self._message_agent_thought) self._agent_loops.append(self._current_loop) self._current_loop = None @@ -202,17 +202,62 @@ def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> Any: self._current_loop.completed_at = time.perf_counter() self._current_loop.latency = self._current_loop.completed_at - self._current_loop.started_at self._current_loop.thought = '[DONE]' - self._message_agent_thought = self.conversation_message_task.on_agent_start( - self.current_chain, - self._current_loop - ) + self._message_agent_thought = self._init_agent_thought() - self.conversation_message_task.on_agent_end( - self._message_agent_thought, self.model_instance, self._current_loop - ) + self._complete_agent_thought(self._message_agent_thought) self._agent_loops.append(self._current_loop) self._current_loop = None self._message_agent_thought = None elif not self._current_loop and self._agent_loops: self._agent_loops[-1].status = 'agent_finish' + + def _init_agent_thought(self) -> MessageAgentThought: + message_agent_thought = MessageAgentThought( + message_id=self.message.id, + message_chain_id=self.message_chain.id, + position=self._current_loop.position, + thought=self._current_loop.thought, + tool=self._current_loop.tool_name, + tool_input=self._current_loop.tool_input, + message=self._current_loop.prompt, + message_price_unit=0, + answer=self._current_loop.completion, + answer_price_unit=0, + created_by_role=('account' if self.message.from_source == 'console' else 'end_user'), + created_by=(self.message.from_account_id + if self.message.from_source == 'console' else self.message.from_end_user_id) + ) + + db.session.add(message_agent_thought) + db.session.commit() + + self.queue_manager.publish_agent_thought(message_agent_thought) + + return message_agent_thought + + def _complete_agent_thought(self, message_agent_thought: MessageAgentThought) -> None: + loop_message_tokens = self._current_loop.prompt_tokens + loop_answer_tokens = self._current_loop.completion_tokens + + # transform usage + llm_usage = self.model_type_instance._calc_response_usage( + self.model_config.model, + self.model_config.credentials, + loop_message_tokens, + loop_answer_tokens + ) + + message_agent_thought.observation = self._current_loop.tool_output + message_agent_thought.tool_process_data = '' # currently not support + message_agent_thought.message_token = loop_message_tokens + message_agent_thought.message_unit_price = llm_usage.prompt_unit_price + message_agent_thought.message_price_unit = llm_usage.prompt_price_unit + message_agent_thought.answer_token = loop_answer_tokens + message_agent_thought.answer_unit_price = llm_usage.completion_unit_price + message_agent_thought.answer_price_unit = llm_usage.completion_price_unit + message_agent_thought.latency = self._current_loop.latency + message_agent_thought.tokens = self._current_loop.prompt_tokens + self._current_loop.completion_tokens + message_agent_thought.total_price = llm_usage.total_price + message_agent_thought.currency = llm_usage.currency + db.session.commit() diff --git a/api/core/callback_handler/dataset_tool_callback_handler.py b/api/core/callback_handler/dataset_tool_callback_handler.py deleted file mode 100644 index b1f9b8c6023752..00000000000000 --- a/api/core/callback_handler/dataset_tool_callback_handler.py +++ /dev/null @@ -1,74 +0,0 @@ -import json -import logging -from json import JSONDecodeError - -from typing import Any, Dict, List, Union, Optional - -from langchain.callbacks.base import BaseCallbackHandler - -from core.callback_handler.entity.dataset_query import DatasetQueryObj -from core.conversation_message_task import ConversationMessageTask - - -class DatasetToolCallbackHandler(BaseCallbackHandler): - """Callback Handler that prints to std out.""" - raise_error: bool = True - - def __init__(self, conversation_message_task: ConversationMessageTask) -> None: - """Initialize callback handler.""" - self.queries = [] - self.conversation_message_task = conversation_message_task - - @property - def always_verbose(self) -> bool: - """Whether to call verbose callbacks even if verbose is False.""" - return True - - @property - def ignore_llm(self) -> bool: - """Whether to ignore LLM callbacks.""" - return True - - @property - def ignore_chain(self) -> bool: - """Whether to ignore chain callbacks.""" - return True - - @property - def ignore_agent(self) -> bool: - """Whether to ignore agent callbacks.""" - return False - - def on_tool_start( - self, - serialized: Dict[str, Any], - input_str: str, - **kwargs: Any, - ) -> None: - tool_name: str = serialized.get('name') - dataset_id = tool_name.removeprefix('dataset-') - - try: - input_dict = json.loads(input_str.replace("'", "\"")) - query = input_dict.get('query') - except JSONDecodeError: - query = input_str - - self.conversation_message_task.on_dataset_query_end(DatasetQueryObj(dataset_id=dataset_id, query=query)) - - def on_tool_end( - self, - output: str, - color: Optional[str] = None, - observation_prefix: Optional[str] = None, - llm_prefix: Optional[str] = None, - **kwargs: Any, - ) -> None: - pass - - - def on_tool_error( - self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any - ) -> None: - """Do nothing.""" - logging.debug("Dataset tool on_llm_error: %s", error) diff --git a/api/core/callback_handler/entity/chain_result.py b/api/core/callback_handler/entity/chain_result.py deleted file mode 100644 index 596486cdb07644..00000000000000 --- a/api/core/callback_handler/entity/chain_result.py +++ /dev/null @@ -1,16 +0,0 @@ -from pydantic import BaseModel - - -class ChainResult(BaseModel): - type: str = None - prompt: dict = None - completion: dict = None - - status: str = 'chain_started' - completed: bool = False - - started_at: float = None - completed_at: float = None - - agent_result: dict = None - """only when type is 'AgentExecutor'""" diff --git a/api/core/callback_handler/entity/dataset_query.py b/api/core/callback_handler/entity/dataset_query.py deleted file mode 100644 index 23705e55ac2c4a..00000000000000 --- a/api/core/callback_handler/entity/dataset_query.py +++ /dev/null @@ -1,6 +0,0 @@ -from pydantic import BaseModel - - -class DatasetQueryObj(BaseModel): - dataset_id: str = None - query: str = None diff --git a/api/core/callback_handler/entity/llm_message.py b/api/core/callback_handler/entity/llm_message.py deleted file mode 100644 index 61967340c7379a..00000000000000 --- a/api/core/callback_handler/entity/llm_message.py +++ /dev/null @@ -1,8 +0,0 @@ -from pydantic import BaseModel - - -class LLMMessage(BaseModel): - prompt: str = '' - prompt_tokens: int = 0 - completion: str = '' - completion_tokens: int = 0 diff --git a/api/core/callback_handler/index_tool_callback_handler.py b/api/core/callback_handler/index_tool_callback_handler.py index ec91d67290d6a1..bfd305e2f727d2 100644 --- a/api/core/callback_handler/index_tool_callback_handler.py +++ b/api/core/callback_handler/index_tool_callback_handler.py @@ -1,17 +1,44 @@ -from typing import List +from typing import List, Union from langchain.schema import Document -from core.conversation_message_task import ConversationMessageTask +from core.application_queue_manager import ApplicationQueueManager +from core.entities.application_entities import InvokeFrom from extensions.ext_database import db -from models.dataset import DocumentSegment +from models.dataset import DocumentSegment, DatasetQuery +from models.model import DatasetRetrieverResource class DatasetIndexToolCallbackHandler: """Callback handler for dataset tool.""" - def __init__(self, conversation_message_task: ConversationMessageTask) -> None: - self.conversation_message_task = conversation_message_task + def __init__(self, queue_manager: ApplicationQueueManager, + app_id: str, + message_id: str, + user_id: str, + invoke_from: InvokeFrom) -> None: + self._queue_manager = queue_manager + self._app_id = app_id + self._message_id = message_id + self._user_id = user_id + self._invoke_from = invoke_from + + def on_query(self, query: str, dataset_id: str) -> None: + """ + Handle query. + """ + dataset_query = DatasetQuery( + dataset_id=dataset_id, + content=query, + source='app', + source_app_id=self._app_id, + created_by_role=('account' + if self._invoke_from in [InvokeFrom.EXPLORE, InvokeFrom.DEBUGGER] else 'end_user'), + created_by=self._user_id + ) + + db.session.add(dataset_query) + db.session.commit() def on_tool_end(self, documents: List[Document]) -> None: """Handle tool end.""" @@ -30,4 +57,27 @@ def on_tool_end(self, documents: List[Document]) -> None: def return_retriever_resource_info(self, resource: List): """Handle return_retriever_resource_info.""" - self.conversation_message_task.on_dataset_query_finish(resource) + if resource and len(resource) > 0: + for item in resource: + dataset_retriever_resource = DatasetRetrieverResource( + message_id=self._message_id, + position=item.get('position'), + dataset_id=item.get('dataset_id'), + dataset_name=item.get('dataset_name'), + document_id=item.get('document_id'), + document_name=item.get('document_name'), + data_source_type=item.get('data_source_type'), + segment_id=item.get('segment_id'), + score=item.get('score') if 'score' in item else None, + hit_count=item.get('hit_count') if 'hit_count' else None, + word_count=item.get('word_count') if 'word_count' in item else None, + segment_position=item.get('segment_position') if 'segment_position' in item else None, + index_node_hash=item.get('index_node_hash') if 'index_node_hash' in item else None, + content=item.get('content'), + retriever_from=item.get('retriever_from'), + created_by=self._user_id + ) + db.session.add(dataset_retriever_resource) + db.session.commit() + + self._queue_manager.publish_retriever_resources(resource) diff --git a/api/core/callback_handler/llm_callback_handler.py b/api/core/callback_handler/llm_callback_handler.py deleted file mode 100644 index 8cd8c693d036ce..00000000000000 --- a/api/core/callback_handler/llm_callback_handler.py +++ /dev/null @@ -1,284 +0,0 @@ -import logging -import threading -import time -from typing import Any, Dict, List, Union, Optional - -from flask import Flask, current_app -from langchain.callbacks.base import BaseCallbackHandler -from langchain.schema import LLMResult, BaseMessage -from pydantic import BaseModel - -from core.callback_handler.entity.llm_message import LLMMessage -from core.conversation_message_task import ConversationMessageTask, ConversationTaskStoppedException, \ - ConversationTaskInterruptException -from core.model_providers.models.entity.message import to_prompt_messages, PromptMessage, LCHumanMessageWithFiles, \ - ImagePromptMessageFile -from core.model_providers.models.llm.base import BaseLLM -from core.moderation.base import ModerationOutputsResult, ModerationAction -from core.moderation.factory import ModerationFactory - - -class ModerationRule(BaseModel): - type: str - config: Dict[str, Any] - - -class LLMCallbackHandler(BaseCallbackHandler): - raise_error: bool = True - - def __init__(self, model_instance: BaseLLM, - conversation_message_task: ConversationMessageTask): - self.model_instance = model_instance - self.llm_message = LLMMessage() - self.start_at = None - self.conversation_message_task = conversation_message_task - - self.output_moderation_handler = None - self.init_output_moderation() - - def init_output_moderation(self): - app_model_config = self.conversation_message_task.app_model_config - sensitive_word_avoidance_dict = app_model_config.sensitive_word_avoidance_dict - - if sensitive_word_avoidance_dict and sensitive_word_avoidance_dict.get("enabled"): - self.output_moderation_handler = OutputModerationHandler( - tenant_id=self.conversation_message_task.tenant_id, - app_id=self.conversation_message_task.app.id, - rule=ModerationRule( - type=sensitive_word_avoidance_dict.get("type"), - config=sensitive_word_avoidance_dict.get("config") - ), - on_message_replace_func=self.conversation_message_task.on_message_replace - ) - - @property - def always_verbose(self) -> bool: - """Whether to call verbose callbacks even if verbose is False.""" - return True - - def on_chat_model_start( - self, - serialized: Dict[str, Any], - messages: List[List[BaseMessage]], - **kwargs: Any - ) -> Any: - real_prompts = [] - for message in messages[0]: - if message.type == 'human': - role = 'user' - elif message.type == 'ai': - role = 'assistant' - else: - role = 'system' - - real_prompts.append({ - "role": role, - "text": message.content, - "files": [{ - "type": file.type.value, - "data": file.data[:10] + '...[TRUNCATED]...' + file.data[-10:], - "detail": file.detail.value if isinstance(file, ImagePromptMessageFile) else None, - } for file in (message.files if isinstance(message, LCHumanMessageWithFiles) else [])] - }) - - self.llm_message.prompt = real_prompts - self.llm_message.prompt_tokens = self.model_instance.get_num_tokens(to_prompt_messages(messages[0])) - - def on_llm_start( - self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any - ) -> None: - self.llm_message.prompt = [{ - "role": 'user', - "text": prompts[0] - }] - - self.llm_message.prompt_tokens = self.model_instance.get_num_tokens([PromptMessage(content=prompts[0])]) - - def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None: - if self.output_moderation_handler: - self.output_moderation_handler.stop_thread() - - self.llm_message.completion = self.output_moderation_handler.moderation_completion( - completion=response.generations[0][0].text, - public_event=True if self.conversation_message_task.streaming else False - ) - else: - self.llm_message.completion = response.generations[0][0].text - - if not self.conversation_message_task.streaming: - self.conversation_message_task.append_message_text(self.llm_message.completion) - - if response.llm_output and 'token_usage' in response.llm_output: - if 'prompt_tokens' in response.llm_output['token_usage']: - self.llm_message.prompt_tokens = response.llm_output['token_usage']['prompt_tokens'] - - if 'completion_tokens' in response.llm_output['token_usage']: - self.llm_message.completion_tokens = response.llm_output['token_usage']['completion_tokens'] - else: - self.llm_message.completion_tokens = self.model_instance.get_num_tokens( - [PromptMessage(content=self.llm_message.completion)]) - else: - self.llm_message.completion_tokens = self.model_instance.get_num_tokens( - [PromptMessage(content=self.llm_message.completion)]) - - self.conversation_message_task.save_message(self.llm_message) - - def on_llm_new_token(self, token: str, **kwargs: Any) -> None: - if self.output_moderation_handler and self.output_moderation_handler.should_direct_output(): - # stop subscribe new token when output moderation should direct output - ex = ConversationTaskInterruptException() - self.on_llm_error(error=ex) - raise ex - - try: - self.conversation_message_task.append_message_text(token) - self.llm_message.completion += token - - if self.output_moderation_handler: - self.output_moderation_handler.append_new_token(token) - except ConversationTaskStoppedException as ex: - self.on_llm_error(error=ex) - raise ex - - def on_llm_error( - self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any - ) -> None: - """Do nothing.""" - if self.output_moderation_handler: - self.output_moderation_handler.stop_thread() - - if isinstance(error, ConversationTaskStoppedException): - if self.conversation_message_task.streaming: - self.llm_message.completion_tokens = self.model_instance.get_num_tokens( - [PromptMessage(content=self.llm_message.completion)] - ) - self.conversation_message_task.save_message(llm_message=self.llm_message, by_stopped=True) - if isinstance(error, ConversationTaskInterruptException): - self.llm_message.completion = self.output_moderation_handler.get_final_output() - self.llm_message.completion_tokens = self.model_instance.get_num_tokens( - [PromptMessage(content=self.llm_message.completion)] - ) - self.conversation_message_task.save_message(llm_message=self.llm_message) - else: - logging.debug("on_llm_error: %s", error) - - -class OutputModerationHandler(BaseModel): - DEFAULT_BUFFER_SIZE: int = 300 - - tenant_id: str - app_id: str - - rule: ModerationRule - on_message_replace_func: Any - - thread: Optional[threading.Thread] = None - thread_running: bool = True - buffer: str = '' - is_final_chunk: bool = False - final_output: Optional[str] = None - - class Config: - arbitrary_types_allowed = True - - def should_direct_output(self): - return self.final_output is not None - - def get_final_output(self): - return self.final_output - - def append_new_token(self, token: str): - self.buffer += token - - if not self.thread: - self.thread = self.start_thread() - - def moderation_completion(self, completion: str, public_event: bool = False) -> str: - self.buffer = completion - self.is_final_chunk = True - - result = self.moderation( - tenant_id=self.tenant_id, - app_id=self.app_id, - moderation_buffer=completion - ) - - if not result or not result.flagged: - return completion - - if result.action == ModerationAction.DIRECT_OUTPUT: - final_output = result.preset_response - else: - final_output = result.text - - if public_event: - self.on_message_replace_func(final_output) - - return final_output - - def start_thread(self) -> threading.Thread: - buffer_size = int(current_app.config.get('MODERATION_BUFFER_SIZE', self.DEFAULT_BUFFER_SIZE)) - thread = threading.Thread(target=self.worker, kwargs={ - 'flask_app': current_app._get_current_object(), - 'buffer_size': buffer_size if buffer_size > 0 else self.DEFAULT_BUFFER_SIZE - }) - - thread.start() - - return thread - - def stop_thread(self): - if self.thread and self.thread.is_alive(): - self.thread_running = False - - def worker(self, flask_app: Flask, buffer_size: int): - with flask_app.app_context(): - current_length = 0 - while self.thread_running: - moderation_buffer = self.buffer - buffer_length = len(moderation_buffer) - if not self.is_final_chunk: - chunk_length = buffer_length - current_length - if 0 <= chunk_length < buffer_size: - time.sleep(1) - continue - - current_length = buffer_length - - result = self.moderation( - tenant_id=self.tenant_id, - app_id=self.app_id, - moderation_buffer=moderation_buffer - ) - - if not result or not result.flagged: - continue - - if result.action == ModerationAction.DIRECT_OUTPUT: - final_output = result.preset_response - self.final_output = final_output - else: - final_output = result.text + self.buffer[len(moderation_buffer):] - - # trigger replace event - if self.thread_running: - self.on_message_replace_func(final_output) - - if result.action == ModerationAction.DIRECT_OUTPUT: - break - - def moderation(self, tenant_id: str, app_id: str, moderation_buffer: str) -> Optional[ModerationOutputsResult]: - try: - moderation_factory = ModerationFactory( - name=self.rule.type, - app_id=app_id, - tenant_id=tenant_id, - config=self.rule.config - ) - - result: ModerationOutputsResult = moderation_factory.moderation_for_outputs(moderation_buffer) - return result - except Exception as e: - logging.error("Moderation Output error: %s", e) - - return None diff --git a/api/core/callback_handler/main_chain_gather_callback_handler.py b/api/core/callback_handler/main_chain_gather_callback_handler.py deleted file mode 100644 index aef7fb36a48207..00000000000000 --- a/api/core/callback_handler/main_chain_gather_callback_handler.py +++ /dev/null @@ -1,76 +0,0 @@ -import logging -import time - -from typing import Any, Dict, Union - -from langchain.callbacks.base import BaseCallbackHandler - -from core.callback_handler.entity.chain_result import ChainResult -from core.conversation_message_task import ConversationMessageTask - - -class MainChainGatherCallbackHandler(BaseCallbackHandler): - """Callback Handler that prints to std out.""" - raise_error: bool = True - - def __init__(self, conversation_message_task: ConversationMessageTask) -> None: - """Initialize callback handler.""" - self._current_chain_result = None - self._current_chain_message = None - self.conversation_message_task = conversation_message_task - self.agent_callback = None - - def clear_chain_results(self) -> None: - self._current_chain_result = None - self._current_chain_message = None - if self.agent_callback: - self.agent_callback.current_chain = None - - @property - def always_verbose(self) -> bool: - """Whether to call verbose callbacks even if verbose is False.""" - return True - - @property - def ignore_llm(self) -> bool: - """Whether to ignore LLM callbacks.""" - return True - - @property - def ignore_agent(self) -> bool: - """Whether to ignore agent callbacks.""" - return True - - def on_chain_start( - self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any - ) -> None: - """Print out that we are entering a chain.""" - if not self._current_chain_result: - chain_type = serialized['id'][-1] - if chain_type: - self._current_chain_result = ChainResult( - type=chain_type, - prompt=inputs, - started_at=time.perf_counter() - ) - self._current_chain_message = self.conversation_message_task.init_chain(self._current_chain_result) - if self.agent_callback: - self.agent_callback.current_chain = self._current_chain_message - - def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None: - """Print out that we finished a chain.""" - if self._current_chain_result and self._current_chain_result.status == 'chain_started': - self._current_chain_result.status = 'chain_ended' - self._current_chain_result.completion = outputs - self._current_chain_result.completed = True - self._current_chain_result.completed_at = time.perf_counter() - - self.conversation_message_task.on_chain_end(self._current_chain_message, self._current_chain_result) - - self.clear_chain_results() - - def on_chain_error( - self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any - ) -> None: - logging.debug("Dataset tool on_chain_error: %s", error) - self.clear_chain_results() diff --git a/api/core/callback_handler/std_out_callback_handler.py b/api/core/callback_handler/std_out_callback_handler.py index e0ff7950e54296..750d9d7399d613 100644 --- a/api/core/callback_handler/std_out_callback_handler.py +++ b/api/core/callback_handler/std_out_callback_handler.py @@ -79,8 +79,11 @@ def on_agent_action( """Run on agent action.""" tool = action.tool tool_input = action.tool_input - action_name_position = action.log.index("\nAction:") + 1 if action.log else -1 - thought = action.log[:action_name_position].strip() if action.log else '' + try: + action_name_position = action.log.index("\nAction:") + 1 if action.log else -1 + thought = action.log[:action_name_position].strip() if action.log else '' + except ValueError: + thought = '' log = f"Thought: {thought}\nTool: {tool}\nTool Input: {tool_input}" print_text("\n[on_agent_action]\n" + log + "\n", color='green') diff --git a/api/core/chain/llm_chain.py b/api/core/chain/llm_chain.py index 2a5b4b61a948e2..4939ad9b138695 100644 --- a/api/core/chain/llm_chain.py +++ b/api/core/chain/llm_chain.py @@ -5,15 +5,19 @@ from langchain.schema import LLMResult, Generation from langchain.schema.language_model import BaseLanguageModel -from core.model_providers.models.entity.message import to_prompt_messages -from core.model_providers.models.llm.base import BaseLLM +from core.agent.agent.agent_llm_callback import AgentLLMCallback +from core.entities.application_entities import ModelConfigEntity +from core.model_manager import ModelInstance +from core.entities.message_entities import lc_messages_to_prompt_messages from core.third_party.langchain.llms.fake import FakeLLM class LLMChain(LCLLMChain): - model_instance: BaseLLM + model_config: ModelConfigEntity """The language model instance to use.""" llm: BaseLanguageModel = FakeLLM(response="") + parameters: Dict[str, Any] = {} + agent_llm_callback: Optional[AgentLLMCallback] = None def generate( self, @@ -23,14 +27,23 @@ def generate( """Generate LLM result from inputs.""" prompts, stop = self.prep_prompts(input_list, run_manager=run_manager) messages = prompts[0].to_messages() - prompt_messages = to_prompt_messages(messages) - result = self.model_instance.run( - messages=prompt_messages, - stop=stop + prompt_messages = lc_messages_to_prompt_messages(messages) + + model_instance = ModelInstance( + provider_model_bundle=self.model_config.provider_model_bundle, + model=self.model_config.model, + ) + + result = model_instance.invoke_llm( + prompt_messages=prompt_messages, + stream=False, + stop=stop, + callbacks=[self.agent_llm_callback] if self.agent_llm_callback else None, + model_parameters=self.parameters ) generations = [ - [Generation(text=result.content)] + [Generation(text=result.message.content)] ] return LLMResult(generations=generations) diff --git a/api/core/completion.py b/api/core/completion.py deleted file mode 100644 index d219814db6118a..00000000000000 --- a/api/core/completion.py +++ /dev/null @@ -1,501 +0,0 @@ -import concurrent -import json -import logging -from concurrent.futures import ThreadPoolExecutor -from typing import Optional, List, Union, Tuple - -from flask import current_app, Flask -from requests.exceptions import ChunkedEncodingError - -from core.agent.agent_executor import AgentExecuteResult, PlanningStrategy -from core.callback_handler.main_chain_gather_callback_handler import MainChainGatherCallbackHandler -from core.callback_handler.llm_callback_handler import LLMCallbackHandler -from core.conversation_message_task import ConversationMessageTask, ConversationTaskStoppedException, \ - ConversationTaskInterruptException -from core.embedding.cached_embedding import CacheEmbedding -from core.external_data_tool.factory import ExternalDataToolFactory -from core.file.file_obj import FileObj -from core.index.vector_index.vector_index import VectorIndex -from core.model_providers.error import LLMBadRequestError -from core.memory.read_only_conversation_token_db_buffer_shared_memory import \ - ReadOnlyConversationTokenDBBufferSharedMemory -from core.model_providers.model_factory import ModelFactory -from core.model_providers.models.entity.message import PromptMessage, PromptMessageFile -from core.model_providers.models.llm.base import BaseLLM -from core.orchestrator_rule_parser import OrchestratorRuleParser -from core.prompt.prompt_template import PromptTemplateParser -from core.prompt.prompt_transform import PromptTransform -from models.dataset import Dataset -from models.model import App, AppModelConfig, Account, Conversation, EndUser -from core.moderation.base import ModerationException, ModerationAction -from core.moderation.factory import ModerationFactory -from services.annotation_service import AppAnnotationService -from services.dataset_service import DatasetCollectionBindingService - - -class Completion: - @classmethod - def generate(cls, task_id: str, app: App, app_model_config: AppModelConfig, query: str, inputs: dict, - files: List[FileObj], user: Union[Account, EndUser], conversation: Optional[Conversation], - streaming: bool, is_override: bool = False, retriever_from: str = 'dev', - auto_generate_name: bool = True, from_source: str = 'console'): - """ - errors: ProviderTokenNotInitError - """ - query = PromptTemplateParser.remove_template_variables(query) - - memory = None - if conversation: - # get memory of conversation (read-only) - memory = cls.get_memory_from_conversation( - tenant_id=app.tenant_id, - app_model_config=app_model_config, - conversation=conversation, - return_messages=False - ) - - inputs = conversation.inputs - - final_model_instance = ModelFactory.get_text_generation_model_from_model_config( - tenant_id=app.tenant_id, - model_config=app_model_config.model_dict, - streaming=streaming - ) - - conversation_message_task = ConversationMessageTask( - task_id=task_id, - app=app, - app_model_config=app_model_config, - user=user, - conversation=conversation, - is_override=is_override, - inputs=inputs, - query=query, - files=files, - streaming=streaming, - model_instance=final_model_instance, - auto_generate_name=auto_generate_name - ) - - prompt_message_files = [file.prompt_message_file for file in files] - - rest_tokens_for_context_and_memory = cls.get_validate_rest_tokens( - mode=app.mode, - model_instance=final_model_instance, - app_model_config=app_model_config, - query=query, - inputs=inputs, - files=prompt_message_files - ) - - # init orchestrator rule parser - orchestrator_rule_parser = OrchestratorRuleParser( - tenant_id=app.tenant_id, - app_model_config=app_model_config - ) - - try: - chain_callback = MainChainGatherCallbackHandler(conversation_message_task) - - try: - # process sensitive_word_avoidance - inputs, query = cls.moderation_for_inputs(app.id, app.tenant_id, app_model_config, inputs, query) - except ModerationException as e: - cls.run_final_llm( - model_instance=final_model_instance, - mode=app.mode, - app_model_config=app_model_config, - query=query, - inputs=inputs, - files=prompt_message_files, - agent_execute_result=None, - conversation_message_task=conversation_message_task, - memory=memory, - fake_response=str(e) - ) - return - # check annotation reply - annotation_reply = cls.query_app_annotations_to_reply(conversation_message_task, from_source) - if annotation_reply: - return - # fill in variable inputs from external data tools if exists - external_data_tools = app_model_config.external_data_tools_list - if external_data_tools: - inputs = cls.fill_in_inputs_from_external_data_tools( - tenant_id=app.tenant_id, - app_id=app.id, - external_data_tools=external_data_tools, - inputs=inputs, - query=query - ) - - # get agent executor - agent_executor = orchestrator_rule_parser.to_agent_executor( - conversation_message_task=conversation_message_task, - memory=memory, - rest_tokens=rest_tokens_for_context_and_memory, - chain_callback=chain_callback, - tenant_id=app.tenant_id, - retriever_from=retriever_from - ) - - query_for_agent = cls.get_query_for_agent(app, app_model_config, query, inputs) - - # run agent executor - agent_execute_result = None - if query_for_agent and agent_executor: - should_use_agent = agent_executor.should_use_agent(query_for_agent) - if should_use_agent: - agent_execute_result = agent_executor.run(query_for_agent) - - # When no extra pre prompt is specified, - # the output of the agent can be used directly as the main output content without calling LLM again - fake_response = None - if not app_model_config.pre_prompt and agent_execute_result and agent_execute_result.output \ - and agent_execute_result.strategy not in [PlanningStrategy.ROUTER, - PlanningStrategy.REACT_ROUTER]: - fake_response = agent_execute_result.output - - # run the final llm - cls.run_final_llm( - model_instance=final_model_instance, - mode=app.mode, - app_model_config=app_model_config, - query=query, - inputs=inputs, - files=prompt_message_files, - agent_execute_result=agent_execute_result, - conversation_message_task=conversation_message_task, - memory=memory, - fake_response=fake_response - ) - except (ConversationTaskInterruptException, ConversationTaskStoppedException): - return - except ChunkedEncodingError as e: - # Interrupt by LLM (like OpenAI), handle it. - logging.warning(f'ChunkedEncodingError: {e}') - return - - @classmethod - def moderation_for_inputs(cls, app_id: str, tenant_id: str, app_model_config: AppModelConfig, inputs: dict, - query: str): - if not app_model_config.sensitive_word_avoidance_dict['enabled']: - return inputs, query - - type = app_model_config.sensitive_word_avoidance_dict['type'] - - moderation = ModerationFactory(type, app_id, tenant_id, - app_model_config.sensitive_word_avoidance_dict['config']) - moderation_result = moderation.moderation_for_inputs(inputs, query) - - if not moderation_result.flagged: - return inputs, query - - if moderation_result.action == ModerationAction.DIRECT_OUTPUT: - raise ModerationException(moderation_result.preset_response) - elif moderation_result.action == ModerationAction.OVERRIDED: - inputs = moderation_result.inputs - query = moderation_result.query - - return inputs, query - - @classmethod - def fill_in_inputs_from_external_data_tools(cls, tenant_id: str, app_id: str, external_data_tools: list[dict], - inputs: dict, query: str) -> dict: - """ - Fill in variable inputs from external data tools if exists. - - :param tenant_id: workspace id - :param app_id: app id - :param external_data_tools: external data tools configs - :param inputs: the inputs - :param query: the query - :return: the filled inputs - """ - # Group tools by type and config - grouped_tools = {} - for tool in external_data_tools: - if not tool.get("enabled"): - continue - - tool_key = (tool.get("type"), json.dumps(tool.get("config"), sort_keys=True)) - grouped_tools.setdefault(tool_key, []).append(tool) - - results = {} - with ThreadPoolExecutor() as executor: - futures = {} - for tool in external_data_tools: - if not tool.get("enabled"): - continue - - future = executor.submit( - cls.query_external_data_tool, current_app._get_current_object(), tenant_id, app_id, tool, - inputs, query - ) - - futures[future] = tool - - for future in concurrent.futures.as_completed(futures): - tool_variable, result = future.result() - results[tool_variable] = result - - inputs.update(results) - return inputs - - @classmethod - def query_external_data_tool(cls, flask_app: Flask, tenant_id: str, app_id: str, external_data_tool: dict, - inputs: dict, query: str) -> Tuple[Optional[str], Optional[str]]: - with flask_app.app_context(): - tool_variable = external_data_tool.get("variable") - tool_type = external_data_tool.get("type") - tool_config = external_data_tool.get("config") - - external_data_tool_factory = ExternalDataToolFactory( - name=tool_type, - tenant_id=tenant_id, - app_id=app_id, - variable=tool_variable, - config=tool_config - ) - - # query external data tool - result = external_data_tool_factory.query( - inputs=inputs, - query=query - ) - - return tool_variable, result - - @classmethod - def get_query_for_agent(cls, app: App, app_model_config: AppModelConfig, query: str, inputs: dict) -> str: - if app.mode != 'completion': - return query - - return inputs.get(app_model_config.dataset_query_variable, "") - - @classmethod - def run_final_llm(cls, model_instance: BaseLLM, mode: str, app_model_config: AppModelConfig, query: str, - inputs: dict, - files: List[PromptMessageFile], - agent_execute_result: Optional[AgentExecuteResult], - conversation_message_task: ConversationMessageTask, - memory: Optional[ReadOnlyConversationTokenDBBufferSharedMemory], - fake_response: Optional[str]): - prompt_transform = PromptTransform() - - # get llm prompt - if app_model_config.prompt_type == 'simple': - prompt_messages, stop_words = prompt_transform.get_prompt( - app_mode=mode, - pre_prompt=app_model_config.pre_prompt, - inputs=inputs, - query=query, - files=files, - context=agent_execute_result.output if agent_execute_result else None, - memory=memory, - model_instance=model_instance - ) - else: - prompt_messages = prompt_transform.get_advanced_prompt( - app_mode=mode, - app_model_config=app_model_config, - inputs=inputs, - query=query, - files=files, - context=agent_execute_result.output if agent_execute_result else None, - memory=memory, - model_instance=model_instance - ) - - model_config = app_model_config.model_dict - completion_params = model_config.get("completion_params", {}) - stop_words = completion_params.get("stop", []) - - cls.recale_llm_max_tokens( - model_instance=model_instance, - prompt_messages=prompt_messages, - ) - - response = model_instance.run( - messages=prompt_messages, - stop=stop_words if stop_words else None, - callbacks=[LLMCallbackHandler(model_instance, conversation_message_task)], - fake_response=fake_response - ) - return response - - @classmethod - def get_history_messages_from_memory(cls, memory: ReadOnlyConversationTokenDBBufferSharedMemory, - max_token_limit: int) -> str: - """Get memory messages.""" - memory.max_token_limit = max_token_limit - memory_key = memory.memory_variables[0] - external_context = memory.load_memory_variables({}) - return external_context[memory_key] - - @classmethod - def query_app_annotations_to_reply(cls, conversation_message_task: ConversationMessageTask, - from_source: str) -> bool: - """Get memory messages.""" - app_model_config = conversation_message_task.app_model_config - app = conversation_message_task.app - annotation_reply = app_model_config.annotation_reply_dict - if annotation_reply['enabled']: - try: - score_threshold = annotation_reply.get('score_threshold', 1) - embedding_provider_name = annotation_reply['embedding_model']['embedding_provider_name'] - embedding_model_name = annotation_reply['embedding_model']['embedding_model_name'] - # get embedding model - embedding_model = ModelFactory.get_embedding_model( - tenant_id=app.tenant_id, - model_provider_name=embedding_provider_name, - model_name=embedding_model_name - ) - embeddings = CacheEmbedding(embedding_model) - - dataset_collection_binding = DatasetCollectionBindingService.get_dataset_collection_binding( - embedding_provider_name, - embedding_model_name, - 'annotation' - ) - - dataset = Dataset( - id=app.id, - tenant_id=app.tenant_id, - indexing_technique='high_quality', - embedding_model_provider=embedding_provider_name, - embedding_model=embedding_model_name, - collection_binding_id=dataset_collection_binding.id - ) - - vector_index = VectorIndex( - dataset=dataset, - config=current_app.config, - embeddings=embeddings, - attributes=['doc_id', 'annotation_id', 'app_id'] - ) - - documents = vector_index.search( - conversation_message_task.query, - search_type='similarity_score_threshold', - search_kwargs={ - 'k': 1, - 'score_threshold': score_threshold, - 'filter': { - 'group_id': [dataset.id] - } - } - ) - if documents: - annotation_id = documents[0].metadata['annotation_id'] - score = documents[0].metadata['score'] - annotation = AppAnnotationService.get_annotation_by_id(annotation_id) - if annotation: - conversation_message_task.annotation_end(annotation.content, annotation.id, annotation.account.name) - # insert annotation history - AppAnnotationService.add_annotation_history(annotation.id, - app.id, - annotation.question, - annotation.content, - conversation_message_task.query, - conversation_message_task.user.id, - conversation_message_task.message.id, - from_source, - score) - return True - except Exception as e: - logging.warning(f'Query annotation failed, exception: {str(e)}.') - return False - return False - - @classmethod - def get_memory_from_conversation(cls, tenant_id: str, app_model_config: AppModelConfig, - conversation: Conversation, - **kwargs) -> ReadOnlyConversationTokenDBBufferSharedMemory: - # only for calc token in memory - memory_model_instance = ModelFactory.get_text_generation_model_from_model_config( - tenant_id=tenant_id, - model_config=app_model_config.model_dict - ) - - # use llm config from conversation - memory = ReadOnlyConversationTokenDBBufferSharedMemory( - conversation=conversation, - model_instance=memory_model_instance, - max_token_limit=kwargs.get("max_token_limit", 2048), - memory_key=kwargs.get("memory_key", "chat_history"), - return_messages=kwargs.get("return_messages", True), - input_key=kwargs.get("input_key", "input"), - output_key=kwargs.get("output_key", "output"), - message_limit=kwargs.get("message_limit", 10), - ) - - return memory - - @classmethod - def get_validate_rest_tokens(cls, mode: str, model_instance: BaseLLM, app_model_config: AppModelConfig, - query: str, inputs: dict, files: List[PromptMessageFile]) -> int: - model_limited_tokens = model_instance.model_rules.max_tokens.max - max_tokens = model_instance.get_model_kwargs().max_tokens - - if model_limited_tokens is None: - return -1 - - if max_tokens is None: - max_tokens = 0 - - prompt_transform = PromptTransform() - - # get prompt without memory and context - if app_model_config.prompt_type == 'simple': - prompt_messages, _ = prompt_transform.get_prompt( - app_mode=mode, - pre_prompt=app_model_config.pre_prompt, - inputs=inputs, - query=query, - files=files, - context=None, - memory=None, - model_instance=model_instance - ) - else: - prompt_messages = prompt_transform.get_advanced_prompt( - app_mode=mode, - app_model_config=app_model_config, - inputs=inputs, - query=query, - files=files, - context=None, - memory=None, - model_instance=model_instance - ) - - prompt_tokens = model_instance.get_num_tokens(prompt_messages) - rest_tokens = model_limited_tokens - max_tokens - prompt_tokens - if rest_tokens < 0: - raise LLMBadRequestError("Query or prefix prompt is too long, you can reduce the prefix prompt, " - "or shrink the max token, or switch to a llm with a larger token limit size.") - - return rest_tokens - - @classmethod - def recale_llm_max_tokens(cls, model_instance: BaseLLM, prompt_messages: List[PromptMessage]): - # recalc max_tokens if sum(prompt_token + max_tokens) over model token limit - model_limited_tokens = model_instance.model_rules.max_tokens.max - max_tokens = model_instance.get_model_kwargs().max_tokens - - if model_limited_tokens is None: - return - - if max_tokens is None: - max_tokens = 0 - - prompt_tokens = model_instance.get_num_tokens(prompt_messages) - - if prompt_tokens + max_tokens > model_limited_tokens: - max_tokens = max(model_limited_tokens - prompt_tokens, 16) - - # update model instance max tokens - model_kwargs = model_instance.get_model_kwargs() - model_kwargs.max_tokens = max_tokens - model_instance.set_model_kwargs(model_kwargs) diff --git a/api/core/conversation_message_task.py b/api/core/conversation_message_task.py deleted file mode 100644 index bf700b3bec5a7f..00000000000000 --- a/api/core/conversation_message_task.py +++ /dev/null @@ -1,517 +0,0 @@ -import json -import time -from typing import Optional, Union, List - -from core.callback_handler.entity.agent_loop import AgentLoop -from core.callback_handler.entity.dataset_query import DatasetQueryObj -from core.callback_handler.entity.llm_message import LLMMessage -from core.callback_handler.entity.chain_result import ChainResult -from core.file.file_obj import FileObj -from core.model_providers.model_factory import ModelFactory -from core.model_providers.models.entity.message import to_prompt_messages, MessageType, PromptMessageFile -from core.model_providers.models.llm.base import BaseLLM -from core.prompt.prompt_builder import PromptBuilder -from core.prompt.prompt_template import PromptTemplateParser -from events.message_event import message_was_created -from extensions.ext_database import db -from extensions.ext_redis import redis_client -from models.dataset import DatasetQuery -from models.model import AppModelConfig, Conversation, Account, Message, EndUser, App, MessageAgentThought, \ - MessageChain, DatasetRetrieverResource, MessageFile - - -class ConversationMessageTask: - def __init__(self, task_id: str, app: App, app_model_config: AppModelConfig, user: Account, - inputs: dict, query: str, files: List[FileObj], streaming: bool, - model_instance: BaseLLM, conversation: Optional[Conversation] = None, is_override: bool = False, - auto_generate_name: bool = True): - self.start_at = time.perf_counter() - - self.task_id = task_id - - self.app = app - self.tenant_id = app.tenant_id - self.app_model_config = app_model_config - self.is_override = is_override - - self.user = user - self.inputs = inputs - self.query = query - self.files = files - self.streaming = streaming - - self.conversation = conversation - self.is_new_conversation = False - - self.model_instance = model_instance - - self.message = None - - self.retriever_resource = None - self.auto_generate_name = auto_generate_name - - self.model_dict = self.app_model_config.model_dict - self.provider_name = self.model_dict.get('provider') - self.model_name = self.model_dict.get('name') - self.mode = app.mode - - self.init() - - self._pub_handler = PubHandler( - user=self.user, - task_id=self.task_id, - message=self.message, - conversation=self.conversation, - chain_pub=False, # disabled currently - agent_thought_pub=True - ) - - def init(self): - - override_model_configs = None - if self.is_override: - override_model_configs = self.app_model_config.to_dict() - - introduction = '' - system_instruction = '' - system_instruction_tokens = 0 - if self.mode == 'chat': - introduction = self.app_model_config.opening_statement - if introduction: - prompt_template = PromptTemplateParser(template=introduction) - prompt_inputs = {k: self.inputs[k] for k in prompt_template.variable_keys if k in self.inputs} - try: - introduction = prompt_template.format(prompt_inputs) - except KeyError: - pass - - if self.app_model_config.pre_prompt: - system_message = PromptBuilder.to_system_message(self.app_model_config.pre_prompt, self.inputs) - system_instruction = system_message.content - model_instance = ModelFactory.get_text_generation_model( - tenant_id=self.tenant_id, - model_provider_name=self.provider_name, - model_name=self.model_name - ) - system_instruction_tokens = model_instance.get_num_tokens(to_prompt_messages([system_message])) - - if not self.conversation: - self.is_new_conversation = True - self.conversation = Conversation( - app_id=self.app.id, - app_model_config_id=self.app_model_config.id, - model_provider=self.provider_name, - model_id=self.model_name, - override_model_configs=json.dumps(override_model_configs) if override_model_configs else None, - mode=self.mode, - name='New conversation', - inputs=self.inputs, - introduction=introduction, - system_instruction=system_instruction, - system_instruction_tokens=system_instruction_tokens, - status='normal', - from_source=('console' if isinstance(self.user, Account) else 'api'), - from_end_user_id=(self.user.id if isinstance(self.user, EndUser) else None), - from_account_id=(self.user.id if isinstance(self.user, Account) else None), - ) - - db.session.add(self.conversation) - db.session.commit() - - self.message = Message( - app_id=self.app.id, - model_provider=self.provider_name, - model_id=self.model_name, - override_model_configs=json.dumps(override_model_configs) if override_model_configs else None, - conversation_id=self.conversation.id, - inputs=self.inputs, - query=self.query, - message="", - message_tokens=0, - message_unit_price=0, - message_price_unit=0, - answer="", - answer_tokens=0, - answer_unit_price=0, - answer_price_unit=0, - provider_response_latency=0, - total_price=0, - currency=self.model_instance.get_currency(), - from_source=('console' if isinstance(self.user, Account) else 'api'), - from_end_user_id=(self.user.id if isinstance(self.user, EndUser) else None), - from_account_id=(self.user.id if isinstance(self.user, Account) else None), - agent_based=self.app_model_config.agent_mode_dict.get('enabled'), - ) - - db.session.add(self.message) - db.session.commit() - - for file in self.files: - message_file = MessageFile( - message_id=self.message.id, - type=file.type.value, - transfer_method=file.transfer_method.value, - url=file.url, - upload_file_id=file.upload_file_id, - created_by_role=('account' if isinstance(self.user, Account) else 'end_user'), - created_by=self.user.id - ) - db.session.add(message_file) - db.session.commit() - - def append_message_text(self, text: str): - if text is not None: - self._pub_handler.pub_text(text) - - def save_message(self, llm_message: LLMMessage, by_stopped: bool = False): - message_tokens = llm_message.prompt_tokens - answer_tokens = llm_message.completion_tokens - - message_unit_price = self.model_instance.get_tokens_unit_price(MessageType.USER) - message_price_unit = self.model_instance.get_price_unit(MessageType.USER) - answer_unit_price = self.model_instance.get_tokens_unit_price(MessageType.ASSISTANT) - answer_price_unit = self.model_instance.get_price_unit(MessageType.ASSISTANT) - - message_total_price = self.model_instance.calc_tokens_price(message_tokens, MessageType.USER) - answer_total_price = self.model_instance.calc_tokens_price(answer_tokens, MessageType.ASSISTANT) - total_price = message_total_price + answer_total_price - - self.message.message = llm_message.prompt - self.message.message_tokens = message_tokens - self.message.message_unit_price = message_unit_price - self.message.message_price_unit = message_price_unit - self.message.answer = PromptTemplateParser.remove_template_variables( - llm_message.completion.strip()) if llm_message.completion else '' - self.message.answer_tokens = answer_tokens - self.message.answer_unit_price = answer_unit_price - self.message.answer_price_unit = answer_price_unit - self.message.provider_response_latency = time.perf_counter() - self.start_at - self.message.total_price = total_price - - db.session.commit() - - message_was_created.send( - self.message, - conversation=self.conversation, - is_first_message=self.is_new_conversation, - auto_generate_name=self.auto_generate_name - ) - - if not by_stopped: - self.end() - - def init_chain(self, chain_result: ChainResult): - message_chain = MessageChain( - message_id=self.message.id, - type=chain_result.type, - input=json.dumps(chain_result.prompt), - output='' - ) - - db.session.add(message_chain) - db.session.commit() - - return message_chain - - def on_chain_end(self, message_chain: MessageChain, chain_result: ChainResult): - message_chain.output = json.dumps(chain_result.completion) - db.session.commit() - - self._pub_handler.pub_chain(message_chain) - - def on_agent_start(self, message_chain: MessageChain, agent_loop: AgentLoop) -> MessageAgentThought: - message_agent_thought = MessageAgentThought( - message_id=self.message.id, - message_chain_id=message_chain.id, - position=agent_loop.position, - thought=agent_loop.thought, - tool=agent_loop.tool_name, - tool_input=agent_loop.tool_input, - message=agent_loop.prompt, - message_price_unit=0, - answer=agent_loop.completion, - answer_price_unit=0, - created_by_role=('account' if isinstance(self.user, Account) else 'end_user'), - created_by=self.user.id - ) - - db.session.add(message_agent_thought) - db.session.commit() - - self._pub_handler.pub_agent_thought(message_agent_thought) - - return message_agent_thought - - def on_agent_end(self, message_agent_thought: MessageAgentThought, agent_model_instance: BaseLLM, - agent_loop: AgentLoop): - agent_message_unit_price = agent_model_instance.get_tokens_unit_price(MessageType.USER) - agent_message_price_unit = agent_model_instance.get_price_unit(MessageType.USER) - agent_answer_unit_price = agent_model_instance.get_tokens_unit_price(MessageType.ASSISTANT) - agent_answer_price_unit = agent_model_instance.get_price_unit(MessageType.ASSISTANT) - - loop_message_tokens = agent_loop.prompt_tokens - loop_answer_tokens = agent_loop.completion_tokens - - loop_message_total_price = agent_model_instance.calc_tokens_price(loop_message_tokens, MessageType.USER) - loop_answer_total_price = agent_model_instance.calc_tokens_price(loop_answer_tokens, MessageType.ASSISTANT) - loop_total_price = loop_message_total_price + loop_answer_total_price - - message_agent_thought.observation = agent_loop.tool_output - message_agent_thought.tool_process_data = '' # currently not support - message_agent_thought.message_token = loop_message_tokens - message_agent_thought.message_unit_price = agent_message_unit_price - message_agent_thought.message_price_unit = agent_message_price_unit - message_agent_thought.answer_token = loop_answer_tokens - message_agent_thought.answer_unit_price = agent_answer_unit_price - message_agent_thought.answer_price_unit = agent_answer_price_unit - message_agent_thought.latency = agent_loop.latency - message_agent_thought.tokens = agent_loop.prompt_tokens + agent_loop.completion_tokens - message_agent_thought.total_price = loop_total_price - message_agent_thought.currency = agent_model_instance.get_currency() - db.session.commit() - - def on_dataset_query_end(self, dataset_query_obj: DatasetQueryObj): - dataset_query = DatasetQuery( - dataset_id=dataset_query_obj.dataset_id, - content=dataset_query_obj.query, - source='app', - source_app_id=self.app.id, - created_by_role=('account' if isinstance(self.user, Account) else 'end_user'), - created_by=self.user.id - ) - - db.session.add(dataset_query) - db.session.commit() - - def on_dataset_query_finish(self, resource: List): - if resource and len(resource) > 0: - for item in resource: - dataset_retriever_resource = DatasetRetrieverResource( - message_id=self.message.id, - position=item.get('position'), - dataset_id=item.get('dataset_id'), - dataset_name=item.get('dataset_name'), - document_id=item.get('document_id'), - document_name=item.get('document_name'), - data_source_type=item.get('data_source_type'), - segment_id=item.get('segment_id'), - score=item.get('score') if 'score' in item else None, - hit_count=item.get('hit_count') if 'hit_count' else None, - word_count=item.get('word_count') if 'word_count' in item else None, - segment_position=item.get('segment_position') if 'segment_position' in item else None, - index_node_hash=item.get('index_node_hash') if 'index_node_hash' in item else None, - content=item.get('content'), - retriever_from=item.get('retriever_from'), - created_by=self.user.id - ) - db.session.add(dataset_retriever_resource) - db.session.commit() - self.retriever_resource = resource - - def on_message_replace(self, text: str): - if text is not None: - self._pub_handler.pub_message_replace(text) - - def message_end(self): - self._pub_handler.pub_message_end(self.retriever_resource) - - def end(self): - self._pub_handler.pub_message_end(self.retriever_resource) - self._pub_handler.pub_end() - - def annotation_end(self, text: str, annotation_id: str, annotation_author_name: str): - self._pub_handler.pub_annotation(text, annotation_id, annotation_author_name, self.start_at) - self._pub_handler.pub_end() - - -class PubHandler: - def __init__(self, user: Union[Account, EndUser], task_id: str, - message: Message, conversation: Conversation, - chain_pub: bool = False, agent_thought_pub: bool = False): - self._channel = PubHandler.generate_channel_name(user, task_id) - self._stopped_cache_key = PubHandler.generate_stopped_cache_key(user, task_id) - - self._task_id = task_id - self._message = message - self._conversation = conversation - self._chain_pub = chain_pub - self._agent_thought_pub = agent_thought_pub - - @classmethod - def generate_channel_name(cls, user: Union[Account, EndUser], task_id: str): - if not user: - raise ValueError("user is required") - - user_str = 'account-' + str(user.id) if isinstance(user, Account) else 'end-user-' + str(user.id) - return "generate_result:{}-{}".format(user_str, task_id) - - @classmethod - def generate_stopped_cache_key(cls, user: Union[Account, EndUser], task_id: str): - user_str = 'account-' + str(user.id) if isinstance(user, Account) else 'end-user-' + str(user.id) - return "generate_result_stopped:{}-{}".format(user_str, task_id) - - def pub_text(self, text: str): - content = { - 'event': 'message', - 'data': { - 'task_id': self._task_id, - 'message_id': str(self._message.id), - 'text': text, - 'mode': self._conversation.mode, - 'conversation_id': str(self._conversation.id) - } - } - - redis_client.publish(self._channel, json.dumps(content)) - - if self._is_stopped(): - self.pub_end() - raise ConversationTaskStoppedException() - - def pub_message_replace(self, text: str): - content = { - 'event': 'message_replace', - 'data': { - 'task_id': self._task_id, - 'message_id': str(self._message.id), - 'text': text, - 'mode': self._conversation.mode, - 'conversation_id': str(self._conversation.id) - } - } - - redis_client.publish(self._channel, json.dumps(content)) - - if self._is_stopped(): - self.pub_end() - raise ConversationTaskStoppedException() - - def pub_chain(self, message_chain: MessageChain): - if self._chain_pub: - content = { - 'event': 'chain', - 'data': { - 'task_id': self._task_id, - 'message_id': self._message.id, - 'chain_id': message_chain.id, - 'type': message_chain.type, - 'input': json.loads(message_chain.input), - 'output': json.loads(message_chain.output), - 'mode': self._conversation.mode, - 'conversation_id': self._conversation.id - } - } - - redis_client.publish(self._channel, json.dumps(content)) - - if self._is_stopped(): - self.pub_end() - raise ConversationTaskStoppedException() - - def pub_agent_thought(self, message_agent_thought: MessageAgentThought): - if self._agent_thought_pub: - content = { - 'event': 'agent_thought', - 'data': { - 'id': message_agent_thought.id, - 'task_id': self._task_id, - 'message_id': self._message.id, - 'chain_id': message_agent_thought.message_chain_id, - 'position': message_agent_thought.position, - 'thought': message_agent_thought.thought, - 'tool': message_agent_thought.tool, - 'tool_input': message_agent_thought.tool_input, - 'mode': self._conversation.mode, - 'conversation_id': self._conversation.id - } - } - - redis_client.publish(self._channel, json.dumps(content)) - - if self._is_stopped(): - self.pub_end() - raise ConversationTaskStoppedException() - - def pub_message_end(self, retriever_resource: List): - content = { - 'event': 'message_end', - 'data': { - 'task_id': self._task_id, - 'message_id': self._message.id, - 'mode': self._conversation.mode, - 'conversation_id': self._conversation.id, - } - } - if retriever_resource: - content['data']['retriever_resources'] = retriever_resource - redis_client.publish(self._channel, json.dumps(content)) - - if self._is_stopped(): - self.pub_end() - raise ConversationTaskStoppedException() - - def pub_annotation(self, text: str, annotation_id: str, annotation_author_name: str, start_at: float): - content = { - 'event': 'annotation', - 'data': { - 'task_id': self._task_id, - 'message_id': self._message.id, - 'mode': self._conversation.mode, - 'conversation_id': self._conversation.id, - 'text': text, - 'annotation_id': annotation_id, - 'annotation_author_name': annotation_author_name - } - } - self._message.answer = text - self._message.provider_response_latency = time.perf_counter() - start_at - - db.session.commit() - - redis_client.publish(self._channel, json.dumps(content)) - - if self._is_stopped(): - self.pub_end() - raise ConversationTaskStoppedException() - - def pub_end(self): - content = { - 'event': 'end', - } - - redis_client.publish(self._channel, json.dumps(content)) - - @classmethod - def pub_error(cls, user: Union[Account, EndUser], task_id: str, e): - content = { - 'error': type(e).__name__, - 'description': e.description if getattr(e, 'description', None) is not None else str(e) - } - - channel = cls.generate_channel_name(user, task_id) - redis_client.publish(channel, json.dumps(content)) - - def _is_stopped(self): - return redis_client.get(self._stopped_cache_key) is not None - - @classmethod - def ping(cls, user: Union[Account, EndUser], task_id: str): - content = { - 'event': 'ping' - } - - channel = cls.generate_channel_name(user, task_id) - redis_client.publish(channel, json.dumps(content)) - - @classmethod - def stop(cls, user: Union[Account, EndUser], task_id: str): - stopped_cache_key = cls.generate_stopped_cache_key(user, task_id) - redis_client.setex(stopped_cache_key, 600, 1) - - -class ConversationTaskStoppedException(Exception): - pass - - -class ConversationTaskInterruptException(Exception): - pass diff --git a/api/core/docstore/dataset_docstore.py b/api/core/docstore/dataset_docstore.py index 63435174073c9d..77a5dde9ed3a68 100644 --- a/api/core/docstore/dataset_docstore.py +++ b/api/core/docstore/dataset_docstore.py @@ -1,9 +1,11 @@ -from typing import Any, Dict, Optional, Sequence +from typing import Any, Dict, Optional, Sequence, cast from langchain.schema import Document from sqlalchemy import func -from core.model_providers.model_factory import ModelFactory +from core.model_manager import ModelManager +from core.model_runtime.entities.model_entities import ModelType +from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel from extensions.ext_database import db from models.dataset import Dataset, DocumentSegment @@ -69,10 +71,12 @@ def add_documents( max_position = 0 embedding_model = None if self._dataset.indexing_technique == 'high_quality': - embedding_model = ModelFactory.get_embedding_model( + model_manager = ModelManager() + embedding_model = model_manager.get_model_instance( tenant_id=self._dataset.tenant_id, - model_provider_name=self._dataset.embedding_model_provider, - model_name=self._dataset.embedding_model + provider=self._dataset.embedding_model_provider, + model_type=ModelType.TEXT_EMBEDDING, + model=self._dataset.embedding_model ) for doc in docs: @@ -89,7 +93,16 @@ def add_documents( ) # calc embedding use tokens - tokens = embedding_model.get_num_tokens(doc.page_content) if embedding_model else 0 + if embedding_model: + model_type_instance = embedding_model.model_type_instance + model_type_instance = cast(TextEmbeddingModel, model_type_instance) + tokens = model_type_instance.get_num_tokens( + model=embedding_model.model, + credentials=embedding_model.credentials, + texts=[doc.page_content] + ) + else: + tokens = 0 if not segment_document: max_position += 1 diff --git a/api/core/embedding/cached_embedding.py b/api/core/embedding/cached_embedding.py index dbdfe026d90de9..0022c8b141670b 100644 --- a/api/core/embedding/cached_embedding.py +++ b/api/core/embedding/cached_embedding.py @@ -1,19 +1,22 @@ import logging -from typing import List +from typing import List, Optional import numpy as np from langchain.embeddings.base import Embeddings from sqlalchemy.exc import IntegrityError -from core.model_providers.models.embedding.base import BaseEmbedding +from core.model_manager import ModelInstance from extensions.ext_database import db from libs import helper from models.dataset import Embedding +logger = logging.getLogger(__name__) + class CacheEmbedding(Embeddings): - def __init__(self, embeddings: BaseEmbedding): - self._embeddings = embeddings + def __init__(self, model_instance: ModelInstance, user: Optional[str] = None) -> None: + self._model_instance = model_instance + self._user = user def embed_documents(self, texts: List[str]) -> List[List[float]]: """Embed search docs.""" @@ -22,7 +25,7 @@ def embed_documents(self, texts: List[str]) -> List[List[float]]: embedding_queue_indices = [] for i, text in enumerate(texts): hash = helper.generate_text_hash(text) - embedding = db.session.query(Embedding).filter_by(model_name=self._embeddings.name, hash=hash).first() + embedding = db.session.query(Embedding).filter_by(model_name=self._model_instance.model, hash=hash).first() if embedding: text_embeddings[i] = embedding.get_embedding() else: @@ -30,15 +33,21 @@ def embed_documents(self, texts: List[str]) -> List[List[float]]: if embedding_queue_indices: try: - embedding_results = self._embeddings.client.embed_documents([texts[i] for i in embedding_queue_indices]) + embedding_result = self._model_instance.invoke_text_embedding( + texts=[texts[i] for i in embedding_queue_indices], + user=self._user + ) + + embedding_results = embedding_result.embeddings except Exception as ex: - raise self._embeddings.handle_exceptions(ex) + logger.error('Failed to embed documents: ', ex) + raise ex for i, indice in enumerate(embedding_queue_indices): hash = helper.generate_text_hash(texts[indice]) try: - embedding = Embedding(model_name=self._embeddings.name, hash=hash) + embedding = Embedding(model_name=self._model_instance.model, hash=hash) vector = embedding_results[i] normalized_embedding = (vector / np.linalg.norm(vector)).tolist() text_embeddings[indice] = normalized_embedding @@ -58,18 +67,23 @@ def embed_query(self, text: str) -> List[float]: """Embed query text.""" # use doc embedding cache or store if not exists hash = helper.generate_text_hash(text) - embedding = db.session.query(Embedding).filter_by(model_name=self._embeddings.name, hash=hash).first() + embedding = db.session.query(Embedding).filter_by(model_name=self._model_instance.model, hash=hash).first() if embedding: return embedding.get_embedding() try: - embedding_results = self._embeddings.client.embed_query(text) + embedding_result = self._model_instance.invoke_text_embedding( + texts=[text], + user=self._user + ) + + embedding_results = embedding_result.embeddings[0] embedding_results = (embedding_results / np.linalg.norm(embedding_results)).tolist() except Exception as ex: - raise self._embeddings.handle_exceptions(ex) + raise ex try: - embedding = Embedding(model_name=self._embeddings.name, hash=hash) + embedding = Embedding(model_name=self._model_instance.model, hash=hash) embedding.set_embedding(embedding_results) db.session.add(embedding) db.session.commit() @@ -79,4 +93,3 @@ def embed_query(self, text: str) -> List[float]: logging.exception('Failed to add embedding to db') return embedding_results - diff --git a/api/core/model_providers/models/embedding/__init__.py b/api/core/entities/__init__.py similarity index 100% rename from api/core/model_providers/models/embedding/__init__.py rename to api/core/entities/__init__.py diff --git a/api/core/entities/application_entities.py b/api/core/entities/application_entities.py new file mode 100644 index 00000000000000..7e34eed51ee24d --- /dev/null +++ b/api/core/entities/application_entities.py @@ -0,0 +1,265 @@ +from enum import Enum +from typing import Optional, Any, cast + +from pydantic import BaseModel + +from core.entities.provider_configuration import ProviderModelBundle +from core.file.file_obj import FileObj +from core.model_runtime.entities.message_entities import PromptMessageRole +from core.model_runtime.entities.model_entities import AIModelEntity + + +class ModelConfigEntity(BaseModel): + """ + Model Config Entity. + """ + provider: str + model: str + model_schema: AIModelEntity + mode: str + provider_model_bundle: ProviderModelBundle + credentials: dict[str, Any] = {} + parameters: dict[str, Any] = {} + stop: list[str] = [] + + +class AdvancedChatMessageEntity(BaseModel): + """ + Advanced Chat Message Entity. + """ + text: str + role: PromptMessageRole + + +class AdvancedChatPromptTemplateEntity(BaseModel): + """ + Advanced Chat Prompt Template Entity. + """ + messages: list[AdvancedChatMessageEntity] + + +class AdvancedCompletionPromptTemplateEntity(BaseModel): + """ + Advanced Completion Prompt Template Entity. + """ + class RolePrefixEntity(BaseModel): + """ + Role Prefix Entity. + """ + user: str + assistant: str + + prompt: str + role_prefix: Optional[RolePrefixEntity] = None + + +class PromptTemplateEntity(BaseModel): + """ + Prompt Template Entity. + """ + class PromptType(Enum): + """ + Prompt Type. + 'simple', 'advanced' + """ + SIMPLE = 'simple' + ADVANCED = 'advanced' + + @classmethod + def value_of(cls, value: str) -> 'PromptType': + """ + Get value of given mode. + + :param value: mode value + :return: mode + """ + for mode in cls: + if mode.value == value: + return mode + raise ValueError(f'invalid prompt type value {value}') + + prompt_type: PromptType + simple_prompt_template: Optional[str] = None + advanced_chat_prompt_template: Optional[AdvancedChatPromptTemplateEntity] = None + advanced_completion_prompt_template: Optional[AdvancedCompletionPromptTemplateEntity] = None + + +class ExternalDataVariableEntity(BaseModel): + """ + External Data Variable Entity. + """ + variable: str + type: str + config: dict[str, Any] = {} + + +class DatasetRetrieveConfigEntity(BaseModel): + """ + Dataset Retrieve Config Entity. + """ + class RetrieveStrategy(Enum): + """ + Dataset Retrieve Strategy. + 'single' or 'multiple' + """ + SINGLE = 'single' + MULTIPLE = 'multiple' + + @classmethod + def value_of(cls, value: str) -> 'RetrieveStrategy': + """ + Get value of given mode. + + :param value: mode value + :return: mode + """ + for mode in cls: + if mode.value == value: + return mode + raise ValueError(f'invalid retrieve strategy value {value}') + + query_variable: Optional[str] = None # Only when app mode is completion + + retrieve_strategy: RetrieveStrategy + single_strategy: Optional[str] = None # for temp + top_k: Optional[int] = None + score_threshold: Optional[float] = None + reranking_model: Optional[dict] = None + + +class DatasetEntity(BaseModel): + """ + Dataset Config Entity. + """ + dataset_ids: list[str] + retrieve_config: DatasetRetrieveConfigEntity + + +class SensitiveWordAvoidanceEntity(BaseModel): + """ + Sensitive Word Avoidance Entity. + """ + type: str + config: dict[str, Any] = {} + + +class FileUploadEntity(BaseModel): + """ + File Upload Entity. + """ + image_config: Optional[dict[str, Any]] = None + + +class AgentToolEntity(BaseModel): + """ + Agent Tool Entity. + """ + tool_id: str + config: dict[str, Any] = {} + + +class AgentEntity(BaseModel): + """ + Agent Entity. + """ + class Strategy(Enum): + """ + Agent Strategy. + """ + CHAIN_OF_THOUGHT = 'chain-of-thought' + FUNCTION_CALLING = 'function-calling' + + provider: str + model: str + strategy: Strategy + tools: list[AgentToolEntity] = [] + + +class AppOrchestrationConfigEntity(BaseModel): + """ + App Orchestration Config Entity. + """ + model_config: ModelConfigEntity + prompt_template: PromptTemplateEntity + external_data_variables: list[ExternalDataVariableEntity] = [] + agent: Optional[AgentEntity] = None + + # features + dataset: Optional[DatasetEntity] = None + file_upload: Optional[FileUploadEntity] = None + opening_statement: Optional[str] = None + suggested_questions_after_answer: bool = False + show_retrieve_source: bool = False + more_like_this: bool = False + speech_to_text: bool = False + sensitive_word_avoidance: Optional[SensitiveWordAvoidanceEntity] = None + + +class InvokeFrom(Enum): + """ + Invoke From. + """ + SERVICE_API = 'service-api' + WEB_APP = 'web-app' + EXPLORE = 'explore' + DEBUGGER = 'debugger' + + @classmethod + def value_of(cls, value: str) -> 'InvokeFrom': + """ + Get value of given mode. + + :param value: mode value + :return: mode + """ + for mode in cls: + if mode.value == value: + return mode + raise ValueError(f'invalid invoke from value {value}') + + def to_source(self) -> str: + """ + Get source of invoke from. + + :return: source + """ + if self == InvokeFrom.WEB_APP: + return 'web_app' + elif self == InvokeFrom.DEBUGGER: + return 'dev' + elif self == InvokeFrom.EXPLORE: + return 'explore_app' + elif self == InvokeFrom.SERVICE_API: + return 'api' + + return 'dev' + + +class ApplicationGenerateEntity(BaseModel): + """ + Application Generate Entity. + """ + task_id: str + tenant_id: str + + app_id: str + app_model_config_id: str + # for save + app_model_config_dict: dict + app_model_config_override: bool + + # Converted from app_model_config to Entity object, or directly covered by external input + app_orchestration_config_entity: AppOrchestrationConfigEntity + + conversation_id: Optional[str] = None + inputs: dict[str, str] + query: Optional[str] = None + files: list[FileObj] = [] + user_id: str + + # extras + stream: bool + invoke_from: InvokeFrom + + # extra parameters, like: auto_generate_conversation_name + extras: dict[str, Any] = {} diff --git a/api/core/entities/message_entities.py b/api/core/entities/message_entities.py new file mode 100644 index 00000000000000..d72f436aad8926 --- /dev/null +++ b/api/core/entities/message_entities.py @@ -0,0 +1,128 @@ +import enum +from typing import Any, cast + +from langchain.schema import HumanMessage, AIMessage, SystemMessage, BaseMessage, FunctionMessage +from pydantic import BaseModel + +from core.model_runtime.entities.message_entities import PromptMessage, UserPromptMessage, TextPromptMessageContent, \ + ImagePromptMessageContent, AssistantPromptMessage, SystemPromptMessage, ToolPromptMessage + + +class PromptMessageFileType(enum.Enum): + IMAGE = 'image' + + @staticmethod + def value_of(value): + for member in PromptMessageFileType: + if member.value == value: + return member + raise ValueError(f"No matching enum found for value '{value}'") + + +class PromptMessageFile(BaseModel): + type: PromptMessageFileType + data: Any + + +class ImagePromptMessageFile(PromptMessageFile): + class DETAIL(enum.Enum): + LOW = 'low' + HIGH = 'high' + + type: PromptMessageFileType = PromptMessageFileType.IMAGE + detail: DETAIL = DETAIL.LOW + + +class LCHumanMessageWithFiles(HumanMessage): + # content: Union[str, List[Union[str, Dict]]] + content: str + files: list[PromptMessageFile] + + +def lc_messages_to_prompt_messages(messages: list[BaseMessage]) -> list[PromptMessage]: + prompt_messages = [] + for message in messages: + if isinstance(message, HumanMessage): + if isinstance(message, LCHumanMessageWithFiles): + file_prompt_message_contents = [] + for file in message.files: + if file.type == PromptMessageFileType.IMAGE: + file = cast(ImagePromptMessageFile, file) + file_prompt_message_contents.append(ImagePromptMessageContent( + data=file.data, + detail=ImagePromptMessageContent.DETAIL.HIGH + if file.detail.value == "high" else ImagePromptMessageContent.DETAIL.LOW + )) + + prompt_message_contents = [TextPromptMessageContent(data=message.content)] + prompt_message_contents.extend(file_prompt_message_contents) + + prompt_messages.append(UserPromptMessage(content=prompt_message_contents)) + else: + prompt_messages.append(UserPromptMessage(content=message.content)) + elif isinstance(message, AIMessage): + message_kwargs = { + 'content': message.content + } + + if 'function_call' in message.additional_kwargs: + message_kwargs['tool_calls'] = [ + AssistantPromptMessage.ToolCall( + id=message.additional_kwargs['function_call']['id'], + type='function', + function=AssistantPromptMessage.ToolCall.ToolCallFunction( + name=message.additional_kwargs['function_call']['name'], + arguments=message.additional_kwargs['function_call']['arguments'] + ) + ) + ] + + prompt_messages.append(AssistantPromptMessage(**message_kwargs)) + elif isinstance(message, SystemMessage): + prompt_messages.append(SystemPromptMessage(content=message.content)) + elif isinstance(message, FunctionMessage): + prompt_messages.append(ToolPromptMessage(content=message.content, tool_call_id=message.name)) + + return prompt_messages + + +def prompt_messages_to_lc_messages(prompt_messages: list[PromptMessage]) -> list[BaseMessage]: + messages = [] + for prompt_message in prompt_messages: + if isinstance(prompt_message, UserPromptMessage): + if isinstance(prompt_message.content, str): + messages.append(HumanMessage(content=prompt_message.content)) + else: + message_contents = [] + for content in prompt_message.content: + if isinstance(content, TextPromptMessageContent): + message_contents.append(content.data) + elif isinstance(content, ImagePromptMessageContent): + message_contents.append({ + 'type': 'image', + 'data': content.data, + 'detail': content.detail.value + }) + + messages.append(HumanMessage(content=message_contents)) + elif isinstance(prompt_message, AssistantPromptMessage): + message_kwargs = { + 'content': prompt_message.content + } + + if prompt_message.tool_calls: + message_kwargs['additional_kwargs'] = { + 'function_call': { + 'id': prompt_message.tool_calls[0].id, + 'name': prompt_message.tool_calls[0].function.name, + 'arguments': prompt_message.tool_calls[0].function.arguments + } + } + + messages.append(AIMessage(**message_kwargs)) + elif isinstance(prompt_message, SystemPromptMessage): + messages.append(SystemMessage(content=prompt_message.content)) + elif isinstance(prompt_message, ToolPromptMessage): + messages.append(FunctionMessage(name=prompt_message.tool_call_id, content=prompt_message.content)) + + return messages diff --git a/api/core/entities/model_entities.py b/api/core/entities/model_entities.py new file mode 100644 index 00000000000000..6b49ec92484578 --- /dev/null +++ b/api/core/entities/model_entities.py @@ -0,0 +1,71 @@ +from enum import Enum +from typing import Optional + +from pydantic import BaseModel + +from core.model_runtime.entities.common_entities import I18nObject +from core.model_runtime.entities.model_entities import ProviderModel, ModelType +from core.model_runtime.entities.provider_entities import SimpleProviderEntity, ProviderEntity + + +class ModelStatus(Enum): + """ + Enum class for model status. + """ + ACTIVE = "active" + NO_CONFIGURE = "no-configure" + QUOTA_EXCEEDED = "quota-exceeded" + NO_PERMISSION = "no-permission" + + +class SimpleModelProviderEntity(BaseModel): + """ + Simple provider. + """ + provider: str + label: I18nObject + icon_small: Optional[I18nObject] = None + icon_large: Optional[I18nObject] = None + supported_model_types: list[ModelType] + + def __init__(self, provider_entity: ProviderEntity) -> None: + """ + Init simple provider. + + :param provider_entity: provider entity + """ + super().__init__( + provider=provider_entity.provider, + label=provider_entity.label, + icon_small=provider_entity.icon_small, + icon_large=provider_entity.icon_large, + supported_model_types=provider_entity.supported_model_types + ) + + +class ModelWithProviderEntity(ProviderModel): + """ + Model with provider entity. + """ + provider: SimpleModelProviderEntity + status: ModelStatus + + +class DefaultModelProviderEntity(BaseModel): + """ + Default model provider entity. + """ + provider: str + label: I18nObject + icon_small: Optional[I18nObject] = None + icon_large: Optional[I18nObject] = None + supported_model_types: list[ModelType] + + +class DefaultModelEntity(BaseModel): + """ + Default model entity. + """ + model: str + model_type: ModelType + provider: DefaultModelProviderEntity diff --git a/api/core/entities/provider_configuration.py b/api/core/entities/provider_configuration.py new file mode 100644 index 00000000000000..2228560a87ff97 --- /dev/null +++ b/api/core/entities/provider_configuration.py @@ -0,0 +1,657 @@ +import datetime +import json +import time +from json import JSONDecodeError +from typing import Optional, List, Dict, Tuple, Iterator + +from pydantic import BaseModel + +from core.entities.model_entities import ModelWithProviderEntity, ModelStatus, SimpleModelProviderEntity +from core.entities.provider_entities import SystemConfiguration, CustomConfiguration, SystemConfigurationStatus +from core.helper import encrypter +from core.model_runtime.entities.model_entities import ModelType +from core.model_runtime.entities.provider_entities import ProviderEntity, CredentialFormSchema, FormType +from core.model_runtime.model_providers import model_provider_factory +from core.model_runtime.model_providers.__base.ai_model import AIModel +from core.model_runtime.model_providers.__base.model_provider import ModelProvider +from core.model_runtime.utils import encoders +from extensions.ext_database import db +from models.provider import ProviderType, Provider, ProviderModel, TenantPreferredModelProvider + + +class ProviderConfiguration(BaseModel): + """ + Model class for provider configuration. + """ + tenant_id: str + provider: ProviderEntity + preferred_provider_type: ProviderType + using_provider_type: ProviderType + system_configuration: SystemConfiguration + custom_configuration: CustomConfiguration + + def get_current_credentials(self, model_type: ModelType, model: str) -> Optional[dict]: + """ + Get current credentials. + + :param model_type: model type + :param model: model name + :return: + """ + if self.using_provider_type == ProviderType.SYSTEM: + return self.system_configuration.credentials + else: + if self.custom_configuration.models: + for model_configuration in self.custom_configuration.models: + if model_configuration.model_type == model_type and model_configuration.model == model: + return model_configuration.credentials + + if self.custom_configuration.provider: + return self.custom_configuration.provider.credentials + else: + return None + + def get_system_configuration_status(self) -> SystemConfigurationStatus: + """ + Get system configuration status. + :return: + """ + if self.system_configuration.enabled is False: + return SystemConfigurationStatus.UNSUPPORTED + + current_quota_type = self.system_configuration.current_quota_type + current_quota_configuration = next( + (q for q in self.system_configuration.quota_configurations if q.quota_type == current_quota_type), + None + ) + + return SystemConfigurationStatus.ACTIVE if current_quota_configuration.is_valid else \ + SystemConfigurationStatus.QUOTA_EXCEEDED + + def is_custom_configuration_available(self) -> bool: + """ + Check custom configuration available. + :return: + """ + return (self.custom_configuration.provider is not None + or len(self.custom_configuration.models) > 0) + + def get_custom_credentials(self, obfuscated: bool = False) -> Optional[dict]: + """ + Get custom credentials. + + :param obfuscated: obfuscated secret data in credentials + :return: + """ + if self.custom_configuration.provider is None: + return None + + credentials = self.custom_configuration.provider.credentials + if not obfuscated: + return credentials + + # Obfuscate credentials + return self._obfuscated_credentials( + credentials=credentials, + credential_form_schemas=self.provider.provider_credential_schema.credential_form_schemas + if self.provider.provider_credential_schema else [] + ) + + def custom_credentials_validate(self, credentials: dict) -> Tuple[Provider, dict]: + """ + Validate custom credentials. + :param credentials: provider credentials + :return: + """ + # get provider + provider_record = db.session.query(Provider) \ + .filter( + Provider.tenant_id == self.tenant_id, + Provider.provider_name == self.provider.provider, + Provider.provider_type == ProviderType.CUSTOM.value + ).first() + + # Get provider credential secret variables + provider_credential_secret_variables = self._extract_secret_variables( + self.provider.provider_credential_schema.credential_form_schemas + if self.provider.provider_credential_schema else [] + ) + + if provider_record: + try: + original_credentials = json.loads(provider_record.encrypted_config) if provider_record.encrypted_config else {} + except JSONDecodeError: + original_credentials = {} + + # encrypt credentials + for key, value in credentials.items(): + if key in provider_credential_secret_variables: + # if send [__HIDDEN__] in secret input, it will be same as original value + if value == '[__HIDDEN__]' and key in original_credentials: + credentials[key] = encrypter.decrypt_token(self.tenant_id, original_credentials[key]) + + model_provider_factory.provider_credentials_validate( + self.provider.provider, + credentials + ) + + for key, value in credentials.items(): + if key in provider_credential_secret_variables: + credentials[key] = encrypter.encrypt_token(self.tenant_id, value) + + return provider_record, credentials + + def add_or_update_custom_credentials(self, credentials: dict) -> None: + """ + Add or update custom provider credentials. + :param credentials: + :return: + """ + # validate custom provider config + provider_record, credentials = self.custom_credentials_validate(credentials) + + # save provider + # Note: Do not switch the preferred provider, which allows users to use quotas first + if provider_record: + provider_record.encrypted_config = json.dumps(credentials) + provider_record.is_valid = True + provider_record.updated_at = datetime.datetime.utcnow() + db.session.commit() + else: + provider_record = Provider( + tenant_id=self.tenant_id, + provider_name=self.provider.provider, + provider_type=ProviderType.CUSTOM.value, + encrypted_config=json.dumps(credentials), + is_valid=True + ) + db.session.add(provider_record) + db.session.commit() + + self.switch_preferred_provider_type(ProviderType.CUSTOM) + + def delete_custom_credentials(self) -> None: + """ + Delete custom provider credentials. + :return: + """ + # get provider + provider_record = db.session.query(Provider) \ + .filter( + Provider.tenant_id == self.tenant_id, + Provider.provider_name == self.provider.provider, + Provider.provider_type == ProviderType.CUSTOM.value + ).first() + + # delete provider + if provider_record: + self.switch_preferred_provider_type(ProviderType.SYSTEM) + + db.session.delete(provider_record) + db.session.commit() + + def get_custom_model_credentials(self, model_type: ModelType, model: str, obfuscated: bool = False) \ + -> Optional[dict]: + """ + Get custom model credentials. + + :param model_type: model type + :param model: model name + :param obfuscated: obfuscated secret data in credentials + :return: + """ + if not self.custom_configuration.models: + return None + + for model_configuration in self.custom_configuration.models: + if model_configuration.model_type == model_type and model_configuration.model == model: + credentials = model_configuration.credentials + if not obfuscated: + return credentials + + # Obfuscate credentials + return self._obfuscated_credentials( + credentials=credentials, + credential_form_schemas=self.provider.model_credential_schema.credential_form_schemas + if self.provider.model_credential_schema else [] + ) + + return None + + def custom_model_credentials_validate(self, model_type: ModelType, model: str, credentials: dict) \ + -> Tuple[ProviderModel, dict]: + """ + Validate custom model credentials. + + :param model_type: model type + :param model: model name + :param credentials: model credentials + :return: + """ + # get provider model + provider_model_record = db.session.query(ProviderModel) \ + .filter( + ProviderModel.tenant_id == self.tenant_id, + ProviderModel.provider_name == self.provider.provider, + ProviderModel.model_name == model, + ProviderModel.model_type == model_type.to_origin_model_type() + ).first() + + # Get provider credential secret variables + provider_credential_secret_variables = self._extract_secret_variables( + self.provider.model_credential_schema.credential_form_schemas + if self.provider.model_credential_schema else [] + ) + + if provider_model_record: + try: + original_credentials = json.loads(provider_model_record.encrypted_config) if provider_model_record.encrypted_config else {} + except JSONDecodeError: + original_credentials = {} + + # decrypt credentials + for key, value in credentials.items(): + if key in provider_credential_secret_variables: + # if send [__HIDDEN__] in secret input, it will be same as original value + if value == '[__HIDDEN__]' and key in original_credentials: + credentials[key] = encrypter.decrypt_token(self.tenant_id, original_credentials[key]) + + model_provider_factory.model_credentials_validate( + provider=self.provider.provider, + model_type=model_type, + model=model, + credentials=credentials + ) + + model_schema = ( + model_provider_factory.get_provider_instance(self.provider.provider) + .get_model_instance(model_type)._get_customizable_model_schema( + model=model, + credentials=credentials + ) + ) + + if model_schema: + credentials['schema'] = json.dumps(encoders.jsonable_encoder(model_schema)) + + for key, value in credentials.items(): + if key in provider_credential_secret_variables: + credentials[key] = encrypter.encrypt_token(self.tenant_id, value) + + return provider_model_record, credentials + + def add_or_update_custom_model_credentials(self, model_type: ModelType, model: str, credentials: dict) -> None: + """ + Add or update custom model credentials. + + :param model_type: model type + :param model: model name + :param credentials: model credentials + :return: + """ + # validate custom model config + provider_model_record, credentials = self.custom_model_credentials_validate(model_type, model, credentials) + + # save provider model + # Note: Do not switch the preferred provider, which allows users to use quotas first + if provider_model_record: + provider_model_record.encrypted_config = json.dumps(credentials) + provider_model_record.is_valid = True + provider_model_record.updated_at = datetime.datetime.utcnow() + db.session.commit() + else: + provider_model_record = ProviderModel( + tenant_id=self.tenant_id, + provider_name=self.provider.provider, + model_name=model, + model_type=model_type.to_origin_model_type(), + encrypted_config=json.dumps(credentials), + is_valid=True + ) + db.session.add(provider_model_record) + db.session.commit() + + def delete_custom_model_credentials(self, model_type: ModelType, model: str) -> None: + """ + Delete custom model credentials. + :param model_type: model type + :param model: model name + :return: + """ + # get provider model + provider_model_record = db.session.query(ProviderModel) \ + .filter( + ProviderModel.tenant_id == self.tenant_id, + ProviderModel.provider_name == self.provider.provider, + ProviderModel.model_name == model, + ProviderModel.model_type == model_type.to_origin_model_type() + ).first() + + # delete provider model + if provider_model_record: + db.session.delete(provider_model_record) + db.session.commit() + + def get_provider_instance(self) -> ModelProvider: + """ + Get provider instance. + :return: + """ + return model_provider_factory.get_provider_instance(self.provider.provider) + + def get_model_type_instance(self, model_type: ModelType) -> AIModel: + """ + Get current model type instance. + + :param model_type: model type + :return: + """ + # Get provider instance + provider_instance = self.get_provider_instance() + + # Get model instance of LLM + return provider_instance.get_model_instance(model_type) + + def switch_preferred_provider_type(self, provider_type: ProviderType) -> None: + """ + Switch preferred provider type. + :param provider_type: + :return: + """ + if provider_type == self.preferred_provider_type: + return + + if provider_type == ProviderType.SYSTEM and not self.system_configuration.enabled: + return + + # get preferred provider + preferred_model_provider = db.session.query(TenantPreferredModelProvider) \ + .filter( + TenantPreferredModelProvider.tenant_id == self.tenant_id, + TenantPreferredModelProvider.provider_name == self.provider.provider + ).first() + + if preferred_model_provider: + preferred_model_provider.preferred_provider_type = provider_type.value + else: + preferred_model_provider = TenantPreferredModelProvider( + tenant_id=self.tenant_id, + provider_name=self.provider.provider, + preferred_provider_type=provider_type.value + ) + db.session.add(preferred_model_provider) + + db.session.commit() + + def _extract_secret_variables(self, credential_form_schemas: list[CredentialFormSchema]) -> list[str]: + """ + Extract secret input form variables. + + :param credential_form_schemas: + :return: + """ + secret_input_form_variables = [] + for credential_form_schema in credential_form_schemas: + if credential_form_schema.type == FormType.SECRET_INPUT: + secret_input_form_variables.append(credential_form_schema.variable) + + return secret_input_form_variables + + def _obfuscated_credentials(self, credentials: dict, credential_form_schemas: list[CredentialFormSchema]) -> dict: + """ + Obfuscated credentials. + + :param credentials: credentials + :param credential_form_schemas: credential form schemas + :return: + """ + # Get provider credential secret variables + credential_secret_variables = self._extract_secret_variables( + credential_form_schemas + ) + + # Obfuscate provider credentials + copy_credentials = credentials.copy() + for key, value in copy_credentials.items(): + if key in credential_secret_variables: + copy_credentials[key] = encrypter.obfuscated_token(value) + + return copy_credentials + + def get_provider_model(self, model_type: ModelType, + model: str, + only_active: bool = False) -> Optional[ModelWithProviderEntity]: + """ + Get provider model. + :param model_type: model type + :param model: model name + :param only_active: return active model only + :return: + """ + provider_models = self.get_provider_models(model_type, only_active) + + for provider_model in provider_models: + if provider_model.model == model: + return provider_model + + return None + + def get_provider_models(self, model_type: Optional[ModelType] = None, + only_active: bool = False) -> list[ModelWithProviderEntity]: + """ + Get provider models. + :param model_type: model type + :param only_active: only active models + :return: + """ + provider_instance = self.get_provider_instance() + + model_types = [] + if model_type: + model_types.append(model_type) + else: + model_types = provider_instance.get_provider_schema().supported_model_types + + if self.using_provider_type == ProviderType.SYSTEM: + provider_models = self._get_system_provider_models( + model_types=model_types, + provider_instance=provider_instance + ) + else: + provider_models = self._get_custom_provider_models( + model_types=model_types, + provider_instance=provider_instance + ) + + if only_active: + provider_models = [m for m in provider_models if m.status == ModelStatus.ACTIVE] + + # resort provider_models + return sorted(provider_models, key=lambda x: x.model_type.value) + + def _get_system_provider_models(self, + model_types: list[ModelType], + provider_instance: ModelProvider) -> list[ModelWithProviderEntity]: + """ + Get system provider models. + + :param model_types: model types + :param provider_instance: provider instance + :return: + """ + provider_models = [] + for model_type in model_types: + provider_models.extend( + [ + ModelWithProviderEntity( + **m.dict(), + provider=SimpleModelProviderEntity(self.provider), + status=ModelStatus.ACTIVE + ) + for m in provider_instance.models(model_type) + ] + ) + + for quota_configuration in self.system_configuration.quota_configurations: + if self.system_configuration.current_quota_type != quota_configuration.quota_type: + continue + + restrict_llms = quota_configuration.restrict_llms + if not restrict_llms: + break + + # if llm name not in restricted llm list, remove it + for m in provider_models: + if m.model_type == ModelType.LLM and m.model not in restrict_llms: + m.status = ModelStatus.NO_PERMISSION + elif not quota_configuration.is_valid: + m.status = ModelStatus.QUOTA_EXCEEDED + + return provider_models + + def _get_custom_provider_models(self, + model_types: list[ModelType], + provider_instance: ModelProvider) -> list[ModelWithProviderEntity]: + """ + Get custom provider models. + + :param model_types: model types + :param provider_instance: provider instance + :return: + """ + provider_models = [] + + credentials = None + if self.custom_configuration.provider: + credentials = self.custom_configuration.provider.credentials + + for model_type in model_types: + if model_type not in self.provider.supported_model_types: + continue + + models = provider_instance.models(model_type) + for m in models: + provider_models.append( + ModelWithProviderEntity( + **m.dict(), + provider=SimpleModelProviderEntity(self.provider), + status=ModelStatus.ACTIVE if credentials else ModelStatus.NO_CONFIGURE + ) + ) + + # custom models + for model_configuration in self.custom_configuration.models: + if model_configuration.model_type not in model_types: + continue + + custom_model_schema = ( + provider_instance.get_model_instance(model_configuration.model_type) + .get_customizable_model_schema_from_credentials( + model_configuration.model, + model_configuration.credentials + ) + ) + + if not custom_model_schema: + continue + + provider_models.append( + ModelWithProviderEntity( + **custom_model_schema.dict(), + provider=SimpleModelProviderEntity(self.provider), + status=ModelStatus.ACTIVE + ) + ) + + return provider_models + + +class ProviderConfigurations(BaseModel): + """ + Model class for provider configuration dict. + """ + tenant_id: str + configurations: Dict[str, ProviderConfiguration] = {} + + def __init__(self, tenant_id: str): + super().__init__(tenant_id=tenant_id) + + def get_models(self, + provider: Optional[str] = None, + model_type: Optional[ModelType] = None, + only_active: bool = False) \ + -> list[ModelWithProviderEntity]: + """ + Get available models. + + If preferred provider type is `system`: + Get the current **system mode** if provider supported, + if all system modes are not available (no quota), it is considered to be the **custom credential mode**. + If there is no model configured in custom mode, it is treated as no_configure. + system > custom > no_configure + + If preferred provider type is `custom`: + If custom credentials are configured, it is treated as custom mode. + Otherwise, get the current **system mode** if supported, + If all system modes are not available (no quota), it is treated as no_configure. + custom > system > no_configure + + If real mode is `system`, use system credentials to get models, + paid quotas > provider free quotas > system free quotas + include pre-defined models (exclude GPT-4, status marked as `no_permission`). + If real mode is `custom`, use workspace custom credentials to get models, + include pre-defined models, custom models(manual append). + If real mode is `no_configure`, only return pre-defined models from `model runtime`. + (model status marked as `no_configure` if preferred provider type is `custom` otherwise `quota_exceeded`) + model status marked as `active` is available. + + :param provider: provider name + :param model_type: model type + :param only_active: only active models + :return: + """ + all_models = [] + for provider_configuration in self.values(): + if provider and provider_configuration.provider.provider != provider: + continue + + all_models.extend(provider_configuration.get_provider_models(model_type, only_active)) + + return all_models + + def to_list(self) -> List[ProviderConfiguration]: + """ + Convert to list. + + :return: + """ + return list(self.values()) + + def __getitem__(self, key): + return self.configurations[key] + + def __setitem__(self, key, value): + self.configurations[key] = value + + def __iter__(self): + return iter(self.configurations) + + def values(self) -> Iterator[ProviderConfiguration]: + return self.configurations.values() + + def get(self, key, default=None): + return self.configurations.get(key, default) + + +class ProviderModelBundle(BaseModel): + """ + Provider model bundle. + """ + configuration: ProviderConfiguration + provider_instance: ModelProvider + model_type_instance: AIModel + + class Config: + """Configuration for this pydantic object.""" + + arbitrary_types_allowed = True diff --git a/api/core/entities/provider_entities.py b/api/core/entities/provider_entities.py new file mode 100644 index 00000000000000..d8905c71af4387 --- /dev/null +++ b/api/core/entities/provider_entities.py @@ -0,0 +1,67 @@ +from enum import Enum +from typing import Optional + +from pydantic import BaseModel + +from core.model_runtime.entities.model_entities import ModelType +from models.provider import ProviderQuotaType + + +class QuotaUnit(Enum): + TIMES = 'times' + TOKENS = 'tokens' + + +class SystemConfigurationStatus(Enum): + """ + Enum class for system configuration status. + """ + ACTIVE = 'active' + QUOTA_EXCEEDED = 'quota-exceeded' + UNSUPPORTED = 'unsupported' + + +class QuotaConfiguration(BaseModel): + """ + Model class for provider quota configuration. + """ + quota_type: ProviderQuotaType + quota_unit: QuotaUnit + quota_limit: int + quota_used: int + is_valid: bool + restrict_llms: list[str] = [] + + +class SystemConfiguration(BaseModel): + """ + Model class for provider system configuration. + """ + enabled: bool + current_quota_type: Optional[ProviderQuotaType] = None + quota_configurations: list[QuotaConfiguration] = [] + credentials: Optional[dict] = None + + +class CustomProviderConfiguration(BaseModel): + """ + Model class for provider custom configuration. + """ + credentials: dict + + +class CustomModelConfiguration(BaseModel): + """ + Model class for provider custom model configuration. + """ + model: str + model_type: ModelType + credentials: dict + + +class CustomConfiguration(BaseModel): + """ + Model class for provider custom configuration. + """ + provider: Optional[CustomProviderConfiguration] = None + models: list[CustomModelConfiguration] = [] diff --git a/api/core/entities/queue_entities.py b/api/core/entities/queue_entities.py new file mode 100644 index 00000000000000..20b434b05d8b39 --- /dev/null +++ b/api/core/entities/queue_entities.py @@ -0,0 +1,118 @@ +from enum import Enum +from typing import Any + +from pydantic import BaseModel + +from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk + + +class QueueEvent(Enum): + """ + QueueEvent enum + """ + MESSAGE = "message" + MESSAGE_REPLACE = "message-replace" + MESSAGE_END = "message-end" + RETRIEVER_RESOURCES = "retriever-resources" + ANNOTATION_REPLY = "annotation-reply" + AGENT_THOUGHT = "agent-thought" + ERROR = "error" + PING = "ping" + STOP = "stop" + + +class AppQueueEvent(BaseModel): + """ + QueueEvent entity + """ + event: QueueEvent + + +class QueueMessageEvent(AppQueueEvent): + """ + QueueMessageEvent entity + """ + event = QueueEvent.MESSAGE + chunk: LLMResultChunk + + +class QueueMessageReplaceEvent(AppQueueEvent): + """ + QueueMessageReplaceEvent entity + """ + event = QueueEvent.MESSAGE_REPLACE + text: str + + +class QueueRetrieverResourcesEvent(AppQueueEvent): + """ + QueueRetrieverResourcesEvent entity + """ + event = QueueEvent.RETRIEVER_RESOURCES + retriever_resources: list[dict] + + +class AnnotationReplyEvent(AppQueueEvent): + """ + AnnotationReplyEvent entity + """ + event = QueueEvent.ANNOTATION_REPLY + message_annotation_id: str + + +class QueueMessageEndEvent(AppQueueEvent): + """ + QueueMessageEndEvent entity + """ + event = QueueEvent.MESSAGE_END + llm_result: LLMResult + + +class QueueAgentThoughtEvent(AppQueueEvent): + """ + QueueAgentThoughtEvent entity + """ + event = QueueEvent.AGENT_THOUGHT + agent_thought_id: str + + +class QueueErrorEvent(AppQueueEvent): + """ + QueueErrorEvent entity + """ + event = QueueEvent.ERROR + error: Any + + +class QueuePingEvent(AppQueueEvent): + """ + QueuePingEvent entity + """ + event = QueueEvent.PING + + +class QueueStopEvent(AppQueueEvent): + """ + QueueStopEvent entity + """ + class StopBy(Enum): + """ + Stop by enum + """ + USER_MANUAL = "user-manual" + ANNOTATION_REPLY = "annotation-reply" + OUTPUT_MODERATION = "output-moderation" + + event = QueueEvent.STOP + stopped_by: StopBy + + +class QueueMessage(BaseModel): + """ + QueueMessage entity + """ + task_id: str + message_id: str + conversation_id: str + app_mode: str + event: AppQueueEvent diff --git a/api/core/model_providers/models/entity/__init__.py b/api/core/errors/__init__.py similarity index 100% rename from api/core/model_providers/models/entity/__init__.py rename to api/core/errors/__init__.py diff --git a/api/core/model_providers/error.py b/api/core/errors/error.py similarity index 63% rename from api/core/model_providers/error.py rename to api/core/errors/error.py index 9bba8401f26bc8..fddfb345fd78c7 100644 --- a/api/core/model_providers/error.py +++ b/api/core/errors/error.py @@ -14,26 +14,6 @@ class LLMBadRequestError(LLMError): description = "Bad Request" -class LLMAPIConnectionError(LLMError): - """Raised when the LLM returns API connection error.""" - description = "API Connection Error" - - -class LLMAPIUnavailableError(LLMError): - """Raised when the LLM returns API unavailable error.""" - description = "API Unavailable Error" - - -class LLMRateLimitError(LLMError): - """Raised when the LLM returns rate limit error.""" - description = "Rate Limit Error" - - -class LLMAuthorizationError(LLMError): - """Raised when the LLM returns authorization error.""" - description = "Authorization Error" - - class ProviderTokenNotInitError(Exception): """ Custom exception raised when the provider token is not initialized. diff --git a/api/core/model_providers/models/llm/__init__.py b/api/core/external_data_tool/weather_search/__init__.py similarity index 100% rename from api/core/model_providers/models/llm/__init__.py rename to api/core/external_data_tool/weather_search/__init__.py diff --git a/api/core/external_data_tool/weather_search/schema.json b/api/core/external_data_tool/weather_search/schema.json new file mode 100644 index 00000000000000..6e8baffe59b276 --- /dev/null +++ b/api/core/external_data_tool/weather_search/schema.json @@ -0,0 +1,35 @@ +{ + "label": { + "en-US": "Weather Search", + "zh-Hans": "天气查询" + }, + "form_schema": [ + { + "type": "select", + "label": { + "en-US": "Temperature Unit", + "zh-Hans": "温度单位" + }, + "variable": "temperature_unit", + "required": true, + "options": [ + { + "label": { + "en-US": "Fahrenheit", + "zh-Hans": "华氏度" + }, + "value": "fahrenheit" + }, + { + "label": { + "en-US": "Centigrade", + "zh-Hans": "摄氏度" + }, + "value": "centigrade" + } + ], + "default": "centigrade", + "placeholder": "Please select temperature unit" + } + ] +} \ No newline at end of file diff --git a/api/core/external_data_tool/weather_search/weather_search.py b/api/core/external_data_tool/weather_search/weather_search.py new file mode 100644 index 00000000000000..70f11b72aee9ad --- /dev/null +++ b/api/core/external_data_tool/weather_search/weather_search.py @@ -0,0 +1,45 @@ +from typing import Optional + +from core.external_data_tool.base import ExternalDataTool + + +class WeatherSearch(ExternalDataTool): + """ + The name of custom type must be unique, keep the same with directory and file name. + """ + name: str = "weather_search" + + @classmethod + def validate_config(cls, tenant_id: str, config: dict) -> None: + """ + schema.json validation. It will be called when user save the config. + + Example: + .. code-block:: python + config = { + "temperature_unit": "centigrade" + } + + :param tenant_id: the id of workspace + :param config: the variables of form config + :return: + """ + + if not config.get('temperature_unit'): + raise ValueError('temperature unit is required') + + def query(self, inputs: dict, query: Optional[str] = None) -> str: + """ + Query the external data tool. + + :param inputs: user inputs + :param query: the query of chat app + :return: the tool query result + """ + city = inputs.get('city') + temperature_unit = self.config.get('temperature_unit') + + if temperature_unit == 'fahrenheit': + return f'Weather in {city} is 32°F' + else: + return f'Weather in {city} is 0°C' diff --git a/api/core/model_providers/models/moderation/__init__.py b/api/core/features/__init__.py similarity index 100% rename from api/core/model_providers/models/moderation/__init__.py rename to api/core/features/__init__.py diff --git a/api/core/features/agent_runner.py b/api/core/features/agent_runner.py new file mode 100644 index 00000000000000..2c63c6669919ea --- /dev/null +++ b/api/core/features/agent_runner.py @@ -0,0 +1,325 @@ +import logging +from typing import cast, Optional, List + +from langchain import WikipediaAPIWrapper +from langchain.callbacks.base import BaseCallbackHandler +from langchain.tools import BaseTool, WikipediaQueryRun, Tool +from pydantic import BaseModel, Field + +from core.agent.agent.agent_llm_callback import AgentLLMCallback +from core.agent.agent_executor import PlanningStrategy, AgentConfiguration, AgentExecutor +from core.application_queue_manager import ApplicationQueueManager +from core.callback_handler.agent_loop_gather_callback_handler import AgentLoopGatherCallbackHandler +from core.callback_handler.index_tool_callback_handler import DatasetIndexToolCallbackHandler +from core.callback_handler.std_out_callback_handler import DifyStdOutCallbackHandler +from core.entities.application_entities import ModelConfigEntity, InvokeFrom, \ + AgentEntity, AgentToolEntity, AppOrchestrationConfigEntity +from core.memory.token_buffer_memory import TokenBufferMemory +from core.model_runtime.entities.model_entities import ModelFeature, ModelType +from core.model_runtime.model_providers import model_provider_factory +from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel +from core.tool.current_datetime_tool import DatetimeTool +from core.tool.dataset_retriever_tool import DatasetRetrieverTool +from core.tool.provider.serpapi_provider import SerpAPIToolProvider +from core.tool.serpapi_wrapper import OptimizedSerpAPIWrapper, OptimizedSerpAPIInput +from core.tool.web_reader_tool import WebReaderTool +from extensions.ext_database import db +from models.dataset import Dataset +from models.model import Message + +logger = logging.getLogger(__name__) + + +class AgentRunnerFeature: + def __init__(self, tenant_id: str, + app_orchestration_config: AppOrchestrationConfigEntity, + model_config: ModelConfigEntity, + config: AgentEntity, + queue_manager: ApplicationQueueManager, + message: Message, + user_id: str, + agent_llm_callback: AgentLLMCallback, + callback: AgentLoopGatherCallbackHandler, + memory: Optional[TokenBufferMemory] = None,) -> None: + """ + Agent runner + :param tenant_id: tenant id + :param app_orchestration_config: app orchestration config + :param model_config: model config + :param config: dataset config + :param queue_manager: queue manager + :param message: message + :param user_id: user id + :param agent_llm_callback: agent llm callback + :param callback: callback + :param memory: memory + """ + self.tenant_id = tenant_id + self.app_orchestration_config = app_orchestration_config + self.model_config = model_config + self.config = config + self.queue_manager = queue_manager + self.message = message + self.user_id = user_id + self.agent_llm_callback = agent_llm_callback + self.callback = callback + self.memory = memory + + def run(self, query: str, + invoke_from: InvokeFrom) -> Optional[str]: + """ + Retrieve agent loop result. + :param query: query + :param invoke_from: invoke from + :return: + """ + provider = self.config.provider + model = self.config.model + tool_configs = self.config.tools + + # check model is support tool calling + provider_instance = model_provider_factory.get_provider_instance(provider=provider) + model_type_instance = provider_instance.get_model_instance(ModelType.LLM) + model_type_instance = cast(LargeLanguageModel, model_type_instance) + + # get model schema + model_schema = model_type_instance.get_model_schema( + model=model, + credentials=self.model_config.credentials + ) + + if not model_schema: + return None + + planning_strategy = PlanningStrategy.REACT + features = model_schema.features + if features: + if ModelFeature.TOOL_CALL in features \ + or ModelFeature.MULTI_TOOL_CALL in features: + planning_strategy = PlanningStrategy.FUNCTION_CALL + + tools = self.to_tools( + tool_configs=tool_configs, + invoke_from=invoke_from, + callbacks=[self.callback, DifyStdOutCallbackHandler()], + ) + + if len(tools) == 0: + return None + + agent_configuration = AgentConfiguration( + strategy=planning_strategy, + model_config=self.model_config, + tools=tools, + memory=self.memory, + max_iterations=10, + max_execution_time=400.0, + early_stopping_method="generate", + agent_llm_callback=self.agent_llm_callback, + callbacks=[self.callback, DifyStdOutCallbackHandler()] + ) + + agent_executor = AgentExecutor(agent_configuration) + + try: + # check if should use agent + should_use_agent = agent_executor.should_use_agent(query) + if not should_use_agent: + return None + + result = agent_executor.run(query) + return result.output + except Exception as ex: + logger.exception("agent_executor run failed") + return None + + def to_tools(self, tool_configs: list[AgentToolEntity], + invoke_from: InvokeFrom, + callbacks: list[BaseCallbackHandler]) \ + -> Optional[List[BaseTool]]: + """ + Convert tool configs to tools + :param tool_configs: tool configs + :param invoke_from: invoke from + :param callbacks: callbacks + """ + tools = [] + for tool_config in tool_configs: + tool = None + if tool_config.tool_id == "dataset": + tool = self.to_dataset_retriever_tool( + tool_config=tool_config.config, + invoke_from=invoke_from + ) + elif tool_config.tool_id == "web_reader": + tool = self.to_web_reader_tool( + tool_config=tool_config.config, + invoke_from=invoke_from + ) + elif tool_config.tool_id == "google_search": + tool = self.to_google_search_tool( + tool_config=tool_config.config, + invoke_from=invoke_from + ) + elif tool_config.tool_id == "wikipedia": + tool = self.to_wikipedia_tool( + tool_config=tool_config.config, + invoke_from=invoke_from + ) + elif tool_config.tool_id == "current_datetime": + tool = self.to_current_datetime_tool( + tool_config=tool_config.config, + invoke_from=invoke_from + ) + + if tool: + if tool.callbacks is not None: + tool.callbacks.extend(callbacks) + else: + tool.callbacks = callbacks + + tools.append(tool) + + return tools + + def to_dataset_retriever_tool(self, tool_config: dict, + invoke_from: InvokeFrom) \ + -> Optional[BaseTool]: + """ + A dataset tool is a tool that can be used to retrieve information from a dataset + :param tool_config: tool config + :param invoke_from: invoke from + """ + show_retrieve_source = self.app_orchestration_config.show_retrieve_source + + hit_callback = DatasetIndexToolCallbackHandler( + queue_manager=self.queue_manager, + app_id=self.message.app_id, + message_id=self.message.id, + user_id=self.user_id, + invoke_from=invoke_from + ) + + # get dataset from dataset id + dataset = db.session.query(Dataset).filter( + Dataset.tenant_id == self.tenant_id, + Dataset.id == tool_config.get("id") + ).first() + + # pass if dataset is not available + if not dataset: + return None + + # pass if dataset is not available + if (dataset and dataset.available_document_count == 0 + and dataset.available_document_count == 0): + return None + + # get retrieval model config + default_retrieval_model = { + 'search_method': 'semantic_search', + 'reranking_enable': False, + 'reranking_model': { + 'reranking_provider_name': '', + 'reranking_model_name': '' + }, + 'top_k': 2, + 'score_threshold_enabled': False + } + + retrieval_model_config = dataset.retrieval_model \ + if dataset.retrieval_model else default_retrieval_model + + # get top k + top_k = retrieval_model_config['top_k'] + + # get score threshold + score_threshold = None + score_threshold_enabled = retrieval_model_config.get("score_threshold_enabled") + if score_threshold_enabled: + score_threshold = retrieval_model_config.get("score_threshold") + + tool = DatasetRetrieverTool.from_dataset( + dataset=dataset, + top_k=top_k, + score_threshold=score_threshold, + hit_callbacks=[hit_callback], + return_resource=show_retrieve_source, + retriever_from=invoke_from.to_source() + ) + + return tool + + def to_web_reader_tool(self, tool_config: dict, + invoke_from: InvokeFrom) -> Optional[BaseTool]: + """ + A tool for reading web pages + :param tool_config: tool config + :param invoke_from: invoke from + :return: + """ + model_parameters = { + "temperature": 0, + "max_tokens": 500 + } + + tool = WebReaderTool( + model_config=self.model_config, + model_parameters=model_parameters, + max_chunk_length=4000, + continue_reading=True + ) + + return tool + + def to_google_search_tool(self, tool_config: dict, + invoke_from: InvokeFrom) -> Optional[BaseTool]: + """ + A tool for performing a Google search and extracting snippets and webpages + :param tool_config: tool config + :param invoke_from: invoke from + :return: + """ + tool_provider = SerpAPIToolProvider(tenant_id=self.tenant_id) + func_kwargs = tool_provider.credentials_to_func_kwargs() + if not func_kwargs: + return None + + tool = Tool( + name="google_search", + description="A tool for performing a Google search and extracting snippets and webpages " + "when you need to search for something you don't know or when your information " + "is not up to date. " + "Input should be a search query.", + func=OptimizedSerpAPIWrapper(**func_kwargs).run, + args_schema=OptimizedSerpAPIInput + ) + + return tool + + def to_current_datetime_tool(self, tool_config: dict, + invoke_from: InvokeFrom) -> Optional[BaseTool]: + """ + A tool for getting the current date and time + :param tool_config: tool config + :param invoke_from: invoke from + :return: + """ + return DatetimeTool() + + def to_wikipedia_tool(self, tool_config: dict, + invoke_from: InvokeFrom) -> Optional[BaseTool]: + """ + A tool for searching Wikipedia + :param tool_config: tool config + :param invoke_from: invoke from + :return: + """ + class WikipediaInput(BaseModel): + query: str = Field(..., description="search query.") + + return WikipediaQueryRun( + name="wikipedia", + api_wrapper=WikipediaAPIWrapper(doc_content_chars_max=4000), + args_schema=WikipediaInput + ) diff --git a/api/core/features/annotation_reply.py b/api/core/features/annotation_reply.py new file mode 100644 index 00000000000000..060a6c20c3bd02 --- /dev/null +++ b/api/core/features/annotation_reply.py @@ -0,0 +1,119 @@ +import logging +from typing import Optional + +from flask import current_app + +from core.embedding.cached_embedding import CacheEmbedding +from core.entities.application_entities import InvokeFrom +from core.index.vector_index.vector_index import VectorIndex +from core.model_manager import ModelManager +from core.model_runtime.entities.model_entities import ModelType +from extensions.ext_database import db +from models.dataset import Dataset +from models.model import App, Message, AppAnnotationSetting, MessageAnnotation +from services.annotation_service import AppAnnotationService +from services.dataset_service import DatasetCollectionBindingService + +logger = logging.getLogger(__name__) + + +class AnnotationReplyFeature: + def query(self, app_record: App, + message: Message, + query: str, + user_id: str, + invoke_from: InvokeFrom) -> Optional[MessageAnnotation]: + """ + Query app annotations to reply + :param app_record: app record + :param message: message + :param query: query + :param user_id: user id + :param invoke_from: invoke from + :return: + """ + annotation_setting = db.session.query(AppAnnotationSetting).filter( + AppAnnotationSetting.app_id == app_record.id).first() + + if not annotation_setting: + return None + + collection_binding_detail = annotation_setting.collection_binding_detail + + try: + score_threshold = annotation_setting.score_threshold or 1 + embedding_provider_name = collection_binding_detail.provider_name + embedding_model_name = collection_binding_detail.model_name + + model_manager = ModelManager() + model_instance = model_manager.get_model_instance( + tenant_id=app_record.tenant_id, + provider=embedding_provider_name, + model_type=ModelType.TEXT_EMBEDDING, + model=embedding_model_name + ) + + # get embedding model + embeddings = CacheEmbedding(model_instance) + + dataset_collection_binding = DatasetCollectionBindingService.get_dataset_collection_binding( + embedding_provider_name, + embedding_model_name, + 'annotation' + ) + + dataset = Dataset( + id=app_record.id, + tenant_id=app_record.tenant_id, + indexing_technique='high_quality', + embedding_model_provider=embedding_provider_name, + embedding_model=embedding_model_name, + collection_binding_id=dataset_collection_binding.id + ) + + vector_index = VectorIndex( + dataset=dataset, + config=current_app.config, + embeddings=embeddings, + attributes=['doc_id', 'annotation_id', 'app_id'] + ) + + documents = vector_index.search( + query=query, + search_type='similarity_score_threshold', + search_kwargs={ + 'k': 1, + 'score_threshold': score_threshold, + 'filter': { + 'group_id': [dataset.id] + } + } + ) + + if documents: + annotation_id = documents[0].metadata['annotation_id'] + score = documents[0].metadata['score'] + annotation = AppAnnotationService.get_annotation_by_id(annotation_id) + if annotation: + if invoke_from in [InvokeFrom.SERVICE_API, InvokeFrom.WEB_APP]: + from_source = 'api' + else: + from_source = 'console' + + # insert annotation history + AppAnnotationService.add_annotation_history(annotation.id, + app_record.id, + annotation.question, + annotation.content, + query, + user_id, + message.id, + from_source, + score) + + return annotation + except Exception as e: + logger.warning(f'Query annotation failed, exception: {str(e)}.') + return None + + return None diff --git a/api/core/features/dataset_retrieval.py b/api/core/features/dataset_retrieval.py new file mode 100644 index 00000000000000..3476bf9ad9050c --- /dev/null +++ b/api/core/features/dataset_retrieval.py @@ -0,0 +1,181 @@ +from typing import cast, Optional, List + +from langchain.tools import BaseTool + +from core.agent.agent_executor import PlanningStrategy, AgentConfiguration, AgentExecutor +from core.callback_handler.index_tool_callback_handler import DatasetIndexToolCallbackHandler +from core.entities.application_entities import DatasetEntity, ModelConfigEntity, InvokeFrom, DatasetRetrieveConfigEntity +from core.memory.token_buffer_memory import TokenBufferMemory +from core.model_runtime.entities.model_entities import ModelFeature +from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel +from core.tool.dataset_multi_retriever_tool import DatasetMultiRetrieverTool +from core.tool.dataset_retriever_tool import DatasetRetrieverTool +from extensions.ext_database import db +from models.dataset import Dataset + + +class DatasetRetrievalFeature: + def retrieve(self, tenant_id: str, + model_config: ModelConfigEntity, + config: DatasetEntity, + query: str, + invoke_from: InvokeFrom, + show_retrieve_source: bool, + hit_callback: DatasetIndexToolCallbackHandler, + memory: Optional[TokenBufferMemory] = None) -> Optional[str]: + """ + Retrieve dataset. + :param tenant_id: tenant id + :param model_config: model config + :param config: dataset config + :param query: query + :param invoke_from: invoke from + :param show_retrieve_source: show retrieve source + :param hit_callback: hit callback + :param memory: memory + :return: + """ + dataset_ids = config.dataset_ids + retrieve_config = config.retrieve_config + + # check model is support tool calling + model_type_instance = model_config.provider_model_bundle.model_type_instance + model_type_instance = cast(LargeLanguageModel, model_type_instance) + + # get model schema + model_schema = model_type_instance.get_model_schema( + model=model_config.model, + credentials=model_config.credentials + ) + + if not model_schema: + return None + + planning_strategy = PlanningStrategy.REACT_ROUTER + features = model_schema.features + if features: + if ModelFeature.TOOL_CALL in features \ + or ModelFeature.MULTI_TOOL_CALL in features: + planning_strategy = PlanningStrategy.ROUTER + + dataset_retriever_tools = self.to_dataset_retriever_tool( + tenant_id=tenant_id, + dataset_ids=dataset_ids, + retrieve_config=retrieve_config, + return_resource=show_retrieve_source, + invoke_from=invoke_from, + hit_callback=hit_callback + ) + + if len(dataset_retriever_tools) == 0: + return None + + agent_configuration = AgentConfiguration( + strategy=planning_strategy, + model_config=model_config, + tools=dataset_retriever_tools, + memory=memory, + max_iterations=10, + max_execution_time=400.0, + early_stopping_method="generate" + ) + + agent_executor = AgentExecutor(agent_configuration) + + should_use_agent = agent_executor.should_use_agent(query) + if not should_use_agent: + return None + + result = agent_executor.run(query) + + return result.output + + def to_dataset_retriever_tool(self, tenant_id: str, + dataset_ids: list[str], + retrieve_config: DatasetRetrieveConfigEntity, + return_resource: bool, + invoke_from: InvokeFrom, + hit_callback: DatasetIndexToolCallbackHandler) \ + -> Optional[List[BaseTool]]: + """ + A dataset tool is a tool that can be used to retrieve information from a dataset + :param tenant_id: tenant id + :param dataset_ids: dataset ids + :param retrieve_config: retrieve config + :param return_resource: return resource + :param invoke_from: invoke from + :param hit_callback: hit callback + """ + tools = [] + available_datasets = [] + for dataset_id in dataset_ids: + # get dataset from dataset id + dataset = db.session.query(Dataset).filter( + Dataset.tenant_id == tenant_id, + Dataset.id == dataset_id + ).first() + + # pass if dataset is not available + if not dataset: + continue + + # pass if dataset is not available + if (dataset and dataset.available_document_count == 0 + and dataset.available_document_count == 0): + continue + + available_datasets.append(dataset) + + if retrieve_config.retrieve_strategy == DatasetRetrieveConfigEntity.RetrieveStrategy.SINGLE: + # get retrieval model config + default_retrieval_model = { + 'search_method': 'semantic_search', + 'reranking_enable': False, + 'reranking_model': { + 'reranking_provider_name': '', + 'reranking_model_name': '' + }, + 'top_k': 2, + 'score_threshold_enabled': False + } + + for dataset in available_datasets: + retrieval_model_config = dataset.retrieval_model \ + if dataset.retrieval_model else default_retrieval_model + + # get top k + top_k = retrieval_model_config['top_k'] + + # get score threshold + score_threshold = None + score_threshold_enabled = retrieval_model_config.get("score_threshold_enabled") + if score_threshold_enabled: + score_threshold = retrieval_model_config.get("score_threshold") + + tool = DatasetRetrieverTool.from_dataset( + dataset=dataset, + top_k=top_k, + score_threshold=score_threshold, + hit_callbacks=[hit_callback], + return_resource=return_resource, + retriever_from=invoke_from.to_source() + ) + + tools.append(tool) + elif retrieve_config.retrieve_strategy == DatasetRetrieveConfigEntity.RetrieveStrategy.MULTIPLE: + tool = DatasetMultiRetrieverTool.from_dataset( + dataset_ids=[dataset.id for dataset in available_datasets], + tenant_id=tenant_id, + top_k=retrieve_config.top_k or 2, + score_threshold=(retrieve_config.score_threshold or 0.5) + if retrieve_config.reranking_model.get('score_threshold_enabled', False) else None, + hit_callbacks=[hit_callback], + return_resource=return_resource, + retriever_from=invoke_from.to_source(), + reranking_provider_name=retrieve_config.reranking_model.get('reranking_provider_name'), + reranking_model_name=retrieve_config.reranking_model.get('reranking_model_name') + ) + + tools.append(tool) + + return tools diff --git a/api/core/features/external_data_fetch.py b/api/core/features/external_data_fetch.py new file mode 100644 index 00000000000000..272b7cee952c8c --- /dev/null +++ b/api/core/features/external_data_fetch.py @@ -0,0 +1,96 @@ +import concurrent +import json +import logging + +from concurrent.futures import ThreadPoolExecutor +from typing import Tuple, Optional + +from flask import current_app, Flask + +from core.entities.application_entities import ExternalDataVariableEntity +from core.external_data_tool.factory import ExternalDataToolFactory + +logger = logging.getLogger(__name__) + + +class ExternalDataFetchFeature: + def fetch(self, tenant_id: str, + app_id: str, + external_data_tools: list[ExternalDataVariableEntity], + inputs: dict, + query: str) -> dict: + """ + Fill in variable inputs from external data tools if exists. + + :param tenant_id: workspace id + :param app_id: app id + :param external_data_tools: external data tools configs + :param inputs: the inputs + :param query: the query + :return: the filled inputs + """ + # Group tools by type and config + grouped_tools = {} + for tool in external_data_tools: + tool_key = (tool.type, json.dumps(tool.config, sort_keys=True)) + grouped_tools.setdefault(tool_key, []).append(tool) + + results = {} + with ThreadPoolExecutor() as executor: + futures = {} + for tool in external_data_tools: + future = executor.submit( + self._query_external_data_tool, + current_app._get_current_object(), + tenant_id, + app_id, + tool, + inputs, + query + ) + + futures[future] = tool + + for future in concurrent.futures.as_completed(futures): + tool_variable, result = future.result() + results[tool_variable] = result + + inputs.update(results) + return inputs + + def _query_external_data_tool(self, flask_app: Flask, + tenant_id: str, + app_id: str, + external_data_tool: ExternalDataVariableEntity, + inputs: dict, + query: str) -> Tuple[Optional[str], Optional[str]]: + """ + Query external data tool. + :param flask_app: flask app + :param tenant_id: tenant id + :param app_id: app id + :param external_data_tool: external data tool + :param inputs: inputs + :param query: query + :return: + """ + with flask_app.app_context(): + tool_variable = external_data_tool.variable + tool_type = external_data_tool.type + tool_config = external_data_tool.config + + external_data_tool_factory = ExternalDataToolFactory( + name=tool_type, + tenant_id=tenant_id, + app_id=app_id, + variable=tool_variable, + config=tool_config + ) + + # query external data tool + result = external_data_tool_factory.query( + inputs=inputs, + query=query + ) + + return tool_variable, result diff --git a/api/core/features/hosting_moderation.py b/api/core/features/hosting_moderation.py new file mode 100644 index 00000000000000..d8ae7adcac5d47 --- /dev/null +++ b/api/core/features/hosting_moderation.py @@ -0,0 +1,32 @@ +import logging + +from core.entities.application_entities import ApplicationGenerateEntity +from core.helper import moderation +from core.model_runtime.entities.message_entities import PromptMessage + +logger = logging.getLogger(__name__) + + +class HostingModerationFeature: + def check(self, application_generate_entity: ApplicationGenerateEntity, + prompt_messages: list[PromptMessage]) -> bool: + """ + Check hosting moderation + :param application_generate_entity: application generate entity + :param prompt_messages: prompt messages + :return: + """ + app_orchestration_config = application_generate_entity.app_orchestration_config_entity + model_config = app_orchestration_config.model_config + + text = "" + for prompt_message in prompt_messages: + if isinstance(prompt_message.content, str): + text += prompt_message.content + "\n" + + moderation_result = moderation.check_moderation( + model_config, + text + ) + + return moderation_result diff --git a/api/core/features/moderation.py b/api/core/features/moderation.py new file mode 100644 index 00000000000000..9735fad0e70ebe --- /dev/null +++ b/api/core/features/moderation.py @@ -0,0 +1,50 @@ +import logging +from typing import Tuple + +from core.entities.application_entities import AppOrchestrationConfigEntity +from core.moderation.base import ModerationAction, ModerationException +from core.moderation.factory import ModerationFactory + +logger = logging.getLogger(__name__) + + +class ModerationFeature: + def check(self, app_id: str, + tenant_id: str, + app_orchestration_config_entity: AppOrchestrationConfigEntity, + inputs: dict, + query: str) -> Tuple[bool, dict, str]: + """ + Process sensitive_word_avoidance. + :param app_id: app id + :param tenant_id: tenant id + :param app_orchestration_config_entity: app orchestration config entity + :param inputs: inputs + :param query: query + :return: + """ + if not app_orchestration_config_entity.sensitive_word_avoidance: + return False, inputs, query + + sensitive_word_avoidance_config = app_orchestration_config_entity.sensitive_word_avoidance + moderation_type = sensitive_word_avoidance_config.type + + moderation_factory = ModerationFactory( + name=moderation_type, + app_id=app_id, + tenant_id=tenant_id, + config=sensitive_word_avoidance_config.config + ) + + moderation_result = moderation_factory.moderation_for_inputs(inputs, query) + + if not moderation_result.flagged: + return False, inputs, query + + if moderation_result.action == ModerationAction.DIRECT_OUTPUT: + raise ModerationException(moderation_result.preset_response) + elif moderation_result.action == ModerationAction.OVERRIDED: + inputs = moderation_result.inputs + query = moderation_result.query + + return True, inputs, query diff --git a/api/core/file/file_obj.py b/api/core/file/file_obj.py index e2487e7ed1adb2..7f6e79b15b9b41 100644 --- a/api/core/file/file_obj.py +++ b/api/core/file/file_obj.py @@ -4,7 +4,7 @@ from pydantic import BaseModel from core.file.upload_file_parser import UploadFileParser -from core.model_providers.models.entity.message import PromptMessageFile, ImagePromptMessageFile +from core.model_runtime.entities.message_entities import ImagePromptMessageContent from extensions.ext_database import db from models.model import UploadFile @@ -50,14 +50,14 @@ def preview_url(self) -> Optional[str]: return self._get_data(force_url=True) @property - def prompt_message_file(self) -> PromptMessageFile: + def prompt_message_content(self) -> ImagePromptMessageContent: if self.type == FileType.IMAGE: image_config = self.file_config.get('image') - return ImagePromptMessageFile( + return ImagePromptMessageContent( data=self.data, - detail=ImagePromptMessageFile.DETAIL.HIGH - if image_config.get("detail") == "high" else ImagePromptMessageFile.DETAIL.LOW + detail=ImagePromptMessageContent.DETAIL.HIGH + if image_config.get("detail") == "high" else ImagePromptMessageContent.DETAIL.LOW ) def _get_data(self, force_url: bool = False) -> Optional[str]: diff --git a/api/core/generator/llm_generator.py b/api/core/generator/llm_generator.py index 87a934e55d54e6..acb7a6d2c92e92 100644 --- a/api/core/generator/llm_generator.py +++ b/api/core/generator/llm_generator.py @@ -3,10 +3,10 @@ from langchain.schema import OutputParserException -from core.model_providers.error import LLMError, ProviderTokenNotInitError -from core.model_providers.model_factory import ModelFactory -from core.model_providers.models.entity.message import PromptMessage, MessageType -from core.model_providers.models.entity.model_params import ModelKwargs +from core.model_manager import ModelManager +from core.model_runtime.entities.message_entities import UserPromptMessage, SystemPromptMessage +from core.model_runtime.entities.model_entities import ModelType +from core.model_runtime.errors.invoke import InvokeAuthorizationError, InvokeError from core.prompt.output_parser.rule_config_generator import RuleConfigGeneratorOutputParser from core.prompt.output_parser.suggested_questions_after_answer import SuggestedQuestionsAfterAnswerOutputParser @@ -26,17 +26,22 @@ def generate_conversation_name(cls, tenant_id: str, query): prompt += query + "\n" - model_instance = ModelFactory.get_text_generation_model( + model_manager = ModelManager() + model_instance = model_manager.get_default_model_instance( tenant_id=tenant_id, - model_kwargs=ModelKwargs( - temperature=1, - max_tokens=100 - ) + model_type=ModelType.LLM, ) - prompts = [PromptMessage(content=prompt)] - response = model_instance.run(prompts) - answer = response.content + prompts = [UserPromptMessage(content=prompt)] + response = model_instance.invoke_llm( + prompt_messages=prompts, + model_parameters={ + "max_tokens": 100, + "temperature": 1 + }, + stream=False + ) + answer = response.message.content result_dict = json.loads(answer) answer = result_dict['Your Output'] @@ -62,22 +67,28 @@ def generate_suggested_questions_after_answer(cls, tenant_id: str, histories: st }) try: - model_instance = ModelFactory.get_text_generation_model( + model_manager = ModelManager() + model_instance = model_manager.get_default_model_instance( tenant_id=tenant_id, - model_kwargs=ModelKwargs( - max_tokens=256, - temperature=0 - ) + model_type=ModelType.LLM, ) - except ProviderTokenNotInitError: + except InvokeAuthorizationError: return [] - prompt_messages = [PromptMessage(content=prompt)] + prompt_messages = [UserPromptMessage(content=prompt)] try: - output = model_instance.run(prompt_messages) - questions = output_parser.parse(output.content) - except LLMError: + response = model_instance.invoke_llm( + prompt_messages=prompt_messages, + model_parameters={ + "max_tokens": 256, + "temperature": 0 + }, + stream=False + ) + + questions = output_parser.parse(response.message.content) + except InvokeError: questions = [] except Exception as e: logging.exception(e) @@ -105,20 +116,26 @@ def generate_rule_config(cls, tenant_id: str, audiences: str, hoping_to_solve: s remove_template_variables=False ) - model_instance = ModelFactory.get_text_generation_model( + model_manager = ModelManager() + model_instance = model_manager.get_default_model_instance( tenant_id=tenant_id, - model_kwargs=ModelKwargs( - max_tokens=512, - temperature=0 - ) + model_type=ModelType.LLM, ) - prompt_messages = [PromptMessage(content=prompt)] + prompt_messages = [UserPromptMessage(content=prompt)] try: - output = model_instance.run(prompt_messages) - rule_config = output_parser.parse(output.content) - except LLMError as e: + response = model_instance.invoke_llm( + prompt_messages=prompt_messages, + model_parameters={ + "max_tokens": 512, + "temperature": 0 + }, + stream=False + ) + + rule_config = output_parser.parse(response.message.content) + except InvokeError as e: raise e except OutputParserException: raise ValueError('Please give a valid input for intended audience or hoping to solve problems.') @@ -136,18 +153,24 @@ def generate_rule_config(cls, tenant_id: str, audiences: str, hoping_to_solve: s def generate_qa_document(cls, tenant_id: str, query, document_language: str): prompt = GENERATOR_QA_PROMPT.format(language=document_language) - model_instance = ModelFactory.get_text_generation_model( + model_manager = ModelManager() + model_instance = model_manager.get_default_model_instance( tenant_id=tenant_id, - model_kwargs=ModelKwargs( - max_tokens=2000 - ) + model_type=ModelType.LLM, ) - prompts = [ - PromptMessage(content=prompt, type=MessageType.SYSTEM), - PromptMessage(content=query) + prompt_messages = [ + SystemPromptMessage(content=prompt), + UserPromptMessage(content=query) ] - response = model_instance.run(prompts) - answer = response.content + response = model_instance.invoke_llm( + prompt_messages=prompt_messages, + model_parameters={ + "max_tokens": 2000 + }, + stream=False + ) + + answer = response.message.content return answer.strip() diff --git a/api/core/helper/encrypter.py b/api/core/helper/encrypter.py index fa94867ba280c5..4d57d2d5fef627 100644 --- a/api/core/helper/encrypter.py +++ b/api/core/helper/encrypter.py @@ -18,3 +18,17 @@ def encrypt_token(tenant_id: str, token: str): def decrypt_token(tenant_id: str, token: str): return rsa.decrypt(base64.b64decode(token), tenant_id) + + +def batch_decrypt_token(tenant_id: str, tokens: list[str]): + rsa_key, cipher_rsa = rsa.get_decrypt_decoding(tenant_id) + + return [rsa.decrypt_token_with_decoding(base64.b64decode(token), rsa_key, cipher_rsa) for token in tokens] + + +def get_decrypt_decoding(tenant_id: str): + return rsa.get_decrypt_decoding(tenant_id) + + +def decrypt_token_with_decoding(token: str, rsa_key, cipher_rsa): + return rsa.decrypt_token_with_decoding(base64.b64decode(token), rsa_key, cipher_rsa) diff --git a/api/core/helper/lru_cache.py b/api/core/helper/lru_cache.py new file mode 100644 index 00000000000000..518962c1652df7 --- /dev/null +++ b/api/core/helper/lru_cache.py @@ -0,0 +1,22 @@ +from collections import OrderedDict +from typing import Any + + +class LRUCache: + def __init__(self, capacity: int): + self.cache = OrderedDict() + self.capacity = capacity + + def get(self, key: Any) -> Any: + if key not in self.cache: + return None + else: + self.cache.move_to_end(key) # move the key to the end of the OrderedDict + return self.cache[key] + + def put(self, key: Any, value: Any) -> None: + if key in self.cache: + self.cache.move_to_end(key) + self.cache[key] = value + if len(self.cache) > self.capacity: + self.cache.popitem(last=False) # pop the first item diff --git a/api/core/helper/moderation.py b/api/core/helper/moderation.py index ac71af9ef4db9f..86d6b498da35e7 100644 --- a/api/core/helper/moderation.py +++ b/api/core/helper/moderation.py @@ -1,18 +1,27 @@ import logging import random -import openai - -from core.model_providers.error import LLMBadRequestError -from core.model_providers.providers.base import BaseModelProvider -from core.model_providers.providers.hosted import hosted_config, hosted_model_providers +from core.entities.application_entities import ModelConfigEntity +from core.model_runtime.errors.invoke import InvokeBadRequestError +from core.model_runtime.model_providers.openai.moderation.moderation import OpenAIModerationModel +from extensions.ext_hosting_provider import hosting_configuration from models.provider import ProviderType +logger = logging.getLogger(__name__) + + +def check_moderation(model_config: ModelConfigEntity, text: str) -> bool: + moderation_config = hosting_configuration.moderation_config + if (moderation_config and moderation_config.enabled is True + and 'openai' in hosting_configuration.provider_map + and hosting_configuration.provider_map['openai'].enabled is True + ): + using_provider_type = model_config.provider_model_bundle.configuration.using_provider_type + provider_name = model_config.provider + if using_provider_type == ProviderType.SYSTEM \ + and provider_name in moderation_config.providers: + hosting_openai_config = hosting_configuration.provider_map['openai'] -def check_moderation(model_provider: BaseModelProvider, text: str) -> bool: - if hosted_config.moderation.enabled is True and hosted_model_providers.openai: - if model_provider.provider.provider_type == ProviderType.SYSTEM.value \ - and model_provider.provider_name in hosted_config.moderation.providers: # 2000 text per chunk length = 2000 text_chunks = [text[i:i + length] for i in range(0, len(text), length)] @@ -23,14 +32,17 @@ def check_moderation(model_provider: BaseModelProvider, text: str) -> bool: text_chunk = random.choice(text_chunks) try: - moderation_result = openai.Moderation.create(input=text_chunk, - api_key=hosted_model_providers.openai.api_key) + model_type_instance = OpenAIModerationModel() + moderation_result = model_type_instance.invoke( + model='text-moderation-stable', + credentials=hosting_openai_config.credentials, + text=text_chunk + ) + + if moderation_result is True: + return True except Exception as ex: - logging.exception(ex) - raise LLMBadRequestError('Rate limit exceeded, please try again later.') - - for result in moderation_result.results: - if result['flagged'] is True: - return False + logger.exception(ex) + raise InvokeBadRequestError('Rate limit exceeded, please try again later.') - return True + return False diff --git a/api/core/hosting_configuration.py b/api/core/hosting_configuration.py new file mode 100644 index 00000000000000..ad58dd382e461e --- /dev/null +++ b/api/core/hosting_configuration.py @@ -0,0 +1,213 @@ +import os +from typing import Optional + +from flask import Flask +from pydantic import BaseModel + +from core.entities.provider_entities import QuotaUnit +from models.provider import ProviderQuotaType + + +class HostingQuota(BaseModel): + quota_type: ProviderQuotaType + restrict_llms: list[str] = [] + + +class TrialHostingQuota(HostingQuota): + quota_type: ProviderQuotaType = ProviderQuotaType.TRIAL + quota_limit: int = 0 + """Quota limit for the hosting provider models. -1 means unlimited.""" + + +class PaidHostingQuota(HostingQuota): + quota_type: ProviderQuotaType = ProviderQuotaType.PAID + stripe_price_id: str = None + increase_quota: int = 1 + min_quantity: int = 20 + max_quantity: int = 100 + + +class FreeHostingQuota(HostingQuota): + quota_type: ProviderQuotaType = ProviderQuotaType.FREE + + +class HostingProvider(BaseModel): + enabled: bool = False + credentials: Optional[dict] = None + quota_unit: Optional[QuotaUnit] = None + quotas: list[HostingQuota] = [] + + +class HostedModerationConfig(BaseModel): + enabled: bool = False + providers: list[str] = [] + + +class HostingConfiguration: + provider_map: dict[str, HostingProvider] = {} + moderation_config: HostedModerationConfig = None + + def init_app(self, app: Flask): + if app.config.get('EDITION') != 'CLOUD': + return + + self.provider_map["openai"] = self.init_openai() + self.provider_map["anthropic"] = self.init_anthropic() + self.provider_map["minimax"] = self.init_minimax() + self.provider_map["spark"] = self.init_spark() + self.provider_map["zhipuai"] = self.init_zhipuai() + + self.moderation_config = self.init_moderation_config() + + def init_openai(self) -> HostingProvider: + quota_unit = QuotaUnit.TIMES + if os.environ.get("HOSTED_OPENAI_ENABLED") and os.environ.get("HOSTED_OPENAI_ENABLED").lower() == 'true': + credentials = { + "openai_api_key": os.environ.get("HOSTED_OPENAI_API_KEY"), + } + + if os.environ.get("HOSTED_OPENAI_API_BASE"): + credentials["openai_api_base"] = os.environ.get("HOSTED_OPENAI_API_BASE") + + if os.environ.get("HOSTED_OPENAI_API_ORGANIZATION"): + credentials["openai_organization"] = os.environ.get("HOSTED_OPENAI_API_ORGANIZATION") + + quotas = [] + hosted_quota_limit = int(os.environ.get("HOSTED_OPENAI_QUOTA_LIMIT", "200")) + if hosted_quota_limit != -1 or hosted_quota_limit > 0: + trial_quota = TrialHostingQuota( + quota_limit=hosted_quota_limit, + restrict_llms=[ + "gpt-3.5-turbo", + "gpt-3.5-turbo-1106", + "gpt-3.5-turbo-instruct", + "gpt-3.5-turbo-16k", + "text-davinci-003" + ] + ) + quotas.append(trial_quota) + + if os.environ.get("HOSTED_OPENAI_PAID_ENABLED") and os.environ.get( + "HOSTED_OPENAI_PAID_ENABLED").lower() == 'true': + paid_quota = PaidHostingQuota( + stripe_price_id=os.environ.get("HOSTED_OPENAI_PAID_STRIPE_PRICE_ID"), + increase_quota=int(os.environ.get("HOSTED_OPENAI_PAID_INCREASE_QUOTA", "1")), + min_quantity=int(os.environ.get("HOSTED_OPENAI_PAID_MIN_QUANTITY", "1")), + max_quantity=int(os.environ.get("HOSTED_OPENAI_PAID_MAX_QUANTITY", "1")) + ) + quotas.append(paid_quota) + + return HostingProvider( + enabled=True, + credentials=credentials, + quota_unit=quota_unit, + quotas=quotas + ) + + return HostingProvider( + enabled=False, + quota_unit=quota_unit, + ) + + def init_anthropic(self) -> HostingProvider: + quota_unit = QuotaUnit.TOKENS + if os.environ.get("HOSTED_ANTHROPIC_ENABLED") and os.environ.get("HOSTED_ANTHROPIC_ENABLED").lower() == 'true': + credentials = { + "anthropic_api_key": os.environ.get("HOSTED_ANTHROPIC_API_KEY"), + } + + if os.environ.get("HOSTED_ANTHROPIC_API_BASE"): + credentials["anthropic_api_url"] = os.environ.get("HOSTED_ANTHROPIC_API_BASE") + + quotas = [] + hosted_quota_limit = int(os.environ.get("HOSTED_ANTHROPIC_QUOTA_LIMIT", "0")) + if hosted_quota_limit != -1 or hosted_quota_limit > 0: + trial_quota = TrialHostingQuota( + quota_limit=hosted_quota_limit + ) + quotas.append(trial_quota) + + if os.environ.get("HOSTED_ANTHROPIC_PAID_ENABLED") and os.environ.get( + "HOSTED_ANTHROPIC_PAID_ENABLED").lower() == 'true': + paid_quota = PaidHostingQuota( + stripe_price_id=os.environ.get("HOSTED_ANTHROPIC_PAID_STRIPE_PRICE_ID"), + increase_quota=int(os.environ.get("HOSTED_ANTHROPIC_PAID_INCREASE_QUOTA", "1000000")), + min_quantity=int(os.environ.get("HOSTED_ANTHROPIC_PAID_MIN_QUANTITY", "20")), + max_quantity=int(os.environ.get("HOSTED_ANTHROPIC_PAID_MAX_QUANTITY", "100")) + ) + quotas.append(paid_quota) + + return HostingProvider( + enabled=True, + credentials=credentials, + quota_unit=quota_unit, + quotas=quotas + ) + + return HostingProvider( + enabled=False, + quota_unit=quota_unit, + ) + + def init_minimax(self) -> HostingProvider: + quota_unit = QuotaUnit.TOKENS + if os.environ.get("HOSTED_MINIMAX_ENABLED") and os.environ.get("HOSTED_MINIMAX_ENABLED").lower() == 'true': + quotas = [FreeHostingQuota()] + + return HostingProvider( + enabled=True, + credentials=None, # use credentials from the provider + quota_unit=quota_unit, + quotas=quotas + ) + + return HostingProvider( + enabled=False, + quota_unit=quota_unit, + ) + + def init_spark(self) -> HostingProvider: + quota_unit = QuotaUnit.TOKENS + if os.environ.get("HOSTED_SPARK_ENABLED") and os.environ.get("HOSTED_SPARK_ENABLED").lower() == 'true': + quotas = [FreeHostingQuota()] + + return HostingProvider( + enabled=True, + credentials=None, # use credentials from the provider + quota_unit=quota_unit, + quotas=quotas + ) + + return HostingProvider( + enabled=False, + quota_unit=quota_unit, + ) + + def init_zhipuai(self) -> HostingProvider: + quota_unit = QuotaUnit.TOKENS + if os.environ.get("HOSTED_ZHIPUAI_ENABLED") and os.environ.get("HOSTED_ZHIPUAI_ENABLED").lower() == 'true': + quotas = [FreeHostingQuota()] + + return HostingProvider( + enabled=True, + credentials=None, # use credentials from the provider + quota_unit=quota_unit, + quotas=quotas + ) + + return HostingProvider( + enabled=False, + quota_unit=quota_unit, + ) + + def init_moderation_config(self) -> HostedModerationConfig: + if os.environ.get("HOSTED_MODERATION_ENABLED") and os.environ.get("HOSTED_MODERATION_ENABLED").lower() == 'true' \ + and os.environ.get("HOSTED_MODERATION_PROVIDERS"): + return HostedModerationConfig( + enabled=True, + providers=os.environ.get("HOSTED_MODERATION_PROVIDERS").split(',') + ) + + return HostedModerationConfig( + enabled=False + ) diff --git a/api/core/index/index.py b/api/core/index/index.py index be5ca315104f35..ce11171d0c544b 100644 --- a/api/core/index/index.py +++ b/api/core/index/index.py @@ -1,18 +1,12 @@ -import json - from flask import current_app from langchain.embeddings import OpenAIEmbeddings from core.embedding.cached_embedding import CacheEmbedding from core.index.keyword_table_index.keyword_table_index import KeywordTableIndex, KeywordTableConfig from core.index.vector_index.vector_index import VectorIndex -from core.model_providers.model_factory import ModelFactory -from core.model_providers.models.embedding.openai_embedding import OpenAIEmbedding -from core.model_providers.models.entity.model_params import ModelKwargs -from core.model_providers.models.llm.openai_model import OpenAIModel -from core.model_providers.providers.openai_provider import OpenAIProvider +from core.model_manager import ModelManager +from core.model_runtime.entities.model_entities import ModelType from models.dataset import Dataset -from models.provider import Provider, ProviderType class IndexBuilder: @@ -22,10 +16,12 @@ def get_index(cls, dataset: Dataset, indexing_technique: str, ignore_high_qualit if not ignore_high_quality_check and dataset.indexing_technique != 'high_quality': return None - embedding_model = ModelFactory.get_embedding_model( + model_manager = ModelManager() + embedding_model = model_manager.get_model_instance( tenant_id=dataset.tenant_id, - model_provider_name=dataset.embedding_model_provider, - model_name=dataset.embedding_model + model_type=ModelType.TEXT_EMBEDDING, + provider=dataset.embedding_model_provider, + model=dataset.embedding_model ) embeddings = CacheEmbedding(embedding_model) diff --git a/api/core/indexing_runner.py b/api/core/indexing_runner.py index 89fba99cab76b9..d7e0843c8460c2 100644 --- a/api/core/indexing_runner.py +++ b/api/core/indexing_runner.py @@ -18,9 +18,11 @@ from core.docstore.dataset_docstore import DatasetDocumentStore from core.generator.llm_generator import LLMGenerator from core.index.index import IndexBuilder -from core.model_providers.error import ProviderTokenNotInitError -from core.model_providers.model_factory import ModelFactory -from core.model_providers.models.entity.message import MessageType +from core.model_manager import ModelManager +from core.errors.error import ProviderTokenNotInitError +from core.model_runtime.entities.model_entities import ModelType, PriceType +from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel +from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel from core.spiltter.fixed_text_splitter import FixedRecursiveCharacterTextSplitter from extensions.ext_database import db from extensions.ext_redis import redis_client @@ -36,6 +38,7 @@ class IndexingRunner: def __init__(self): self.storage = storage + self.model_manager = ModelManager() def run(self, dataset_documents: List[DatasetDocument]): """Run the indexing process.""" @@ -210,7 +213,7 @@ def file_indexing_estimate(self, tenant_id: str, file_details: List[UploadFile], """ Estimate the indexing for the document. """ - embedding_model = None + embedding_model_instance = None if dataset_id: dataset = Dataset.query.filter_by( id=dataset_id @@ -218,15 +221,17 @@ def file_indexing_estimate(self, tenant_id: str, file_details: List[UploadFile], if not dataset: raise ValueError('Dataset not found.') if dataset.indexing_technique == 'high_quality' or indexing_technique == 'high_quality': - embedding_model = ModelFactory.get_embedding_model( - tenant_id=dataset.tenant_id, - model_provider_name=dataset.embedding_model_provider, - model_name=dataset.embedding_model + embedding_model_instance = self.model_manager.get_model_instance( + tenant_id=tenant_id, + provider=dataset.embedding_model_provider, + model_type=ModelType.TEXT_EMBEDDING, + model=dataset.embedding_model ) else: if indexing_technique == 'high_quality': - embedding_model = ModelFactory.get_embedding_model( - tenant_id=tenant_id + embedding_model_instance = self.model_manager.get_default_model_instance( + tenant_id=tenant_id, + model_type=ModelType.TEXT_EMBEDDING, ) tokens = 0 preview_texts = [] @@ -255,32 +260,56 @@ def file_indexing_estimate(self, tenant_id: str, file_details: List[UploadFile], for document in documents: if len(preview_texts) < 5: preview_texts.append(document.page_content) - if indexing_technique == 'high_quality' or embedding_model: - tokens += embedding_model.get_num_tokens(self.filter_string(document.page_content)) + if indexing_technique == 'high_quality' or embedding_model_instance: + embedding_model_type_instance = embedding_model_instance.model_type_instance + embedding_model_type_instance = cast(TextEmbeddingModel, embedding_model_type_instance) + tokens += embedding_model_type_instance.get_num_tokens( + model=embedding_model_instance.model, + credentials=embedding_model_instance.credentials, + texts=[self.filter_string(document.page_content)] + ) if doc_form and doc_form == 'qa_model': - text_generation_model = ModelFactory.get_text_generation_model( - tenant_id=tenant_id + model_instance = self.model_manager.get_default_model_instance( + tenant_id=tenant_id, + model_type=ModelType.LLM ) + + model_type_instance = model_instance.model_type_instance + model_type_instance = cast(LargeLanguageModel, model_type_instance) + if len(preview_texts) > 0: # qa model document response = LLMGenerator.generate_qa_document(current_user.current_tenant_id, preview_texts[0], doc_language) document_qa_list = self.format_split_text(response) + price_info = model_type_instance.get_price( + model=model_instance.model, + credentials=model_instance.credentials, + price_type=PriceType.INPUT, + tokens=total_segments * 2000, + ) return { "total_segments": total_segments * 20, "tokens": total_segments * 2000, - "total_price": '{:f}'.format( - text_generation_model.calc_tokens_price(total_segments * 2000, MessageType.USER)), - "currency": embedding_model.get_currency(), + "total_price": '{:f}'.format(price_info.total_amount), + "currency": price_info.currency, "qa_preview": document_qa_list, "preview": preview_texts } + if embedding_model_instance: + embedding_model_type_instance = cast(TextEmbeddingModel, embedding_model_instance.model_type_instance) + embedding_price_info = embedding_model_type_instance.get_price( + model=embedding_model_instance.model, + credentials=embedding_model_instance.credentials, + price_type=PriceType.INPUT, + tokens=tokens + ) return { "total_segments": total_segments, "tokens": tokens, - "total_price": '{:f}'.format(embedding_model.calc_tokens_price(tokens)) if embedding_model else 0, - "currency": embedding_model.get_currency() if embedding_model else 'USD', + "total_price": '{:f}'.format(embedding_price_info.total_amount) if embedding_model_instance else 0, + "currency": embedding_price_info.currency if embedding_model_instance else 'USD', "preview": preview_texts } @@ -290,7 +319,7 @@ def notion_indexing_estimate(self, tenant_id: str, notion_info_list: list, tmp_p """ Estimate the indexing for the document. """ - embedding_model = None + embedding_model_instance = None if dataset_id: dataset = Dataset.query.filter_by( id=dataset_id @@ -298,15 +327,17 @@ def notion_indexing_estimate(self, tenant_id: str, notion_info_list: list, tmp_p if not dataset: raise ValueError('Dataset not found.') if dataset.indexing_technique == 'high_quality' or indexing_technique == 'high_quality': - embedding_model = ModelFactory.get_embedding_model( - tenant_id=dataset.tenant_id, - model_provider_name=dataset.embedding_model_provider, - model_name=dataset.embedding_model + embedding_model_instance = self.model_manager.get_model_instance( + tenant_id=tenant_id, + provider=dataset.embedding_model_provider, + model_type=ModelType.TEXT_EMBEDDING, + model=dataset.embedding_model ) else: if indexing_technique == 'high_quality': - embedding_model = ModelFactory.get_embedding_model( - tenant_id=tenant_id + embedding_model_instance = self.model_manager.get_default_model_instance( + tenant_id=tenant_id, + model_type=ModelType.TEXT_EMBEDDING ) # load data from notion tokens = 0 @@ -349,35 +380,63 @@ def notion_indexing_estimate(self, tenant_id: str, notion_info_list: list, tmp_p processing_rule=processing_rule ) total_segments += len(documents) + + embedding_model_type_instance = embedding_model_instance.model_type_instance + embedding_model_type_instance = cast(TextEmbeddingModel, embedding_model_type_instance) + for document in documents: if len(preview_texts) < 5: preview_texts.append(document.page_content) - if indexing_technique == 'high_quality' or embedding_model: - tokens += embedding_model.get_num_tokens(document.page_content) + if indexing_technique == 'high_quality' or embedding_model_instance: + tokens += embedding_model_type_instance.get_num_tokens( + model=embedding_model_instance.model, + credentials=embedding_model_instance.credentials, + texts=[document.page_content] + ) if doc_form and doc_form == 'qa_model': - text_generation_model = ModelFactory.get_text_generation_model( - tenant_id=tenant_id + model_instance = self.model_manager.get_default_model_instance( + tenant_id=tenant_id, + model_type=ModelType.LLM ) + + model_type_instance = model_instance.model_type_instance + model_type_instance = cast(LargeLanguageModel, model_type_instance) if len(preview_texts) > 0: # qa model document response = LLMGenerator.generate_qa_document(current_user.current_tenant_id, preview_texts[0], doc_language) document_qa_list = self.format_split_text(response) + + price_info = model_type_instance.get_price( + model=model_instance.model, + credentials=model_instance.credentials, + price_type=PriceType.INPUT, + tokens=total_segments * 2000, + ) + return { "total_segments": total_segments * 20, "tokens": total_segments * 2000, - "total_price": '{:f}'.format( - text_generation_model.calc_tokens_price(total_segments * 2000, MessageType.USER)), - "currency": embedding_model.get_currency(), + "total_price": '{:f}'.format(price_info.total_amount), + "currency": price_info.currency, "qa_preview": document_qa_list, "preview": preview_texts } + + embedding_model_type_instance = embedding_model_instance.model_type_instance + embedding_model_type_instance = cast(TextEmbeddingModel, embedding_model_type_instance) + embedding_price_info = embedding_model_type_instance.get_price( + model=embedding_model_instance.model, + credentials=embedding_model_instance.credentials, + price_type=PriceType.INPUT, + tokens=tokens + ) return { "total_segments": total_segments, "tokens": tokens, - "total_price": '{:f}'.format(embedding_model.calc_tokens_price(tokens)) if embedding_model else 0, - "currency": embedding_model.get_currency() if embedding_model else 'USD', + "total_price": '{:f}'.format(embedding_price_info.total_amount) if embedding_model_instance else 0, + "currency": embedding_price_info.currency if embedding_model_instance else 'USD', "preview": preview_texts } @@ -656,25 +715,36 @@ def _build_index(self, dataset: Dataset, dataset_document: DatasetDocument, docu """ vector_index = IndexBuilder.get_index(dataset, 'high_quality') keyword_table_index = IndexBuilder.get_index(dataset, 'economy') - embedding_model = None + embedding_model_instance = None if dataset.indexing_technique == 'high_quality': - embedding_model = ModelFactory.get_embedding_model( + embedding_model_instance = self.model_manager.get_model_instance( tenant_id=dataset.tenant_id, - model_provider_name=dataset.embedding_model_provider, - model_name=dataset.embedding_model + provider=dataset.embedding_model_provider, + model_type=ModelType.TEXT_EMBEDDING, + model=dataset.embedding_model ) # chunk nodes by chunk size indexing_start_at = time.perf_counter() tokens = 0 chunk_size = 100 + + embedding_model_type_instance = None + if embedding_model_instance: + embedding_model_type_instance = embedding_model_instance.model_type_instance + embedding_model_type_instance = cast(TextEmbeddingModel, embedding_model_type_instance) + for i in range(0, len(documents), chunk_size): # check document is paused self._check_document_paused_status(dataset_document.id) chunk_documents = documents[i:i + chunk_size] - if dataset.indexing_technique == 'high_quality' or embedding_model: + if dataset.indexing_technique == 'high_quality' or embedding_model_type_instance: tokens += sum( - embedding_model.get_num_tokens(document.page_content) + embedding_model_type_instance.get_num_tokens( + embedding_model_instance.model, + embedding_model_instance.credentials, + [document.page_content] + ) for document in chunk_documents ) diff --git a/api/core/memory/read_only_conversation_token_db_buffer_shared_memory.py b/api/core/memory/read_only_conversation_token_db_buffer_shared_memory.py deleted file mode 100644 index 9840c5c22da5c3..00000000000000 --- a/api/core/memory/read_only_conversation_token_db_buffer_shared_memory.py +++ /dev/null @@ -1,95 +0,0 @@ -from typing import Any, List, Dict - -from langchain.memory.chat_memory import BaseChatMemory -from langchain.schema import get_buffer_string, BaseMessage - -from core.file.message_file_parser import MessageFileParser -from core.model_providers.models.entity.message import PromptMessage, MessageType, to_lc_messages -from core.model_providers.models.llm.base import BaseLLM -from extensions.ext_database import db -from models.model import Conversation, Message - - -class ReadOnlyConversationTokenDBBufferSharedMemory(BaseChatMemory): - conversation: Conversation - human_prefix: str = "Human" - ai_prefix: str = "Assistant" - model_instance: BaseLLM - memory_key: str = "chat_history" - max_token_limit: int = 2000 - message_limit: int = 10 - - @property - def buffer(self) -> List[BaseMessage]: - """String buffer of memory.""" - app_model = self.conversation.app - - # fetch limited messages desc, and return reversed - messages = db.session.query(Message).filter( - Message.conversation_id == self.conversation.id, - Message.answer != '' - ).order_by(Message.created_at.desc()).limit(self.message_limit).all() - - messages = list(reversed(messages)) - message_file_parser = MessageFileParser(tenant_id=app_model.tenant_id, app_id=self.conversation.app_id) - - chat_messages: List[PromptMessage] = [] - for message in messages: - files = message.message_files - if files: - file_objs = message_file_parser.transform_message_files( - files, message.app_model_config - ) - - prompt_message_files = [file_obj.prompt_message_file for file_obj in file_objs] - chat_messages.append(PromptMessage( - content=message.query, - type=MessageType.USER, - files=prompt_message_files - )) - else: - chat_messages.append(PromptMessage(content=message.query, type=MessageType.USER)) - - chat_messages.append(PromptMessage(content=message.answer, type=MessageType.ASSISTANT)) - - if not chat_messages: - return [] - - # prune the chat message if it exceeds the max token limit - curr_buffer_length = self.model_instance.get_num_tokens(chat_messages) - if curr_buffer_length > self.max_token_limit: - pruned_memory = [] - while curr_buffer_length > self.max_token_limit and chat_messages: - pruned_memory.append(chat_messages.pop(0)) - curr_buffer_length = self.model_instance.get_num_tokens(chat_messages) - - return to_lc_messages(chat_messages) - - @property - def memory_variables(self) -> List[str]: - """Will always return list of memory variables. - - :meta private: - """ - return [self.memory_key] - - def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]: - """Return history buffer.""" - buffer: Any = self.buffer - if self.return_messages: - final_buffer: Any = buffer - else: - final_buffer = get_buffer_string( - buffer, - human_prefix=self.human_prefix, - ai_prefix=self.ai_prefix, - ) - return {self.memory_key: final_buffer} - - def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None: - """Nothing should be saved or changed""" - pass - - def clear(self) -> None: - """Nothing to clear, got a memory like a vault.""" - pass diff --git a/api/core/memory/read_only_conversation_token_db_string_buffer_shared_memory.py b/api/core/memory/read_only_conversation_token_db_string_buffer_shared_memory.py deleted file mode 100644 index 0edd2445e298d7..00000000000000 --- a/api/core/memory/read_only_conversation_token_db_string_buffer_shared_memory.py +++ /dev/null @@ -1,36 +0,0 @@ -from typing import Any, List, Dict - -from langchain.memory.chat_memory import BaseChatMemory -from langchain.schema import get_buffer_string - -from core.memory.read_only_conversation_token_db_buffer_shared_memory import \ - ReadOnlyConversationTokenDBBufferSharedMemory - - -class ReadOnlyConversationTokenDBStringBufferSharedMemory(BaseChatMemory): - memory: ReadOnlyConversationTokenDBBufferSharedMemory - - @property - def memory_variables(self) -> List[str]: - """Return memory variables.""" - return self.memory.memory_variables - - def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]: - """Load memory variables from memory.""" - buffer: Any = self.memory.buffer - - final_buffer = get_buffer_string( - buffer, - human_prefix=self.memory.human_prefix, - ai_prefix=self.memory.ai_prefix, - ) - - return {self.memory.memory_key: final_buffer} - - def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None: - """Nothing should be saved or changed""" - pass - - def clear(self) -> None: - """Nothing to clear, got a memory like a vault.""" - pass \ No newline at end of file diff --git a/api/core/memory/token_buffer_memory.py b/api/core/memory/token_buffer_memory.py new file mode 100644 index 00000000000000..285cb3eeb1114d --- /dev/null +++ b/api/core/memory/token_buffer_memory.py @@ -0,0 +1,109 @@ +from core.file.message_file_parser import MessageFileParser +from core.model_manager import ModelInstance +from core.model_runtime.entities.message_entities import PromptMessage, TextPromptMessageContent, UserPromptMessage, \ + AssistantPromptMessage, PromptMessageRole +from core.model_runtime.entities.model_entities import ModelType +from core.model_runtime.model_providers import model_provider_factory +from extensions.ext_database import db +from models.model import Conversation, Message + + +class TokenBufferMemory: + def __init__(self, conversation: Conversation, model_instance: ModelInstance) -> None: + self.conversation = conversation + self.model_instance = model_instance + + def get_history_prompt_messages(self, max_token_limit: int = 2000, + message_limit: int = 10) -> list[PromptMessage]: + """ + Get history prompt messages. + :param max_token_limit: max token limit + :param message_limit: message limit + """ + app_record = self.conversation.app + + # fetch limited messages, and return reversed + messages = db.session.query(Message).filter( + Message.conversation_id == self.conversation.id, + Message.answer != '' + ).order_by(Message.created_at.desc()).limit(message_limit).all() + + messages = list(reversed(messages)) + message_file_parser = MessageFileParser( + tenant_id=app_record.tenant_id, + app_id=app_record.id + ) + + prompt_messages = [] + for message in messages: + files = message.message_files + if files: + file_objs = message_file_parser.transform_message_files( + files, message.app_model_config + ) + + prompt_message_contents = [TextPromptMessageContent(data=message.query)] + for file_obj in file_objs: + prompt_message_contents.append(file_obj.prompt_message_content) + + prompt_messages.append(UserPromptMessage(content=prompt_message_contents)) + else: + prompt_messages.append(UserPromptMessage(content=message.query)) + + prompt_messages.append(AssistantPromptMessage(content=message.answer)) + + if not prompt_messages: + return [] + + # prune the chat message if it exceeds the max token limit + provider_instance = model_provider_factory.get_provider_instance(self.model_instance.provider) + model_type_instance = provider_instance.get_model_instance(ModelType.LLM) + + curr_message_tokens = model_type_instance.get_num_tokens( + self.model_instance.model, + self.model_instance.credentials, + prompt_messages + ) + + if curr_message_tokens > max_token_limit: + pruned_memory = [] + while curr_message_tokens > max_token_limit and prompt_messages: + pruned_memory.append(prompt_messages.pop(0)) + curr_message_tokens = model_type_instance.get_num_tokens( + self.model_instance.model, + self.model_instance.credentials, + prompt_messages + ) + + return prompt_messages + + def get_history_prompt_text(self, human_prefix: str = "Human", + ai_prefix: str = "Assistant", + max_token_limit: int = 2000, + message_limit: int = 10) -> str: + """ + Get history prompt text. + :param human_prefix: human prefix + :param ai_prefix: ai prefix + :param max_token_limit: max token limit + :param message_limit: message limit + :return: + """ + prompt_messages = self.get_history_prompt_messages( + max_token_limit=max_token_limit, + message_limit=message_limit + ) + + string_messages = [] + for m in prompt_messages: + if m.role == PromptMessageRole.USER: + role = human_prefix + elif m.role == PromptMessageRole.ASSISTANT: + role = ai_prefix + else: + continue + + message = f"{role}: {m.content}" + string_messages.append(message) + + return "\n".join(string_messages) \ No newline at end of file diff --git a/api/core/model_manager.py b/api/core/model_manager.py new file mode 100644 index 00000000000000..ffbec8578e535a --- /dev/null +++ b/api/core/model_manager.py @@ -0,0 +1,209 @@ +from typing import Optional, Union, Generator, cast, List, IO + +from core.entities.provider_configuration import ProviderModelBundle +from core.errors.error import ProviderTokenNotInitError +from core.model_runtime.callbacks.base_callback import Callback +from core.model_runtime.entities.llm_entities import LLMResult +from core.model_runtime.entities.message_entities import PromptMessageTool, PromptMessage +from core.model_runtime.entities.model_entities import ModelType +from core.model_runtime.entities.rerank_entities import RerankResult +from core.model_runtime.entities.text_embedding_entities import TextEmbeddingResult +from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel +from core.model_runtime.model_providers.__base.moderation_model import ModerationModel +from core.model_runtime.model_providers.__base.rerank_model import RerankModel +from core.model_runtime.model_providers.__base.speech2text_model import Speech2TextModel +from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel +from core.provider_manager import ProviderManager + + +class ModelInstance: + """ + Model instance class + """ + + def __init__(self, provider_model_bundle: ProviderModelBundle, model: str) -> None: + self._provider_model_bundle = provider_model_bundle + self.model = model + self.provider = provider_model_bundle.configuration.provider.provider + self.credentials = self._fetch_credentials_from_bundle(provider_model_bundle, model) + self.model_type_instance = self._provider_model_bundle.model_type_instance + + def _fetch_credentials_from_bundle(self, provider_model_bundle: ProviderModelBundle, model: str) -> dict: + """ + Fetch credentials from provider model bundle + :param provider_model_bundle: provider model bundle + :param model: model name + :return: + """ + credentials = provider_model_bundle.configuration.get_current_credentials( + model_type=provider_model_bundle.model_type_instance.model_type, + model=model + ) + + if credentials is None: + raise ProviderTokenNotInitError(f"Model {model} credentials is not initialized.") + + return credentials + + def invoke_llm(self, prompt_messages: list[PromptMessage], model_parameters: Optional[dict] = None, + tools: Optional[list[PromptMessageTool]] = None, stop: Optional[List[str]] = None, + stream: bool = True, user: Optional[str] = None, callbacks: list[Callback] = None) \ + -> Union[LLMResult, Generator]: + """ + Invoke large language model + + :param prompt_messages: prompt messages + :param model_parameters: model parameters + :param tools: tools for tool calling + :param stop: stop words + :param stream: is stream response + :param user: unique user id + :param callbacks: callbacks + :return: full response or stream response chunk generator result + """ + if not isinstance(self.model_type_instance, LargeLanguageModel): + raise Exception(f"Model type instance is not LargeLanguageModel") + + self.model_type_instance = cast(LargeLanguageModel, self.model_type_instance) + return self.model_type_instance.invoke( + model=self.model, + credentials=self.credentials, + prompt_messages=prompt_messages, + model_parameters=model_parameters, + tools=tools, + stop=stop, + stream=stream, + user=user, + callbacks=callbacks + ) + + def invoke_text_embedding(self, texts: list[str], user: Optional[str] = None) \ + -> TextEmbeddingResult: + """ + Invoke large language model + + :param texts: texts to embed + :param user: unique user id + :return: embeddings result + """ + if not isinstance(self.model_type_instance, TextEmbeddingModel): + raise Exception(f"Model type instance is not TextEmbeddingModel") + + self.model_type_instance = cast(TextEmbeddingModel, self.model_type_instance) + return self.model_type_instance.invoke( + model=self.model, + credentials=self.credentials, + texts=texts, + user=user + ) + + def invoke_rerank(self, query: str, docs: list[str], score_threshold: Optional[float] = None, top_n: Optional[int] = None, + user: Optional[str] = None) \ + -> RerankResult: + """ + Invoke rerank model + + :param query: search query + :param docs: docs for reranking + :param score_threshold: score threshold + :param top_n: top n + :param user: unique user id + :return: rerank result + """ + if not isinstance(self.model_type_instance, RerankModel): + raise Exception(f"Model type instance is not RerankModel") + + self.model_type_instance = cast(RerankModel, self.model_type_instance) + return self.model_type_instance.invoke( + model=self.model, + credentials=self.credentials, + query=query, + docs=docs, + score_threshold=score_threshold, + top_n=top_n, + user=user + ) + + def invoke_moderation(self, text: str, user: Optional[str] = None) \ + -> bool: + """ + Invoke moderation model + + :param text: text to moderate + :param user: unique user id + :return: false if text is safe, true otherwise + """ + if not isinstance(self.model_type_instance, ModerationModel): + raise Exception(f"Model type instance is not ModerationModel") + + self.model_type_instance = cast(ModerationModel, self.model_type_instance) + return self.model_type_instance.invoke( + model=self.model, + credentials=self.credentials, + text=text, + user=user + ) + + def invoke_speech2text(self, file: IO[bytes], user: Optional[str] = None) \ + -> str: + """ + Invoke large language model + + :param file: audio file + :param user: unique user id + :return: text for given audio file + """ + if not isinstance(self.model_type_instance, Speech2TextModel): + raise Exception(f"Model type instance is not Speech2TextModel") + + self.model_type_instance = cast(Speech2TextModel, self.model_type_instance) + return self.model_type_instance.invoke( + model=self.model, + credentials=self.credentials, + file=file, + user=user + ) + + +class ModelManager: + def __init__(self) -> None: + self._provider_manager = ProviderManager() + + def get_model_instance(self, tenant_id: str, provider: str, model_type: ModelType, model: str) -> ModelInstance: + """ + Get model instance + :param tenant_id: tenant id + :param provider: provider name + :param model_type: model type + :param model: model name + :return: + """ + provider_model_bundle = self._provider_manager.get_provider_model_bundle( + tenant_id=tenant_id, + provider=provider, + model_type=model_type + ) + + return ModelInstance(provider_model_bundle, model) + + def get_default_model_instance(self, tenant_id: str, model_type: ModelType) -> ModelInstance: + """ + Get default model instance + :param tenant_id: tenant id + :param model_type: model type + :return: + """ + default_model_entity = self._provider_manager.get_default_model( + tenant_id=tenant_id, + model_type=model_type + ) + + if not default_model_entity: + raise ProviderTokenNotInitError(f"Default model not found for {model_type}") + + return self.get_model_instance( + tenant_id=tenant_id, + provider=default_model_entity.provider.provider, + model_type=model_type, + model=default_model_entity.model + ) diff --git a/api/core/model_providers/model_factory.py b/api/core/model_providers/model_factory.py deleted file mode 100644 index f663707b075553..00000000000000 --- a/api/core/model_providers/model_factory.py +++ /dev/null @@ -1,335 +0,0 @@ -from typing import Optional - -from langchain.callbacks.base import Callbacks - -from core.model_providers.error import ProviderTokenNotInitError, LLMBadRequestError -from core.model_providers.model_provider_factory import ModelProviderFactory, DEFAULT_MODELS -from core.model_providers.models.base import BaseProviderModel -from core.model_providers.models.embedding.base import BaseEmbedding -from core.model_providers.models.entity.model_params import ModelKwargs, ModelType -from core.model_providers.models.llm.base import BaseLLM -from core.model_providers.models.moderation.base import BaseModeration -from core.model_providers.models.reranking.base import BaseReranking -from core.model_providers.models.speech2text.base import BaseSpeech2Text -from extensions.ext_database import db -from models.provider import TenantDefaultModel - - -class ModelFactory: - - @classmethod - def get_text_generation_model_from_model_config(cls, tenant_id: str, - model_config: dict, - streaming: bool = False, - callbacks: Callbacks = None) -> Optional[BaseLLM]: - provider_name = model_config.get("provider") - model_name = model_config.get("name") - completion_params = model_config.get("completion_params", {}) - - return cls.get_text_generation_model( - tenant_id=tenant_id, - model_provider_name=provider_name, - model_name=model_name, - model_kwargs=ModelKwargs( - temperature=completion_params.get('temperature', 0), - max_tokens=completion_params.get('max_tokens', 256), - top_p=completion_params.get('top_p', 0), - frequency_penalty=completion_params.get('frequency_penalty', 0.1), - presence_penalty=completion_params.get('presence_penalty', 0.1) - ), - streaming=streaming, - callbacks=callbacks - ) - - @classmethod - def get_text_generation_model(cls, - tenant_id: str, - model_provider_name: Optional[str] = None, - model_name: Optional[str] = None, - model_kwargs: Optional[ModelKwargs] = None, - streaming: bool = False, - callbacks: Callbacks = None, - deduct_quota: bool = True) -> Optional[BaseLLM]: - """ - get text generation model. - - :param tenant_id: a string representing the ID of the tenant. - :param model_provider_name: - :param model_name: - :param model_kwargs: - :param streaming: - :param callbacks: - :param deduct_quota: - :return: - """ - is_default_model = False - if model_provider_name is None and model_name is None: - default_model = cls.get_default_model(tenant_id, ModelType.TEXT_GENERATION) - - if not default_model: - raise LLMBadRequestError(f"Default model is not available. " - f"Please configure a Default System Reasoning Model " - f"in the Settings -> Model Provider.") - - model_provider_name = default_model.provider_name - model_name = default_model.model_name - is_default_model = True - - # get model provider - model_provider = ModelProviderFactory.get_preferred_model_provider(tenant_id, model_provider_name) - - if not model_provider: - raise ProviderTokenNotInitError(f"Model {model_name} provider credentials is not initialized.") - - # init text generation model - model_class = model_provider.get_model_class(model_type=ModelType.TEXT_GENERATION) - - try: - model_instance = model_class( - model_provider=model_provider, - name=model_name, - model_kwargs=model_kwargs, - streaming=streaming, - callbacks=callbacks - ) - except LLMBadRequestError as e: - if is_default_model: - raise LLMBadRequestError(f"Default model {model_name} is not available. " - f"Please check your model provider credentials.") - else: - raise e - - if is_default_model or not deduct_quota: - model_instance.deduct_quota = False - - return model_instance - - @classmethod - def get_embedding_model(cls, - tenant_id: str, - model_provider_name: Optional[str] = None, - model_name: Optional[str] = None) -> Optional[BaseEmbedding]: - """ - get embedding model. - - :param tenant_id: a string representing the ID of the tenant. - :param model_provider_name: - :param model_name: - :return: - """ - if model_provider_name is None and model_name is None: - default_model = cls.get_default_model(tenant_id, ModelType.EMBEDDINGS) - - if not default_model: - raise LLMBadRequestError(f"Default model is not available. " - f"Please configure a Default Embedding Model " - f"in the Settings -> Model Provider.") - - model_provider_name = default_model.provider_name - model_name = default_model.model_name - - # get model provider - model_provider = ModelProviderFactory.get_preferred_model_provider(tenant_id, model_provider_name) - - if not model_provider: - raise ProviderTokenNotInitError(f"Model {model_name} provider credentials is not initialized.") - - # init embedding model - model_class = model_provider.get_model_class(model_type=ModelType.EMBEDDINGS) - return model_class( - model_provider=model_provider, - name=model_name - ) - - - @classmethod - def get_reranking_model(cls, - tenant_id: str, - model_provider_name: Optional[str] = None, - model_name: Optional[str] = None) -> Optional[BaseReranking]: - """ - get reranking model. - - :param tenant_id: a string representing the ID of the tenant. - :param model_provider_name: - :param model_name: - :return: - """ - if (model_provider_name is None or len(model_provider_name) == 0) and (model_name is None or len(model_name) == 0): - default_model = cls.get_default_model(tenant_id, ModelType.RERANKING) - - if not default_model: - raise LLMBadRequestError(f"Default model is not available. " - f"Please configure a Default Reranking Model " - f"in the Settings -> Model Provider.") - - model_provider_name = default_model.provider_name - model_name = default_model.model_name - - # get model provider - model_provider = ModelProviderFactory.get_preferred_model_provider(tenant_id, model_provider_name) - - if not model_provider: - raise ProviderTokenNotInitError(f"Model {model_name} provider credentials is not initialized.") - - # init reranking model - model_class = model_provider.get_model_class(model_type=ModelType.RERANKING) - return model_class( - model_provider=model_provider, - name=model_name - ) - - @classmethod - def get_speech2text_model(cls, - tenant_id: str, - model_provider_name: Optional[str] = None, - model_name: Optional[str] = None) -> Optional[BaseSpeech2Text]: - """ - get speech to text model. - - :param tenant_id: a string representing the ID of the tenant. - :param model_provider_name: - :param model_name: - :return: - """ - if model_provider_name is None and model_name is None: - default_model = cls.get_default_model(tenant_id, ModelType.SPEECH_TO_TEXT) - - if not default_model: - raise LLMBadRequestError(f"Default model is not available. " - f"Please configure a Default Speech-to-Text Model " - f"in the Settings -> Model Provider.") - - model_provider_name = default_model.provider_name - model_name = default_model.model_name - - # get model provider - model_provider = ModelProviderFactory.get_preferred_model_provider(tenant_id, model_provider_name) - - if not model_provider: - raise ProviderTokenNotInitError(f"Model {model_name} provider credentials is not initialized.") - - # init speech to text model - model_class = model_provider.get_model_class(model_type=ModelType.SPEECH_TO_TEXT) - return model_class( - model_provider=model_provider, - name=model_name - ) - - @classmethod - def get_moderation_model(cls, - tenant_id: str, - model_provider_name: str, - model_name: str) -> Optional[BaseModeration]: - """ - get moderation model. - - :param tenant_id: a string representing the ID of the tenant. - :param model_provider_name: - :param model_name: - :return: - """ - # get model provider - model_provider = ModelProviderFactory.get_preferred_model_provider(tenant_id, model_provider_name) - - if not model_provider: - raise ProviderTokenNotInitError(f"Model {model_name} provider credentials is not initialized.") - - # init moderation model - model_class = model_provider.get_model_class(model_type=ModelType.MODERATION) - return model_class( - model_provider=model_provider, - name=model_name - ) - - @classmethod - def get_default_model(cls, tenant_id: str, model_type: ModelType) -> TenantDefaultModel: - """ - get default model of model type. - - :param tenant_id: - :param model_type: - :return: - """ - # get default model - default_model = db.session.query(TenantDefaultModel) \ - .filter( - TenantDefaultModel.tenant_id == tenant_id, - TenantDefaultModel.model_type == model_type.value - ).first() - - if not default_model: - model_provider_rules = ModelProviderFactory.get_provider_rules() - for model_provider_name, model_provider_rule in model_provider_rules.items(): - model_provider = ModelProviderFactory.get_preferred_model_provider(tenant_id, model_provider_name) - if not model_provider: - continue - - model_list = model_provider.get_supported_model_list(model_type) - if model_list: - model_info = model_list[0] - default_model = TenantDefaultModel( - tenant_id=tenant_id, - model_type=model_type.value, - provider_name=model_provider_name, - model_name=model_info['id'] - ) - db.session.add(default_model) - db.session.commit() - break - - return default_model - - @classmethod - def update_default_model(cls, - tenant_id: str, - model_type: ModelType, - provider_name: str, - model_name: str) -> TenantDefaultModel: - """ - update default model of model type. - - :param tenant_id: - :param model_type: - :param provider_name: - :param model_name: - :return: - """ - model_provider_name = ModelProviderFactory.get_provider_names() - if provider_name not in model_provider_name: - raise ValueError(f'Invalid provider name: {provider_name}') - - model_provider = ModelProviderFactory.get_preferred_model_provider(tenant_id, provider_name) - - if not model_provider: - raise ProviderTokenNotInitError(f"Model {model_name} provider credentials is not initialized.") - - model_list = model_provider.get_supported_model_list(model_type) - model_ids = [model['id'] for model in model_list] - if model_name not in model_ids: - raise ValueError(f'Invalid model name: {model_name}') - - # get default model - default_model = db.session.query(TenantDefaultModel) \ - .filter( - TenantDefaultModel.tenant_id == tenant_id, - TenantDefaultModel.model_type == model_type.value - ).first() - - if default_model: - # update default model - default_model.provider_name = provider_name - default_model.model_name = model_name - db.session.commit() - else: - # create default model - default_model = TenantDefaultModel( - tenant_id=tenant_id, - model_type=model_type.value, - provider_name=provider_name, - model_name=model_name, - ) - db.session.add(default_model) - db.session.commit() - - return default_model diff --git a/api/core/model_providers/model_provider_factory.py b/api/core/model_providers/model_provider_factory.py deleted file mode 100644 index de6b8287f8dece..00000000000000 --- a/api/core/model_providers/model_provider_factory.py +++ /dev/null @@ -1,276 +0,0 @@ -from typing import Type - -from sqlalchemy.exc import IntegrityError - -from core.model_providers.models.entity.model_params import ModelType -from core.model_providers.providers.base import BaseModelProvider -from core.model_providers.rules import provider_rules -from extensions.ext_database import db -from models.provider import TenantPreferredModelProvider, ProviderType, Provider, ProviderQuotaType - -DEFAULT_MODELS = { - ModelType.TEXT_GENERATION.value: { - 'provider_name': 'openai', - 'model_name': 'gpt-3.5-turbo', - }, - ModelType.EMBEDDINGS.value: { - 'provider_name': 'openai', - 'model_name': 'text-embedding-ada-002', - }, - ModelType.SPEECH_TO_TEXT.value: { - 'provider_name': 'openai', - 'model_name': 'whisper-1', - } -} - - -class ModelProviderFactory: - @classmethod - def get_model_provider_class(cls, provider_name: str) -> Type[BaseModelProvider]: - if provider_name == 'openai': - from core.model_providers.providers.openai_provider import OpenAIProvider - return OpenAIProvider - elif provider_name == 'anthropic': - from core.model_providers.providers.anthropic_provider import AnthropicProvider - return AnthropicProvider - elif provider_name == 'minimax': - from core.model_providers.providers.minimax_provider import MinimaxProvider - return MinimaxProvider - elif provider_name == 'spark': - from core.model_providers.providers.spark_provider import SparkProvider - return SparkProvider - elif provider_name == 'tongyi': - from core.model_providers.providers.tongyi_provider import TongyiProvider - return TongyiProvider - elif provider_name == 'wenxin': - from core.model_providers.providers.wenxin_provider import WenxinProvider - return WenxinProvider - elif provider_name == 'zhipuai': - from core.model_providers.providers.zhipuai_provider import ZhipuAIProvider - return ZhipuAIProvider - elif provider_name == 'chatglm': - from core.model_providers.providers.chatglm_provider import ChatGLMProvider - return ChatGLMProvider - elif provider_name == 'baichuan': - from core.model_providers.providers.baichuan_provider import BaichuanProvider - return BaichuanProvider - elif provider_name == 'azure_openai': - from core.model_providers.providers.azure_openai_provider import AzureOpenAIProvider - return AzureOpenAIProvider - elif provider_name == 'replicate': - from core.model_providers.providers.replicate_provider import ReplicateProvider - return ReplicateProvider - elif provider_name == 'huggingface_hub': - from core.model_providers.providers.huggingface_hub_provider import HuggingfaceHubProvider - return HuggingfaceHubProvider - elif provider_name == 'xinference': - from core.model_providers.providers.xinference_provider import XinferenceProvider - return XinferenceProvider - elif provider_name == 'openllm': - from core.model_providers.providers.openllm_provider import OpenLLMProvider - return OpenLLMProvider - elif provider_name == 'localai': - from core.model_providers.providers.localai_provider import LocalAIProvider - return LocalAIProvider - elif provider_name == 'cohere': - from core.model_providers.providers.cohere_provider import CohereProvider - return CohereProvider - elif provider_name == 'jina': - from core.model_providers.providers.jina_provider import JinaProvider - return JinaProvider - else: - raise NotImplementedError - - @classmethod - def get_provider_names(cls): - """ - Returns a list of provider names. - """ - return list(provider_rules.keys()) - - @classmethod - def get_provider_rules(cls): - """ - Returns a list of provider rules. - - :return: - """ - return provider_rules - - @classmethod - def get_provider_rule(cls, provider_name: str): - """ - Returns provider rule. - """ - return provider_rules[provider_name] - - @classmethod - def get_preferred_model_provider(cls, tenant_id: str, model_provider_name: str): - """ - get preferred model provider. - - :param tenant_id: a string representing the ID of the tenant. - :param model_provider_name: - :return: - """ - # get preferred provider - preferred_provider = cls._get_preferred_provider(tenant_id, model_provider_name) - if not preferred_provider or not preferred_provider.is_valid: - return None - - # init model provider - model_provider_class = ModelProviderFactory.get_model_provider_class(model_provider_name) - return model_provider_class(provider=preferred_provider) - - @classmethod - def get_preferred_type_by_preferred_model_provider(cls, - tenant_id: str, - model_provider_name: str, - preferred_model_provider: TenantPreferredModelProvider): - """ - get preferred provider type by preferred model provider. - - :param model_provider_name: - :param preferred_model_provider: - :return: - """ - if not preferred_model_provider: - model_provider_rules = ModelProviderFactory.get_provider_rule(model_provider_name) - support_provider_types = model_provider_rules['support_provider_types'] - - if ProviderType.CUSTOM.value in support_provider_types: - custom_provider = db.session.query(Provider) \ - .filter( - Provider.tenant_id == tenant_id, - Provider.provider_name == model_provider_name, - Provider.provider_type == ProviderType.CUSTOM.value, - Provider.is_valid == True - ).first() - - if custom_provider: - return ProviderType.CUSTOM.value - - model_provider = cls.get_model_provider_class(model_provider_name) - - if ProviderType.SYSTEM.value in support_provider_types \ - and model_provider.is_provider_type_system_supported(): - return ProviderType.SYSTEM.value - elif ProviderType.CUSTOM.value in support_provider_types: - return ProviderType.CUSTOM.value - else: - return preferred_model_provider.preferred_provider_type - - @classmethod - def _get_preferred_provider(cls, tenant_id: str, model_provider_name: str): - """ - get preferred provider of tenant. - - :param tenant_id: - :param model_provider_name: - :return: - """ - # get preferred provider type - preferred_provider_type = cls._get_preferred_provider_type(tenant_id, model_provider_name) - - # get providers by preferred provider type - providers = db.session.query(Provider) \ - .filter( - Provider.tenant_id == tenant_id, - Provider.provider_name == model_provider_name, - Provider.provider_type == preferred_provider_type - ).all() - - no_system_provider = False - if preferred_provider_type == ProviderType.SYSTEM.value: - quota_type_to_provider_dict = {} - for provider in providers: - quota_type_to_provider_dict[provider.quota_type] = provider - - model_provider_rules = ModelProviderFactory.get_provider_rule(model_provider_name) - for quota_type_enum in ProviderQuotaType: - quota_type = quota_type_enum.value - if quota_type in model_provider_rules['system_config']['supported_quota_types']: - if quota_type in quota_type_to_provider_dict.keys(): - provider = quota_type_to_provider_dict[quota_type] - if provider.is_valid and provider.quota_limit > provider.quota_used: - return provider - elif quota_type == ProviderQuotaType.TRIAL.value: - try: - provider = Provider( - tenant_id=tenant_id, - provider_name=model_provider_name, - provider_type=ProviderType.SYSTEM.value, - is_valid=True, - quota_type=ProviderQuotaType.TRIAL.value, - quota_limit=model_provider_rules['system_config']['quota_limit'], - quota_used=0 - ) - db.session.add(provider) - db.session.commit() - except IntegrityError: - db.session.rollback() - provider = db.session.query(Provider) \ - .filter( - Provider.tenant_id == tenant_id, - Provider.provider_name == model_provider_name, - Provider.provider_type == ProviderType.SYSTEM.value, - Provider.quota_type == ProviderQuotaType.TRIAL.value - ).first() - - if provider.quota_limit == 0: - return None - - return provider - - no_system_provider = True - - if no_system_provider: - providers = db.session.query(Provider) \ - .filter( - Provider.tenant_id == tenant_id, - Provider.provider_name == model_provider_name, - Provider.provider_type == ProviderType.CUSTOM.value - ).all() - - if preferred_provider_type == ProviderType.CUSTOM.value or no_system_provider: - if providers: - return providers[0] - else: - try: - provider = Provider( - tenant_id=tenant_id, - provider_name=model_provider_name, - provider_type=ProviderType.CUSTOM.value, - is_valid=False - ) - db.session.add(provider) - db.session.commit() - except IntegrityError: - db.session.rollback() - provider = db.session.query(Provider) \ - .filter( - Provider.tenant_id == tenant_id, - Provider.provider_name == model_provider_name, - Provider.provider_type == ProviderType.CUSTOM.value - ).first() - - return provider - - return None - - @classmethod - def _get_preferred_provider_type(cls, tenant_id: str, model_provider_name: str): - """ - get preferred provider type of tenant. - - :param tenant_id: - :param model_provider_name: - :return: - """ - preferred_model_provider = db.session.query(TenantPreferredModelProvider) \ - .filter( - TenantPreferredModelProvider.tenant_id == tenant_id, - TenantPreferredModelProvider.provider_name == model_provider_name - ).first() - - return cls.get_preferred_type_by_preferred_model_provider(tenant_id, model_provider_name, preferred_model_provider) diff --git a/api/core/model_providers/models/base.py b/api/core/model_providers/models/base.py deleted file mode 100644 index 01f83efa848ba1..00000000000000 --- a/api/core/model_providers/models/base.py +++ /dev/null @@ -1,22 +0,0 @@ -from abc import ABC -from typing import Any - -from core.model_providers.providers.base import BaseModelProvider - - -class BaseProviderModel(ABC): - _client: Any - _model_provider: BaseModelProvider - - def __init__(self, model_provider: BaseModelProvider, client: Any): - self._model_provider = model_provider - self._client = client - - @property - def client(self): - return self._client - - @property - def model_provider(self): - return self._model_provider - diff --git a/api/core/model_providers/models/embedding/azure_openai_embedding.py b/api/core/model_providers/models/embedding/azure_openai_embedding.py deleted file mode 100644 index 506b32c55713bb..00000000000000 --- a/api/core/model_providers/models/embedding/azure_openai_embedding.py +++ /dev/null @@ -1,78 +0,0 @@ -import decimal -import logging - -import openai -import tiktoken -from langchain.embeddings import OpenAIEmbeddings - -from core.model_providers.error import LLMBadRequestError, LLMAuthorizationError, LLMRateLimitError, \ - LLMAPIUnavailableError, LLMAPIConnectionError -from core.model_providers.models.embedding.base import BaseEmbedding -from core.model_providers.providers.base import BaseModelProvider - -AZURE_OPENAI_API_VERSION = '2023-07-01-preview' - - -class AzureOpenAIEmbedding(BaseEmbedding): - def __init__(self, model_provider: BaseModelProvider, name: str): - self.credentials = model_provider.get_model_credentials( - model_name=name, - model_type=self.type - ) - - client = OpenAIEmbeddings( - deployment=name, - openai_api_type='azure', - openai_api_version=AZURE_OPENAI_API_VERSION, - chunk_size=16, - max_retries=1, - openai_api_key=self.credentials.get('openai_api_key'), - openai_api_base=self.credentials.get('openai_api_base') - ) - - super().__init__(model_provider, client, name) - - @property - def base_model_name(self) -> str: - """ - get base model name (not deployment) - - :return: str - """ - return self.credentials.get("base_model_name") - - def get_num_tokens(self, text: str) -> int: - """ - get num tokens of text. - - :param text: - :return: - """ - if len(text) == 0: - return 0 - - enc = tiktoken.encoding_for_model(self.credentials.get('base_model_name')) - - tokenized_text = enc.encode(text) - - # calculate the number of tokens in the encoded text - return len(tokenized_text) - - def handle_exceptions(self, ex: Exception) -> Exception: - if isinstance(ex, openai.error.InvalidRequestError): - logging.warning("Invalid request to Azure OpenAI API.") - return LLMBadRequestError(str(ex)) - elif isinstance(ex, openai.error.APIConnectionError): - logging.warning("Failed to connect to Azure OpenAI API.") - return LLMAPIConnectionError(ex.__class__.__name__ + ":" + str(ex)) - elif isinstance(ex, (openai.error.APIError, openai.error.ServiceUnavailableError, openai.error.Timeout)): - logging.warning("Azure OpenAI service unavailable.") - return LLMAPIUnavailableError(ex.__class__.__name__ + ":" + str(ex)) - elif isinstance(ex, openai.error.RateLimitError): - return LLMRateLimitError('Azure ' + str(ex)) - elif isinstance(ex, openai.error.AuthenticationError): - return LLMAuthorizationError('Azure ' + str(ex)) - elif isinstance(ex, openai.error.OpenAIError): - return LLMBadRequestError('Azure ' + ex.__class__.__name__ + ":" + str(ex)) - else: - return ex diff --git a/api/core/model_providers/models/embedding/base.py b/api/core/model_providers/models/embedding/base.py deleted file mode 100644 index 25f30894b0390f..00000000000000 --- a/api/core/model_providers/models/embedding/base.py +++ /dev/null @@ -1,102 +0,0 @@ -from abc import abstractmethod -from typing import Any -import decimal - -import tiktoken -from langchain.schema.language_model import _get_token_ids_default_method - -from core.model_providers.models.base import BaseProviderModel -from core.model_providers.models.entity.model_params import ModelType -from core.model_providers.providers.base import BaseModelProvider -import logging -logger = logging.getLogger(__name__) - -class BaseEmbedding(BaseProviderModel): - name: str - type: ModelType = ModelType.EMBEDDINGS - - def __init__(self, model_provider: BaseModelProvider, client: Any, name: str): - super().__init__(model_provider, client) - self.name = name - - @property - def base_model_name(self) -> str: - """ - get base model name - - :return: str - """ - return self.name - - @property - def price_config(self) -> dict: - def get_or_default(): - default_price_config = { - 'completion': decimal.Decimal('0'), - 'unit': decimal.Decimal('0'), - 'currency': 'USD' - } - rules = self.model_provider.get_rules() - price_config = rules['price_config'][self.base_model_name] if 'price_config' in rules else default_price_config - price_config = { - 'completion': decimal.Decimal(price_config['completion']), - 'unit': decimal.Decimal(price_config['unit']), - 'currency': price_config['currency'] - } - return price_config - - self._price_config = self._price_config if hasattr(self, '_price_config') else get_or_default() - - logger.debug(f"model: {self.name} price_config: {self._price_config}") - return self._price_config - - def calc_tokens_price(self, tokens: int) -> decimal.Decimal: - """ - calc tokens total price. - - :param tokens: - :return: decimal.Decimal('0.0000001') - """ - unit_price = self.price_config['completion'] - unit = self.price_config['unit'] - total_price = tokens * unit_price * unit - total_price = total_price.quantize(decimal.Decimal('0.0000001'), rounding=decimal.ROUND_HALF_UP) - logging.debug(f"tokens={tokens}, unit_price={unit_price}, unit={unit}, total_price:{total_price}") - return total_price - - def get_tokens_unit_price(self) -> decimal.Decimal: - """ - get token price. - - :return: decimal.Decimal('0.0001') - - """ - unit_price = self.price_config['completion'] - unit_price = unit_price.quantize(decimal.Decimal('0.0001'), rounding=decimal.ROUND_HALF_UP) - logger.debug(f'unit_price:{unit_price}') - return unit_price - - def get_num_tokens(self, text: str) -> int: - """ - get num tokens of text. - - :param text: - :return: - """ - if len(text) == 0: - return 0 - - return len(_get_token_ids_default_method(text)) - - def get_currency(self): - """ - get token currency. - - :return: get from price config, default 'USD' - """ - currency = self.price_config['currency'] - return currency - - @abstractmethod - def handle_exceptions(self, ex: Exception) -> Exception: - raise NotImplementedError diff --git a/api/core/model_providers/models/embedding/huggingface_embedding.py b/api/core/model_providers/models/embedding/huggingface_embedding.py deleted file mode 100644 index 61af1cede8bfe2..00000000000000 --- a/api/core/model_providers/models/embedding/huggingface_embedding.py +++ /dev/null @@ -1,22 +0,0 @@ -from core.model_providers.error import LLMBadRequestError -from core.model_providers.providers.base import BaseModelProvider -from core.third_party.langchain.embeddings.huggingface_hub_embedding import HuggingfaceHubEmbeddings -from core.model_providers.models.embedding.base import BaseEmbedding - - -class HuggingfaceEmbedding(BaseEmbedding): - def __init__(self, model_provider: BaseModelProvider, name: str): - credentials = model_provider.get_model_credentials( - model_name=name, - model_type=self.type - ) - - client = HuggingfaceHubEmbeddings( - model=name, - **credentials - ) - - super().__init__(model_provider, client, name) - - def handle_exceptions(self, ex: Exception) -> Exception: - return LLMBadRequestError(f"Huggingface embedding: {str(ex)}") diff --git a/api/core/model_providers/models/embedding/jina_embedding.py b/api/core/model_providers/models/embedding/jina_embedding.py deleted file mode 100644 index 7c611af1c58dcc..00000000000000 --- a/api/core/model_providers/models/embedding/jina_embedding.py +++ /dev/null @@ -1,25 +0,0 @@ -from core.model_providers.error import LLMBadRequestError -from core.model_providers.models.embedding.base import BaseEmbedding -from core.model_providers.providers.base import BaseModelProvider -from core.third_party.langchain.embeddings.jina_embedding import JinaEmbeddings - - -class JinaEmbedding(BaseEmbedding): - def __init__(self, model_provider: BaseModelProvider, name: str): - credentials = model_provider.get_model_credentials( - model_name=name, - model_type=self.type - ) - - client = JinaEmbeddings( - model=name, - **credentials - ) - - super().__init__(model_provider, client, name) - - def handle_exceptions(self, ex: Exception) -> Exception: - if isinstance(ex, ValueError): - return LLMBadRequestError(f"Jina: {str(ex)}") - else: - return ex diff --git a/api/core/model_providers/models/embedding/localai_embedding.py b/api/core/model_providers/models/embedding/localai_embedding.py deleted file mode 100644 index 437e63c5ab62a4..00000000000000 --- a/api/core/model_providers/models/embedding/localai_embedding.py +++ /dev/null @@ -1,29 +0,0 @@ -from langchain.embeddings import LocalAIEmbeddings - -from replicate.exceptions import ModelError, ReplicateError - -from core.model_providers.error import LLMBadRequestError -from core.model_providers.providers.base import BaseModelProvider -from core.model_providers.models.embedding.base import BaseEmbedding - - -class LocalAIEmbedding(BaseEmbedding): - def __init__(self, model_provider: BaseModelProvider, name: str): - credentials = model_provider.get_model_credentials( - model_name=name, - model_type=self.type - ) - - client = LocalAIEmbeddings( - model=name, - openai_api_key="1", - openai_api_base=credentials['server_url'], - ) - - super().__init__(model_provider, client, name) - - def handle_exceptions(self, ex: Exception) -> Exception: - if isinstance(ex, (ModelError, ReplicateError)): - return LLMBadRequestError(f"LocalAI embedding: {str(ex)}") - else: - return ex diff --git a/api/core/model_providers/models/embedding/minimax_embedding.py b/api/core/model_providers/models/embedding/minimax_embedding.py deleted file mode 100644 index 690ca9946fc3af..00000000000000 --- a/api/core/model_providers/models/embedding/minimax_embedding.py +++ /dev/null @@ -1,26 +0,0 @@ -from langchain.embeddings import MiniMaxEmbeddings - -from core.model_providers.error import LLMBadRequestError -from core.model_providers.models.embedding.base import BaseEmbedding -from core.model_providers.providers.base import BaseModelProvider - - -class MinimaxEmbedding(BaseEmbedding): - def __init__(self, model_provider: BaseModelProvider, name: str): - credentials = model_provider.get_model_credentials( - model_name=name, - model_type=self.type - ) - - client = MiniMaxEmbeddings( - model=name, - **credentials - ) - - super().__init__(model_provider, client, name) - - def handle_exceptions(self, ex: Exception) -> Exception: - if isinstance(ex, ValueError): - return LLMBadRequestError(f"Minimax: {str(ex)}") - else: - return ex diff --git a/api/core/model_providers/models/embedding/openai_embedding.py b/api/core/model_providers/models/embedding/openai_embedding.py deleted file mode 100644 index 54444b121ca907..00000000000000 --- a/api/core/model_providers/models/embedding/openai_embedding.py +++ /dev/null @@ -1,62 +0,0 @@ -import decimal -import logging - -import openai -import tiktoken -from langchain.embeddings import OpenAIEmbeddings - -from core.model_providers.error import LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, \ - LLMRateLimitError, LLMAuthorizationError -from core.model_providers.models.embedding.base import BaseEmbedding -from core.model_providers.providers.base import BaseModelProvider - - -class OpenAIEmbedding(BaseEmbedding): - def __init__(self, model_provider: BaseModelProvider, name: str): - credentials = model_provider.get_model_credentials( - model_name=name, - model_type=self.type - ) - - client = OpenAIEmbeddings( - max_retries=1, - **credentials - ) - - super().__init__(model_provider, client, name) - - def get_num_tokens(self, text: str) -> int: - """ - get num tokens of text. - - :param text: - :return: - """ - if len(text) == 0: - return 0 - - enc = tiktoken.encoding_for_model(self.name) - - tokenized_text = enc.encode(text) - - # calculate the number of tokens in the encoded text - return len(tokenized_text) - - def handle_exceptions(self, ex: Exception) -> Exception: - if isinstance(ex, openai.error.InvalidRequestError): - logging.warning("Invalid request to OpenAI API.") - return LLMBadRequestError(str(ex)) - elif isinstance(ex, openai.error.APIConnectionError): - logging.warning("Failed to connect to OpenAI API.") - return LLMAPIConnectionError(ex.__class__.__name__ + ":" + str(ex)) - elif isinstance(ex, (openai.error.APIError, openai.error.ServiceUnavailableError, openai.error.Timeout)): - logging.warning("OpenAI service unavailable.") - return LLMAPIUnavailableError(ex.__class__.__name__ + ":" + str(ex)) - elif isinstance(ex, openai.error.RateLimitError): - return LLMRateLimitError(str(ex)) - elif isinstance(ex, openai.error.AuthenticationError): - return LLMAuthorizationError(str(ex)) - elif isinstance(ex, openai.error.OpenAIError): - return LLMBadRequestError(ex.__class__.__name__ + ":" + str(ex)) - else: - return ex diff --git a/api/core/model_providers/models/embedding/openllm_embedding.py b/api/core/model_providers/models/embedding/openllm_embedding.py deleted file mode 100644 index 8d27815bfc53d7..00000000000000 --- a/api/core/model_providers/models/embedding/openllm_embedding.py +++ /dev/null @@ -1,22 +0,0 @@ -from core.third_party.langchain.embeddings.openllm_embedding import OpenLLMEmbeddings - -from core.model_providers.error import LLMBadRequestError -from core.model_providers.providers.base import BaseModelProvider -from core.model_providers.models.embedding.base import BaseEmbedding - - -class OpenLLMEmbedding(BaseEmbedding): - def __init__(self, model_provider: BaseModelProvider, name: str): - credentials = model_provider.get_model_credentials( - model_name=name, - model_type=self.type - ) - - client = OpenLLMEmbeddings( - server_url=credentials['server_url'] - ) - - super().__init__(model_provider, client, name) - - def handle_exceptions(self, ex: Exception) -> Exception: - return LLMBadRequestError(f"OpenLLM embedding: {str(ex)}") diff --git a/api/core/model_providers/models/embedding/replicate_embedding.py b/api/core/model_providers/models/embedding/replicate_embedding.py deleted file mode 100644 index 962593fcdb08eb..00000000000000 --- a/api/core/model_providers/models/embedding/replicate_embedding.py +++ /dev/null @@ -1,29 +0,0 @@ -import decimal - -from replicate.exceptions import ModelError, ReplicateError - -from core.model_providers.error import LLMBadRequestError -from core.model_providers.providers.base import BaseModelProvider -from core.third_party.langchain.embeddings.replicate_embedding import ReplicateEmbeddings -from core.model_providers.models.embedding.base import BaseEmbedding - - -class ReplicateEmbedding(BaseEmbedding): - def __init__(self, model_provider: BaseModelProvider, name: str): - credentials = model_provider.get_model_credentials( - model_name=name, - model_type=self.type - ) - - client = ReplicateEmbeddings( - model=name + ':' + credentials.get('model_version'), - replicate_api_token=credentials.get('replicate_api_token') - ) - - super().__init__(model_provider, client, name) - - def handle_exceptions(self, ex: Exception) -> Exception: - if isinstance(ex, (ModelError, ReplicateError)): - return LLMBadRequestError(f"Replicate: {str(ex)}") - else: - return ex diff --git a/api/core/model_providers/models/embedding/xinference_embedding.py b/api/core/model_providers/models/embedding/xinference_embedding.py deleted file mode 100644 index 81f9756a166bff..00000000000000 --- a/api/core/model_providers/models/embedding/xinference_embedding.py +++ /dev/null @@ -1,22 +0,0 @@ -from core.model_providers.error import LLMBadRequestError -from core.model_providers.providers.base import BaseModelProvider -from core.model_providers.models.embedding.base import BaseEmbedding -from core.third_party.langchain.embeddings.xinference_embedding import XinferenceEmbeddings - - -class XinferenceEmbedding(BaseEmbedding): - def __init__(self, model_provider: BaseModelProvider, name: str): - credentials = model_provider.get_model_credentials( - model_name=name, - model_type=self.type - ) - - client = XinferenceEmbeddings( - server_url=credentials['server_url'], - model_uid=credentials['model_uid'], - ) - - super().__init__(model_provider, client, name) - - def handle_exceptions(self, ex: Exception) -> Exception: - return LLMBadRequestError(f"Xinference embedding: {str(ex)}") diff --git a/api/core/model_providers/models/embedding/zhipuai_embedding.py b/api/core/model_providers/models/embedding/zhipuai_embedding.py deleted file mode 100644 index 97d5056c37de76..00000000000000 --- a/api/core/model_providers/models/embedding/zhipuai_embedding.py +++ /dev/null @@ -1,22 +0,0 @@ -from core.model_providers.error import LLMBadRequestError -from core.model_providers.providers.base import BaseModelProvider -from core.model_providers.models.embedding.base import BaseEmbedding -from core.third_party.langchain.embeddings.zhipuai_embedding import ZhipuAIEmbeddings - - -class ZhipuAIEmbedding(BaseEmbedding): - def __init__(self, model_provider: BaseModelProvider, name: str): - credentials = model_provider.get_model_credentials( - model_name=name, - model_type=self.type - ) - - client = ZhipuAIEmbeddings( - model=name, - **credentials, - ) - - super().__init__(model_provider, client, name) - - def handle_exceptions(self, ex: Exception) -> Exception: - return LLMBadRequestError(f"ZhipuAI embedding: {str(ex)}") diff --git a/api/core/model_providers/models/entity/message.py b/api/core/model_providers/models/entity/message.py deleted file mode 100644 index e3e49ba0f42883..00000000000000 --- a/api/core/model_providers/models/entity/message.py +++ /dev/null @@ -1,113 +0,0 @@ -import enum -from typing import Any, cast, Union, List, Dict - -from langchain.schema import HumanMessage, AIMessage, SystemMessage, BaseMessage, FunctionMessage -from pydantic import BaseModel - - -class LLMRunResult(BaseModel): - content: str - prompt_tokens: int - completion_tokens: int - source: list = None - function_call: dict = None - - -class MessageType(enum.Enum): - USER = 'user' - ASSISTANT = 'assistant' - SYSTEM = 'system' - - -class PromptMessageFileType(enum.Enum): - IMAGE = 'image' - - @staticmethod - def value_of(value): - for member in PromptMessageFileType: - if member.value == value: - return member - raise ValueError(f"No matching enum found for value '{value}'") - - - -class PromptMessageFile(BaseModel): - type: PromptMessageFileType - data: Any - - -class ImagePromptMessageFile(PromptMessageFile): - class DETAIL(enum.Enum): - LOW = 'low' - HIGH = 'high' - - type: PromptMessageFileType = PromptMessageFileType.IMAGE - detail: DETAIL = DETAIL.LOW - - -class PromptMessage(BaseModel): - type: MessageType = MessageType.USER - content: str = '' - files: list[PromptMessageFile] = [] - function_call: dict = None - - -class LCHumanMessageWithFiles(HumanMessage): - # content: Union[str, List[Union[str, Dict]]] - content: str - files: list[PromptMessageFile] - - -def to_lc_messages(messages: list[PromptMessage]): - lc_messages = [] - for message in messages: - if message.type == MessageType.USER: - if not message.files: - lc_messages.append(HumanMessage(content=message.content)) - else: - lc_messages.append(LCHumanMessageWithFiles(content=message.content, files=message.files)) - elif message.type == MessageType.ASSISTANT: - additional_kwargs = {} - if message.function_call: - additional_kwargs['function_call'] = message.function_call - lc_messages.append(AIMessage(content=message.content, additional_kwargs=additional_kwargs)) - elif message.type == MessageType.SYSTEM: - lc_messages.append(SystemMessage(content=message.content)) - - return lc_messages - - -def to_prompt_messages(messages: list[BaseMessage]): - prompt_messages = [] - for message in messages: - if isinstance(message, HumanMessage): - if isinstance(message, LCHumanMessageWithFiles): - prompt_messages.append(PromptMessage( - content=message.content, - type=MessageType.USER, - files=message.files - )) - else: - prompt_messages.append(PromptMessage(content=message.content, type=MessageType.USER)) - elif isinstance(message, AIMessage): - message_kwargs = { - 'content': message.content, - 'type': MessageType.ASSISTANT - } - - if 'function_call' in message.additional_kwargs: - message_kwargs['function_call'] = message.additional_kwargs['function_call'] - - prompt_messages.append(PromptMessage(**message_kwargs)) - elif isinstance(message, SystemMessage): - prompt_messages.append(PromptMessage(content=message.content, type=MessageType.SYSTEM)) - elif isinstance(message, FunctionMessage): - prompt_messages.append(PromptMessage(content=message.content, type=MessageType.USER)) - return prompt_messages - - -def str_to_prompt_messages(texts: list[str]): - prompt_messages = [] - for text in texts: - prompt_messages.append(PromptMessage(content=text)) - return prompt_messages diff --git a/api/core/model_providers/models/entity/model_params.py b/api/core/model_providers/models/entity/model_params.py deleted file mode 100644 index 0effa75e6e4aa5..00000000000000 --- a/api/core/model_providers/models/entity/model_params.py +++ /dev/null @@ -1,60 +0,0 @@ -import enum -from typing import Optional, TypeVar, Generic - -from langchain.load.serializable import Serializable -from pydantic import BaseModel - - -class ModelMode(enum.Enum): - COMPLETION = 'completion' - CHAT = 'chat' - - -class ModelType(enum.Enum): - TEXT_GENERATION = 'text-generation' - EMBEDDINGS = 'embeddings' - SPEECH_TO_TEXT = 'speech2text' - IMAGE = 'image' - VIDEO = 'video' - MODERATION = 'moderation' - RERANKING = 'reranking' - @staticmethod - def value_of(value): - for member in ModelType: - if member.value == value: - return member - raise ValueError(f"No matching enum found for value '{value}'") - - -class ModelKwargs(BaseModel): - max_tokens: Optional[int] - temperature: Optional[float] - top_p: Optional[float] - presence_penalty: Optional[float] - frequency_penalty: Optional[float] - - -class KwargRuleType(enum.Enum): - STRING = 'string' - INTEGER = 'integer' - FLOAT = 'float' - - -T = TypeVar('T') - - -class KwargRule(Generic[T], BaseModel): - enabled: bool = True - min: Optional[T] = None - max: Optional[T] = None - default: Optional[T] = None - alias: Optional[str] = None - precision: Optional[int] = None - - -class ModelKwargsRules(BaseModel): - max_tokens: KwargRule = KwargRule[int](enabled=False) - temperature: KwargRule = KwargRule[float](enabled=False) - top_p: KwargRule = KwargRule[float](enabled=False) - presence_penalty: KwargRule = KwargRule[float](enabled=False) - frequency_penalty: KwargRule = KwargRule[float](enabled=False) diff --git a/api/core/model_providers/models/entity/provider.py b/api/core/model_providers/models/entity/provider.py deleted file mode 100644 index d9a32ec7db96ee..00000000000000 --- a/api/core/model_providers/models/entity/provider.py +++ /dev/null @@ -1,11 +0,0 @@ -from enum import Enum - - -class ProviderQuotaUnit(Enum): - TIMES = 'times' - TOKENS = 'tokens' - - -class ModelFeature(Enum): - AGENT_THOUGHT = 'agent_thought' - VISION = 'vision' diff --git a/api/core/model_providers/models/llm/anthropic_model.py b/api/core/model_providers/models/llm/anthropic_model.py deleted file mode 100644 index 5ba7e29c1dab39..00000000000000 --- a/api/core/model_providers/models/llm/anthropic_model.py +++ /dev/null @@ -1,79 +0,0 @@ -import logging -from typing import List, Optional, Any - -import anthropic -from langchain.callbacks.manager import Callbacks -from langchain.schema import LLMResult - -from core.model_providers.error import LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, \ - LLMRateLimitError, LLMAuthorizationError -from core.model_providers.models.llm.base import BaseLLM -from core.model_providers.models.entity.message import PromptMessage, MessageType -from core.model_providers.models.entity.model_params import ModelMode, ModelKwargs -from core.third_party.langchain.llms.anthropic_llm import AnthropicLLM - - -class AnthropicModel(BaseLLM): - model_mode: ModelMode = ModelMode.CHAT - - def _init_client(self) -> Any: - provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, self.model_kwargs) - return AnthropicLLM( - model=self.name, - streaming=self.streaming, - callbacks=self.callbacks, - default_request_timeout=60, - **self.credentials, - **provider_model_kwargs - ) - - def _run(self, messages: List[PromptMessage], - stop: Optional[List[str]] = None, - callbacks: Callbacks = None, - **kwargs) -> LLMResult: - """ - run predict by prompt messages and stop words. - - :param messages: - :param stop: - :param callbacks: - :return: - """ - prompts = self._get_prompt_from_messages(messages) - return self._client.generate([prompts], stop, callbacks) - - def get_num_tokens(self, messages: List[PromptMessage]) -> int: - """ - get num tokens of prompt messages. - - :param messages: - :return: - """ - prompts = self._get_prompt_from_messages(messages) - return max(self._client.get_num_tokens_from_messages(prompts) - len(prompts), 0) - - def _set_model_kwargs(self, model_kwargs: ModelKwargs): - provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, model_kwargs) - for k, v in provider_model_kwargs.items(): - if hasattr(self.client, k): - setattr(self.client, k, v) - - def handle_exceptions(self, ex: Exception) -> Exception: - if isinstance(ex, anthropic.APIConnectionError): - logging.warning("Failed to connect to Anthropic API.") - return LLMAPIConnectionError(f"Anthropic: The server could not be reached, cause: {ex.__cause__}") - elif isinstance(ex, anthropic.RateLimitError): - return LLMRateLimitError("Anthropic: A 429 status code was received; we should back off a bit.") - elif isinstance(ex, anthropic.AuthenticationError): - return LLMAuthorizationError(f"Anthropic: {ex.message}") - elif isinstance(ex, anthropic.BadRequestError): - return LLMBadRequestError(f"Anthropic: {ex.message}") - elif isinstance(ex, anthropic.APIStatusError): - return LLMAPIUnavailableError(f"Anthropic: code: {ex.status_code}, cause: {ex.message}") - else: - return ex - - @property - def support_streaming(self): - return True - diff --git a/api/core/model_providers/models/llm/azure_openai_model.py b/api/core/model_providers/models/llm/azure_openai_model.py deleted file mode 100644 index 38977e3ad694d2..00000000000000 --- a/api/core/model_providers/models/llm/azure_openai_model.py +++ /dev/null @@ -1,171 +0,0 @@ -import decimal -import logging -from functools import wraps -from typing import List, Optional, Any - -import openai -from langchain.callbacks.manager import Callbacks -from langchain.schema import LLMResult - -from core.model_providers.providers.base import BaseModelProvider -from core.third_party.langchain.llms.azure_chat_open_ai import EnhanceAzureChatOpenAI -from core.third_party.langchain.llms.azure_open_ai import EnhanceAzureOpenAI -from core.model_providers.error import LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, \ - LLMRateLimitError, LLMAuthorizationError -from core.model_providers.models.llm.base import BaseLLM -from core.model_providers.models.entity.message import PromptMessage, MessageType -from core.model_providers.models.entity.model_params import ModelMode, ModelKwargs - -AZURE_OPENAI_API_VERSION = '2023-07-01-preview' - - -FUNCTION_CALL_MODELS = [ - 'gpt-4', - 'gpt-4-32k', - 'gpt-35-turbo', - 'gpt-35-turbo-16k', - 'gpt-4-1106-preview' -] - -class AzureOpenAIModel(BaseLLM): - def __init__(self, model_provider: BaseModelProvider, - name: str, - model_kwargs: ModelKwargs, - streaming: bool = False, - callbacks: Callbacks = None): - if name == 'text-davinci-003': - self.model_mode = ModelMode.COMPLETION - else: - self.model_mode = ModelMode.CHAT - super().__init__(model_provider, name, model_kwargs, streaming, callbacks) - - def _init_client(self) -> Any: - provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, self.model_kwargs) - if self.name == 'text-davinci-003': - client = EnhanceAzureOpenAI( - deployment_name=self.name, - streaming=self.streaming, - request_timeout=60, - openai_api_type='azure', - openai_api_version=AZURE_OPENAI_API_VERSION, - openai_api_key=self.credentials.get('openai_api_key'), - openai_api_base=self.credentials.get('openai_api_base'), - callbacks=self.callbacks, - **provider_model_kwargs - ) - else: - extra_model_kwargs = { - 'top_p': provider_model_kwargs.get('top_p'), - 'frequency_penalty': provider_model_kwargs.get('frequency_penalty'), - 'presence_penalty': provider_model_kwargs.get('presence_penalty'), - } - - client = EnhanceAzureChatOpenAI( - deployment_name=self.name, - temperature=provider_model_kwargs.get('temperature'), - max_tokens=provider_model_kwargs.get('max_tokens'), - model_kwargs=extra_model_kwargs, - streaming=self.streaming, - request_timeout=60, - openai_api_type='azure', - openai_api_version=AZURE_OPENAI_API_VERSION, - openai_api_key=self.credentials.get('openai_api_key'), - openai_api_base=self.credentials.get('openai_api_base'), - callbacks=self.callbacks, - ) - - return client - - def _run(self, messages: List[PromptMessage], - stop: Optional[List[str]] = None, - callbacks: Callbacks = None, - **kwargs) -> LLMResult: - """ - run predict by prompt messages and stop words. - - :param messages: - :param stop: - :param callbacks: - :return: - """ - prompts = self._get_prompt_from_messages(messages) - generate_kwargs = { - 'stop': stop, - 'callbacks': callbacks - } - - if isinstance(prompts, str): - generate_kwargs['prompts'] = [prompts] - else: - generate_kwargs['messages'] = [prompts] - - if 'functions' in kwargs: - generate_kwargs['functions'] = kwargs['functions'] - - return self._client.generate(**generate_kwargs) - - @property - def base_model_name(self) -> str: - """ - get base model name (not deployment) - - :return: str - """ - return self.credentials.get("base_model_name") - - def get_num_tokens(self, messages: List[PromptMessage]) -> int: - """ - get num tokens of prompt messages. - - :param messages: - :return: - """ - prompts = self._get_prompt_from_messages(messages) - if isinstance(prompts, str): - return self._client.get_num_tokens(prompts) - else: - return max(self._client.get_num_tokens_from_messages(prompts) - len(prompts), 0) - - def _set_model_kwargs(self, model_kwargs: ModelKwargs): - provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, model_kwargs) - if self.name == 'text-davinci-003': - for k, v in provider_model_kwargs.items(): - if hasattr(self.client, k): - setattr(self.client, k, v) - else: - extra_model_kwargs = { - 'top_p': provider_model_kwargs.get('top_p'), - 'frequency_penalty': provider_model_kwargs.get('frequency_penalty'), - 'presence_penalty': provider_model_kwargs.get('presence_penalty'), - } - - self.client.temperature = provider_model_kwargs.get('temperature') - self.client.max_tokens = provider_model_kwargs.get('max_tokens') - self.client.model_kwargs = extra_model_kwargs - - def handle_exceptions(self, ex: Exception) -> Exception: - if isinstance(ex, openai.error.InvalidRequestError): - logging.warning("Invalid request to Azure OpenAI API.") - return LLMBadRequestError(str(ex)) - elif isinstance(ex, openai.error.APIConnectionError): - logging.warning("Failed to connect to Azure OpenAI API.") - return LLMAPIConnectionError(ex.__class__.__name__ + ":" + str(ex)) - elif isinstance(ex, (openai.error.APIError, openai.error.ServiceUnavailableError, openai.error.Timeout)): - logging.warning("Azure OpenAI service unavailable.") - return LLMAPIUnavailableError(ex.__class__.__name__ + ":" + str(ex)) - elif isinstance(ex, openai.error.RateLimitError): - return LLMRateLimitError('Azure ' + str(ex)) - elif isinstance(ex, openai.error.AuthenticationError): - return LLMAuthorizationError('Azure ' + str(ex)) - elif isinstance(ex, openai.error.OpenAIError): - return LLMBadRequestError('Azure ' + ex.__class__.__name__ + ":" + str(ex)) - else: - return ex - - @property - def support_streaming(self): - return True - - @property - def support_function_call(self): - return self.base_model_name in FUNCTION_CALL_MODELS diff --git a/api/core/model_providers/models/llm/baichuan_model.py b/api/core/model_providers/models/llm/baichuan_model.py deleted file mode 100644 index e614547fa3d517..00000000000000 --- a/api/core/model_providers/models/llm/baichuan_model.py +++ /dev/null @@ -1,61 +0,0 @@ -from typing import List, Optional, Any - -from langchain.callbacks.manager import Callbacks -from langchain.schema import LLMResult - -from core.model_providers.error import LLMBadRequestError -from core.model_providers.models.llm.base import BaseLLM -from core.model_providers.models.entity.message import PromptMessage -from core.model_providers.models.entity.model_params import ModelMode, ModelKwargs -from core.third_party.langchain.llms.baichuan_llm import BaichuanChatLLM - - -class BaichuanModel(BaseLLM): - model_mode: ModelMode = ModelMode.CHAT - - def _init_client(self) -> Any: - provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, self.model_kwargs) - return BaichuanChatLLM( - streaming=self.streaming, - callbacks=self.callbacks, - **self.credentials, - **provider_model_kwargs - ) - - def _run(self, messages: List[PromptMessage], - stop: Optional[List[str]] = None, - callbacks: Callbacks = None, - **kwargs) -> LLMResult: - """ - run predict by prompt messages and stop words. - - :param messages: - :param stop: - :param callbacks: - :return: - """ - prompts = self._get_prompt_from_messages(messages) - return self._client.generate([prompts], stop, callbacks) - - def get_num_tokens(self, messages: List[PromptMessage]) -> int: - """ - get num tokens of prompt messages. - - :param messages: - :return: - """ - prompts = self._get_prompt_from_messages(messages) - return max(self._client.get_num_tokens_from_messages(prompts), 0) - - def _set_model_kwargs(self, model_kwargs: ModelKwargs): - provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, model_kwargs) - for k, v in provider_model_kwargs.items(): - if hasattr(self.client, k): - setattr(self.client, k, v) - - def handle_exceptions(self, ex: Exception) -> Exception: - return LLMBadRequestError(f"Baichuan: {str(ex)}") - - @property - def support_streaming(self): - return True diff --git a/api/core/model_providers/models/llm/base.py b/api/core/model_providers/models/llm/base.py deleted file mode 100644 index 9c81170156108f..00000000000000 --- a/api/core/model_providers/models/llm/base.py +++ /dev/null @@ -1,361 +0,0 @@ -from abc import abstractmethod -from typing import List, Optional, Any, Union -import decimal -import logging - -from langchain.callbacks.manager import Callbacks -from langchain.schema import LLMResult, BaseMessage, ChatGeneration - -from core.callback_handler.std_out_callback_handler import DifyStreamingStdOutCallbackHandler, DifyStdOutCallbackHandler -from core.helper import moderation -from core.model_providers.models.base import BaseProviderModel -from core.model_providers.models.entity.message import PromptMessage, MessageType, LLMRunResult, to_lc_messages -from core.model_providers.models.entity.model_params import ModelType, ModelKwargs, ModelMode, ModelKwargsRules -from core.model_providers.providers.base import BaseModelProvider -from core.third_party.langchain.llms.fake import FakeLLM - -logger = logging.getLogger(__name__) - - -class BaseLLM(BaseProviderModel): - model_mode: ModelMode = ModelMode.COMPLETION - name: str - model_kwargs: ModelKwargs - credentials: dict - streaming: bool = False - type: ModelType = ModelType.TEXT_GENERATION - deduct_quota: bool = True - - def __init__(self, model_provider: BaseModelProvider, - name: str, - model_kwargs: ModelKwargs, - streaming: bool = False, - callbacks: Callbacks = None): - self.name = name - self.model_rules = model_provider.get_model_parameter_rules(name, self.type) - self.model_kwargs = model_kwargs if model_kwargs else ModelKwargs( - max_tokens=None, - temperature=None, - top_p=None, - presence_penalty=None, - frequency_penalty=None - ) - self.credentials = model_provider.get_model_credentials( - model_name=name, - model_type=self.type - ) - self.streaming = streaming - - if streaming: - default_callback = DifyStreamingStdOutCallbackHandler() - else: - default_callback = DifyStdOutCallbackHandler() - - if not callbacks: - callbacks = [default_callback] - else: - callbacks.append(default_callback) - - self.callbacks = callbacks - - client = self._init_client() - super().__init__(model_provider, client) - - @abstractmethod - def _init_client(self) -> Any: - raise NotImplementedError - - @property - def base_model_name(self) -> str: - """ - get llm base model name - - :return: str - """ - return self.name - - @property - def price_config(self) -> dict: - def get_or_default(): - default_price_config = { - 'prompt': decimal.Decimal('0'), - 'completion': decimal.Decimal('0'), - 'unit': decimal.Decimal('0'), - 'currency': 'USD' - } - rules = self.model_provider.get_rules() - price_config = rules['price_config'][ - self.base_model_name] if 'price_config' in rules else default_price_config - price_config = { - 'prompt': decimal.Decimal(price_config['prompt']), - 'completion': decimal.Decimal(price_config['completion']), - 'unit': decimal.Decimal(price_config['unit']), - 'currency': price_config['currency'] - } - return price_config - - self._price_config = self._price_config if hasattr(self, '_price_config') else get_or_default() - - logger.debug(f"model: {self.name} price_config: {self._price_config}") - return self._price_config - - def run(self, messages: List[PromptMessage], - stop: Optional[List[str]] = None, - callbacks: Callbacks = None, - **kwargs) -> LLMRunResult: - """ - run predict by prompt messages and stop words. - - :param messages: - :param stop: - :param callbacks: - :return: - """ - moderation_result = moderation.check_moderation( - self.model_provider, - "\n".join([message.content for message in messages]) - ) - - if not moderation_result: - kwargs['fake_response'] = "I apologize for any confusion, " \ - "but I'm an AI assistant to be helpful, harmless, and honest." - - if self.deduct_quota: - self.model_provider.check_quota_over_limit() - - if not callbacks: - callbacks = self.callbacks - else: - callbacks.extend(self.callbacks) - - if 'fake_response' in kwargs and kwargs['fake_response']: - prompts = self._get_prompt_from_messages(messages, ModelMode.CHAT) - fake_llm = FakeLLM( - response=kwargs['fake_response'], - num_token_func=self.get_num_tokens, - streaming=self.streaming, - callbacks=callbacks - ) - result = fake_llm.generate([prompts]) - else: - try: - result = self._run( - messages=messages, - stop=stop, - callbacks=callbacks if not (self.streaming and not self.support_streaming) else None, - **kwargs - ) - except Exception as ex: - raise self.handle_exceptions(ex) - - function_call = None - if isinstance(result.generations[0][0], ChatGeneration): - completion_content = result.generations[0][0].message.content - if 'function_call' in result.generations[0][0].message.additional_kwargs: - function_call = result.generations[0][0].message.additional_kwargs.get('function_call') - else: - completion_content = result.generations[0][0].text - - if self.streaming and not self.support_streaming: - # use FakeLLM to simulate streaming when current model not support streaming but streaming is True - prompts = self._get_prompt_from_messages(messages, ModelMode.CHAT) - fake_llm = FakeLLM( - response=completion_content, - num_token_func=self.get_num_tokens, - streaming=self.streaming, - callbacks=callbacks - ) - fake_llm.generate([prompts]) - - if result.llm_output and result.llm_output['token_usage']: - prompt_tokens = result.llm_output['token_usage']['prompt_tokens'] - completion_tokens = result.llm_output['token_usage']['completion_tokens'] - total_tokens = result.llm_output['token_usage']['total_tokens'] - else: - prompt_tokens = self.get_num_tokens(messages) - completion_tokens = self.get_num_tokens( - [PromptMessage(content=completion_content, type=MessageType.ASSISTANT)]) - total_tokens = prompt_tokens + completion_tokens - - self.model_provider.update_last_used() - - if self.deduct_quota: - self.model_provider.deduct_quota(total_tokens) - - return LLMRunResult( - content=completion_content, - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens, - function_call=function_call - ) - - @abstractmethod - def _run(self, messages: List[PromptMessage], - stop: Optional[List[str]] = None, - callbacks: Callbacks = None, - **kwargs) -> LLMResult: - """ - run predict by prompt messages and stop words. - - :param messages: - :param stop: - :param callbacks: - :return: - """ - raise NotImplementedError - - @abstractmethod - def get_num_tokens(self, messages: List[PromptMessage]) -> int: - """ - get num tokens of prompt messages. - - :param messages: - :return: - """ - raise NotImplementedError - - def calc_tokens_price(self, tokens: int, message_type: MessageType) -> decimal.Decimal: - """ - calc tokens total price. - - :param tokens: - :param message_type: - :return: - """ - if message_type == MessageType.USER or message_type == MessageType.SYSTEM: - unit_price = self.price_config['prompt'] - else: - unit_price = self.price_config['completion'] - unit = self.get_price_unit(message_type) - - total_price = tokens * unit_price * unit - total_price = total_price.quantize(decimal.Decimal('0.0000001'), rounding=decimal.ROUND_HALF_UP) - logging.debug(f"tokens={tokens}, unit_price={unit_price}, unit={unit}, total_price:{total_price}") - return total_price - - def get_tokens_unit_price(self, message_type: MessageType) -> decimal.Decimal: - """ - get token price. - - :param message_type: - :return: decimal.Decimal('0.0001') - """ - if message_type == MessageType.USER or message_type == MessageType.SYSTEM: - unit_price = self.price_config['prompt'] - else: - unit_price = self.price_config['completion'] - unit_price = unit_price.quantize(decimal.Decimal('0.0001'), rounding=decimal.ROUND_HALF_UP) - logging.debug(f"unit_price={unit_price}") - return unit_price - - def get_price_unit(self, message_type: MessageType) -> decimal.Decimal: - """ - get price unit. - - :param message_type: - :return: decimal.Decimal('0.000001') - """ - if message_type == MessageType.USER or message_type == MessageType.SYSTEM: - price_unit = self.price_config['unit'] - else: - price_unit = self.price_config['unit'] - - price_unit = price_unit.quantize(decimal.Decimal('0.000001'), rounding=decimal.ROUND_HALF_UP) - logging.debug(f"price_unit={price_unit}") - return price_unit - - def get_currency(self) -> str: - """ - get token currency. - - :return: get from price config, default 'USD' - """ - currency = self.price_config['currency'] - return currency - - def get_model_kwargs(self): - return self.model_kwargs - - def set_model_kwargs(self, model_kwargs: ModelKwargs): - self.model_kwargs = model_kwargs - self._set_model_kwargs(model_kwargs) - - @abstractmethod - def _set_model_kwargs(self, model_kwargs: ModelKwargs): - raise NotImplementedError - - @abstractmethod - def handle_exceptions(self, ex: Exception) -> Exception: - """ - Handle llm run exceptions. - - :param ex: - :return: - """ - raise NotImplementedError - - def add_callbacks(self, callbacks: Callbacks): - """ - Add callbacks to client. - - :param callbacks: - :return: - """ - if not self.client.callbacks: - self.client.callbacks = callbacks - else: - self.client.callbacks.extend(callbacks) - - @property - def support_streaming(self): - return False - - @property - def support_function_call(self): - return False - - def _get_prompt_from_messages(self, messages: List[PromptMessage], - model_mode: Optional[ModelMode] = None) -> Union[str , List[BaseMessage]]: - if not model_mode: - model_mode = self.model_mode - - if model_mode == ModelMode.COMPLETION: - if len(messages) == 0: - return '' - - return messages[0].content - else: - if len(messages) == 0: - return [] - - return to_lc_messages(messages) - - def _to_model_kwargs_input(self, model_rules: ModelKwargsRules, model_kwargs: ModelKwargs) -> dict: - """ - convert model kwargs to provider model kwargs. - - :param model_rules: - :param model_kwargs: - :return: - """ - model_kwargs_input = {} - for key, value in model_kwargs.dict().items(): - rule = getattr(model_rules, key) - if not rule.enabled: - continue - - if rule.alias: - key = rule.alias - - if rule.default is not None and value is None: - value = rule.default - - if rule.min is not None: - value = max(value, rule.min) - - if rule.max is not None: - value = min(value, rule.max) - - model_kwargs_input[key] = value - - return model_kwargs_input diff --git a/api/core/model_providers/models/llm/chatglm_model.py b/api/core/model_providers/models/llm/chatglm_model.py deleted file mode 100644 index cb6e98d5e27f0a..00000000000000 --- a/api/core/model_providers/models/llm/chatglm_model.py +++ /dev/null @@ -1,102 +0,0 @@ -import logging -from typing import List, Optional, Any - -import openai -from langchain.callbacks.manager import Callbacks -from langchain.schema import LLMResult, get_buffer_string - -from core.model_providers.error import LLMBadRequestError, LLMRateLimitError, LLMAuthorizationError, \ - LLMAPIUnavailableError, LLMAPIConnectionError -from core.model_providers.models.llm.base import BaseLLM -from core.model_providers.models.entity.message import PromptMessage, MessageType -from core.model_providers.models.entity.model_params import ModelMode, ModelKwargs -from core.third_party.langchain.llms.chat_open_ai import EnhanceChatOpenAI - - -class ChatGLMModel(BaseLLM): - model_mode: ModelMode = ModelMode.CHAT - - def _init_client(self) -> Any: - provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, self.model_kwargs) - - extra_model_kwargs = { - 'top_p': provider_model_kwargs.get('top_p') - } - - if provider_model_kwargs.get('max_length') is not None: - extra_model_kwargs['max_length'] = provider_model_kwargs.get('max_length') - - client = EnhanceChatOpenAI( - model_name=self.name, - temperature=provider_model_kwargs.get('temperature'), - max_tokens=provider_model_kwargs.get('max_tokens'), - model_kwargs=extra_model_kwargs, - streaming=self.streaming, - callbacks=self.callbacks, - request_timeout=60, - openai_api_key="1", - openai_api_base=self.credentials['api_base'] + '/v1' - ) - - return client - - def _run(self, messages: List[PromptMessage], - stop: Optional[List[str]] = None, - callbacks: Callbacks = None, - **kwargs) -> LLMResult: - """ - run predict by prompt messages and stop words. - - :param messages: - :param stop: - :param callbacks: - :return: - """ - prompts = self._get_prompt_from_messages(messages) - return self._client.generate([prompts], stop, callbacks) - - def get_num_tokens(self, messages: List[PromptMessage]) -> int: - """ - get num tokens of prompt messages. - - :param messages: - :return: - """ - prompts = self._get_prompt_from_messages(messages) - return max(sum([self._client.get_num_tokens(get_buffer_string([m])) for m in prompts]) - len(prompts), 0) - - def get_currency(self): - return 'RMB' - - def _set_model_kwargs(self, model_kwargs: ModelKwargs): - provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, model_kwargs) - extra_model_kwargs = { - 'top_p': provider_model_kwargs.get('top_p') - } - - self.client.temperature = provider_model_kwargs.get('temperature') - self.client.max_tokens = provider_model_kwargs.get('max_tokens') - self.client.model_kwargs = extra_model_kwargs - - def handle_exceptions(self, ex: Exception) -> Exception: - if isinstance(ex, openai.error.InvalidRequestError): - logging.warning("Invalid request to ChatGLM API.") - return LLMBadRequestError(str(ex)) - elif isinstance(ex, openai.error.APIConnectionError): - logging.warning("Failed to connect to ChatGLM API.") - return LLMAPIConnectionError(ex.__class__.__name__ + ":" + str(ex)) - elif isinstance(ex, (openai.error.APIError, openai.error.ServiceUnavailableError, openai.error.Timeout)): - logging.warning("ChatGLM service unavailable.") - return LLMAPIUnavailableError(ex.__class__.__name__ + ":" + str(ex)) - elif isinstance(ex, openai.error.RateLimitError): - return LLMRateLimitError(str(ex)) - elif isinstance(ex, openai.error.AuthenticationError): - return LLMAuthorizationError(str(ex)) - elif isinstance(ex, openai.error.OpenAIError): - return LLMBadRequestError(ex.__class__.__name__ + ":" + str(ex)) - else: - return ex - - @classmethod - def support_streaming(cls): - return True \ No newline at end of file diff --git a/api/core/model_providers/models/llm/huggingface_hub_model.py b/api/core/model_providers/models/llm/huggingface_hub_model.py deleted file mode 100644 index ca3f1d2cf72657..00000000000000 --- a/api/core/model_providers/models/llm/huggingface_hub_model.py +++ /dev/null @@ -1,84 +0,0 @@ -from typing import List, Optional, Any - -from langchain.callbacks.manager import Callbacks -from langchain.schema import LLMResult - -from core.model_providers.error import LLMBadRequestError -from core.model_providers.models.llm.base import BaseLLM -from core.model_providers.models.entity.message import PromptMessage -from core.model_providers.models.entity.model_params import ModelMode, ModelKwargs -from core.third_party.langchain.llms.huggingface_endpoint_llm import HuggingFaceEndpointLLM -from core.third_party.langchain.llms.huggingface_hub_llm import HuggingFaceHubLLM - - -class HuggingfaceHubModel(BaseLLM): - model_mode: ModelMode = ModelMode.COMPLETION - - def _init_client(self) -> Any: - provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, self.model_kwargs) - if self.credentials['huggingfacehub_api_type'] == 'inference_endpoints': - streaming = self.streaming - - if 'baichuan' in self.name.lower(): - streaming = False - - client = HuggingFaceEndpointLLM( - endpoint_url=self.credentials['huggingfacehub_endpoint_url'], - task=self.credentials['task_type'], - model_kwargs=provider_model_kwargs, - huggingfacehub_api_token=self.credentials['huggingfacehub_api_token'], - callbacks=self.callbacks, - streaming=streaming - ) - else: - client = HuggingFaceHubLLM( - repo_id=self.name, - task=self.credentials['task_type'], - model_kwargs=provider_model_kwargs, - huggingfacehub_api_token=self.credentials['huggingfacehub_api_token'], - callbacks=self.callbacks, - ) - - return client - - def _run(self, messages: List[PromptMessage], - stop: Optional[List[str]] = None, - callbacks: Callbacks = None, - **kwargs) -> LLMResult: - """ - run predict by prompt messages and stop words. - - :param messages: - :param stop: - :param callbacks: - :return: - """ - prompts = self._get_prompt_from_messages(messages) - return self._client.generate([prompts], stop, callbacks) - - def get_num_tokens(self, messages: List[PromptMessage]) -> int: - """ - get num tokens of prompt messages. - - :param messages: - :return: - """ - prompts = self._get_prompt_from_messages(messages) - return self._client.get_num_tokens(prompts) - - def _set_model_kwargs(self, model_kwargs: ModelKwargs): - provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, model_kwargs) - self.client.model_kwargs = provider_model_kwargs - - def handle_exceptions(self, ex: Exception) -> Exception: - return LLMBadRequestError(f"Huggingface Hub: {str(ex)}") - - @property - def support_streaming(self): - if self.credentials['huggingfacehub_api_type'] == 'inference_endpoints': - if 'baichuan' in self.name.lower(): - return False - - return True - else: - return False diff --git a/api/core/model_providers/models/llm/localai_model.py b/api/core/model_providers/models/llm/localai_model.py deleted file mode 100644 index 072b2f14b8e4f5..00000000000000 --- a/api/core/model_providers/models/llm/localai_model.py +++ /dev/null @@ -1,131 +0,0 @@ -import logging -from typing import List, Optional, Any - -import openai -from langchain.callbacks.manager import Callbacks -from langchain.schema import LLMResult, get_buffer_string - -from core.model_providers.error import LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, \ - LLMRateLimitError, LLMAuthorizationError -from core.model_providers.providers.base import BaseModelProvider -from core.third_party.langchain.llms.chat_open_ai import EnhanceChatOpenAI -from core.third_party.langchain.llms.open_ai import EnhanceOpenAI -from core.model_providers.models.llm.base import BaseLLM -from core.model_providers.models.entity.message import PromptMessage -from core.model_providers.models.entity.model_params import ModelMode, ModelKwargs - - -class LocalAIModel(BaseLLM): - def __init__(self, model_provider: BaseModelProvider, - name: str, - model_kwargs: ModelKwargs, - streaming: bool = False, - callbacks: Callbacks = None): - credentials = model_provider.get_model_credentials( - model_name=name, - model_type=self.type - ) - - if credentials['completion_type'] == 'chat_completion': - self.model_mode = ModelMode.CHAT - else: - self.model_mode = ModelMode.COMPLETION - - super().__init__(model_provider, name, model_kwargs, streaming, callbacks) - - def _init_client(self) -> Any: - provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, self.model_kwargs) - if self.model_mode == ModelMode.COMPLETION: - client = EnhanceOpenAI( - model_name=self.name, - streaming=self.streaming, - callbacks=self.callbacks, - request_timeout=60, - openai_api_key="1", - openai_api_base=self.credentials['server_url'] + '/v1', - **provider_model_kwargs - ) - else: - extra_model_kwargs = { - 'top_p': provider_model_kwargs.get('top_p') - } - - client = EnhanceChatOpenAI( - model_name=self.name, - temperature=provider_model_kwargs.get('temperature'), - max_tokens=provider_model_kwargs.get('max_tokens'), - model_kwargs=extra_model_kwargs, - streaming=self.streaming, - callbacks=self.callbacks, - request_timeout=60, - openai_api_key="1", - openai_api_base=self.credentials['server_url'] + '/v1' - ) - - return client - - def _run(self, messages: List[PromptMessage], - stop: Optional[List[str]] = None, - callbacks: Callbacks = None, - **kwargs) -> LLMResult: - """ - run predict by prompt messages and stop words. - - :param messages: - :param stop: - :param callbacks: - :return: - """ - prompts = self._get_prompt_from_messages(messages) - return self._client.generate([prompts], stop, callbacks) - - def get_num_tokens(self, messages: List[PromptMessage]) -> int: - """ - get num tokens of prompt messages. - - :param messages: - :return: - """ - prompts = self._get_prompt_from_messages(messages) - if isinstance(prompts, str): - return self._client.get_num_tokens(prompts) - else: - return max(sum([self._client.get_num_tokens(get_buffer_string([m])) for m in prompts]) - len(prompts), 0) - - def _set_model_kwargs(self, model_kwargs: ModelKwargs): - provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, model_kwargs) - if self.model_mode == ModelMode.COMPLETION: - for k, v in provider_model_kwargs.items(): - if hasattr(self.client, k): - setattr(self.client, k, v) - else: - extra_model_kwargs = { - 'top_p': provider_model_kwargs.get('top_p') - } - - self.client.temperature = provider_model_kwargs.get('temperature') - self.client.max_tokens = provider_model_kwargs.get('max_tokens') - self.client.model_kwargs = extra_model_kwargs - - def handle_exceptions(self, ex: Exception) -> Exception: - if isinstance(ex, openai.error.InvalidRequestError): - logging.warning("Invalid request to LocalAI API.") - return LLMBadRequestError(str(ex)) - elif isinstance(ex, openai.error.APIConnectionError): - logging.warning("Failed to connect to LocalAI API.") - return LLMAPIConnectionError(ex.__class__.__name__ + ":" + str(ex)) - elif isinstance(ex, (openai.error.APIError, openai.error.ServiceUnavailableError, openai.error.Timeout)): - logging.warning("LocalAI service unavailable.") - return LLMAPIUnavailableError(ex.__class__.__name__ + ":" + str(ex)) - elif isinstance(ex, openai.error.RateLimitError): - return LLMRateLimitError(str(ex)) - elif isinstance(ex, openai.error.AuthenticationError): - return LLMAuthorizationError(str(ex)) - elif isinstance(ex, openai.error.OpenAIError): - return LLMBadRequestError(ex.__class__.__name__ + ":" + str(ex)) - else: - return ex - - @classmethod - def support_streaming(cls): - return True diff --git a/api/core/model_providers/models/llm/minimax_model.py b/api/core/model_providers/models/llm/minimax_model.py deleted file mode 100644 index 83fca8fd75fca4..00000000000000 --- a/api/core/model_providers/models/llm/minimax_model.py +++ /dev/null @@ -1,68 +0,0 @@ -from typing import List, Optional, Any - -from langchain.callbacks.manager import Callbacks -from langchain.schema import LLMResult - -from core.model_providers.error import LLMBadRequestError -from core.model_providers.models.llm.base import BaseLLM -from core.model_providers.models.entity.message import PromptMessage -from core.model_providers.models.entity.model_params import ModelMode, ModelKwargs -from core.third_party.langchain.llms.minimax_llm import MinimaxChatLLM - - -class MinimaxModel(BaseLLM): - model_mode: ModelMode = ModelMode.CHAT - - def _init_client(self) -> Any: - provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, self.model_kwargs) - return MinimaxChatLLM( - model=self.name, - streaming=self.streaming, - callbacks=self.callbacks, - **self.credentials, - **provider_model_kwargs - ) - - def _run(self, messages: List[PromptMessage], - stop: Optional[List[str]] = None, - callbacks: Callbacks = None, - **kwargs) -> LLMResult: - """ - run predict by prompt messages and stop words. - - :param messages: - :param stop: - :param callbacks: - :return: - """ - prompts = self._get_prompt_from_messages(messages) - return self._client.generate([prompts], stop, callbacks) - - def get_num_tokens(self, messages: List[PromptMessage]) -> int: - """ - get num tokens of prompt messages. - - :param messages: - :return: - """ - prompts = self._get_prompt_from_messages(messages) - return max(self._client.get_num_tokens_from_messages(prompts), 0) - - def get_currency(self): - return 'RMB' - - def _set_model_kwargs(self, model_kwargs: ModelKwargs): - provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, model_kwargs) - for k, v in provider_model_kwargs.items(): - if hasattr(self.client, k): - setattr(self.client, k, v) - - def handle_exceptions(self, ex: Exception) -> Exception: - if isinstance(ex, ValueError): - return LLMBadRequestError(f"Minimax: {str(ex)}") - else: - return ex - - @property - def support_streaming(self): - return True diff --git a/api/core/model_providers/models/llm/openai_model.py b/api/core/model_providers/models/llm/openai_model.py deleted file mode 100644 index 08331885b8daad..00000000000000 --- a/api/core/model_providers/models/llm/openai_model.py +++ /dev/null @@ -1,215 +0,0 @@ -import logging -from typing import List, Optional, Any - -import openai -from langchain.callbacks.manager import Callbacks -from langchain.schema import LLMResult - -from core.model_providers.providers.base import BaseModelProvider -from core.third_party.langchain.llms.chat_open_ai import EnhanceChatOpenAI -from core.model_providers.error import LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, \ - LLMRateLimitError, LLMAuthorizationError, ModelCurrentlyNotSupportError -from core.third_party.langchain.llms.open_ai import EnhanceOpenAI -from core.model_providers.models.llm.base import BaseLLM -from core.model_providers.models.entity.message import PromptMessage, MessageType -from core.model_providers.models.entity.model_params import ModelMode, ModelKwargs -from models.provider import ProviderType, ProviderQuotaType - -COMPLETION_MODELS = [ - 'gpt-3.5-turbo-instruct', # 4,096 tokens - 'text-davinci-003', # 4,097 tokens -] - -CHAT_MODELS = [ - 'gpt-4-1106-preview', # 128,000 tokens - 'gpt-4-vision-preview', # 128,000 tokens - 'gpt-4', # 8,192 tokens - 'gpt-4-32k', # 32,768 tokens - 'gpt-3.5-turbo-1106', # 16,384 tokens - 'gpt-3.5-turbo', # 4,096 tokens - 'gpt-3.5-turbo-16k', # 16,384 tokens -] - -MODEL_MAX_TOKENS = { - 'gpt-4-1106-preview': 128000, - 'gpt-4-vision-preview': 128000, - 'gpt-4': 8192, - 'gpt-4-32k': 32768, - 'gpt-3.5-turbo-1106': 16384, - 'gpt-3.5-turbo': 4096, - 'gpt-3.5-turbo-instruct': 4097, - 'gpt-3.5-turbo-16k': 16384, - 'text-davinci-003': 4097, -} - -FUNCTION_CALL_MODELS = [ - 'gpt-4-1106-preview', - 'gpt-4', - 'gpt-4-32k', - 'gpt-3.5-turbo-1106', - 'gpt-3.5-turbo', - 'gpt-3.5-turbo-16k' -] - - -class OpenAIModel(BaseLLM): - def __init__(self, model_provider: BaseModelProvider, - name: str, - model_kwargs: ModelKwargs, - streaming: bool = False, - callbacks: Callbacks = None): - if name in COMPLETION_MODELS: - self.model_mode = ModelMode.COMPLETION - else: - self.model_mode = ModelMode.CHAT - - super().__init__(model_provider, name, model_kwargs, streaming, callbacks) - - def _init_client(self) -> Any: - provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, self.model_kwargs) - if self.name in COMPLETION_MODELS: - client = EnhanceOpenAI( - model_name=self.name, - streaming=self.streaming, - callbacks=self.callbacks, - request_timeout=60, - **self.credentials, - **provider_model_kwargs - ) - else: - # Fine-tuning is currently only available for the following base models: - # davinci, curie, babbage, and ada. - # This means that except for the fixed `completion` model, - # all other fine-tuned models are `completion` models. - extra_model_kwargs = { - 'top_p': provider_model_kwargs.get('top_p'), - 'frequency_penalty': provider_model_kwargs.get('frequency_penalty'), - 'presence_penalty': provider_model_kwargs.get('presence_penalty'), - } - - client = EnhanceChatOpenAI( - model_name=self.name, - temperature=provider_model_kwargs.get('temperature'), - max_tokens=provider_model_kwargs.get('max_tokens'), - model_kwargs=extra_model_kwargs, - streaming=self.streaming, - callbacks=self.callbacks, - request_timeout=60, - **self.credentials - ) - - return client - - def _run(self, messages: List[PromptMessage], - stop: Optional[List[str]] = None, - callbacks: Callbacks = None, - **kwargs) -> LLMResult: - """ - run predict by prompt messages and stop words. - - :param messages: - :param stop: - :param callbacks: - :return: - """ - if self.name.startswith('gpt-4') \ - and self.model_provider.provider.provider_type == ProviderType.SYSTEM.value \ - and self.model_provider.provider.quota_type == ProviderQuotaType.TRIAL.value: - raise ModelCurrentlyNotSupportError("Dify Hosted OpenAI GPT-4 currently not support.") - - prompts = self._get_prompt_from_messages(messages) - - generate_kwargs = { - 'stop': stop, - 'callbacks': callbacks - } - - if isinstance(prompts, str): - generate_kwargs['prompts'] = [prompts] - else: - generate_kwargs['messages'] = [prompts] - - if 'functions' in kwargs: - generate_kwargs['functions'] = kwargs['functions'] - - return self._client.generate(**generate_kwargs) - - def get_num_tokens(self, messages: List[PromptMessage]) -> int: - """ - get num tokens of prompt messages. - - :param messages: - :return: - """ - prompts = self._get_prompt_from_messages(messages) - if isinstance(prompts, str): - return self._client.get_num_tokens(prompts) - else: - return max(self._client.get_num_tokens_from_messages(prompts) - len(prompts), 0) - - def _set_model_kwargs(self, model_kwargs: ModelKwargs): - provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, model_kwargs) - if self.name in COMPLETION_MODELS: - for k, v in provider_model_kwargs.items(): - if hasattr(self.client, k): - setattr(self.client, k, v) - else: - extra_model_kwargs = { - 'top_p': provider_model_kwargs.get('top_p'), - 'frequency_penalty': provider_model_kwargs.get('frequency_penalty'), - 'presence_penalty': provider_model_kwargs.get('presence_penalty'), - } - - self.client.temperature = provider_model_kwargs.get('temperature') - self.client.max_tokens = provider_model_kwargs.get('max_tokens') - self.client.model_kwargs = extra_model_kwargs - - def handle_exceptions(self, ex: Exception) -> Exception: - if isinstance(ex, openai.error.InvalidRequestError): - logging.warning("Invalid request to OpenAI API.") - return LLMBadRequestError(str(ex)) - elif isinstance(ex, openai.error.APIConnectionError): - logging.warning("Failed to connect to OpenAI API.") - return LLMAPIConnectionError(ex.__class__.__name__ + ":" + str(ex)) - elif isinstance(ex, (openai.error.APIError, openai.error.ServiceUnavailableError, openai.error.Timeout)): - logging.warning("OpenAI service unavailable.") - return LLMAPIUnavailableError(ex.__class__.__name__ + ":" + str(ex)) - elif isinstance(ex, openai.error.RateLimitError): - return LLMRateLimitError(str(ex)) - elif isinstance(ex, openai.error.AuthenticationError): - return LLMAuthorizationError(str(ex)) - elif isinstance(ex, openai.error.OpenAIError): - return LLMBadRequestError(ex.__class__.__name__ + ":" + str(ex)) - else: - return ex - - @property - def support_streaming(self): - return True - - @property - def support_function_call(self): - return self.name in FUNCTION_CALL_MODELS - - # def is_model_valid_or_raise(self): - # """ - # check is a valid model. - # - # :return: - # """ - # credentials = self._model_provider.get_credentials() - # - # try: - # result = openai.Model.retrieve( - # id=self.name, - # api_key=credentials.get('openai_api_key'), - # request_timeout=60 - # ) - # - # if 'id' not in result or result['id'] != self.name: - # raise LLMNotExistsError(f"OpenAI Model {self.name} not exists.") - # except openai.error.OpenAIError as e: - # raise LLMNotExistsError(f"OpenAI Model {self.name} not exists, cause: {e.__class__.__name__}:{str(e)}") - # except Exception as e: - # logging.exception("OpenAI Model retrieve failed.") - # raise e diff --git a/api/core/model_providers/models/llm/openllm_model.py b/api/core/model_providers/models/llm/openllm_model.py deleted file mode 100644 index c92877fd8b6ae6..00000000000000 --- a/api/core/model_providers/models/llm/openllm_model.py +++ /dev/null @@ -1,56 +0,0 @@ -from typing import List, Optional, Any - -from langchain.callbacks.manager import Callbacks -from langchain.schema import LLMResult - -from core.model_providers.error import LLMBadRequestError -from core.model_providers.models.llm.base import BaseLLM -from core.model_providers.models.entity.message import PromptMessage -from core.model_providers.models.entity.model_params import ModelMode, ModelKwargs -from core.third_party.langchain.llms.openllm import OpenLLM - - -class OpenLLMModel(BaseLLM): - model_mode: ModelMode = ModelMode.COMPLETION - - def _init_client(self) -> Any: - self.provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, self.model_kwargs) - - client = OpenLLM( - server_url=self.credentials.get('server_url'), - callbacks=self.callbacks, - llm_kwargs=self.provider_model_kwargs - ) - - return client - - def _run(self, messages: List[PromptMessage], - stop: Optional[List[str]] = None, - callbacks: Callbacks = None, - **kwargs) -> LLMResult: - """ - run predict by prompt messages and stop words. - - :param messages: - :param stop: - :param callbacks: - :return: - """ - prompts = self._get_prompt_from_messages(messages) - return self._client.generate([prompts], stop, callbacks) - - def get_num_tokens(self, messages: List[PromptMessage]) -> int: - """ - get num tokens of prompt messages. - - :param messages: - :return: - """ - prompts = self._get_prompt_from_messages(messages) - return max(self._client.get_num_tokens(prompts), 0) - - def _set_model_kwargs(self, model_kwargs: ModelKwargs): - pass - - def handle_exceptions(self, ex: Exception) -> Exception: - return LLMBadRequestError(f"OpenLLM: {str(ex)}") diff --git a/api/core/model_providers/models/llm/replicate_model.py b/api/core/model_providers/models/llm/replicate_model.py deleted file mode 100644 index becc212ad9f8cf..00000000000000 --- a/api/core/model_providers/models/llm/replicate_model.py +++ /dev/null @@ -1,96 +0,0 @@ -import decimal -from functools import wraps -from typing import List, Optional, Any - -from langchain.callbacks.manager import Callbacks -from langchain.schema import LLMResult, get_buffer_string -from replicate.exceptions import ReplicateError, ModelError - -from core.model_providers.providers.base import BaseModelProvider -from core.model_providers.error import LLMBadRequestError -from core.third_party.langchain.llms.replicate_llm import EnhanceReplicate -from core.model_providers.models.llm.base import BaseLLM -from core.model_providers.models.entity.message import PromptMessage, MessageType -from core.model_providers.models.entity.model_params import ModelMode, ModelKwargs - - -class ReplicateModel(BaseLLM): - def __init__(self, model_provider: BaseModelProvider, - name: str, - model_kwargs: ModelKwargs, - streaming: bool = False, - callbacks: Callbacks = None): - self.model_mode = ModelMode.CHAT if name.endswith('-chat') else ModelMode.COMPLETION - - super().__init__(model_provider, name, model_kwargs, streaming, callbacks) - - def _init_client(self) -> Any: - provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, self.model_kwargs) - - return EnhanceReplicate( - model=self.name + ':' + self.credentials.get('model_version'), - input=provider_model_kwargs, - streaming=self.streaming, - replicate_api_token=self.credentials.get('replicate_api_token'), - callbacks=self.callbacks, - ) - - def _run(self, messages: List[PromptMessage], - stop: Optional[List[str]] = None, - callbacks: Callbacks = None, - **kwargs) -> LLMResult: - """ - run predict by prompt messages and stop words. - - :param messages: - :param stop: - :param callbacks: - :return: - """ - prompts = self._get_prompt_from_messages(messages) - extra_kwargs = {} - if isinstance(prompts, list): - system_messages = [message for message in messages if message.type == 'system'] - if system_messages: - system_message = system_messages[0] - extra_kwargs['system_prompt'] = system_message.content - prompts = [message for message in messages if message.type != 'system'] - - prompts = get_buffer_string(prompts) - - # The maximum length the generated tokens can have. - # Corresponds to the length of the input prompt + max_new_tokens. - if 'max_length' in self._client.input: - self._client.input['max_length'] = min( - self._client.input['max_length'] + self.get_num_tokens(messages), - self.model_rules.max_tokens.max - ) - - return self._client.generate([prompts], stop, callbacks, **extra_kwargs) - - def get_num_tokens(self, messages: List[PromptMessage]) -> int: - """ - get num tokens of prompt messages. - - :param messages: - :return: - """ - prompts = self._get_prompt_from_messages(messages) - if isinstance(prompts, list): - prompts = get_buffer_string(prompts) - - return self._client.get_num_tokens(prompts) - - def _set_model_kwargs(self, model_kwargs: ModelKwargs): - provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, model_kwargs) - self.client.input = provider_model_kwargs - - def handle_exceptions(self, ex: Exception) -> Exception: - if isinstance(ex, (ModelError, ReplicateError)): - return LLMBadRequestError(f"Replicate: {str(ex)}") - else: - return ex - - @property - def support_streaming(self): - return True diff --git a/api/core/model_providers/models/llm/spark_model.py b/api/core/model_providers/models/llm/spark_model.py deleted file mode 100644 index ecbcb103e0ed38..00000000000000 --- a/api/core/model_providers/models/llm/spark_model.py +++ /dev/null @@ -1,70 +0,0 @@ -import decimal -from typing import List, Optional, Any - -from langchain.callbacks.manager import Callbacks -from langchain.schema import LLMResult - -from core.model_providers.error import LLMBadRequestError -from core.model_providers.models.llm.base import BaseLLM -from core.model_providers.models.entity.message import PromptMessage, MessageType -from core.model_providers.models.entity.model_params import ModelMode, ModelKwargs -from core.third_party.langchain.llms.spark import ChatSpark -from core.third_party.spark.spark_llm import SparkError - - -class SparkModel(BaseLLM): - model_mode: ModelMode = ModelMode.CHAT - - def _init_client(self) -> Any: - provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, self.model_kwargs) - return ChatSpark( - model_name=self.name, - streaming=self.streaming, - callbacks=self.callbacks, - **self.credentials, - **provider_model_kwargs - ) - - def _run(self, messages: List[PromptMessage], - stop: Optional[List[str]] = None, - callbacks: Callbacks = None, - **kwargs) -> LLMResult: - """ - run predict by prompt messages and stop words. - - :param messages: - :param stop: - :param callbacks: - :return: - """ - prompts = self._get_prompt_from_messages(messages) - return self._client.generate([prompts], stop, callbacks) - - def get_num_tokens(self, messages: List[PromptMessage]) -> int: - """ - get num tokens of prompt messages. - - :param messages: - :return: - """ - contents = [message.content for message in messages] - return max(self._client.get_num_tokens("".join(contents)), 0) - - def get_currency(self): - return 'RMB' - - def _set_model_kwargs(self, model_kwargs: ModelKwargs): - provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, model_kwargs) - for k, v in provider_model_kwargs.items(): - if hasattr(self.client, k): - setattr(self.client, k, v) - - def handle_exceptions(self, ex: Exception) -> Exception: - if isinstance(ex, SparkError): - return LLMBadRequestError(f"Spark: {str(ex)}") - else: - return ex - - @property - def support_streaming(self): - return True diff --git a/api/core/model_providers/models/llm/tongyi_model.py b/api/core/model_providers/models/llm/tongyi_model.py deleted file mode 100644 index 319505817ffbea..00000000000000 --- a/api/core/model_providers/models/llm/tongyi_model.py +++ /dev/null @@ -1,72 +0,0 @@ -import decimal -from functools import wraps -from typing import List, Optional, Any - -from langchain.callbacks.manager import Callbacks -from langchain.schema import LLMResult -from requests import HTTPError - -from core.model_providers.error import LLMBadRequestError -from core.model_providers.models.llm.base import BaseLLM -from core.model_providers.models.entity.message import PromptMessage, MessageType -from core.model_providers.models.entity.model_params import ModelMode, ModelKwargs -from core.third_party.langchain.llms.tongyi_llm import EnhanceTongyi - - -class TongyiModel(BaseLLM): - model_mode: ModelMode = ModelMode.COMPLETION - - def _init_client(self) -> Any: - provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, self.model_kwargs) - return EnhanceTongyi( - model_name=self.name, - max_retries=1, - streaming=self.streaming, - callbacks=self.callbacks, - **self.credentials, - **provider_model_kwargs - ) - - def _run(self, messages: List[PromptMessage], - stop: Optional[List[str]] = None, - callbacks: Callbacks = None, - **kwargs) -> LLMResult: - """ - run predict by prompt messages and stop words. - - :param messages: - :param stop: - :param callbacks: - :return: - """ - prompts = self._get_prompt_from_messages(messages) - return self._client.generate([prompts], stop, callbacks) - - def get_num_tokens(self, messages: List[PromptMessage]) -> int: - """ - get num tokens of prompt messages. - - :param messages: - :return: - """ - prompts = self._get_prompt_from_messages(messages) - return max(self._client.get_num_tokens(prompts), 0) - - def get_currency(self): - return 'RMB' - - def _set_model_kwargs(self, model_kwargs: ModelKwargs): - provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, model_kwargs) - for k, v in provider_model_kwargs.items(): - if hasattr(self.client, k): - setattr(self.client, k, v) - - def handle_exceptions(self, ex: Exception) -> Exception: - if isinstance(ex, (ValueError, HTTPError)): - return LLMBadRequestError(f"Tongyi: {str(ex)}") - else: - return ex - - @property - def support_streaming(self): - return True diff --git a/api/core/model_providers/models/llm/wenxin_model.py b/api/core/model_providers/models/llm/wenxin_model.py deleted file mode 100644 index c912ffb5d32c74..00000000000000 --- a/api/core/model_providers/models/llm/wenxin_model.py +++ /dev/null @@ -1,69 +0,0 @@ -import decimal -from typing import List, Optional, Any - -from langchain.callbacks.manager import Callbacks -from langchain.schema import LLMResult - -from core.model_providers.error import LLMBadRequestError -from core.model_providers.models.llm.base import BaseLLM -from core.model_providers.models.entity.message import PromptMessage -from core.model_providers.models.entity.model_params import ModelMode, ModelKwargs -from core.third_party.langchain.llms.wenxin import Wenxin - - -class WenxinModel(BaseLLM): - model_mode: ModelMode = ModelMode.CHAT - - def _init_client(self) -> Any: - provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, self.model_kwargs) - return Wenxin( - model=self.name, - streaming=self.streaming, - callbacks=self.callbacks, - **self.credentials, - **provider_model_kwargs - ) - - def _run(self, messages: List[PromptMessage], - stop: Optional[List[str]] = None, - callbacks: Callbacks = None, - **kwargs) -> LLMResult: - """ - run predict by prompt messages and stop words. - - :param messages: - :param stop: - :param callbacks: - :return: - """ - prompts = self._get_prompt_from_messages(messages) - - generate_kwargs = {'stop': stop, 'callbacks': callbacks, 'messages': [prompts]} - - if 'functions' in kwargs: - generate_kwargs['functions'] = kwargs['functions'] - - return self._client.generate(**generate_kwargs) - - def get_num_tokens(self, messages: List[PromptMessage]) -> int: - """ - get num tokens of prompt messages. - - :param messages: - :return: - """ - prompts = self._get_prompt_from_messages(messages) - return max(self._client.get_num_tokens_from_messages(prompts), 0) - - def _set_model_kwargs(self, model_kwargs: ModelKwargs): - provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, model_kwargs) - for k, v in provider_model_kwargs.items(): - if hasattr(self.client, k): - setattr(self.client, k, v) - - def handle_exceptions(self, ex: Exception) -> Exception: - return LLMBadRequestError(f"Wenxin: {str(ex)}") - - @property - def support_streaming(self): - return True diff --git a/api/core/model_providers/models/llm/xinference_model.py b/api/core/model_providers/models/llm/xinference_model.py deleted file mode 100644 index 2239ef1336856c..00000000000000 --- a/api/core/model_providers/models/llm/xinference_model.py +++ /dev/null @@ -1,70 +0,0 @@ -from typing import List, Optional, Any - -from langchain.callbacks.manager import Callbacks -from langchain.schema import LLMResult - -from core.model_providers.error import LLMBadRequestError -from core.model_providers.models.llm.base import BaseLLM -from core.model_providers.models.entity.message import PromptMessage -from core.model_providers.models.entity.model_params import ModelMode, ModelKwargs -from core.third_party.langchain.llms.xinference_llm import XinferenceLLM - - -class XinferenceModel(BaseLLM): - model_mode: ModelMode = ModelMode.COMPLETION - - def _init_client(self) -> Any: - self.provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, self.model_kwargs) - - client = XinferenceLLM( - server_url=self.credentials['server_url'], - model_uid=self.credentials['model_uid'], - ) - - client.callbacks = self.callbacks - - return client - - def _run(self, messages: List[PromptMessage], - stop: Optional[List[str]] = None, - callbacks: Callbacks = None, - **kwargs) -> LLMResult: - """ - run predict by prompt messages and stop words. - - :param messages: - :param stop: - :param callbacks: - :return: - """ - prompts = self._get_prompt_from_messages(messages) - return self._client.generate( - [prompts], - stop, - callbacks, - generate_config={ - "stop": stop, - "stream": self.streaming, - **self.provider_model_kwargs, - } - ) - - def get_num_tokens(self, messages: List[PromptMessage]) -> int: - """ - get num tokens of prompt messages. - - :param messages: - :return: - """ - prompts = self._get_prompt_from_messages(messages) - return max(self._client.get_num_tokens(prompts), 0) - - def _set_model_kwargs(self, model_kwargs: ModelKwargs): - pass - - def handle_exceptions(self, ex: Exception) -> Exception: - return LLMBadRequestError(f"Xinference: {str(ex)}") - - @property - def support_streaming(self): - return True diff --git a/api/core/model_providers/models/llm/zhipuai_model.py b/api/core/model_providers/models/llm/zhipuai_model.py deleted file mode 100644 index f94eb4e7298ea0..00000000000000 --- a/api/core/model_providers/models/llm/zhipuai_model.py +++ /dev/null @@ -1,62 +0,0 @@ -from typing import List, Optional, Any - -from langchain.callbacks.manager import Callbacks -from langchain.schema import LLMResult - -from core.model_providers.error import LLMBadRequestError -from core.model_providers.models.llm.base import BaseLLM -from core.model_providers.models.entity.message import PromptMessage -from core.model_providers.models.entity.model_params import ModelMode, ModelKwargs -from core.third_party.langchain.llms.zhipuai_llm import ZhipuAIChatLLM - - -class ZhipuAIModel(BaseLLM): - model_mode: ModelMode = ModelMode.CHAT - - def _init_client(self) -> Any: - provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, self.model_kwargs) - return ZhipuAIChatLLM( - model=self.name, - streaming=self.streaming, - callbacks=self.callbacks, - **self.credentials, - **provider_model_kwargs - ) - - def _run(self, messages: List[PromptMessage], - stop: Optional[List[str]] = None, - callbacks: Callbacks = None, - **kwargs) -> LLMResult: - """ - run predict by prompt messages and stop words. - - :param messages: - :param stop: - :param callbacks: - :return: - """ - prompts = self._get_prompt_from_messages(messages) - return self._client.generate([prompts], stop, callbacks) - - def get_num_tokens(self, messages: List[PromptMessage]) -> int: - """ - get num tokens of prompt messages. - - :param messages: - :return: - """ - prompts = self._get_prompt_from_messages(messages) - return max(self._client.get_num_tokens_from_messages(prompts), 0) - - def _set_model_kwargs(self, model_kwargs: ModelKwargs): - provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, model_kwargs) - for k, v in provider_model_kwargs.items(): - if hasattr(self.client, k): - setattr(self.client, k, v) - - def handle_exceptions(self, ex: Exception) -> Exception: - return LLMBadRequestError(f"ZhipuAI: {str(ex)}") - - @property - def support_streaming(self): - return True diff --git a/api/core/model_providers/models/moderation/base.py b/api/core/model_providers/models/moderation/base.py deleted file mode 100644 index 0d56739f1a1852..00000000000000 --- a/api/core/model_providers/models/moderation/base.py +++ /dev/null @@ -1,29 +0,0 @@ -from abc import abstractmethod -from typing import Any - -from core.model_providers.models.base import BaseProviderModel -from core.model_providers.models.entity.model_params import ModelType -from core.model_providers.providers.base import BaseModelProvider - - -class BaseModeration(BaseProviderModel): - name: str - type: ModelType = ModelType.MODERATION - - def __init__(self, model_provider: BaseModelProvider, client: Any, name: str): - super().__init__(model_provider, client) - self.name = name - - def run(self, text: str) -> bool: - try: - return self._run(text) - except Exception as ex: - raise self.handle_exceptions(ex) - - @abstractmethod - def _run(self, text: str) -> bool: - raise NotImplementedError - - @abstractmethod - def handle_exceptions(self, ex: Exception) -> Exception: - raise NotImplementedError diff --git a/api/core/model_providers/models/moderation/openai_moderation.py b/api/core/model_providers/models/moderation/openai_moderation.py deleted file mode 100644 index f3dedc542a2b21..00000000000000 --- a/api/core/model_providers/models/moderation/openai_moderation.py +++ /dev/null @@ -1,58 +0,0 @@ -import logging - -import openai - -from core.model_providers.error import LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, \ - LLMRateLimitError, LLMAuthorizationError -from core.model_providers.models.moderation.base import BaseModeration -from core.model_providers.providers.base import BaseModelProvider - -DEFAULT_MODEL = 'whisper-1' - - -class OpenAIModeration(BaseModeration): - - def __init__(self, model_provider: BaseModelProvider, name: str): - super().__init__(model_provider, openai.Moderation, name) - - def _run(self, text: str) -> bool: - credentials = self.model_provider.get_model_credentials( - model_name=self.name, - model_type=self.type - ) - - # 2000 text per chunk - length = 2000 - text_chunks = [text[i:i + length] for i in range(0, len(text), length)] - - max_text_chunks = 32 - chunks = [text_chunks[i:i + max_text_chunks] for i in range(0, len(text_chunks), max_text_chunks)] - - for text_chunk in chunks: - moderation_result = self._client.create(input=text_chunk, - api_key=credentials['openai_api_key']) - - for result in moderation_result.results: - if result['flagged'] is True: - return False - - return True - - def handle_exceptions(self, ex: Exception) -> Exception: - if isinstance(ex, openai.error.InvalidRequestError): - logging.warning("Invalid request to OpenAI API.") - return LLMBadRequestError(str(ex)) - elif isinstance(ex, openai.error.APIConnectionError): - logging.warning("Failed to connect to OpenAI API.") - return LLMAPIConnectionError(ex.__class__.__name__ + ":" + str(ex)) - elif isinstance(ex, (openai.error.APIError, openai.error.ServiceUnavailableError, openai.error.Timeout)): - logging.warning("OpenAI service unavailable.") - return LLMAPIUnavailableError(ex.__class__.__name__ + ":" + str(ex)) - elif isinstance(ex, openai.error.RateLimitError): - return LLMRateLimitError(str(ex)) - elif isinstance(ex, openai.error.AuthenticationError): - return LLMAuthorizationError(str(ex)) - elif isinstance(ex, openai.error.OpenAIError): - return LLMBadRequestError(ex.__class__.__name__ + ":" + str(ex)) - else: - return ex diff --git a/api/core/model_providers/models/reranking/base.py b/api/core/model_providers/models/reranking/base.py deleted file mode 100644 index 85863895f4208d..00000000000000 --- a/api/core/model_providers/models/reranking/base.py +++ /dev/null @@ -1,36 +0,0 @@ -from abc import abstractmethod -from typing import Any, Optional, List -from langchain.schema import Document - -from core.model_providers.models.base import BaseProviderModel -from core.model_providers.models.entity.model_params import ModelType -from core.model_providers.providers.base import BaseModelProvider -import logging - -logger = logging.getLogger(__name__) - - -class BaseReranking(BaseProviderModel): - name: str - type: ModelType = ModelType.RERANKING - - def __init__(self, model_provider: BaseModelProvider, client: Any, name: str): - super().__init__(model_provider, client) - self.name = name - - @property - def base_model_name(self) -> str: - """ - get base model name - - :return: str - """ - return self.name - - @abstractmethod - def rerank(self, query: str, documents: List[Document], score_threshold: Optional[float], top_k: Optional[int]) -> Optional[List[Document]]: - raise NotImplementedError - - @abstractmethod - def handle_exceptions(self, ex: Exception) -> Exception: - raise NotImplementedError diff --git a/api/core/model_providers/models/reranking/cohere_reranking.py b/api/core/model_providers/models/reranking/cohere_reranking.py deleted file mode 100644 index fa2748734d34de..00000000000000 --- a/api/core/model_providers/models/reranking/cohere_reranking.py +++ /dev/null @@ -1,81 +0,0 @@ -import logging -from typing import List, Optional - -import cohere -import openai -from core.model_providers.error import (LLMAPIConnectionError, - LLMAPIUnavailableError, - LLMAuthorizationError, - LLMBadRequestError, LLMRateLimitError) -from core.model_providers.models.reranking.base import BaseReranking -from core.model_providers.providers.base import BaseModelProvider -from langchain.schema import Document - - -class CohereReranking(BaseReranking): - - def __init__(self, model_provider: BaseModelProvider, name: str): - self.credentials = model_provider.get_model_credentials( - model_name=name, - model_type=self.type - ) - - client = cohere.Client(self.credentials.get('api_key')) - - super().__init__(model_provider, client, name) - - def rerank(self, query: str, documents: List[Document], score_threshold: Optional[float], top_k: Optional[int]) -> \ - Optional[List[Document]]: - if not documents: - return [] - docs = [] - doc_id = [] - unique_documents = [] - for document in documents: - if document.metadata['doc_id'] not in doc_id: - doc_id.append(document.metadata['doc_id']) - docs.append(document.page_content) - unique_documents.append(document) - documents = unique_documents - - results = self.client.rerank(query=query, documents=docs, model=self.name, top_n=top_k) - rerank_documents = [] - - for idx, result in enumerate(results): - # format document - rerank_document = Document( - page_content=result.document['text'], - metadata={ - "doc_id": documents[result.index].metadata['doc_id'], - "doc_hash": documents[result.index].metadata['doc_hash'], - "document_id": documents[result.index].metadata['document_id'], - "dataset_id": documents[result.index].metadata['dataset_id'], - 'score': result.relevance_score - } - ) - # score threshold check - if score_threshold is not None: - if result.relevance_score >= score_threshold: - rerank_documents.append(rerank_document) - else: - rerank_documents.append(rerank_document) - return rerank_documents - - def handle_exceptions(self, ex: Exception) -> Exception: - if isinstance(ex, openai.error.InvalidRequestError): - logging.warning("Invalid request to OpenAI API.") - return LLMBadRequestError(str(ex)) - elif isinstance(ex, openai.error.APIConnectionError): - logging.warning("Failed to connect to OpenAI API.") - return LLMAPIConnectionError(ex.__class__.__name__ + ":" + str(ex)) - elif isinstance(ex, (openai.error.APIError, openai.error.ServiceUnavailableError, openai.error.Timeout)): - logging.warning("OpenAI service unavailable.") - return LLMAPIUnavailableError(ex.__class__.__name__ + ":" + str(ex)) - elif isinstance(ex, openai.error.RateLimitError): - return LLMRateLimitError(str(ex)) - elif isinstance(ex, openai.error.AuthenticationError): - return LLMAuthorizationError(str(ex)) - elif isinstance(ex, openai.error.OpenAIError): - return LLMBadRequestError(ex.__class__.__name__ + ":" + str(ex)) - else: - return ex diff --git a/api/core/model_providers/models/reranking/xinference_reranking.py b/api/core/model_providers/models/reranking/xinference_reranking.py deleted file mode 100644 index fda2772c70f767..00000000000000 --- a/api/core/model_providers/models/reranking/xinference_reranking.py +++ /dev/null @@ -1,62 +0,0 @@ -import logging -from typing import List, Optional - -from core.model_providers.error import LLMBadRequestError -from core.model_providers.models.reranking.base import BaseReranking -from core.model_providers.providers.base import BaseModelProvider -from langchain.schema import Document -from xinference_client.client.restful.restful_client import Client - - -class XinferenceReranking(BaseReranking): - - def __init__(self, model_provider: BaseModelProvider, name: str): - self.credentials = model_provider.get_model_credentials( - model_name=name, - model_type=self.type - ) - - client = Client(self.credentials['server_url']) - - super().__init__(model_provider, client, name) - - def rerank(self, query: str, documents: List[Document], score_threshold: Optional[float], top_k: Optional[int]) -> Optional[List[Document]]: - if not documents: - return [] - docs = [] - doc_id = [] - unique_documents = [] - for document in documents: - if document.metadata['doc_id'] not in doc_id: - doc_id.append(document.metadata['doc_id']) - docs.append(document.page_content) - unique_documents.append(document) - documents = unique_documents - - model = self.client.get_model(self.credentials['model_uid']) - response = model.rerank(query=query, documents=docs, top_n=top_k) - rerank_documents = [] - - for idx, result in enumerate(response['results']): - # format document - index = result['index'] - rerank_document = Document( - page_content=result['document'], - metadata={ - "doc_id": documents[index].metadata['doc_id'], - "doc_hash": documents[index].metadata['doc_hash'], - "document_id": documents[index].metadata['document_id'], - "dataset_id": documents[index].metadata['dataset_id'], - 'score': result['relevance_score'] - } - ) - # score threshold check - if score_threshold is not None: - if result['relevance_score'] >= score_threshold: - rerank_documents.append(rerank_document) - else: - rerank_documents.append(rerank_document) - return rerank_documents - - def handle_exceptions(self, ex: Exception) -> Exception: - return LLMBadRequestError(f"Xinference rerank: {str(ex)}") diff --git a/api/core/model_providers/models/speech2text/base.py b/api/core/model_providers/models/speech2text/base.py deleted file mode 100644 index 0b1ec1d558c452..00000000000000 --- a/api/core/model_providers/models/speech2text/base.py +++ /dev/null @@ -1,29 +0,0 @@ -from abc import abstractmethod -from typing import Any - -from core.model_providers.models.base import BaseProviderModel -from core.model_providers.models.entity.model_params import ModelType -from core.model_providers.providers.base import BaseModelProvider - - -class BaseSpeech2Text(BaseProviderModel): - name: str - type: ModelType = ModelType.SPEECH_TO_TEXT - - def __init__(self, model_provider: BaseModelProvider, client: Any, name: str): - super().__init__(model_provider, client) - self.name = name - - def run(self, file): - try: - return self._run(file) - except Exception as ex: - raise self.handle_exceptions(ex) - - @abstractmethod - def _run(self, file): - raise NotImplementedError - - @abstractmethod - def handle_exceptions(self, ex: Exception) -> Exception: - raise NotImplementedError diff --git a/api/core/model_providers/models/speech2text/openai_whisper.py b/api/core/model_providers/models/speech2text/openai_whisper.py deleted file mode 100644 index a1fdc484dd2725..00000000000000 --- a/api/core/model_providers/models/speech2text/openai_whisper.py +++ /dev/null @@ -1,47 +0,0 @@ -import logging - -import openai - -from core.model_providers.error import LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, \ - LLMRateLimitError, LLMAuthorizationError -from core.model_providers.models.speech2text.base import BaseSpeech2Text -from core.model_providers.providers.base import BaseModelProvider - - -class OpenAIWhisper(BaseSpeech2Text): - - def __init__(self, model_provider: BaseModelProvider, name: str): - super().__init__(model_provider, openai.Audio, name) - - def _run(self, file): - credentials = self.model_provider.get_model_credentials( - model_name=self.name, - model_type=self.type - ) - - return self._client.transcribe( - model=self.name, - file=file, - api_key=credentials.get('openai_api_key'), - api_base=credentials.get('openai_api_base'), - organization=credentials.get('openai_organization'), - ) - - def handle_exceptions(self, ex: Exception) -> Exception: - if isinstance(ex, openai.error.InvalidRequestError): - logging.warning("Invalid request to OpenAI API.") - return LLMBadRequestError(str(ex)) - elif isinstance(ex, openai.error.APIConnectionError): - logging.warning("Failed to connect to OpenAI API.") - return LLMAPIConnectionError(ex.__class__.__name__ + ":" + str(ex)) - elif isinstance(ex, (openai.error.APIError, openai.error.ServiceUnavailableError, openai.error.Timeout)): - logging.warning("OpenAI service unavailable.") - return LLMAPIUnavailableError(ex.__class__.__name__ + ":" + str(ex)) - elif isinstance(ex, openai.error.RateLimitError): - return LLMRateLimitError(str(ex)) - elif isinstance(ex, openai.error.AuthenticationError): - return LLMAuthorizationError(str(ex)) - elif isinstance(ex, openai.error.OpenAIError): - return LLMBadRequestError(ex.__class__.__name__ + ":" + str(ex)) - else: - return ex diff --git a/api/core/model_providers/providers/anthropic_provider.py b/api/core/model_providers/providers/anthropic_provider.py deleted file mode 100644 index 7e9c383de5aac0..00000000000000 --- a/api/core/model_providers/providers/anthropic_provider.py +++ /dev/null @@ -1,228 +0,0 @@ -import json -import logging -from json import JSONDecodeError -from typing import Type, Optional - -import anthropic -from flask import current_app -from langchain.schema import HumanMessage - -from core.helper import encrypter -from core.model_providers.models.base import BaseProviderModel -from core.model_providers.models.entity.model_params import ModelKwargsRules, KwargRule, ModelMode -from core.model_providers.models.entity.provider import ModelFeature -from core.model_providers.models.llm.anthropic_model import AnthropicModel -from core.model_providers.models.llm.base import ModelType -from core.model_providers.providers.base import BaseModelProvider, CredentialsValidateFailedError -from core.model_providers.providers.hosted import hosted_model_providers -from core.third_party.langchain.llms.anthropic_llm import AnthropicLLM -from models.provider import ProviderType - - -class AnthropicProvider(BaseModelProvider): - - @property - def provider_name(self): - """ - Returns the name of a provider. - """ - return 'anthropic' - - def _get_fixed_model_list(self, model_type: ModelType) -> list[dict]: - if model_type == ModelType.TEXT_GENERATION: - return [ - { - 'id': 'claude-2.1', - 'name': 'claude-2.1', - 'mode': ModelMode.CHAT.value, - 'features': [ - ModelFeature.AGENT_THOUGHT.value - ] - }, - { - 'id': 'claude-2', - 'name': 'claude-2', - 'mode': ModelMode.CHAT.value, - 'features': [ - ModelFeature.AGENT_THOUGHT.value - ] - }, - { - 'id': 'claude-instant-1', - 'name': 'claude-instant-1', - 'mode': ModelMode.CHAT.value, - }, - ] - else: - return [] - - def _get_text_generation_model_mode(self, model_name) -> str: - return ModelMode.CHAT.value - - def get_model_class(self, model_type: ModelType) -> Type[BaseProviderModel]: - """ - Returns the model class. - - :param model_type: - :return: - """ - if model_type == ModelType.TEXT_GENERATION: - model_class = AnthropicModel - else: - raise NotImplementedError - - return model_class - - def get_model_parameter_rules(self, model_name: str, model_type: ModelType) -> ModelKwargsRules: - """ - get model parameter rules. - - :param model_name: - :param model_type: - :return: - """ - model_max_tokens = { - 'claude-instant-1': 100000, - 'claude-2': 100000, - 'claude-2.1': 200000, - } - - return ModelKwargsRules( - temperature=KwargRule[float](min=0, max=1, default=1, precision=2), - top_p=KwargRule[float](min=0, max=1, default=0.7, precision=2), - presence_penalty=KwargRule[float](enabled=False), - frequency_penalty=KwargRule[float](enabled=False), - max_tokens=KwargRule[int](alias="max_tokens_to_sample", min=10, max=model_max_tokens.get(model_name, 100000), default=256, precision=0), - ) - - @classmethod - def is_provider_credentials_valid_or_raise(cls, credentials: dict): - """ - Validates the given credentials. - """ - if 'anthropic_api_key' not in credentials: - raise CredentialsValidateFailedError('Anthropic API Key must be provided.') - - try: - credential_kwargs = { - 'anthropic_api_key': credentials['anthropic_api_key'] - } - - if 'anthropic_api_url' in credentials: - credential_kwargs['anthropic_api_url'] = credentials['anthropic_api_url'] - - chat_llm = AnthropicLLM( - model='claude-instant-1', - max_tokens_to_sample=10, - temperature=0, - default_request_timeout=60, - **credential_kwargs - ) - - messages = [ - HumanMessage( - content="ping" - ) - ] - - chat_llm(messages) - except anthropic.APIConnectionError as ex: - raise CredentialsValidateFailedError(str(ex)) - except (anthropic.APIStatusError, anthropic.RateLimitError) as ex: - raise CredentialsValidateFailedError(str(ex)) - except Exception as ex: - logging.exception('Anthropic config validation failed') - raise ex - - @classmethod - def encrypt_provider_credentials(cls, tenant_id: str, credentials: dict) -> dict: - credentials['anthropic_api_key'] = encrypter.encrypt_token(tenant_id, credentials['anthropic_api_key']) - return credentials - - def get_provider_credentials(self, obfuscated: bool = False) -> dict: - if self.provider.provider_type == ProviderType.CUSTOM.value: - try: - credentials = json.loads(self.provider.encrypted_config) - except JSONDecodeError: - credentials = { - 'anthropic_api_url': None, - 'anthropic_api_key': None - } - - if credentials['anthropic_api_key']: - credentials['anthropic_api_key'] = encrypter.decrypt_token( - self.provider.tenant_id, - credentials['anthropic_api_key'] - ) - - if obfuscated: - credentials['anthropic_api_key'] = encrypter.obfuscated_token(credentials['anthropic_api_key']) - - if 'anthropic_api_url' not in credentials: - credentials['anthropic_api_url'] = None - - return credentials - else: - if hosted_model_providers.anthropic: - return { - 'anthropic_api_url': hosted_model_providers.anthropic.api_base, - 'anthropic_api_key': hosted_model_providers.anthropic.api_key, - } - else: - return { - 'anthropic_api_url': None, - 'anthropic_api_key': None - } - - @classmethod - def is_provider_type_system_supported(cls) -> bool: - if current_app.config['EDITION'] != 'CLOUD': - return False - - if hosted_model_providers.anthropic: - return True - - return False - - def should_deduct_quota(self): - if hosted_model_providers.anthropic and \ - hosted_model_providers.anthropic.quota_limit and hosted_model_providers.anthropic.quota_limit > -1: - return True - - return False - - @classmethod - def is_model_credentials_valid_or_raise(cls, model_name: str, model_type: ModelType, credentials: dict): - """ - check model credentials valid. - - :param model_name: - :param model_type: - :param credentials: - """ - return - - @classmethod - def encrypt_model_credentials(cls, tenant_id: str, model_name: str, model_type: ModelType, - credentials: dict) -> dict: - """ - encrypt model credentials for save. - - :param tenant_id: - :param model_name: - :param model_type: - :param credentials: - :return: - """ - return {} - - def get_model_credentials(self, model_name: str, model_type: ModelType, obfuscated: bool = False) -> dict: - """ - get credentials for llm use. - - :param model_name: - :param model_type: - :param obfuscated: - :return: - """ - return self.get_provider_credentials(obfuscated) diff --git a/api/core/model_providers/providers/azure_openai_provider.py b/api/core/model_providers/providers/azure_openai_provider.py deleted file mode 100644 index 25621e72517a0b..00000000000000 --- a/api/core/model_providers/providers/azure_openai_provider.py +++ /dev/null @@ -1,438 +0,0 @@ -import json -import logging -from json import JSONDecodeError -from typing import Type - -import openai -from flask import current_app -from langchain.embeddings import OpenAIEmbeddings -from langchain.schema import HumanMessage - -from core.helper import encrypter -from core.model_providers.models.base import BaseProviderModel -from core.model_providers.models.embedding.azure_openai_embedding import AzureOpenAIEmbedding, \ - AZURE_OPENAI_API_VERSION -from core.model_providers.models.entity.model_params import ModelType, ModelKwargsRules, KwargRule, ModelMode -from core.model_providers.models.entity.provider import ModelFeature -from core.model_providers.models.llm.azure_openai_model import AzureOpenAIModel -from core.model_providers.providers.base import BaseModelProvider, CredentialsValidateFailedError -from core.model_providers.providers.hosted import hosted_model_providers -from core.third_party.langchain.llms.azure_chat_open_ai import EnhanceAzureChatOpenAI -from extensions.ext_database import db -from models.provider import ProviderType, ProviderModel, ProviderQuotaType - -BASE_MODELS = [ - 'gpt-4', - 'gpt-4-32k', - 'gpt-4-1106-preview', - 'gpt-4-vision-preview', - 'gpt-35-turbo', - 'gpt-35-turbo-16k', - 'text-davinci-003', - 'text-embedding-ada-002', -] - - -class AzureOpenAIProvider(BaseModelProvider): - - @property - def provider_name(self): - """ - Returns the name of a provider. - """ - return 'azure_openai' - - def get_supported_model_list(self, model_type: ModelType) -> list[dict]: - # convert old provider config to provider models - self._convert_provider_config_to_model_config() - - if self.provider.provider_type == ProviderType.CUSTOM.value: - # get configurable provider models - provider_models = db.session.query(ProviderModel).filter( - ProviderModel.tenant_id == self.provider.tenant_id, - ProviderModel.provider_name == self.provider.provider_name, - ProviderModel.model_type == model_type.value, - ProviderModel.is_valid == True - ).order_by(ProviderModel.created_at.asc()).all() - - model_list = [] - for provider_model in provider_models: - model_dict = { - 'id': provider_model.model_name, - 'name': provider_model.model_name - } - - credentials = json.loads(provider_model.encrypted_config) - - if provider_model.model_type == ModelType.TEXT_GENERATION.value: - model_dict['mode'] = self._get_text_generation_model_mode(credentials['base_model_name']) - - if credentials['base_model_name'] in [ - 'gpt-4', - 'gpt-4-32k', - 'gpt-35-turbo', - 'gpt-35-turbo-16k', - ]: - model_dict['features'] = [ - ModelFeature.AGENT_THOUGHT.value - ] - - model_list.append(model_dict) - else: - model_list = self._get_fixed_model_list(model_type) - - return model_list - - def _get_text_generation_model_mode(self, model_name) -> str: - if model_name == 'text-davinci-003': - return ModelMode.COMPLETION.value - else: - return ModelMode.CHAT.value - - def _get_fixed_model_list(self, model_type: ModelType) -> list[dict]: - if model_type == ModelType.TEXT_GENERATION: - models = [ - { - 'id': 'gpt-3.5-turbo', - 'name': 'gpt-3.5-turbo', - 'mode': ModelMode.CHAT.value, - 'features': [ - ModelFeature.AGENT_THOUGHT.value - ] - }, - { - 'id': 'gpt-3.5-turbo-16k', - 'name': 'gpt-3.5-turbo-16k', - 'mode': ModelMode.CHAT.value, - 'features': [ - ModelFeature.AGENT_THOUGHT.value - ] - }, - { - 'id': 'gpt-4', - 'name': 'gpt-4', - 'mode': ModelMode.CHAT.value, - 'features': [ - ModelFeature.AGENT_THOUGHT.value - ] - }, - { - 'id': 'gpt-4-32k', - 'name': 'gpt-4-32k', - 'mode': ModelMode.CHAT.value, - 'features': [ - ModelFeature.AGENT_THOUGHT.value - ] - }, - { - 'id': 'gpt-4-1106-preview', - 'name': 'gpt-4-1106-preview', - 'mode': ModelMode.CHAT.value, - 'features': [ - ModelFeature.AGENT_THOUGHT.value - ] - }, - { - 'id': 'gpt-4-vision-preview', - 'name': 'gpt-4-vision-preview', - 'mode': ModelMode.CHAT.value, - 'features': [ - ModelFeature.VISION.value - ] - }, - { - 'id': 'text-davinci-003', - 'name': 'text-davinci-003', - 'mode': ModelMode.COMPLETION.value, - } - ] - - if self.provider.provider_type == ProviderType.SYSTEM.value \ - and self.provider.quota_type == ProviderQuotaType.TRIAL.value: - models = [item for item in models if item['id'] not in ['gpt-4', 'gpt-4-32k']] - - return models - elif model_type == ModelType.EMBEDDINGS: - return [ - { - 'id': 'text-embedding-ada-002', - 'name': 'text-embedding-ada-002' - } - ] - else: - return [] - - def get_model_class(self, model_type: ModelType) -> Type[BaseProviderModel]: - """ - Returns the model class. - - :param model_type: - :return: - """ - if model_type == ModelType.TEXT_GENERATION: - model_class = AzureOpenAIModel - elif model_type == ModelType.EMBEDDINGS: - model_class = AzureOpenAIEmbedding - else: - raise NotImplementedError - - return model_class - - def get_model_parameter_rules(self, model_name: str, model_type: ModelType) -> ModelKwargsRules: - """ - get model parameter rules. - - :param model_name: - :param model_type: - :return: - """ - base_model_max_tokens = { - 'gpt-4': 8192, - 'gpt-4-32k': 32768, - 'gpt-4-1106-preview': 4096, - 'gpt-4-vision-preview': 4096, - 'gpt-35-turbo': 4096, - 'gpt-35-turbo-16k': 16384, - 'text-davinci-003': 4097, - } - - model_credentials = self.get_model_credentials(model_name, model_type) - - return ModelKwargsRules( - temperature=KwargRule[float](min=0, max=2, default=1, precision=2), - top_p=KwargRule[float](min=0, max=1, default=1, precision=2), - presence_penalty=KwargRule[float](min=-2, max=2, default=0, precision=2), - frequency_penalty=KwargRule[float](min=-2, max=2, default=0, precision=2), - max_tokens=KwargRule[int](min=10, max=base_model_max_tokens.get( - model_credentials['base_model_name'], - 4097 - ), default=16, precision=0), - ) - - @classmethod - def is_model_credentials_valid_or_raise(cls, model_name: str, model_type: ModelType, credentials: dict): - """ - check model credentials valid. - - :param model_name: - :param model_type: - :param credentials: - """ - if 'openai_api_key' not in credentials: - raise CredentialsValidateFailedError('Azure OpenAI API key is required') - - if 'openai_api_base' not in credentials: - raise CredentialsValidateFailedError('Azure OpenAI API Base Endpoint is required') - - if 'base_model_name' not in credentials: - raise CredentialsValidateFailedError('Base Model Name is required') - - if credentials['base_model_name'] not in BASE_MODELS: - raise CredentialsValidateFailedError('Base Model Name is invalid') - - if model_type == ModelType.TEXT_GENERATION: - try: - client = EnhanceAzureChatOpenAI( - deployment_name=model_name, - temperature=0, - max_tokens=15, - request_timeout=10, - openai_api_type='azure', - openai_api_version='2023-07-01-preview', - openai_api_key=credentials['openai_api_key'], - openai_api_base=credentials['openai_api_base'], - ) - - client.generate([[HumanMessage(content='hi!')]]) - except openai.error.OpenAIError as e: - raise CredentialsValidateFailedError( - f"Azure OpenAI deployment {model_name} not exists, cause: {e.__class__.__name__}:{str(e)}") - except Exception as e: - logging.exception("Azure OpenAI Model retrieve failed.") - raise e - elif model_type == ModelType.EMBEDDINGS: - try: - client = OpenAIEmbeddings( - openai_api_type='azure', - openai_api_version=AZURE_OPENAI_API_VERSION, - deployment=model_name, - chunk_size=16, - max_retries=1, - openai_api_key=credentials['openai_api_key'], - openai_api_base=credentials['openai_api_base'] - ) - - client.embed_query('hi') - except openai.error.OpenAIError as e: - logging.exception("Azure OpenAI Model check error.") - raise CredentialsValidateFailedError( - f"Azure OpenAI deployment {model_name} not exists, cause: {e.__class__.__name__}:{str(e)}") - except Exception as e: - logging.exception("Azure OpenAI Model retrieve failed.") - raise e - - @classmethod - def encrypt_model_credentials(cls, tenant_id: str, model_name: str, model_type: ModelType, - credentials: dict) -> dict: - """ - encrypt model credentials for save. - - :param tenant_id: - :param model_name: - :param model_type: - :param credentials: - :return: - """ - credentials['openai_api_key'] = encrypter.encrypt_token(tenant_id, credentials['openai_api_key']) - return credentials - - def get_model_credentials(self, model_name: str, model_type: ModelType, obfuscated: bool = False) -> dict: - """ - get credentials for llm use. - - :param model_name: - :param model_type: - :param obfuscated: - :return: - """ - if self.provider.provider_type == ProviderType.CUSTOM.value: - # convert old provider config to provider models - self._convert_provider_config_to_model_config() - - provider_model = self._get_provider_model(model_name, model_type) - - if not provider_model.encrypted_config: - return { - 'openai_api_base': '', - 'openai_api_key': '', - 'base_model_name': '' - } - - credentials = json.loads(provider_model.encrypted_config) - if credentials['openai_api_key']: - credentials['openai_api_key'] = encrypter.decrypt_token( - self.provider.tenant_id, - credentials['openai_api_key'] - ) - - if obfuscated: - credentials['openai_api_key'] = encrypter.obfuscated_token(credentials['openai_api_key']) - - return { - 'openai_api_base': credentials['openai_api_base'], - 'openai_api_key': credentials['openai_api_key'], - 'base_model_name': credentials['base_model_name'] - } - else: - if hosted_model_providers.azure_openai: - return { - 'openai_api_base': hosted_model_providers.azure_openai.api_base, - 'openai_api_key': hosted_model_providers.azure_openai.api_key, - 'base_model_name': model_name - } - else: - return { - 'openai_api_base': None, - 'openai_api_key': None, - 'base_model_name': None - } - - @classmethod - def is_provider_type_system_supported(cls) -> bool: - if current_app.config['EDITION'] != 'CLOUD': - return False - - if hosted_model_providers.azure_openai: - return True - - return False - - def should_deduct_quota(self): - if hosted_model_providers.azure_openai \ - and hosted_model_providers.azure_openai.quota_limit and hosted_model_providers.azure_openai.quota_limit > -1: - return True - - return False - - @classmethod - def is_provider_credentials_valid_or_raise(cls, credentials: dict): - return - - @classmethod - def encrypt_provider_credentials(cls, tenant_id: str, credentials: dict) -> dict: - return {} - - def get_provider_credentials(self, obfuscated: bool = False) -> dict: - return {} - - def _convert_provider_config_to_model_config(self): - if self.provider.provider_type == ProviderType.CUSTOM.value \ - and self.provider.is_valid \ - and self.provider.encrypted_config: - try: - credentials = json.loads(self.provider.encrypted_config) - except JSONDecodeError: - credentials = { - 'openai_api_base': '', - 'openai_api_key': '', - 'base_model_name': '' - } - - self._add_provider_model( - model_name='gpt-35-turbo', - model_type=ModelType.TEXT_GENERATION, - provider_credentials=credentials - ) - - self._add_provider_model( - model_name='gpt-35-turbo-16k', - model_type=ModelType.TEXT_GENERATION, - provider_credentials=credentials - ) - - self._add_provider_model( - model_name='gpt-4', - model_type=ModelType.TEXT_GENERATION, - provider_credentials=credentials - ) - - self._add_provider_model( - model_name='gpt-4-1106-preview', - model_type=ModelType.TEXT_GENERATION, - provider_credentials=credentials - ) - - self._add_provider_model( - model_name='gpt-4-vision-preview', - model_type=ModelType.TEXT_GENERATION, - provider_credentials=credentials - ) - - self._add_provider_model( - model_name='text-davinci-003', - model_type=ModelType.TEXT_GENERATION, - provider_credentials=credentials - ) - - self._add_provider_model( - model_name='text-embedding-ada-002', - model_type=ModelType.EMBEDDINGS, - provider_credentials=credentials - ) - - self.provider.encrypted_config = None - db.session.commit() - - def _add_provider_model(self, model_name: str, model_type: ModelType, provider_credentials: dict): - credentials = provider_credentials.copy() - credentials['base_model_name'] = model_name - provider_model = ProviderModel( - tenant_id=self.provider.tenant_id, - provider_name=self.provider.provider_name, - model_name=model_name, - model_type=model_type.value, - encrypted_config=json.dumps(credentials), - is_valid=True - ) - db.session.add(provider_model) - db.session.commit() diff --git a/api/core/model_providers/providers/baichuan_provider.py b/api/core/model_providers/providers/baichuan_provider.py deleted file mode 100644 index 784c9df2c6582b..00000000000000 --- a/api/core/model_providers/providers/baichuan_provider.py +++ /dev/null @@ -1,171 +0,0 @@ -import json -from json import JSONDecodeError -from typing import Type - -from langchain.schema import HumanMessage - -from core.helper import encrypter -from core.model_providers.models.base import BaseProviderModel -from core.model_providers.models.entity.model_params import ModelKwargsRules, KwargRule, ModelType, ModelMode -from core.model_providers.models.llm.baichuan_model import BaichuanModel -from core.model_providers.providers.base import BaseModelProvider, CredentialsValidateFailedError -from core.third_party.langchain.llms.baichuan_llm import BaichuanChatLLM -from models.provider import ProviderType - - -class BaichuanProvider(BaseModelProvider): - - @property - def provider_name(self): - """ - Returns the name of a provider. - """ - return 'baichuan' - - def _get_text_generation_model_mode(self, model_name) -> str: - return ModelMode.CHAT.value - - def _get_fixed_model_list(self, model_type: ModelType) -> list[dict]: - if model_type == ModelType.TEXT_GENERATION: - return [ - { - 'id': 'baichuan2-53b', - 'name': 'Baichuan2-53B', - 'mode': ModelMode.CHAT.value, - } - ] - else: - return [] - - def get_model_class(self, model_type: ModelType) -> Type[BaseProviderModel]: - """ - Returns the model class. - - :param model_type: - :return: - """ - if model_type == ModelType.TEXT_GENERATION: - model_class = BaichuanModel - else: - raise NotImplementedError - - return model_class - - def get_model_parameter_rules(self, model_name: str, model_type: ModelType) -> ModelKwargsRules: - """ - get model parameter rules. - - :param model_name: - :param model_type: - :return: - """ - return ModelKwargsRules( - temperature=KwargRule[float](min=0, max=1, default=0.3, precision=2), - top_p=KwargRule[float](min=0, max=0.99, default=0.85, precision=2), - presence_penalty=KwargRule[float](enabled=False), - frequency_penalty=KwargRule[float](enabled=False), - max_tokens=KwargRule[int](enabled=False), - ) - - @classmethod - def is_provider_credentials_valid_or_raise(cls, credentials: dict): - """ - Validates the given credentials. - """ - if 'api_key' not in credentials: - raise CredentialsValidateFailedError('Baichuan api_key must be provided.') - - if 'secret_key' not in credentials: - raise CredentialsValidateFailedError('Baichuan secret_key must be provided.') - - try: - credential_kwargs = { - 'api_key': credentials['api_key'], - 'secret_key': credentials['secret_key'], - } - - llm = BaichuanChatLLM( - temperature=0, - **credential_kwargs - ) - - llm([HumanMessage(content='ping')]) - except Exception as ex: - raise CredentialsValidateFailedError(str(ex)) - - @classmethod - def encrypt_provider_credentials(cls, tenant_id: str, credentials: dict) -> dict: - credentials['api_key'] = encrypter.encrypt_token(tenant_id, credentials['api_key']) - credentials['secret_key'] = encrypter.encrypt_token(tenant_id, credentials['secret_key']) - return credentials - - def get_provider_credentials(self, obfuscated: bool = False) -> dict: - if self.provider.provider_type == ProviderType.CUSTOM.value: - try: - credentials = json.loads(self.provider.encrypted_config) - except JSONDecodeError: - credentials = { - 'api_key': None, - 'secret_key': None, - } - - if credentials['api_key']: - credentials['api_key'] = encrypter.decrypt_token( - self.provider.tenant_id, - credentials['api_key'] - ) - - if obfuscated: - credentials['api_key'] = encrypter.obfuscated_token(credentials['api_key']) - - if credentials['secret_key']: - credentials['secret_key'] = encrypter.decrypt_token( - self.provider.tenant_id, - credentials['secret_key'] - ) - - if obfuscated: - credentials['secret_key'] = encrypter.obfuscated_token(credentials['secret_key']) - - return credentials - else: - return {} - - def should_deduct_quota(self): - return True - - @classmethod - def is_model_credentials_valid_or_raise(cls, model_name: str, model_type: ModelType, credentials: dict): - """ - check model credentials valid. - - :param model_name: - :param model_type: - :param credentials: - """ - return - - @classmethod - def encrypt_model_credentials(cls, tenant_id: str, model_name: str, model_type: ModelType, - credentials: dict) -> dict: - """ - encrypt model credentials for save. - - :param tenant_id: - :param model_name: - :param model_type: - :param credentials: - :return: - """ - return {} - - def get_model_credentials(self, model_name: str, model_type: ModelType, obfuscated: bool = False) -> dict: - """ - get credentials for llm use. - - :param model_name: - :param model_type: - :param obfuscated: - :return: - """ - return self.get_provider_credentials(obfuscated) diff --git a/api/core/model_providers/providers/base.py b/api/core/model_providers/providers/base.py deleted file mode 100644 index 1ff03f286de64b..00000000000000 --- a/api/core/model_providers/providers/base.py +++ /dev/null @@ -1,294 +0,0 @@ -from abc import ABC, abstractmethod -from datetime import datetime -from typing import Type, Optional - -from flask import current_app -from pydantic import BaseModel - -from core.model_providers.error import QuotaExceededError, LLMBadRequestError -from extensions.ext_database import db -from core.model_providers.models.entity.model_params import ModelType, ModelKwargsRules -from core.model_providers.models.entity.provider import ProviderQuotaUnit -from core.model_providers.rules import provider_rules -from models.provider import Provider, ProviderType, ProviderModel - - -class BaseModelProvider(BaseModel, ABC): - - provider: Provider - - class Config: - """Configuration for this pydantic object.""" - - arbitrary_types_allowed = True - - @property - @abstractmethod - def provider_name(self): - """ - Returns the name of a provider. - """ - raise NotImplementedError - - def get_rules(self): - """ - Returns the rules of a provider. - """ - return provider_rules[self.provider_name] - - def get_supported_model_list(self, model_type: ModelType) -> list[dict]: - """ - get supported model object list for use. - - :param model_type: - :return: - """ - rules = self.get_rules() - if 'custom' not in rules['support_provider_types']: - return self._get_fixed_model_list(model_type) - - if 'model_flexibility' not in rules: - return self._get_fixed_model_list(model_type) - - if rules['model_flexibility'] == 'fixed': - return self._get_fixed_model_list(model_type) - - # get configurable provider models - provider_models = db.session.query(ProviderModel).filter( - ProviderModel.tenant_id == self.provider.tenant_id, - ProviderModel.provider_name == self.provider.provider_name, - ProviderModel.model_type == model_type.value, - ProviderModel.is_valid == True - ).order_by(ProviderModel.created_at.asc()).all() - - provider_model_list = [] - for provider_model in provider_models: - provider_model_dict = { - 'id': provider_model.model_name, - 'name': provider_model.model_name - } - - if model_type == ModelType.TEXT_GENERATION: - provider_model_dict['mode'] = self._get_text_generation_model_mode(provider_model.model_name) - - provider_model_list.append(provider_model_dict) - - return provider_model_list - - @abstractmethod - def _get_fixed_model_list(self, model_type: ModelType) -> list[dict]: - """ - get supported model object list for use. - - :param model_type: - :return: - """ - raise NotImplementedError - - @abstractmethod - def _get_text_generation_model_mode(self, model_name) -> str: - """ - get text generation model mode. - - :param model_name: - :return: - """ - raise NotImplementedError - - @abstractmethod - def get_model_class(self, model_type: ModelType) -> Type: - """ - get specific model class. - - :param model_type: - :return: - """ - raise NotImplementedError - - @classmethod - @abstractmethod - def is_provider_credentials_valid_or_raise(cls, credentials: dict): - """ - check provider credentials valid. - - :param credentials: - """ - raise NotImplementedError - - @classmethod - @abstractmethod - def encrypt_provider_credentials(cls, tenant_id: str, credentials: dict) -> dict: - """ - encrypt provider credentials for save. - - :param tenant_id: - :param credentials: - :return: - """ - raise NotImplementedError - - @abstractmethod - def get_provider_credentials(self, obfuscated: bool = False) -> dict: - """ - get credentials for llm use. - - :param obfuscated: - :return: - """ - raise NotImplementedError - - @classmethod - @abstractmethod - def is_model_credentials_valid_or_raise(cls, model_name: str, model_type: ModelType, credentials: dict): - """ - check model credentials valid. - - :param model_name: - :param model_type: - :param credentials: - """ - raise NotImplementedError - - @classmethod - @abstractmethod - def encrypt_model_credentials(cls, tenant_id: str, model_name: str, model_type: ModelType, - credentials: dict) -> dict: - """ - encrypt model credentials for save. - - :param tenant_id: - :param model_name: - :param model_type: - :param credentials: - :return: - """ - raise NotImplementedError - - @abstractmethod - def get_model_parameter_rules(self, model_name: str, model_type: ModelType) -> ModelKwargsRules: - """ - get model parameter rules. - - :param model_name: - :param model_type: - :return: - """ - raise NotImplementedError - - @abstractmethod - def get_model_credentials(self, model_name: str, model_type: ModelType, obfuscated: bool = False) -> dict: - """ - get credentials for llm use. - - :param model_name: - :param model_type: - :param obfuscated: - :return: - """ - raise NotImplementedError - - @classmethod - def is_provider_type_system_supported(cls) -> bool: - return current_app.config['EDITION'] == 'CLOUD' - - def check_quota_over_limit(self): - """ - check provider quota over limit. - - :return: - """ - if self.provider.provider_type != ProviderType.SYSTEM.value: - return - - rules = self.get_rules() - if 'system' not in rules['support_provider_types']: - return - - provider = db.session.query(Provider).filter( - db.and_( - Provider.id == self.provider.id, - Provider.is_valid == True, - Provider.quota_limit > Provider.quota_used - ) - ).first() - - if not provider: - raise QuotaExceededError() - - def deduct_quota(self, used_tokens: int = 0) -> None: - """ - deduct available quota when provider type is system or paid. - - :return: - """ - if self.provider.provider_type != ProviderType.SYSTEM.value: - return - - rules = self.get_rules() - if 'system' not in rules['support_provider_types']: - return - - if not self.should_deduct_quota(): - return - - if 'system_config' not in rules: - quota_unit = ProviderQuotaUnit.TIMES.value - elif 'quota_unit' not in rules['system_config']: - quota_unit = ProviderQuotaUnit.TIMES.value - else: - quota_unit = rules['system_config']['quota_unit'] - - if quota_unit == ProviderQuotaUnit.TOKENS.value: - used_quota = used_tokens - else: - used_quota = 1 - - db.session.query(Provider).filter( - Provider.tenant_id == self.provider.tenant_id, - Provider.provider_name == self.provider.provider_name, - Provider.provider_type == self.provider.provider_type, - Provider.quota_type == self.provider.quota_type, - Provider.quota_limit > Provider.quota_used - ).update({'quota_used': Provider.quota_used + used_quota}) - db.session.commit() - - def should_deduct_quota(self): - return False - - def update_last_used(self) -> None: - """ - update last used time. - - :return: - """ - db.session.query(Provider).filter( - Provider.tenant_id == self.provider.tenant_id, - Provider.provider_name == self.provider.provider_name - ).update({'last_used': datetime.utcnow()}) - db.session.commit() - - def _get_provider_model(self, model_name: str, model_type: ModelType) -> ProviderModel: - """ - get provider model. - - :param model_name: - :param model_type: - :return: - """ - provider_model = db.session.query(ProviderModel).filter( - ProviderModel.tenant_id == self.provider.tenant_id, - ProviderModel.provider_name == self.provider.provider_name, - ProviderModel.model_name == model_name, - ProviderModel.model_type == model_type.value, - ProviderModel.is_valid == True - ).first() - - if not provider_model: - raise LLMBadRequestError(f"The model {model_name} does not exist. " - f"Please check the configuration.") - - return provider_model - - -class CredentialsValidateFailedError(Exception): - pass diff --git a/api/core/model_providers/providers/chatglm_provider.py b/api/core/model_providers/providers/chatglm_provider.py deleted file mode 100644 index e3db27b2d2441e..00000000000000 --- a/api/core/model_providers/providers/chatglm_provider.py +++ /dev/null @@ -1,165 +0,0 @@ -import json -from json import JSONDecodeError -from typing import Type - -import requests -from langchain.llms import ChatGLM - -from core.helper import encrypter -from core.model_providers.models.base import BaseProviderModel -from core.model_providers.models.entity.model_params import ModelKwargsRules, KwargRule, ModelType, ModelMode -from core.model_providers.models.llm.chatglm_model import ChatGLMModel -from core.model_providers.providers.base import BaseModelProvider, CredentialsValidateFailedError -from models.provider import ProviderType - - -class ChatGLMProvider(BaseModelProvider): - - @property - def provider_name(self): - """ - Returns the name of a provider. - """ - return 'chatglm' - - def _get_fixed_model_list(self, model_type: ModelType) -> list[dict]: - if model_type == ModelType.TEXT_GENERATION: - return [ - { - 'id': 'chatglm3-6b', - 'name': 'ChatGLM3-6B', - 'mode': ModelMode.CHAT.value, - }, - { - 'id': 'chatglm3-6b-32k', - 'name': 'ChatGLM3-6B-32K', - 'mode': ModelMode.CHAT.value, - }, - { - 'id': 'chatglm2-6b', - 'name': 'ChatGLM2-6B', - 'mode': ModelMode.CHAT.value, - } - ] - else: - return [] - - def _get_text_generation_model_mode(self, model_name) -> str: - return ModelMode.CHAT.value - - def get_model_class(self, model_type: ModelType) -> Type[BaseProviderModel]: - """ - Returns the model class. - - :param model_type: - :return: - """ - if model_type == ModelType.TEXT_GENERATION: - model_class = ChatGLMModel - else: - raise NotImplementedError - - return model_class - - def get_model_parameter_rules(self, model_name: str, model_type: ModelType) -> ModelKwargsRules: - """ - get model parameter rules. - - :param model_name: - :param model_type: - :return: - """ - model_max_tokens = { - 'chatglm3-6b-32k': 32000, - 'chatglm3-6b': 8000, - 'chatglm2-6b': 8000, - } - - max_tokens_alias = 'max_length' if model_name == 'chatglm2-6b' else 'max_tokens' - - return ModelKwargsRules( - temperature=KwargRule[float](min=0, max=2, default=1, precision=2), - top_p=KwargRule[float](min=0, max=1, default=0.7, precision=2), - presence_penalty=KwargRule[float](enabled=False), - frequency_penalty=KwargRule[float](enabled=False), - max_tokens=KwargRule[int](alias=max_tokens_alias, min=10, max=model_max_tokens.get(model_name), default=2048, precision=0), - ) - - @classmethod - def is_provider_credentials_valid_or_raise(cls, credentials: dict): - """ - Validates the given credentials. - """ - if 'api_base' not in credentials: - raise CredentialsValidateFailedError('ChatGLM Endpoint URL must be provided.') - - try: - response = requests.get(f"{credentials['api_base']}/v1/models", timeout=5) - - if response.status_code != 200: - raise Exception('ChatGLM Endpoint URL is invalid.') - except Exception as ex: - raise CredentialsValidateFailedError(str(ex)) - - @classmethod - def encrypt_provider_credentials(cls, tenant_id: str, credentials: dict) -> dict: - credentials['api_base'] = encrypter.encrypt_token(tenant_id, credentials['api_base']) - return credentials - - def get_provider_credentials(self, obfuscated: bool = False) -> dict: - if self.provider.provider_type == ProviderType.CUSTOM.value: - try: - credentials = json.loads(self.provider.encrypted_config) - except JSONDecodeError: - credentials = { - 'api_base': None - } - - if credentials['api_base']: - credentials['api_base'] = encrypter.decrypt_token( - self.provider.tenant_id, - credentials['api_base'] - ) - - if obfuscated: - credentials['api_base'] = encrypter.obfuscated_token(credentials['api_base']) - - return credentials - - return {} - - @classmethod - def is_model_credentials_valid_or_raise(cls, model_name: str, model_type: ModelType, credentials: dict): - """ - check model credentials valid. - - :param model_name: - :param model_type: - :param credentials: - """ - return - - @classmethod - def encrypt_model_credentials(cls, tenant_id: str, model_name: str, model_type: ModelType, - credentials: dict) -> dict: - """ - encrypt model credentials for save. - - :param tenant_id: - :param model_name: - :param model_type: - :param credentials: - :return: - """ - return {} - - def get_model_credentials(self, model_name: str, model_type: ModelType, obfuscated: bool = False) -> dict: - """ - get credentials for llm use. - - :param model_name: - :param model_type: - :param obfuscated: - :return: - """ - return self.get_provider_credentials(obfuscated) diff --git a/api/core/model_providers/providers/cohere_provider.py b/api/core/model_providers/providers/cohere_provider.py deleted file mode 100644 index 9fa77dfff2a5c4..00000000000000 --- a/api/core/model_providers/providers/cohere_provider.py +++ /dev/null @@ -1,152 +0,0 @@ -import json -from json import JSONDecodeError -from typing import Type - -from langchain.schema import HumanMessage - -from core.helper import encrypter -from core.model_providers.models.base import BaseProviderModel -from core.model_providers.models.entity.model_params import ModelKwargsRules, KwargRule, ModelType, ModelMode -from core.model_providers.models.reranking.cohere_reranking import CohereReranking -from core.model_providers.providers.base import BaseModelProvider, CredentialsValidateFailedError -from models.provider import ProviderType - - -class CohereProvider(BaseModelProvider): - - @property - def provider_name(self): - """ - Returns the name of a provider. - """ - return 'cohere' - - def _get_text_generation_model_mode(self, model_name) -> str: - return ModelMode.CHAT.value - - def _get_fixed_model_list(self, model_type: ModelType) -> list[dict]: - if model_type == ModelType.RERANKING: - return [ - { - 'id': 'rerank-english-v2.0', - 'name': 'rerank-english-v2.0' - }, - { - 'id': 'rerank-multilingual-v2.0', - 'name': 'rerank-multilingual-v2.0' - } - ] - else: - return [] - - def get_model_class(self, model_type: ModelType) -> Type[BaseProviderModel]: - """ - Returns the model class. - - :param model_type: - :return: - """ - if model_type == ModelType.RERANKING: - model_class = CohereReranking - else: - raise NotImplementedError - - return model_class - - def get_model_parameter_rules(self, model_name: str, model_type: ModelType) -> ModelKwargsRules: - """ - get model parameter rules. - - :param model_name: - :param model_type: - :return: - """ - return ModelKwargsRules( - temperature=KwargRule[float](min=0, max=1, default=0.3, precision=2), - top_p=KwargRule[float](min=0, max=0.99, default=0.85, precision=2), - presence_penalty=KwargRule[float](enabled=False), - frequency_penalty=KwargRule[float](enabled=False), - max_tokens=KwargRule[int](enabled=False), - ) - - @classmethod - def is_provider_credentials_valid_or_raise(cls, credentials: dict): - """ - Validates the given credentials. - """ - if 'api_key' not in credentials: - raise CredentialsValidateFailedError('Cohere api_key must be provided.') - - try: - credential_kwargs = { - 'api_key': credentials['api_key'], - } - # todo validate - except Exception as ex: - raise CredentialsValidateFailedError(str(ex)) - - @classmethod - def encrypt_provider_credentials(cls, tenant_id: str, credentials: dict) -> dict: - credentials['api_key'] = encrypter.encrypt_token(tenant_id, credentials['api_key']) - return credentials - - def get_provider_credentials(self, obfuscated: bool = False) -> dict: - if self.provider.provider_type == ProviderType.CUSTOM.value: - try: - credentials = json.loads(self.provider.encrypted_config) - except JSONDecodeError: - credentials = { - 'api_key': None, - } - - if credentials['api_key']: - credentials['api_key'] = encrypter.decrypt_token( - self.provider.tenant_id, - credentials['api_key'] - ) - - if obfuscated: - credentials['api_key'] = encrypter.obfuscated_token(credentials['api_key']) - - return credentials - else: - return {} - - def should_deduct_quota(self): - return True - - @classmethod - def is_model_credentials_valid_or_raise(cls, model_name: str, model_type: ModelType, credentials: dict): - """ - check model credentials valid. - - :param model_name: - :param model_type: - :param credentials: - """ - return - - @classmethod - def encrypt_model_credentials(cls, tenant_id: str, model_name: str, model_type: ModelType, - credentials: dict) -> dict: - """ - encrypt model credentials for save. - - :param tenant_id: - :param model_name: - :param model_type: - :param credentials: - :return: - """ - return {} - - def get_model_credentials(self, model_name: str, model_type: ModelType, obfuscated: bool = False) -> dict: - """ - get credentials for llm use. - - :param model_name: - :param model_type: - :param obfuscated: - :return: - """ - return self.get_provider_credentials(obfuscated) diff --git a/api/core/model_providers/providers/hosted.py b/api/core/model_providers/providers/hosted.py deleted file mode 100644 index df8a769acbfe36..00000000000000 --- a/api/core/model_providers/providers/hosted.py +++ /dev/null @@ -1,86 +0,0 @@ -import os -from typing import Optional - -import langchain -from flask import Flask -from pydantic import BaseModel - - -class HostedOpenAI(BaseModel): - api_base: str = None - api_organization: str = None - api_key: str - quota_limit: int = 0 - """Quota limit for the openai hosted model. -1 means unlimited.""" - paid_enabled: bool = False - - -class HostedAzureOpenAI(BaseModel): - api_base: str - api_key: str - quota_limit: int = 0 - """Quota limit for the azure openai hosted model. -1 means unlimited.""" - - -class HostedAnthropic(BaseModel): - api_base: str = None - api_key: str - quota_limit: int = 0 - """Quota limit for the anthropic hosted model. -1 means unlimited.""" - paid_enabled: bool = False - - -class HostedModelProviders(BaseModel): - openai: Optional[HostedOpenAI] = None - azure_openai: Optional[HostedAzureOpenAI] = None - anthropic: Optional[HostedAnthropic] = None - - -hosted_model_providers = HostedModelProviders() - - -class HostedModerationConfig(BaseModel): - enabled: bool = False - providers: list[str] = [] - - -class HostedConfig(BaseModel): - moderation = HostedModerationConfig() - - -hosted_config = HostedConfig() - - -def init_app(app: Flask): - if os.environ.get("DEBUG") and os.environ.get("DEBUG").lower() == 'true': - langchain.verbose = True - - if app.config.get("HOSTED_OPENAI_ENABLED"): - hosted_model_providers.openai = HostedOpenAI( - api_base=app.config.get("HOSTED_OPENAI_API_BASE"), - api_organization=app.config.get("HOSTED_OPENAI_API_ORGANIZATION"), - api_key=app.config.get("HOSTED_OPENAI_API_KEY"), - quota_limit=app.config.get("HOSTED_OPENAI_QUOTA_LIMIT"), - paid_enabled=app.config.get("HOSTED_OPENAI_PAID_ENABLED"), - ) - - if app.config.get("HOSTED_AZURE_OPENAI_ENABLED"): - hosted_model_providers.azure_openai = HostedAzureOpenAI( - api_base=app.config.get("HOSTED_AZURE_OPENAI_API_BASE"), - api_key=app.config.get("HOSTED_AZURE_OPENAI_API_KEY"), - quota_limit=app.config.get("HOSTED_AZURE_OPENAI_QUOTA_LIMIT"), - ) - - if app.config.get("HOSTED_ANTHROPIC_ENABLED"): - hosted_model_providers.anthropic = HostedAnthropic( - api_base=app.config.get("HOSTED_ANTHROPIC_API_BASE"), - api_key=app.config.get("HOSTED_ANTHROPIC_API_KEY"), - quota_limit=app.config.get("HOSTED_ANTHROPIC_QUOTA_LIMIT"), - paid_enabled=app.config.get("HOSTED_ANTHROPIC_PAID_ENABLED"), - ) - - if app.config.get("HOSTED_MODERATION_ENABLED") and app.config.get("HOSTED_MODERATION_PROVIDERS"): - hosted_config.moderation = HostedModerationConfig( - enabled=app.config.get("HOSTED_MODERATION_ENABLED"), - providers=app.config.get("HOSTED_MODERATION_PROVIDERS").split(',') - ) diff --git a/api/core/model_providers/providers/huggingface_hub_provider.py b/api/core/model_providers/providers/huggingface_hub_provider.py deleted file mode 100644 index 2cb7ff120a84c8..00000000000000 --- a/api/core/model_providers/providers/huggingface_hub_provider.py +++ /dev/null @@ -1,251 +0,0 @@ -import json -from typing import Type -import requests - -from huggingface_hub import HfApi - -from core.helper import encrypter -from core.model_providers.models.entity.model_params import KwargRule, ModelKwargsRules, ModelType, ModelMode -from core.model_providers.models.llm.huggingface_hub_model import HuggingfaceHubModel -from core.model_providers.providers.base import BaseModelProvider, CredentialsValidateFailedError - -from core.model_providers.models.base import BaseProviderModel -from core.third_party.langchain.llms.huggingface_endpoint_llm import HuggingFaceEndpointLLM -from core.third_party.langchain.embeddings.huggingface_hub_embedding import HuggingfaceHubEmbeddings -from core.model_providers.models.embedding.huggingface_embedding import HuggingfaceEmbedding -from models.provider import ProviderType - -HUGGINGFACE_ENDPOINT_API = 'https://api.endpoints.huggingface.cloud/v2/endpoint/' - - -class HuggingfaceHubProvider(BaseModelProvider): - @property - def provider_name(self): - """ - Returns the name of a provider. - """ - return 'huggingface_hub' - - def _get_fixed_model_list(self, model_type: ModelType) -> list[dict]: - return [] - - def _get_text_generation_model_mode(self, model_name) -> str: - return ModelMode.COMPLETION.value - - def get_model_class(self, model_type: ModelType) -> Type[BaseProviderModel]: - """ - Returns the model class. - - :param model_type: - :return: - """ - if model_type == ModelType.TEXT_GENERATION: - model_class = HuggingfaceHubModel - elif model_type == ModelType.EMBEDDINGS: - model_class = HuggingfaceEmbedding - else: - raise NotImplementedError - - return model_class - - def get_model_parameter_rules(self, model_name: str, model_type: ModelType) -> ModelKwargsRules: - """ - get model parameter rules. - - :param model_name: - :param model_type: - :return: - """ - return ModelKwargsRules( - temperature=KwargRule[float](min=0, max=2, default=1, precision=2), - top_p=KwargRule[float](min=0.01, max=0.99, default=0.7, precision=2), - presence_penalty=KwargRule[float](enabled=False), - frequency_penalty=KwargRule[float](enabled=False), - max_tokens=KwargRule[int](alias='max_new_tokens', min=10, max=4000, default=200, precision=0), - ) - - @classmethod - def is_model_credentials_valid_or_raise(cls, model_name: str, model_type: ModelType, credentials: dict): - """ - check model credentials valid. - - :param model_name: - :param model_type: - :param credentials: - """ - if model_type not in [ModelType.TEXT_GENERATION, ModelType.EMBEDDINGS]: - raise NotImplementedError - - if 'huggingfacehub_api_type' not in credentials \ - or credentials['huggingfacehub_api_type'] not in ['hosted_inference_api', 'inference_endpoints']: - raise CredentialsValidateFailedError('Hugging Face Hub API Type invalid, ' - 'must be hosted_inference_api or inference_endpoints.') - - if 'huggingfacehub_api_token' not in credentials: - raise CredentialsValidateFailedError('Hugging Face Hub API Token must be provided.') - - hfapi = HfApi(token=credentials['huggingfacehub_api_token']) - - try: - hfapi.whoami() - except Exception: - raise CredentialsValidateFailedError("Invalid API Token.") - - if credentials['huggingfacehub_api_type'] == 'inference_endpoints': - if 'huggingfacehub_endpoint_url' not in credentials: - raise CredentialsValidateFailedError('Hugging Face Hub Endpoint URL must be provided.') - - if 'task_type' not in credentials: - raise CredentialsValidateFailedError('Task Type must be provided.') - - if credentials['task_type'] not in ("text2text-generation", "text-generation", 'feature-extraction'): - raise CredentialsValidateFailedError('Task Type must be one of text2text-generation, ' - 'text-generation, feature-extraction.') - - try: - if credentials['task_type'] == 'feature-extraction': - cls.check_embedding_valid(credentials, model_name) - else: - cls.check_llm_valid(credentials) - except Exception as e: - raise CredentialsValidateFailedError(f"{e.__class__.__name__}:{str(e)}") - else: - try: - model_info = hfapi.model_info(repo_id=model_name) - if not model_info: - raise ValueError(f'Model {model_name} not found.') - - if 'inference' in model_info.cardData and not model_info.cardData['inference']: - raise ValueError(f'Inference API has been turned off for this model {model_name}.') - - VALID_TASKS = ("text2text-generation", "text-generation", "feature-extraction") - if model_info.pipeline_tag not in VALID_TASKS: - raise ValueError(f"Model {model_name} is not a valid task, " - f"must be one of {VALID_TASKS}.") - except Exception as e: - raise CredentialsValidateFailedError(f"{e.__class__.__name__}:{str(e)}") - - @classmethod - def check_llm_valid(cls, credentials: dict): - llm = HuggingFaceEndpointLLM( - endpoint_url=credentials['huggingfacehub_endpoint_url'], - task=credentials['task_type'], - model_kwargs={"temperature": 0.5, "max_new_tokens": 200}, - huggingfacehub_api_token=credentials['huggingfacehub_api_token'] - ) - - llm("ping") - - @classmethod - def check_embedding_valid(cls, credentials: dict, model_name: str): - - cls.check_endpoint_url_model_repository_name(credentials, model_name) - - embedding_model = HuggingfaceHubEmbeddings( - model=model_name, - **credentials - ) - - embedding_model.embed_query("ping") - - @classmethod - def check_endpoint_url_model_repository_name(cls, credentials: dict, model_name: str): - try: - url = f'{HUGGINGFACE_ENDPOINT_API}{credentials["huggingface_namespace"]}' - headers = { - 'Authorization': f'Bearer {credentials["huggingfacehub_api_token"]}', - 'Content-Type': 'application/json' - } - - response =requests.get(url=url, headers=headers) - - if response.status_code != 200: - raise ValueError('User Name or Organization Name is invalid.') - - model_repository_name = '' - - for item in response.json().get("items", []): - if item.get("status", {}).get("url") == credentials['huggingfacehub_endpoint_url']: - model_repository_name = item.get("model", {}).get("repository") - break - - if model_repository_name != model_name: - raise ValueError(f'Model Name {model_name} is invalid. Please check it on the inference endpoints console.') - - except Exception as e: - raise ValueError(str(e)) - - - @classmethod - def encrypt_model_credentials(cls, tenant_id: str, model_name: str, model_type: ModelType, - credentials: dict) -> dict: - """ - encrypt model credentials for save. - - :param tenant_id: - :param model_name: - :param model_type: - :param credentials: - :return: - """ - credentials['huggingfacehub_api_token'] = encrypter.encrypt_token(tenant_id, credentials['huggingfacehub_api_token']) - - if credentials['huggingfacehub_api_type'] == 'hosted_inference_api': - hfapi = HfApi(token=credentials['huggingfacehub_api_token']) - model_info = hfapi.model_info(repo_id=model_name) - if not model_info: - raise ValueError(f'Model {model_name} not found.') - - if 'inference' in model_info.cardData and not model_info.cardData['inference']: - raise ValueError(f'Inference API has been turned off for this model {model_name}.') - - credentials['task_type'] = model_info.pipeline_tag - - return credentials - - def get_model_credentials(self, model_name: str, model_type: ModelType, obfuscated: bool = False) -> dict: - """ - get credentials for llm use. - - :param model_name: - :param model_type: - :param obfuscated: - :return: - """ - if self.provider.provider_type != ProviderType.CUSTOM.value: - raise NotImplementedError - - provider_model = self._get_provider_model(model_name, model_type) - - if not provider_model.encrypted_config: - return { - 'huggingfacehub_api_token': None, - 'task_type': None - } - - credentials = json.loads(provider_model.encrypted_config) - - if 'task_type' not in credentials: - credentials['task_type'] = 'text-generation' - - if credentials['huggingfacehub_api_token']: - credentials['huggingfacehub_api_token'] = encrypter.decrypt_token( - self.provider.tenant_id, - credentials['huggingfacehub_api_token'] - ) - - if obfuscated: - credentials['huggingfacehub_api_token'] = encrypter.obfuscated_token(credentials['huggingfacehub_api_token']) - - return credentials - - @classmethod - def is_provider_credentials_valid_or_raise(cls, credentials: dict): - return - - @classmethod - def encrypt_provider_credentials(cls, tenant_id: str, credentials: dict) -> dict: - return {} - - def get_provider_credentials(self, obfuscated: bool = False) -> dict: - return {} diff --git a/api/core/model_providers/providers/jina_provider.py b/api/core/model_providers/providers/jina_provider.py deleted file mode 100644 index fd3402582bf219..00000000000000 --- a/api/core/model_providers/providers/jina_provider.py +++ /dev/null @@ -1,141 +0,0 @@ -import json -from json import JSONDecodeError -from typing import Type - -from core.helper import encrypter -from core.model_providers.models.base import BaseProviderModel -from core.model_providers.models.embedding.jina_embedding import JinaEmbedding -from core.model_providers.models.entity.model_params import ModelType, ModelKwargsRules -from core.model_providers.providers.base import BaseModelProvider, CredentialsValidateFailedError -from core.third_party.langchain.embeddings.jina_embedding import JinaEmbeddings -from models.provider import ProviderType - - -class JinaProvider(BaseModelProvider): - - @property - def provider_name(self): - """ - Returns the name of a provider. - """ - return 'jina' - - def _get_fixed_model_list(self, model_type: ModelType) -> list[dict]: - if model_type == ModelType.EMBEDDINGS: - return [ - { - 'id': 'jina-embeddings-v2-base-en', - 'name': 'jina-embeddings-v2-base-en', - }, - { - 'id': 'jina-embeddings-v2-small-en', - 'name': 'jina-embeddings-v2-small-en', - } - ] - else: - return [] - - def get_model_class(self, model_type: ModelType) -> Type[BaseProviderModel]: - """ - Returns the model class. - - :param model_type: - :return: - """ - if model_type == ModelType.EMBEDDINGS: - model_class = JinaEmbedding - else: - raise NotImplementedError - - return model_class - - @classmethod - def is_provider_credentials_valid_or_raise(cls, credentials: dict): - """ - Validates the given credentials. - """ - if 'api_key' not in credentials: - raise CredentialsValidateFailedError('Jina API Key must be provided.') - - try: - credential_kwargs = { - 'api_key': credentials['api_key'], - } - - embedding = JinaEmbeddings( - model='jina-embeddings-v2-small-en', - **credential_kwargs - ) - - embedding.embed_query("ping") - except Exception as ex: - raise CredentialsValidateFailedError(str(ex)) - - @classmethod - def encrypt_provider_credentials(cls, tenant_id: str, credentials: dict) -> dict: - credentials['api_key'] = encrypter.encrypt_token(tenant_id, credentials['api_key']) - return credentials - - def get_provider_credentials(self, obfuscated: bool = False) -> dict: - if self.provider.provider_type == ProviderType.CUSTOM.value: - try: - credentials = json.loads(self.provider.encrypted_config) - except JSONDecodeError: - credentials = { - 'api_key': None, - } - - if credentials['api_key']: - credentials['api_key'] = encrypter.decrypt_token( - self.provider.tenant_id, - credentials['api_key'] - ) - - if obfuscated: - credentials['api_key'] = encrypter.obfuscated_token(credentials['api_key']) - - return credentials - - return {} - - @classmethod - def is_model_credentials_valid_or_raise(cls, model_name: str, model_type: ModelType, credentials: dict): - """ - check model credentials valid. - - :param model_name: - :param model_type: - :param credentials: - """ - return - - @classmethod - def encrypt_model_credentials(cls, tenant_id: str, model_name: str, model_type: ModelType, - credentials: dict) -> dict: - """ - encrypt model credentials for save. - - :param tenant_id: - :param model_name: - :param model_type: - :param credentials: - :return: - """ - return {} - - def get_model_credentials(self, model_name: str, model_type: ModelType, obfuscated: bool = False) -> dict: - """ - get credentials for llm use. - - :param model_name: - :param model_type: - :param obfuscated: - :return: - """ - return self.get_provider_credentials(obfuscated) - - def _get_text_generation_model_mode(self, model_name) -> str: - raise NotImplementedError - - def get_model_parameter_rules(self, model_name: str, model_type: ModelType) -> ModelKwargsRules: - raise NotImplementedError diff --git a/api/core/model_providers/providers/localai_provider.py b/api/core/model_providers/providers/localai_provider.py deleted file mode 100644 index 89279996f8a50f..00000000000000 --- a/api/core/model_providers/providers/localai_provider.py +++ /dev/null @@ -1,171 +0,0 @@ -import json -from typing import Type - -from langchain.embeddings import LocalAIEmbeddings -from langchain.schema import HumanMessage - -from core.helper import encrypter -from core.model_providers.models.embedding.localai_embedding import LocalAIEmbedding -from core.model_providers.models.entity.model_params import ModelKwargsRules, ModelType, KwargRule, ModelMode -from core.model_providers.models.llm.localai_model import LocalAIModel -from core.model_providers.providers.base import BaseModelProvider, CredentialsValidateFailedError - -from core.model_providers.models.base import BaseProviderModel -from core.third_party.langchain.llms.chat_open_ai import EnhanceChatOpenAI -from core.third_party.langchain.llms.open_ai import EnhanceOpenAI -from models.provider import ProviderType - - -class LocalAIProvider(BaseModelProvider): - @property - def provider_name(self): - """ - Returns the name of a provider. - """ - return 'localai' - - def _get_fixed_model_list(self, model_type: ModelType) -> list[dict]: - return [] - - def _get_text_generation_model_mode(self, model_name) -> str: - credentials = self.get_model_credentials(model_name, ModelType.TEXT_GENERATION) - if credentials['completion_type'] == 'chat_completion': - return ModelMode.CHAT.value - else: - return ModelMode.COMPLETION.value - - def get_model_class(self, model_type: ModelType) -> Type[BaseProviderModel]: - """ - Returns the model class. - - :param model_type: - :return: - """ - if model_type == ModelType.TEXT_GENERATION: - model_class = LocalAIModel - elif model_type == ModelType.EMBEDDINGS: - model_class = LocalAIEmbedding - else: - raise NotImplementedError - - return model_class - - def get_model_parameter_rules(self, model_name: str, model_type: ModelType) -> ModelKwargsRules: - """ - get model parameter rules. - - :param model_name: - :param model_type: - :return: - """ - return ModelKwargsRules( - temperature=KwargRule[float](min=0, max=2, default=0.7, precision=2), - top_p=KwargRule[float](min=0, max=1, default=1, precision=2), - max_tokens=KwargRule[int](min=10, max=4097, default=16, precision=0), - ) - - @classmethod - def is_model_credentials_valid_or_raise(cls, model_name: str, model_type: ModelType, credentials: dict): - """ - check model credentials valid. - - :param model_name: - :param model_type: - :param credentials: - """ - if 'server_url' not in credentials: - raise CredentialsValidateFailedError('LocalAI Server URL must be provided.') - - try: - if model_type == ModelType.EMBEDDINGS: - model = LocalAIEmbeddings( - model=model_name, - openai_api_key='1', - openai_api_base=credentials['server_url'] - ) - - model.embed_query("ping") - else: - if ('completion_type' not in credentials - or credentials['completion_type'] not in ['completion', 'chat_completion']): - raise CredentialsValidateFailedError('LocalAI Completion Type must be provided.') - - if credentials['completion_type'] == 'chat_completion': - model = EnhanceChatOpenAI( - model_name=model_name, - openai_api_key='1', - openai_api_base=credentials['server_url'] + '/v1', - max_tokens=10, - request_timeout=60, - ) - - model([HumanMessage(content='ping')]) - else: - model = EnhanceOpenAI( - model_name=model_name, - openai_api_key='1', - openai_api_base=credentials['server_url'] + '/v1', - max_tokens=10, - request_timeout=60, - ) - - model('ping') - except Exception as ex: - raise CredentialsValidateFailedError(str(ex)) - - @classmethod - def encrypt_model_credentials(cls, tenant_id: str, model_name: str, model_type: ModelType, - credentials: dict) -> dict: - """ - encrypt model credentials for save. - - :param tenant_id: - :param model_name: - :param model_type: - :param credentials: - :return: - """ - credentials['server_url'] = encrypter.encrypt_token(tenant_id, credentials['server_url']) - return credentials - - def get_model_credentials(self, model_name: str, model_type: ModelType, obfuscated: bool = False) -> dict: - """ - get credentials for llm use. - - :param model_name: - :param model_type: - :param obfuscated: - :return: - """ - if self.provider.provider_type != ProviderType.CUSTOM.value: - raise NotImplementedError - - provider_model = self._get_provider_model(model_name, model_type) - - if not provider_model.encrypted_config: - return { - 'server_url': None, - } - - credentials = json.loads(provider_model.encrypted_config) - if credentials['server_url']: - credentials['server_url'] = encrypter.decrypt_token( - self.provider.tenant_id, - credentials['server_url'] - ) - - if obfuscated: - credentials['server_url'] = encrypter.obfuscated_token(credentials['server_url']) - - return credentials - - @classmethod - def is_provider_credentials_valid_or_raise(cls, credentials: dict): - return - - @classmethod - def encrypt_provider_credentials(cls, tenant_id: str, credentials: dict) -> dict: - return {} - - def get_provider_credentials(self, obfuscated: bool = False) -> dict: - return {} diff --git a/api/core/model_providers/providers/minimax_provider.py b/api/core/model_providers/providers/minimax_provider.py deleted file mode 100644 index f643e1e805d262..00000000000000 --- a/api/core/model_providers/providers/minimax_provider.py +++ /dev/null @@ -1,185 +0,0 @@ -import json -from json import JSONDecodeError -from typing import Type - -from langchain.schema import HumanMessage - -from core.helper import encrypter -from core.model_providers.models.base import BaseProviderModel -from core.model_providers.models.embedding.minimax_embedding import MinimaxEmbedding -from core.model_providers.models.entity.model_params import ModelKwargsRules, KwargRule, ModelType, ModelMode -from core.model_providers.models.llm.minimax_model import MinimaxModel -from core.model_providers.providers.base import BaseModelProvider, CredentialsValidateFailedError -from core.third_party.langchain.llms.minimax_llm import MinimaxChatLLM -from models.provider import ProviderType, ProviderQuotaType - - -class MinimaxProvider(BaseModelProvider): - - @property - def provider_name(self): - """ - Returns the name of a provider. - """ - return 'minimax' - - def _get_fixed_model_list(self, model_type: ModelType) -> list[dict]: - if model_type == ModelType.TEXT_GENERATION: - return [ - { - 'id': 'abab5.5-chat', - 'name': 'abab5.5-chat', - 'mode': ModelMode.COMPLETION.value, - }, - { - 'id': 'abab5-chat', - 'name': 'abab5-chat', - 'mode': ModelMode.COMPLETION.value, - } - ] - elif model_type == ModelType.EMBEDDINGS: - return [ - { - 'id': 'embo-01', - 'name': 'embo-01', - } - ] - else: - return [] - - def _get_text_generation_model_mode(self, model_name) -> str: - return ModelMode.COMPLETION.value - - def get_model_class(self, model_type: ModelType) -> Type[BaseProviderModel]: - """ - Returns the model class. - - :param model_type: - :return: - """ - if model_type == ModelType.TEXT_GENERATION: - model_class = MinimaxModel - elif model_type == ModelType.EMBEDDINGS: - model_class = MinimaxEmbedding - else: - raise NotImplementedError - - return model_class - - def get_model_parameter_rules(self, model_name: str, model_type: ModelType) -> ModelKwargsRules: - """ - get model parameter rules. - - :param model_name: - :param model_type: - :return: - """ - model_max_tokens = { - 'abab5.5-chat': 16384, - 'abab5-chat': 6144, - } - - return ModelKwargsRules( - temperature=KwargRule[float](min=0.01, max=1, default=0.9, precision=2), - top_p=KwargRule[float](min=0, max=1, default=0.95, precision=2), - presence_penalty=KwargRule[float](enabled=False), - frequency_penalty=KwargRule[float](enabled=False), - max_tokens=KwargRule[int](min=10, max=model_max_tokens.get(model_name, 6144), default=1024, precision=0), - ) - - @classmethod - def is_provider_credentials_valid_or_raise(cls, credentials: dict): - """ - Validates the given credentials. - """ - if 'minimax_group_id' not in credentials: - raise CredentialsValidateFailedError('MiniMax Group ID must be provided.') - - if 'minimax_api_key' not in credentials: - raise CredentialsValidateFailedError('MiniMax API Key must be provided.') - - try: - credential_kwargs = { - 'minimax_group_id': credentials['minimax_group_id'], - 'minimax_api_key': credentials['minimax_api_key'], - } - - llm = MinimaxChatLLM( - model='abab5.5-chat', - max_tokens=10, - temperature=0.01, - **credential_kwargs - ) - - llm([HumanMessage(content='ping')]) - except Exception as ex: - raise CredentialsValidateFailedError(str(ex)) - - @classmethod - def encrypt_provider_credentials(cls, tenant_id: str, credentials: dict) -> dict: - credentials['minimax_api_key'] = encrypter.encrypt_token(tenant_id, credentials['minimax_api_key']) - return credentials - - def get_provider_credentials(self, obfuscated: bool = False) -> dict: - if self.provider.provider_type == ProviderType.CUSTOM.value \ - or (self.provider.provider_type == ProviderType.SYSTEM.value - and self.provider.quota_type == ProviderQuotaType.FREE.value): - try: - credentials = json.loads(self.provider.encrypted_config) - except JSONDecodeError: - credentials = { - 'minimax_group_id': None, - 'minimax_api_key': None, - } - - if credentials['minimax_api_key']: - credentials['minimax_api_key'] = encrypter.decrypt_token( - self.provider.tenant_id, - credentials['minimax_api_key'] - ) - - if obfuscated: - credentials['minimax_api_key'] = encrypter.obfuscated_token(credentials['minimax_api_key']) - - return credentials - - return {} - - def should_deduct_quota(self): - return True - - @classmethod - def is_model_credentials_valid_or_raise(cls, model_name: str, model_type: ModelType, credentials: dict): - """ - check model credentials valid. - - :param model_name: - :param model_type: - :param credentials: - """ - return - - @classmethod - def encrypt_model_credentials(cls, tenant_id: str, model_name: str, model_type: ModelType, - credentials: dict) -> dict: - """ - encrypt model credentials for save. - - :param tenant_id: - :param model_name: - :param model_type: - :param credentials: - :return: - """ - return {} - - def get_model_credentials(self, model_name: str, model_type: ModelType, obfuscated: bool = False) -> dict: - """ - get credentials for llm use. - - :param model_name: - :param model_type: - :param obfuscated: - :return: - """ - return self.get_provider_credentials(obfuscated) diff --git a/api/core/model_providers/providers/openai_provider.py b/api/core/model_providers/providers/openai_provider.py deleted file mode 100644 index 0e890529c633c4..00000000000000 --- a/api/core/model_providers/providers/openai_provider.py +++ /dev/null @@ -1,318 +0,0 @@ -import json -import logging -from json import JSONDecodeError -from typing import Type, Optional - -from flask import current_app -from openai.error import AuthenticationError, OpenAIError - -import openai - -from core.helper import encrypter -from core.model_providers.models.entity.provider import ModelFeature -from core.model_providers.models.speech2text.openai_whisper import OpenAIWhisper -from core.model_providers.models.base import BaseProviderModel -from core.model_providers.models.embedding.openai_embedding import OpenAIEmbedding -from core.model_providers.models.entity.model_params import ModelKwargsRules, KwargRule, ModelType, ModelMode -from core.model_providers.models.llm.openai_model import OpenAIModel, COMPLETION_MODELS -from core.model_providers.models.moderation.openai_moderation import OpenAIModeration -from core.model_providers.providers.base import BaseModelProvider, CredentialsValidateFailedError -from core.model_providers.providers.hosted import hosted_model_providers -from models.provider import ProviderType, ProviderQuotaType - - -class OpenAIProvider(BaseModelProvider): - - @property - def provider_name(self): - """ - Returns the name of a provider. - """ - return 'openai' - - def _get_fixed_model_list(self, model_type: ModelType) -> list[dict]: - if model_type == ModelType.TEXT_GENERATION: - models = [ - { - 'id': 'gpt-3.5-turbo', - 'name': 'gpt-3.5-turbo', - 'mode': ModelMode.CHAT.value, - 'features': [ - ModelFeature.AGENT_THOUGHT.value - ] - }, - { - 'id': 'gpt-3.5-turbo-1106', - 'name': 'gpt-3.5-turbo-1106', - 'mode': ModelMode.CHAT.value, - 'features': [ - ModelFeature.AGENT_THOUGHT.value - ] - }, - { - 'id': 'gpt-3.5-turbo-instruct', - 'name': 'gpt-3.5-turbo-instruct', - 'mode': ModelMode.COMPLETION.value, - }, - { - 'id': 'gpt-3.5-turbo-16k', - 'name': 'gpt-3.5-turbo-16k', - 'mode': ModelMode.CHAT.value, - 'features': [ - ModelFeature.AGENT_THOUGHT.value - ] - }, - { - 'id': 'gpt-4', - 'name': 'gpt-4', - 'mode': ModelMode.CHAT.value, - 'features': [ - ModelFeature.AGENT_THOUGHT.value - ] - }, - { - 'id': 'gpt-4-1106-preview', - 'name': 'gpt-4-1106-preview', - 'mode': ModelMode.CHAT.value, - 'features': [ - ModelFeature.AGENT_THOUGHT.value - ] - }, - { - 'id': 'gpt-4-vision-preview', - 'name': 'gpt-4-vision-preview', - 'mode': ModelMode.CHAT.value, - 'features': [ - ModelFeature.VISION.value - ] - }, - { - 'id': 'gpt-4-32k', - 'name': 'gpt-4-32k', - 'mode': ModelMode.CHAT.value, - 'features': [ - ModelFeature.AGENT_THOUGHT.value - ] - }, - { - 'id': 'text-davinci-003', - 'name': 'text-davinci-003', - 'mode': ModelMode.COMPLETION.value, - } - ] - - if self.provider.provider_type == ProviderType.SYSTEM.value \ - and self.provider.quota_type == ProviderQuotaType.TRIAL.value: - models = [item for item in models if not item['id'].startswith('gpt-4')] - - return models - elif model_type == ModelType.EMBEDDINGS: - return [ - { - 'id': 'text-embedding-ada-002', - 'name': 'text-embedding-ada-002' - } - ] - elif model_type == ModelType.SPEECH_TO_TEXT: - return [ - { - 'id': 'whisper-1', - 'name': 'whisper-1' - } - ] - elif model_type == ModelType.MODERATION: - return [ - { - 'id': 'text-moderation-stable', - 'name': 'text-moderation-stable' - } - ] - else: - return [] - - def _get_text_generation_model_mode(self, model_name) -> str: - if model_name in COMPLETION_MODELS: - return ModelMode.COMPLETION.value - else: - return ModelMode.CHAT.value - - def get_model_class(self, model_type: ModelType) -> Type[BaseProviderModel]: - """ - Returns the model class. - - :param model_type: - :return: - """ - if model_type == ModelType.TEXT_GENERATION: - model_class = OpenAIModel - elif model_type == ModelType.EMBEDDINGS: - model_class = OpenAIEmbedding - elif model_type == ModelType.MODERATION: - model_class = OpenAIModeration - elif model_type == ModelType.SPEECH_TO_TEXT: - model_class = OpenAIWhisper - else: - raise NotImplementedError - - return model_class - - def get_model_parameter_rules(self, model_name: str, model_type: ModelType) -> ModelKwargsRules: - """ - get model parameter rules. - - :param model_name: - :param model_type: - :return: - """ - model_max_tokens = { - 'gpt-4-1106-preview': 128000, - 'gpt-4-vision-preview': 128000, - 'gpt-4': 8192, - 'gpt-4-32k': 32768, - 'gpt-3.5-turbo-1106': 16384, - 'gpt-3.5-turbo': 4096, - 'gpt-3.5-turbo-instruct': 4097, - 'gpt-3.5-turbo-16k': 16384, - 'text-davinci-003': 4097, - } - - return ModelKwargsRules( - temperature=KwargRule[float](min=0, max=2, default=1, precision=2), - top_p=KwargRule[float](min=0, max=1, default=1, precision=2), - presence_penalty=KwargRule[float](min=-2, max=2, default=0, precision=2), - frequency_penalty=KwargRule[float](min=-2, max=2, default=0, precision=2), - max_tokens=KwargRule[int](min=10, max=model_max_tokens.get(model_name, 4097), default=16, precision=0), - ) - - @classmethod - def is_provider_credentials_valid_or_raise(cls, credentials: dict): - """ - Validates the given credentials. - """ - if 'openai_api_key' not in credentials: - raise CredentialsValidateFailedError('OpenAI API key is required') - - try: - credentials_kwargs = { - "api_key": credentials['openai_api_key'] - } - - if 'openai_api_base' in credentials and credentials['openai_api_base']: - credentials_kwargs['api_base'] = credentials['openai_api_base'] + '/v1' - - if 'openai_organization' in credentials: - credentials_kwargs['organization'] = credentials['openai_organization'] - - openai.ChatCompletion.create( - messages=[{"role": "user", "content": 'ping'}], - model='gpt-3.5-turbo', - timeout=10, - request_timeout=(5, 30), - max_tokens=20, - **credentials_kwargs - ) - except (AuthenticationError, OpenAIError) as ex: - raise CredentialsValidateFailedError(str(ex)) - except Exception as ex: - logging.exception('OpenAI config validation failed') - raise ex - - @classmethod - def encrypt_provider_credentials(cls, tenant_id: str, credentials: dict) -> dict: - credentials['openai_api_key'] = encrypter.encrypt_token(tenant_id, credentials['openai_api_key']) - return credentials - - def get_provider_credentials(self, obfuscated: bool = False) -> dict: - if self.provider.provider_type == ProviderType.CUSTOM.value: - try: - credentials = json.loads(self.provider.encrypted_config) - except JSONDecodeError: - credentials = { - 'openai_api_base': None, - 'openai_api_key': self.provider.encrypted_config, - 'openai_organization': None - } - - if credentials['openai_api_key']: - credentials['openai_api_key'] = encrypter.decrypt_token( - self.provider.tenant_id, - credentials['openai_api_key'] - ) - - if obfuscated: - credentials['openai_api_key'] = encrypter.obfuscated_token(credentials['openai_api_key']) - - if 'openai_api_base' not in credentials or not credentials['openai_api_base']: - credentials['openai_api_base'] = None - else: - credentials['openai_api_base'] = credentials['openai_api_base'] + '/v1' - - if 'openai_organization' not in credentials: - credentials['openai_organization'] = None - - return credentials - else: - if hosted_model_providers.openai: - return { - 'openai_api_base': hosted_model_providers.openai.api_base, - 'openai_api_key': hosted_model_providers.openai.api_key, - 'openai_organization': hosted_model_providers.openai.api_organization - } - else: - return { - 'openai_api_base': None, - 'openai_api_key': None, - 'openai_organization': None - } - - @classmethod - def is_provider_type_system_supported(cls) -> bool: - if current_app.config['EDITION'] != 'CLOUD': - return False - - if hosted_model_providers.openai: - return True - - return False - - def should_deduct_quota(self): - if hosted_model_providers.openai \ - and hosted_model_providers.openai.quota_limit and hosted_model_providers.openai.quota_limit > -1: - return True - - return False - - @classmethod - def is_model_credentials_valid_or_raise(cls, model_name: str, model_type: ModelType, credentials: dict): - """ - check model credentials valid. - - :param model_name: - :param model_type: - :param credentials: - """ - return - - @classmethod - def encrypt_model_credentials(cls, tenant_id: str, model_name: str, model_type: ModelType, credentials: dict) -> dict: - """ - encrypt model credentials for save. - - :param tenant_id: - :param model_name: - :param model_type: - :param credentials: - :return: - """ - return {} - - def get_model_credentials(self, model_name: str, model_type: ModelType, obfuscated: bool = False) -> dict: - """ - get credentials for llm use. - - :param model_name: - :param model_type: - :param obfuscated: - :return: - """ - return self.get_provider_credentials(obfuscated) diff --git a/api/core/model_providers/providers/openllm_provider.py b/api/core/model_providers/providers/openllm_provider.py deleted file mode 100644 index ea0e0b860d9f3e..00000000000000 --- a/api/core/model_providers/providers/openllm_provider.py +++ /dev/null @@ -1,152 +0,0 @@ -import json -from typing import Type - -from core.helper import encrypter -from core.model_providers.models.embedding.openllm_embedding import OpenLLMEmbedding -from core.model_providers.models.entity.model_params import KwargRule, ModelKwargsRules, ModelType, ModelMode -from core.model_providers.models.llm.openllm_model import OpenLLMModel -from core.model_providers.providers.base import BaseModelProvider, CredentialsValidateFailedError - -from core.model_providers.models.base import BaseProviderModel -from core.third_party.langchain.embeddings.openllm_embedding import OpenLLMEmbeddings -from core.third_party.langchain.llms.openllm import OpenLLM -from models.provider import ProviderType - - -class OpenLLMProvider(BaseModelProvider): - @property - def provider_name(self): - """ - Returns the name of a provider. - """ - return 'openllm' - - def _get_fixed_model_list(self, model_type: ModelType) -> list[dict]: - return [] - - def _get_text_generation_model_mode(self, model_name) -> str: - return ModelMode.COMPLETION.value - - def get_model_class(self, model_type: ModelType) -> Type[BaseProviderModel]: - """ - Returns the model class. - - :param model_type: - :return: - """ - if model_type == ModelType.TEXT_GENERATION: - model_class = OpenLLMModel - elif model_type== ModelType.EMBEDDINGS: - model_class = OpenLLMEmbedding - else: - raise NotImplementedError - - return model_class - - def get_model_parameter_rules(self, model_name: str, model_type: ModelType) -> ModelKwargsRules: - """ - get model parameter rules. - - :param model_name: - :param model_type: - :return: - """ - return ModelKwargsRules( - temperature=KwargRule[float](min=0.01, max=2, default=1, precision=2), - top_p=KwargRule[float](min=0, max=1, default=0.7, precision=2), - presence_penalty=KwargRule[float](min=-2, max=2, default=0, precision=2), - frequency_penalty=KwargRule[float](min=-2, max=2, default=0, precision=2), - max_tokens=KwargRule[int](alias='max_new_tokens', min=10, max=4000, default=128, precision=0), - ) - - @classmethod - def is_model_credentials_valid_or_raise(cls, model_name: str, model_type: ModelType, credentials: dict): - """ - check model credentials valid. - - :param model_name: - :param model_type: - :param credentials: - """ - if 'server_url' not in credentials: - raise CredentialsValidateFailedError('OpenLLM Server URL must be provided.') - - try: - credential_kwargs = { - 'server_url': credentials['server_url'] - } - - if model_type == ModelType.TEXT_GENERATION: - llm = OpenLLM( - llm_kwargs={ - 'max_new_tokens': 10 - }, - **credential_kwargs - ) - - llm("ping") - elif model_type == ModelType.EMBEDDINGS: - embedding = OpenLLMEmbeddings( - **credential_kwargs - ) - - embedding.embed_query("ping") - except Exception as ex: - raise CredentialsValidateFailedError(str(ex)) - - @classmethod - def encrypt_model_credentials(cls, tenant_id: str, model_name: str, model_type: ModelType, - credentials: dict) -> dict: - """ - encrypt model credentials for save. - - :param tenant_id: - :param model_name: - :param model_type: - :param credentials: - :return: - """ - credentials['server_url'] = encrypter.encrypt_token(tenant_id, credentials['server_url']) - return credentials - - def get_model_credentials(self, model_name: str, model_type: ModelType, obfuscated: bool = False) -> dict: - """ - get credentials for llm use. - - :param model_name: - :param model_type: - :param obfuscated: - :return: - """ - if self.provider.provider_type != ProviderType.CUSTOM.value: - raise NotImplementedError - - provider_model = self._get_provider_model(model_name, model_type) - - if not provider_model.encrypted_config: - return { - 'server_url': None - } - - credentials = json.loads(provider_model.encrypted_config) - if credentials['server_url']: - credentials['server_url'] = encrypter.decrypt_token( - self.provider.tenant_id, - credentials['server_url'] - ) - - if obfuscated: - credentials['server_url'] = encrypter.obfuscated_token(credentials['server_url']) - - return credentials - - @classmethod - def is_provider_credentials_valid_or_raise(cls, credentials: dict): - return - - @classmethod - def encrypt_provider_credentials(cls, tenant_id: str, credentials: dict) -> dict: - return {} - - def get_provider_credentials(self, obfuscated: bool = False) -> dict: - return {} diff --git a/api/core/model_providers/providers/replicate_provider.py b/api/core/model_providers/providers/replicate_provider.py deleted file mode 100644 index be9a7aa7aebe3c..00000000000000 --- a/api/core/model_providers/providers/replicate_provider.py +++ /dev/null @@ -1,191 +0,0 @@ -import json -import logging -from typing import Type - -import replicate -from replicate.exceptions import ReplicateError - -from core.helper import encrypter -from core.model_providers.models.entity.model_params import KwargRule, KwargRuleType, ModelKwargsRules, ModelType, \ - ModelMode -from core.model_providers.models.llm.replicate_model import ReplicateModel -from core.model_providers.providers.base import BaseModelProvider, CredentialsValidateFailedError - -from core.model_providers.models.base import BaseProviderModel -from core.model_providers.models.embedding.replicate_embedding import ReplicateEmbedding -from models.provider import ProviderType - - -class ReplicateProvider(BaseModelProvider): - @property - def provider_name(self): - """ - Returns the name of a provider. - """ - return 'replicate' - - def _get_fixed_model_list(self, model_type: ModelType) -> list[dict]: - return [] - - def _get_text_generation_model_mode(self, model_name) -> str: - return ModelMode.CHAT.value if model_name.endswith('-chat') else ModelMode.COMPLETION.value - - def get_model_class(self, model_type: ModelType) -> Type[BaseProviderModel]: - """ - Returns the model class. - - :param model_type: - :return: - """ - if model_type == ModelType.TEXT_GENERATION: - model_class = ReplicateModel - elif model_type == ModelType.EMBEDDINGS: - model_class = ReplicateEmbedding - else: - raise NotImplementedError - - return model_class - - def get_model_parameter_rules(self, model_name: str, model_type: ModelType) -> ModelKwargsRules: - """ - get model parameter rules. - - :param model_name: - :param model_type: - :return: - """ - model_credentials = self.get_model_credentials(model_name, model_type) - - model = replicate.Client(api_token=model_credentials.get("replicate_api_token")).models.get(model_name) - - try: - version = model.versions.get(model_credentials['model_version']) - except ReplicateError as e: - raise CredentialsValidateFailedError(f"Model {model_name}:{model_credentials['model_version']} not exists, " - f"cause: {e.__class__.__name__}:{str(e)}") - except Exception as e: - logging.exception("Model validate failed.") - raise e - - model_kwargs_rules = ModelKwargsRules() - for key, value in version.openapi_schema['components']['schemas']['Input']['properties'].items(): - if key not in ['debug', 'prompt'] and value['type'] in ['number', 'integer']: - if key == ['temperature', 'top_p']: - kwarg_rule = KwargRule[float]( - type=KwargRuleType.FLOAT.value if value['type'] == 'number' else KwargRuleType.INTEGER.value, - min=float(value.get('minimum')) if value.get('minimum') is not None else None, - max=float(value.get('maximum')) if value.get('maximum') is not None else None, - default=float(value.get('default')) if value.get('default') is not None else None, - precision = 2 - ) - if key == 'temperature': - model_kwargs_rules.temperature = kwarg_rule - else: - model_kwargs_rules.top_p = kwarg_rule - elif key in ['max_length', 'max_new_tokens']: - model_kwargs_rules.max_tokens = KwargRule[int]( - alias=key, - type=KwargRuleType.INTEGER.value, - min=int(value.get('minimum')) if value.get('minimum') is not None else 1, - max=int(value.get('maximum')) if value.get('maximum') is not None else 8000, - default=int(value.get('default')) if value.get('default') is not None else 500, - precision = 0 - ) - - return model_kwargs_rules - - @classmethod - def is_model_credentials_valid_or_raise(cls, model_name: str, model_type: ModelType, credentials: dict): - """ - check model credentials valid. - - :param model_name: - :param model_type: - :param credentials: - """ - if 'replicate_api_token' not in credentials: - raise CredentialsValidateFailedError('Replicate API Key must be provided.') - - if 'model_version' not in credentials: - raise CredentialsValidateFailedError('Replicate Model Version must be provided.') - - if model_name.count("/") != 1: - raise CredentialsValidateFailedError('Replicate Model Name must be provided, ' - 'format: {user_name}/{model_name}') - - version = credentials['model_version'] - try: - model = replicate.Client(api_token=credentials.get("replicate_api_token")).models.get(model_name) - rst = model.versions.get(version) - - if model_type == ModelType.EMBEDDINGS \ - and 'Embedding' not in rst.openapi_schema['components']['schemas']: - raise CredentialsValidateFailedError(f"Model {model_name}:{version} is not a Embedding model.") - elif model_type == ModelType.TEXT_GENERATION \ - and ('items' not in rst.openapi_schema['components']['schemas']['Output'] - or 'type' not in rst.openapi_schema['components']['schemas']['Output']['items'] - or rst.openapi_schema['components']['schemas']['Output']['items']['type'] != 'string'): - raise CredentialsValidateFailedError(f"Model {model_name}:{version} is not a Text Generation model.") - except ReplicateError as e: - raise CredentialsValidateFailedError( - f"Model {model_name}:{version} not exists, cause: {e.__class__.__name__}:{str(e)}") - except Exception as e: - logging.exception("Replicate config validation failed.") - raise e - - @classmethod - def encrypt_model_credentials(cls, tenant_id: str, model_name: str, model_type: ModelType, - credentials: dict) -> dict: - """ - encrypt model credentials for save. - - :param tenant_id: - :param model_name: - :param model_type: - :param credentials: - :return: - """ - credentials['replicate_api_token'] = encrypter.encrypt_token(tenant_id, credentials['replicate_api_token']) - return credentials - - def get_model_credentials(self, model_name: str, model_type: ModelType, obfuscated: bool = False) -> dict: - """ - get credentials for llm use. - - :param model_name: - :param model_type: - :param obfuscated: - :return: - """ - if self.provider.provider_type != ProviderType.CUSTOM.value: - raise NotImplementedError - - provider_model = self._get_provider_model(model_name, model_type) - - if not provider_model.encrypted_config: - return { - 'replicate_api_token': None, - } - - credentials = json.loads(provider_model.encrypted_config) - if credentials['replicate_api_token']: - credentials['replicate_api_token'] = encrypter.decrypt_token( - self.provider.tenant_id, - credentials['replicate_api_token'] - ) - - if obfuscated: - credentials['replicate_api_token'] = encrypter.obfuscated_token(credentials['replicate_api_token']) - - return credentials - - @classmethod - def is_provider_credentials_valid_or_raise(cls, credentials: dict): - return - - @classmethod - def encrypt_provider_credentials(cls, tenant_id: str, credentials: dict) -> dict: - return {} - - def get_provider_credentials(self, obfuscated: bool = False) -> dict: - return {} diff --git a/api/core/model_providers/providers/spark_provider.py b/api/core/model_providers/providers/spark_provider.py deleted file mode 100644 index bd95db4890ea88..00000000000000 --- a/api/core/model_providers/providers/spark_provider.py +++ /dev/null @@ -1,242 +0,0 @@ -import json -import logging -from json import JSONDecodeError -from typing import Type - -from langchain.schema import HumanMessage - -from core.helper import encrypter -from core.model_providers.models.base import BaseProviderModel -from core.model_providers.models.entity.model_params import ModelKwargsRules, KwargRule, ModelType, ModelMode -from core.model_providers.models.llm.spark_model import SparkModel -from core.model_providers.providers.base import BaseModelProvider, CredentialsValidateFailedError -from core.third_party.langchain.llms.spark import ChatSpark -from core.third_party.spark.spark_llm import SparkError -from models.provider import ProviderType, ProviderQuotaType - - -class SparkProvider(BaseModelProvider): - - @property - def provider_name(self): - """ - Returns the name of a provider. - """ - return 'spark' - - def _get_fixed_model_list(self, model_type: ModelType) -> list[dict]: - if model_type == ModelType.TEXT_GENERATION: - return [ - { - 'id': 'spark-v3', - 'name': 'Spark V3.0', - 'mode': ModelMode.CHAT.value, - }, - { - 'id': 'spark-v2', - 'name': 'Spark V2.0', - 'mode': ModelMode.CHAT.value, - }, - { - 'id': 'spark', - 'name': 'Spark V1.5', - 'mode': ModelMode.CHAT.value, - } - ] - else: - return [] - - def _get_text_generation_model_mode(self, model_name) -> str: - return ModelMode.CHAT.value - - def get_model_class(self, model_type: ModelType) -> Type[BaseProviderModel]: - """ - Returns the model class. - - :param model_type: - :return: - """ - if model_type == ModelType.TEXT_GENERATION: - model_class = SparkModel - else: - raise NotImplementedError - - return model_class - - def get_model_parameter_rules(self, model_name: str, model_type: ModelType) -> ModelKwargsRules: - """ - get model parameter rules. - - :param model_name: - :param model_type: - :return: - """ - return ModelKwargsRules( - temperature=KwargRule[float](min=0, max=1, default=0.5, precision=2), - top_p=KwargRule[float](enabled=False), - presence_penalty=KwargRule[float](enabled=False), - frequency_penalty=KwargRule[float](enabled=False), - max_tokens=KwargRule[int](min=10, max=4096, default=2048, precision=0), - ) - - @classmethod - def is_provider_credentials_valid_or_raise(cls, credentials: dict): - """ - Validates the given credentials. - """ - if 'app_id' not in credentials: - raise CredentialsValidateFailedError('Spark app_id must be provided.') - - if 'api_key' not in credentials: - raise CredentialsValidateFailedError('Spark api_key must be provided.') - - if 'api_secret' not in credentials: - raise CredentialsValidateFailedError('Spark api_secret must be provided.') - - credential_kwargs = { - 'app_id': credentials['app_id'], - 'api_key': credentials['api_key'], - 'api_secret': credentials['api_secret'], - } - - try: - chat_llm = ChatSpark( - model_name='spark-v3', - max_tokens=10, - temperature=0.01, - **credential_kwargs - ) - - messages = [ - HumanMessage( - content="ping" - ) - ] - - chat_llm(messages) - except SparkError as ex: - # try spark v2.1 if v3.1 failed - try: - chat_llm = ChatSpark( - model_name='spark-v2', - max_tokens=10, - temperature=0.01, - **credential_kwargs - ) - - messages = [ - HumanMessage( - content="ping" - ) - ] - - chat_llm(messages) - except SparkError as ex: - # try spark v1.5 if v2.1 failed - try: - chat_llm = ChatSpark( - model_name='spark', - max_tokens=10, - temperature=0.01, - **credential_kwargs - ) - - messages = [ - HumanMessage( - content="ping" - ) - ] - - chat_llm(messages) - except SparkError as ex: - raise CredentialsValidateFailedError(str(ex)) - except Exception as ex: - logging.exception('Spark config validation failed') - raise ex - except Exception as ex: - logging.exception('Spark config validation failed') - raise ex - - @classmethod - def encrypt_provider_credentials(cls, tenant_id: str, credentials: dict) -> dict: - credentials['api_key'] = encrypter.encrypt_token(tenant_id, credentials['api_key']) - credentials['api_secret'] = encrypter.encrypt_token(tenant_id, credentials['api_secret']) - return credentials - - def get_provider_credentials(self, obfuscated: bool = False) -> dict: - if self.provider.provider_type == ProviderType.CUSTOM.value \ - or (self.provider.provider_type == ProviderType.SYSTEM.value - and self.provider.quota_type == ProviderQuotaType.FREE.value): - try: - credentials = json.loads(self.provider.encrypted_config) - except JSONDecodeError: - credentials = { - 'app_id': None, - 'api_key': None, - 'api_secret': None, - } - - if credentials['api_key']: - credentials['api_key'] = encrypter.decrypt_token( - self.provider.tenant_id, - credentials['api_key'] - ) - - if obfuscated: - credentials['api_key'] = encrypter.obfuscated_token(credentials['api_key']) - - if credentials['api_secret']: - credentials['api_secret'] = encrypter.decrypt_token( - self.provider.tenant_id, - credentials['api_secret'] - ) - - if obfuscated: - credentials['api_secret'] = encrypter.obfuscated_token(credentials['api_secret']) - - return credentials - else: - return { - 'app_id': None, - 'api_key': None, - 'api_secret': None, - } - - def should_deduct_quota(self): - return True - - @classmethod - def is_model_credentials_valid_or_raise(cls, model_name: str, model_type: ModelType, credentials: dict): - """ - check model credentials valid. - - :param model_name: - :param model_type: - :param credentials: - """ - return - - @classmethod - def encrypt_model_credentials(cls, tenant_id: str, model_name: str, model_type: ModelType, - credentials: dict) -> dict: - """ - encrypt model credentials for save. - - :param tenant_id: - :param model_name: - :param model_type: - :param credentials: - :return: - """ - return {} - - def get_model_credentials(self, model_name: str, model_type: ModelType, obfuscated: bool = False) -> dict: - """ - get credentials for llm use. - - :param model_name: - :param model_type: - :param obfuscated: - :return: - """ - return self.get_provider_credentials(obfuscated) diff --git a/api/core/model_providers/providers/tongyi_provider.py b/api/core/model_providers/providers/tongyi_provider.py deleted file mode 100644 index 49ff731ac5cb5a..00000000000000 --- a/api/core/model_providers/providers/tongyi_provider.py +++ /dev/null @@ -1,162 +0,0 @@ -import json -from json import JSONDecodeError -from typing import Type - -from core.helper import encrypter -from core.model_providers.models.base import BaseProviderModel -from core.model_providers.models.entity.model_params import ModelKwargsRules, KwargRule, ModelType, ModelMode -from core.model_providers.models.llm.tongyi_model import TongyiModel -from core.model_providers.providers.base import BaseModelProvider, CredentialsValidateFailedError -from core.third_party.langchain.llms.tongyi_llm import EnhanceTongyi -from models.provider import ProviderType - - -class TongyiProvider(BaseModelProvider): - - @property - def provider_name(self): - """ - Returns the name of a provider. - """ - return 'tongyi' - - def _get_fixed_model_list(self, model_type: ModelType) -> list[dict]: - if model_type == ModelType.TEXT_GENERATION: - return [ - { - 'id': 'qwen-turbo', - 'name': 'qwen-turbo', - 'mode': ModelMode.COMPLETION.value, - }, - { - 'id': 'qwen-plus', - 'name': 'qwen-plus', - 'mode': ModelMode.COMPLETION.value, - } - ] - else: - return [] - - def _get_text_generation_model_mode(self, model_name) -> str: - return ModelMode.COMPLETION.value - - def get_model_class(self, model_type: ModelType) -> Type[BaseProviderModel]: - """ - Returns the model class. - - :param model_type: - :return: - """ - if model_type == ModelType.TEXT_GENERATION: - model_class = TongyiModel - else: - raise NotImplementedError - - return model_class - - def get_model_parameter_rules(self, model_name: str, model_type: ModelType) -> ModelKwargsRules: - """ - get model parameter rules. - - :param model_name: - :param model_type: - :return: - """ - model_max_tokens = { - 'qwen-turbo': 6000, - 'qwen-plus': 6000 - } - - return ModelKwargsRules( - temperature=KwargRule[float](min=0.01, max=1, default=1, precision=2), - top_p=KwargRule[float](min=0.01, max=0.99, default=0.5, precision=2), - presence_penalty=KwargRule[float](enabled=False), - frequency_penalty=KwargRule[float](enabled=False), - max_tokens=KwargRule[int](enabled=False, max=model_max_tokens.get(model_name)), - ) - - @classmethod - def is_provider_credentials_valid_or_raise(cls, credentials: dict): - """ - Validates the given credentials. - """ - if 'dashscope_api_key' not in credentials: - raise CredentialsValidateFailedError('Dashscope API Key must be provided.') - - try: - credential_kwargs = { - 'dashscope_api_key': credentials['dashscope_api_key'] - } - - llm = EnhanceTongyi( - model_name='qwen-turbo', - max_retries=1, - **credential_kwargs - ) - - llm("ping") - except Exception as ex: - raise CredentialsValidateFailedError(str(ex)) - - @classmethod - def encrypt_provider_credentials(cls, tenant_id: str, credentials: dict) -> dict: - credentials['dashscope_api_key'] = encrypter.encrypt_token(tenant_id, credentials['dashscope_api_key']) - return credentials - - def get_provider_credentials(self, obfuscated: bool = False) -> dict: - if self.provider.provider_type == ProviderType.CUSTOM.value: - try: - credentials = json.loads(self.provider.encrypted_config) - except JSONDecodeError: - credentials = { - 'dashscope_api_key': None - } - - if credentials['dashscope_api_key']: - credentials['dashscope_api_key'] = encrypter.decrypt_token( - self.provider.tenant_id, - credentials['dashscope_api_key'] - ) - - if obfuscated: - credentials['dashscope_api_key'] = encrypter.obfuscated_token(credentials['dashscope_api_key']) - - return credentials - - return {} - - @classmethod - def is_model_credentials_valid_or_raise(cls, model_name: str, model_type: ModelType, credentials: dict): - """ - check model credentials valid. - - :param model_name: - :param model_type: - :param credentials: - """ - return - - @classmethod - def encrypt_model_credentials(cls, tenant_id: str, model_name: str, model_type: ModelType, - credentials: dict) -> dict: - """ - encrypt model credentials for save. - - :param tenant_id: - :param model_name: - :param model_type: - :param credentials: - :return: - """ - return {} - - def get_model_credentials(self, model_name: str, model_type: ModelType, obfuscated: bool = False) -> dict: - """ - get credentials for llm use. - - :param model_name: - :param model_type: - :param obfuscated: - :return: - """ - return self.get_provider_credentials(obfuscated) diff --git a/api/core/model_providers/providers/wenxin_provider.py b/api/core/model_providers/providers/wenxin_provider.py deleted file mode 100644 index 650acffcc4567c..00000000000000 --- a/api/core/model_providers/providers/wenxin_provider.py +++ /dev/null @@ -1,201 +0,0 @@ -import json -from json import JSONDecodeError -from typing import Type - -from langchain.schema import HumanMessage - -from core.helper import encrypter -from core.model_providers.models.base import BaseProviderModel -from core.model_providers.models.entity.model_params import ModelKwargsRules, KwargRule, ModelType, ModelMode -from core.model_providers.models.llm.wenxin_model import WenxinModel -from core.model_providers.providers.base import BaseModelProvider, CredentialsValidateFailedError -from core.third_party.langchain.llms.wenxin import Wenxin -from models.provider import ProviderType - - -class WenxinProvider(BaseModelProvider): - - @property - def provider_name(self): - """ - Returns the name of a provider. - """ - return 'wenxin' - - def _get_fixed_model_list(self, model_type: ModelType) -> list[dict]: - if model_type == ModelType.TEXT_GENERATION: - return [ - { - 'id': 'ernie-bot-4', - 'name': 'ERNIE-Bot-4', - 'mode': ModelMode.CHAT.value, - }, - { - 'id': 'ernie-bot', - 'name': 'ERNIE-Bot', - 'mode': ModelMode.CHAT.value, - }, - { - 'id': 'ernie-bot-turbo', - 'name': 'ERNIE-Bot-turbo', - 'mode': ModelMode.CHAT.value, - }, - { - 'id': 'bloomz-7b', - 'name': 'BLOOMZ-7B', - 'mode': ModelMode.CHAT.value, - } - ] - else: - return [] - - def _get_text_generation_model_mode(self, model_name) -> str: - return ModelMode.COMPLETION.value - - def get_model_class(self, model_type: ModelType) -> Type[BaseProviderModel]: - """ - Returns the model class. - - :param model_type: - :return: - """ - if model_type == ModelType.TEXT_GENERATION: - model_class = WenxinModel - else: - raise NotImplementedError - - return model_class - - def get_model_parameter_rules(self, model_name: str, model_type: ModelType) -> ModelKwargsRules: - """ - get model parameter rules. - - :param model_name: - :param model_type: - :return: - """ - model_max_tokens = { - 'ernie-bot-4': 4800, - 'ernie-bot': 4800, - 'ernie-bot-turbo': 11200, - } - - if model_name in ['ernie-bot-4', 'ernie-bot', 'ernie-bot-turbo']: - return ModelKwargsRules( - temperature=KwargRule[float](min=0.01, max=1, default=0.95, precision=2), - top_p=KwargRule[float](min=0.01, max=1, default=0.8, precision=2), - presence_penalty=KwargRule[float](enabled=False), - frequency_penalty=KwargRule[float](enabled=False), - max_tokens=KwargRule[int](enabled=False, max=model_max_tokens.get(model_name)), - ) - else: - return ModelKwargsRules( - temperature=KwargRule[float](enabled=False), - top_p=KwargRule[float](enabled=False), - presence_penalty=KwargRule[float](enabled=False), - frequency_penalty=KwargRule[float](enabled=False), - max_tokens=KwargRule[int](enabled=False), - ) - - @classmethod - def is_provider_credentials_valid_or_raise(cls, credentials: dict): - """ - Validates the given credentials. - """ - if 'api_key' not in credentials: - raise CredentialsValidateFailedError('Wenxin api_key must be provided.') - - if 'secret_key' not in credentials: - raise CredentialsValidateFailedError('Wenxin secret_key must be provided.') - - try: - credential_kwargs = { - 'api_key': credentials['api_key'], - 'secret_key': credentials['secret_key'], - } - - llm = Wenxin( - temperature=0.01, - **credential_kwargs - ) - - llm([HumanMessage(content='ping')]) - except Exception as ex: - raise CredentialsValidateFailedError(str(ex)) - - @classmethod - def encrypt_provider_credentials(cls, tenant_id: str, credentials: dict) -> dict: - credentials['api_key'] = encrypter.encrypt_token(tenant_id, credentials['api_key']) - credentials['secret_key'] = encrypter.encrypt_token(tenant_id, credentials['secret_key']) - return credentials - - def get_provider_credentials(self, obfuscated: bool = False) -> dict: - if self.provider.provider_type == ProviderType.CUSTOM.value: - try: - credentials = json.loads(self.provider.encrypted_config) - except JSONDecodeError: - credentials = { - 'api_key': None, - 'secret_key': None, - } - - if credentials['api_key']: - credentials['api_key'] = encrypter.decrypt_token( - self.provider.tenant_id, - credentials['api_key'] - ) - - if obfuscated: - credentials['api_key'] = encrypter.obfuscated_token(credentials['api_key']) - - if credentials['secret_key']: - credentials['secret_key'] = encrypter.decrypt_token( - self.provider.tenant_id, - credentials['secret_key'] - ) - - if obfuscated: - credentials['secret_key'] = encrypter.obfuscated_token(credentials['secret_key']) - - return credentials - else: - return { - 'api_key': None, - 'secret_key': None, - } - - @classmethod - def is_model_credentials_valid_or_raise(cls, model_name: str, model_type: ModelType, credentials: dict): - """ - check model credentials valid. - - :param model_name: - :param model_type: - :param credentials: - """ - return - - @classmethod - def encrypt_model_credentials(cls, tenant_id: str, model_name: str, model_type: ModelType, - credentials: dict) -> dict: - """ - encrypt model credentials for save. - - :param tenant_id: - :param model_name: - :param model_type: - :param credentials: - :return: - """ - return {} - - def get_model_credentials(self, model_name: str, model_type: ModelType, obfuscated: bool = False) -> dict: - """ - get credentials for llm use. - - :param model_name: - :param model_type: - :param obfuscated: - :return: - """ - return self.get_provider_credentials(obfuscated) diff --git a/api/core/model_providers/providers/xinference_provider.py b/api/core/model_providers/providers/xinference_provider.py deleted file mode 100644 index 133c5e7cf8bd9f..00000000000000 --- a/api/core/model_providers/providers/xinference_provider.py +++ /dev/null @@ -1,212 +0,0 @@ -import json -from typing import Type - -import requests -from xinference_client.client.restful.restful_client import Client - -from core.helper import encrypter -from core.model_providers.models.embedding.xinference_embedding import XinferenceEmbedding -from core.model_providers.models.entity.model_params import KwargRule, ModelKwargsRules, ModelType, ModelMode -from core.model_providers.models.llm.xinference_model import XinferenceModel -from core.model_providers.models.reranking.xinference_reranking import XinferenceReranking -from core.model_providers.providers.base import BaseModelProvider, CredentialsValidateFailedError - -from core.model_providers.models.base import BaseProviderModel -from core.third_party.langchain.embeddings.xinference_embedding import XinferenceEmbeddings -from core.third_party.langchain.llms.xinference_llm import XinferenceLLM -from models.provider import ProviderType - - -class XinferenceProvider(BaseModelProvider): - @property - def provider_name(self): - """ - Returns the name of a provider. - """ - return 'xinference' - - def _get_fixed_model_list(self, model_type: ModelType) -> list[dict]: - return [] - - def _get_text_generation_model_mode(self, model_name) -> str: - return ModelMode.COMPLETION.value - - def get_model_class(self, model_type: ModelType) -> Type[BaseProviderModel]: - """ - Returns the model class. - - :param model_type: - :return: - """ - if model_type == ModelType.TEXT_GENERATION: - model_class = XinferenceModel - elif model_type == ModelType.EMBEDDINGS: - model_class = XinferenceEmbedding - elif model_type == ModelType.RERANKING: - model_class = XinferenceReranking - else: - raise NotImplementedError - - return model_class - - def get_model_parameter_rules(self, model_name: str, model_type: ModelType) -> ModelKwargsRules: - """ - get model parameter rules. - - :param model_name: - :param model_type: - :return: - """ - credentials = self.get_model_credentials(model_name, model_type) - if credentials['model_format'] == "ggmlv3" and credentials["model_handle_type"] == "chatglm": - return ModelKwargsRules( - temperature=KwargRule[float](min=0.01, max=2, default=1, precision=2), - top_p=KwargRule[float](min=0, max=1, default=0.7, precision=2), - presence_penalty=KwargRule[float](enabled=False), - frequency_penalty=KwargRule[float](enabled=False), - max_tokens=KwargRule[int](min=10, max=4000, default=256, precision=0), - ) - elif credentials['model_format'] == "ggmlv3": - return ModelKwargsRules( - temperature=KwargRule[float](min=0.01, max=2, default=1, precision=2), - top_p=KwargRule[float](min=0, max=1, default=0.7, precision=2), - presence_penalty=KwargRule[float](min=-2, max=2, default=0, precision=2), - frequency_penalty=KwargRule[float](min=-2, max=2, default=0, precision=2), - max_tokens=KwargRule[int](min=10, max=4000, default=256, precision=0), - ) - else: - return ModelKwargsRules( - temperature=KwargRule[float](min=0.01, max=2, default=1, precision=2), - top_p=KwargRule[float](min=0, max=1, default=0.7, precision=2), - presence_penalty=KwargRule[float](enabled=False), - frequency_penalty=KwargRule[float](enabled=False), - max_tokens=KwargRule[int](min=10, max=4000, default=256, precision=0), - ) - - - @classmethod - def is_model_credentials_valid_or_raise(cls, model_name: str, model_type: ModelType, credentials: dict): - """ - check model credentials valid. - - :param model_name: - :param model_type: - :param credentials: - """ - if 'server_url' not in credentials: - raise CredentialsValidateFailedError('Xinference Server URL must be provided.') - - if 'model_uid' not in credentials: - raise CredentialsValidateFailedError('Xinference Model UID must be provided.') - - try: - credential_kwargs = { - 'server_url': credentials['server_url'], - 'model_uid': credentials['model_uid'], - } - - if model_type == ModelType.TEXT_GENERATION: - llm = XinferenceLLM( - **credential_kwargs - ) - - llm("ping") - elif model_type == ModelType.EMBEDDINGS: - embedding = XinferenceEmbeddings( - **credential_kwargs - ) - - embedding.embed_query("ping") - elif model_type == ModelType.RERANKING: - rerank_client = Client(credential_kwargs['server_url']) - model = rerank_client.get_model(credential_kwargs['model_uid']) - model.rerank(query="ping", documents=["ping", "pong"], top_n=2) - except Exception as ex: - raise CredentialsValidateFailedError(str(ex)) - - @classmethod - def encrypt_model_credentials(cls, tenant_id: str, model_name: str, model_type: ModelType, - credentials: dict) -> dict: - """ - encrypt model credentials for save. - - :param tenant_id: - :param model_name: - :param model_type: - :param credentials: - :return: - """ - if model_type == ModelType.TEXT_GENERATION: - extra_credentials = cls._get_extra_credentials(credentials) - credentials.update(extra_credentials) - - credentials['server_url'] = encrypter.encrypt_token(tenant_id, credentials['server_url']) - - return credentials - - def get_model_credentials(self, model_name: str, model_type: ModelType, obfuscated: bool = False) -> dict: - """ - get credentials for llm use. - - :param model_name: - :param model_type: - :param obfuscated: - :return: - """ - if self.provider.provider_type != ProviderType.CUSTOM.value: - raise NotImplementedError - - provider_model = self._get_provider_model(model_name, model_type) - - if not provider_model.encrypted_config: - return { - 'server_url': None, - 'model_uid': None, - } - - credentials = json.loads(provider_model.encrypted_config) - if credentials['server_url']: - credentials['server_url'] = encrypter.decrypt_token( - self.provider.tenant_id, - credentials['server_url'] - ) - - if obfuscated: - credentials['server_url'] = encrypter.obfuscated_token(credentials['server_url']) - - return credentials - - @classmethod - def _get_extra_credentials(self, credentials: dict) -> dict: - url = f"{credentials['server_url']}/v1/models/{credentials['model_uid']}" - response = requests.get(url) - if response.status_code != 200: - raise RuntimeError( - f"Failed to get the model description, detail: {response.json()['detail']}" - ) - desc = response.json() - - extra_credentials = { - 'model_format': desc['model_format'], - } - if desc["model_format"] == "ggmlv3" and "chatglm" in desc["model_name"]: - extra_credentials['model_handle_type'] = 'chatglm' - elif "generate" in desc["model_ability"]: - extra_credentials['model_handle_type'] = 'generate' - elif "chat" in desc["model_ability"]: - extra_credentials['model_handle_type'] = 'chat' - else: - raise NotImplementedError(f"Model handle type not supported.") - - return extra_credentials - - @classmethod - def is_provider_credentials_valid_or_raise(cls, credentials: dict): - return - - @classmethod - def encrypt_provider_credentials(cls, tenant_id: str, credentials: dict) -> dict: - return {} - - def get_provider_credentials(self, obfuscated: bool = False) -> dict: - return {} diff --git a/api/core/model_providers/providers/zhipuai_provider.py b/api/core/model_providers/providers/zhipuai_provider.py deleted file mode 100644 index 2a7a0f65e04835..00000000000000 --- a/api/core/model_providers/providers/zhipuai_provider.py +++ /dev/null @@ -1,188 +0,0 @@ -import json -from json import JSONDecodeError -from typing import Type - -from langchain.schema import HumanMessage - -from core.helper import encrypter -from core.model_providers.models.base import BaseProviderModel -from core.model_providers.models.embedding.zhipuai_embedding import ZhipuAIEmbedding -from core.model_providers.models.entity.model_params import ModelKwargsRules, KwargRule, ModelType, ModelMode -from core.model_providers.models.llm.zhipuai_model import ZhipuAIModel -from core.model_providers.providers.base import BaseModelProvider, CredentialsValidateFailedError -from core.third_party.langchain.llms.zhipuai_llm import ZhipuAIChatLLM -from models.provider import ProviderType, ProviderQuotaType - - -class ZhipuAIProvider(BaseModelProvider): - - @property - def provider_name(self): - """ - Returns the name of a provider. - """ - return 'zhipuai' - - def _get_fixed_model_list(self, model_type: ModelType) -> list[dict]: - if model_type == ModelType.TEXT_GENERATION: - return [ - { - 'id': 'chatglm_turbo', - 'name': 'chatglm_turbo', - 'mode': ModelMode.CHAT.value, - }, - { - 'id': 'chatglm_pro', - 'name': 'chatglm_pro', - 'mode': ModelMode.CHAT.value, - }, - { - 'id': 'chatglm_std', - 'name': 'chatglm_std', - 'mode': ModelMode.CHAT.value, - }, - { - 'id': 'chatglm_lite', - 'name': 'chatglm_lite', - 'mode': ModelMode.CHAT.value, - }, - { - 'id': 'chatglm_lite_32k', - 'name': 'chatglm_lite_32k', - 'mode': ModelMode.CHAT.value, - } - ] - elif model_type == ModelType.EMBEDDINGS: - return [ - { - 'id': 'text_embedding', - 'name': 'text_embedding', - } - ] - else: - return [] - - def _get_text_generation_model_mode(self, model_name) -> str: - return ModelMode.CHAT.value - - def get_model_class(self, model_type: ModelType) -> Type[BaseProviderModel]: - """ - Returns the model class. - - :param model_type: - :return: - """ - if model_type == ModelType.TEXT_GENERATION: - model_class = ZhipuAIModel - elif model_type == ModelType.EMBEDDINGS: - model_class = ZhipuAIEmbedding - else: - raise NotImplementedError - - return model_class - - def get_model_parameter_rules(self, model_name: str, model_type: ModelType) -> ModelKwargsRules: - """ - get model parameter rules. - - :param model_name: - :param model_type: - :return: - """ - return ModelKwargsRules( - temperature=KwargRule[float](min=0.01, max=1, default=0.95, precision=2), - top_p=KwargRule[float](min=0.1, max=0.9, default=0.8, precision=1), - presence_penalty=KwargRule[float](enabled=False), - frequency_penalty=KwargRule[float](enabled=False), - max_tokens=KwargRule[int](enabled=False), - ) - - @classmethod - def is_provider_credentials_valid_or_raise(cls, credentials: dict): - """ - Validates the given credentials. - """ - if 'api_key' not in credentials: - raise CredentialsValidateFailedError('ZhipuAI api_key must be provided.') - - try: - credential_kwargs = { - 'api_key': credentials['api_key'] - } - - llm = ZhipuAIChatLLM( - temperature=0.01, - **credential_kwargs - ) - - llm([HumanMessage(content='ping')]) - except Exception as ex: - raise CredentialsValidateFailedError(str(ex)) - - @classmethod - def encrypt_provider_credentials(cls, tenant_id: str, credentials: dict) -> dict: - credentials['api_key'] = encrypter.encrypt_token(tenant_id, credentials['api_key']) - return credentials - - def get_provider_credentials(self, obfuscated: bool = False) -> dict: - if self.provider.provider_type == ProviderType.CUSTOM.value \ - or (self.provider.provider_type == ProviderType.SYSTEM.value - and self.provider.quota_type == ProviderQuotaType.FREE.value): - try: - credentials = json.loads(self.provider.encrypted_config) - except JSONDecodeError: - credentials = { - 'api_key': None, - } - - if credentials['api_key']: - credentials['api_key'] = encrypter.decrypt_token( - self.provider.tenant_id, - credentials['api_key'] - ) - - if obfuscated: - credentials['api_key'] = encrypter.obfuscated_token(credentials['api_key']) - - return credentials - else: - return {} - - def should_deduct_quota(self): - return True - - @classmethod - def is_model_credentials_valid_or_raise(cls, model_name: str, model_type: ModelType, credentials: dict): - """ - check model credentials valid. - - :param model_name: - :param model_type: - :param credentials: - """ - return - - @classmethod - def encrypt_model_credentials(cls, tenant_id: str, model_name: str, model_type: ModelType, - credentials: dict) -> dict: - """ - encrypt model credentials for save. - - :param tenant_id: - :param model_name: - :param model_type: - :param credentials: - :return: - """ - return {} - - def get_model_credentials(self, model_name: str, model_type: ModelType, obfuscated: bool = False) -> dict: - """ - get credentials for llm use. - - :param model_name: - :param model_type: - :param obfuscated: - :return: - """ - return self.get_provider_credentials(obfuscated) diff --git a/api/core/model_providers/rules.py b/api/core/model_providers/rules.py deleted file mode 100644 index 5a911500deee10..00000000000000 --- a/api/core/model_providers/rules.py +++ /dev/null @@ -1,47 +0,0 @@ -import json -import os - - -def init_provider_rules(): - # Get the absolute path of the subdirectory - subdirectory_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'rules') - - # Path to the providers.json file - providers_json_file_path = os.path.join(subdirectory_path, '_providers.json') - - try: - # Open the JSON file and read its content - with open(providers_json_file_path, 'r') as json_file: - data = json.load(json_file) - # Store the content in a dictionary with the key as the file name (without extension) - provider_names = data - except FileNotFoundError: - return "JSON file not found or path error" - except json.JSONDecodeError: - return "JSON file decoding error" - - # Dictionary to store the content of all JSON files - json_data = {} - - try: - # Loop through all files in the directory - for provider_name in provider_names: - filename = provider_name + '.json' - - # Path to each JSON file - json_file_path = os.path.join(subdirectory_path, filename) - - # Open each JSON file and read its content - with open(json_file_path, 'r') as json_file: - data = json.load(json_file) - # Store the content in the dictionary with the key as the file name (without extension) - json_data[os.path.splitext(filename)[0]] = data - - return json_data - except FileNotFoundError: - return "JSON file not found or path error" - except json.JSONDecodeError: - return "JSON file decoding error" - - -provider_rules = init_provider_rules() diff --git a/api/core/model_providers/rules/_providers.json b/api/core/model_providers/rules/_providers.json deleted file mode 100644 index 8c500e9ddad5e9..00000000000000 --- a/api/core/model_providers/rules/_providers.json +++ /dev/null @@ -1,19 +0,0 @@ -[ - "openai", - "azure_openai", - "anthropic", - "minimax", - "tongyi", - "spark", - "wenxin", - "zhipuai", - "baichuan", - "chatglm", - "replicate", - "huggingface_hub", - "xinference", - "openllm", - "localai", - "cohere", - "jina" -] diff --git a/api/core/model_providers/rules/anthropic.json b/api/core/model_providers/rules/anthropic.json deleted file mode 100644 index bb02ce845be4dd..00000000000000 --- a/api/core/model_providers/rules/anthropic.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "support_provider_types": [ - "system", - "custom" - ], - "system_config": { - "supported_quota_types": [ - "paid", - "trial" - ], - "quota_unit": "tokens", - "quota_limit": 0 - }, - "model_flexibility": "fixed", - "supported_model_types": [ - "text-generation" - ], - "price_config": { - "claude-instant-1": { - "prompt": "1.63", - "completion": "5.51", - "unit": "0.000001", - "currency": "USD" - }, - "claude-2": { - "prompt": "8.00", - "completion": "24.00", - "unit": "0.000001", - "currency": "USD" - }, - "claude-2.1": { - "prompt": "8.00", - "completion": "24.00", - "unit": "0.000001", - "currency": "USD" - } - } -} \ No newline at end of file diff --git a/api/core/model_providers/rules/azure_openai.json b/api/core/model_providers/rules/azure_openai.json deleted file mode 100644 index 789abd4d437602..00000000000000 --- a/api/core/model_providers/rules/azure_openai.json +++ /dev/null @@ -1,66 +0,0 @@ -{ - "support_provider_types": [ - "custom" - ], - "system_config": null, - "model_flexibility": "configurable", - "supported_model_types": [ - "text-generation", - "embeddings" - ], - "price_config":{ - "gpt-4": { - "prompt": "0.03", - "completion": "0.06", - "unit": "0.001", - "currency": "USD" - }, - "gpt-4-32k": { - "prompt": "0.06", - "completion": "0.12", - "unit": "0.001", - "currency": "USD" - }, - "gpt-4-1106-preview": { - "prompt": "0.01", - "completion": "0.03", - "unit": "0.001", - "currency": "USD" - }, - "gpt-4-vision-preview": { - "prompt": "0.01", - "completion": "0.03", - "unit": "0.001", - "currency": "USD" - }, - "gpt-35-turbo": { - "prompt": "0.002", - "completion": "0.0015", - "unit": "0.001", - "currency": "USD" - }, - "gpt-35-turbo-16k": { - "prompt": "0.003", - "completion": "0.004", - "unit": "0.001", - "currency": "USD" - }, - "text-davinci-002": { - "prompt": "0.02", - "completion": "0.02", - "unit": "0.001", - "currency": "USD" - }, - "text-davinci-003": { - "prompt": "0.02", - "completion": "0.02", - "unit": "0.001", - "currency": "USD" - }, - "text-embedding-ada-002":{ - "completion": "0.0001", - "unit": "0.001", - "currency": "USD" - } - } -} \ No newline at end of file diff --git a/api/core/model_providers/rules/baichuan.json b/api/core/model_providers/rules/baichuan.json deleted file mode 100644 index 70b847cd8ad245..00000000000000 --- a/api/core/model_providers/rules/baichuan.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "support_provider_types": [ - "custom" - ], - "system_config": null, - "model_flexibility": "fixed", - "supported_model_types": [ - "text-generation" - ], - "price_config": { - "baichuan2-53b": { - "prompt": "0.01", - "completion": "0.01", - "unit": "0.001", - "currency": "RMB" - } - } -} \ No newline at end of file diff --git a/api/core/model_providers/rules/chatglm.json b/api/core/model_providers/rules/chatglm.json deleted file mode 100644 index 3ddfb8cf53b047..00000000000000 --- a/api/core/model_providers/rules/chatglm.json +++ /dev/null @@ -1,10 +0,0 @@ -{ - "support_provider_types": [ - "custom" - ], - "system_config": null, - "model_flexibility": "fixed", - "supported_model_types": [ - "text-generation" - ] -} \ No newline at end of file diff --git a/api/core/model_providers/rules/cohere.json b/api/core/model_providers/rules/cohere.json deleted file mode 100644 index 5ce0c9cc5b8177..00000000000000 --- a/api/core/model_providers/rules/cohere.json +++ /dev/null @@ -1,10 +0,0 @@ -{ - "support_provider_types": [ - "custom" - ], - "system_config": null, - "model_flexibility": "fixed", - "supported_model_types": [ - "reranking" - ] -} \ No newline at end of file diff --git a/api/core/model_providers/rules/huggingface_hub.json b/api/core/model_providers/rules/huggingface_hub.json deleted file mode 100644 index 3f1ee225f16b3c..00000000000000 --- a/api/core/model_providers/rules/huggingface_hub.json +++ /dev/null @@ -1,11 +0,0 @@ -{ - "support_provider_types": [ - "custom" - ], - "system_config": null, - "model_flexibility": "configurable", - "supported_model_types": [ - "text-generation", - "embeddings" - ] -} \ No newline at end of file diff --git a/api/core/model_providers/rules/jina.json b/api/core/model_providers/rules/jina.json deleted file mode 100644 index e63bf3975a4425..00000000000000 --- a/api/core/model_providers/rules/jina.json +++ /dev/null @@ -1,10 +0,0 @@ -{ - "support_provider_types": [ - "custom" - ], - "system_config": null, - "model_flexibility": "fixed", - "supported_model_types": [ - "embeddings" - ] -} \ No newline at end of file diff --git a/api/core/model_providers/rules/localai.json b/api/core/model_providers/rules/localai.json deleted file mode 100644 index 3f1ee225f16b3c..00000000000000 --- a/api/core/model_providers/rules/localai.json +++ /dev/null @@ -1,11 +0,0 @@ -{ - "support_provider_types": [ - "custom" - ], - "system_config": null, - "model_flexibility": "configurable", - "supported_model_types": [ - "text-generation", - "embeddings" - ] -} \ No newline at end of file diff --git a/api/core/model_providers/rules/minimax.json b/api/core/model_providers/rules/minimax.json deleted file mode 100644 index 0348ec3dfbc92d..00000000000000 --- a/api/core/model_providers/rules/minimax.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "support_provider_types": [ - "system", - "custom" - ], - "system_config": { - "supported_quota_types": [ - "free" - ], - "quota_unit": "tokens" - }, - "model_flexibility": "fixed", - "supported_model_types": [ - "text-generation", - "embeddings" - ], - "price_config": { - "abab5.5-chat": { - "prompt": "0.015", - "completion": "0.015", - "unit": "0.001", - "currency": "RMB" - }, - "abab5-chat": { - "prompt": "0.015", - "completion": "0.015", - "unit": "0.001", - "currency": "RMB" - }, - "embo-01": { - "completion": "0", - "unit": "0.0001", - "currency": "RMB" - } - } -} \ No newline at end of file diff --git a/api/core/model_providers/rules/openai.json b/api/core/model_providers/rules/openai.json deleted file mode 100644 index 4f1f39b792fdbc..00000000000000 --- a/api/core/model_providers/rules/openai.json +++ /dev/null @@ -1,81 +0,0 @@ -{ - "support_provider_types": [ - "system", - "custom" - ], - "system_config": { - "supported_quota_types": [ - "trial" - ], - "quota_unit": "times", - "quota_limit": 200 - }, - "model_flexibility": "fixed", - "supported_model_types": [ - "text-generation", - "embeddings", - "speech2text", - "moderation" - ], - "price_config": { - "gpt-4": { - "prompt": "0.03", - "completion": "0.06", - "unit": "0.001", - "currency": "USD" - }, - "gpt-4-32k": { - "prompt": "0.06", - "completion": "0.12", - "unit": "0.001", - "currency": "USD" - }, - "gpt-4-1106-preview": { - "prompt": "0.01", - "completion": "0.03", - "unit": "0.001", - "currency": "USD" - }, - "gpt-4-vision-preview": { - "prompt": "0.01", - "completion": "0.03", - "unit": "0.001", - "currency": "USD" - }, - "gpt-3.5-turbo": { - "prompt": "0.0015", - "completion": "0.002", - "unit": "0.001", - "currency": "USD" - }, - "gpt-3.5-turbo-1106": { - "prompt": "0.0010", - "completion": "0.002", - "unit": "0.001", - "currency": "USD" - }, - "gpt-3.5-turbo-instruct": { - "prompt": "0.0015", - "completion": "0.002", - "unit": "0.001", - "currency": "USD" - }, - "gpt-3.5-turbo-16k": { - "prompt": "0.003", - "completion": "0.004", - "unit": "0.001", - "currency": "USD" - }, - "text-davinci-003": { - "prompt": "0.02", - "completion": "0.02", - "unit": "0.001", - "currency": "USD" - }, - "text-embedding-ada-002":{ - "completion": "0.0001", - "unit": "0.001", - "currency": "USD" - } - } -} \ No newline at end of file diff --git a/api/core/model_providers/rules/openllm.json b/api/core/model_providers/rules/openllm.json deleted file mode 100644 index 3f1ee225f16b3c..00000000000000 --- a/api/core/model_providers/rules/openllm.json +++ /dev/null @@ -1,11 +0,0 @@ -{ - "support_provider_types": [ - "custom" - ], - "system_config": null, - "model_flexibility": "configurable", - "supported_model_types": [ - "text-generation", - "embeddings" - ] -} \ No newline at end of file diff --git a/api/core/model_providers/rules/replicate.json b/api/core/model_providers/rules/replicate.json deleted file mode 100644 index 3f1ee225f16b3c..00000000000000 --- a/api/core/model_providers/rules/replicate.json +++ /dev/null @@ -1,11 +0,0 @@ -{ - "support_provider_types": [ - "custom" - ], - "system_config": null, - "model_flexibility": "configurable", - "supported_model_types": [ - "text-generation", - "embeddings" - ] -} \ No newline at end of file diff --git a/api/core/model_providers/rules/spark.json b/api/core/model_providers/rules/spark.json deleted file mode 100644 index 4fa4d9a5698c8e..00000000000000 --- a/api/core/model_providers/rules/spark.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "support_provider_types": [ - "system", - "custom" - ], - "system_config": { - "supported_quota_types": [ - "free" - ], - "quota_unit": "tokens" - }, - "model_flexibility": "fixed", - "supported_model_types": [ - "text-generation" - ], - "price_config": { - "spark": { - "prompt": "0.18", - "completion": "0.18", - "unit": "0.0001", - "currency": "RMB" - }, - "spark-v2": { - "prompt": "0.36", - "completion": "0.36", - "unit": "0.0001", - "currency": "RMB" - }, - "spark-v3": { - "prompt": "0.36", - "completion": "0.36", - "unit": "0.0001", - "currency": "RMB" - } - } -} \ No newline at end of file diff --git a/api/core/model_providers/rules/tongyi.json b/api/core/model_providers/rules/tongyi.json deleted file mode 100644 index 319fbcaf9f7a92..00000000000000 --- a/api/core/model_providers/rules/tongyi.json +++ /dev/null @@ -1,24 +0,0 @@ -{ - "support_provider_types": [ - "custom" - ], - "system_config": null, - "model_flexibility": "fixed", - "supported_model_types": [ - "text-generation" - ], - "price_config": { - "qwen-turbo": { - "prompt": "0.012", - "completion": "0.012", - "unit": "0.001", - "currency": "RMB" - }, - "qwen-plus": { - "prompt": "0.14", - "completion": "0.14", - "unit": "0.001", - "currency": "RMB" - } - } -} \ No newline at end of file diff --git a/api/core/model_providers/rules/wenxin.json b/api/core/model_providers/rules/wenxin.json deleted file mode 100644 index 193dccc411a6d5..00000000000000 --- a/api/core/model_providers/rules/wenxin.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "support_provider_types": [ - "custom" - ], - "system_config": null, - "model_flexibility": "fixed", - "supported_model_types": [ - "text-generation" - ], - "price_config": { - "ernie-bot-4": { - "prompt": "0", - "completion": "0", - "unit": "0.001", - "currency": "RMB" - }, - "ernie-bot": { - "prompt": "0.012", - "completion": "0.012", - "unit": "0.001", - "currency": "RMB" - }, - "ernie-bot-turbo": { - "prompt": "0.008", - "completion": "0.008", - "unit": "0.001", - "currency": "RMB" - }, - "bloomz-7b": { - "prompt": "0.006", - "completion": "0.006", - "unit": "0.001", - "currency": "RMB" - } - } -} \ No newline at end of file diff --git a/api/core/model_providers/rules/xinference.json b/api/core/model_providers/rules/xinference.json deleted file mode 100644 index 3e426a927b502c..00000000000000 --- a/api/core/model_providers/rules/xinference.json +++ /dev/null @@ -1,12 +0,0 @@ -{ - "support_provider_types": [ - "custom" - ], - "system_config": null, - "model_flexibility": "configurable", - "supported_model_types": [ - "text-generation", - "embeddings", - "reranking" - ] -} \ No newline at end of file diff --git a/api/core/model_providers/rules/zhipuai.json b/api/core/model_providers/rules/zhipuai.json deleted file mode 100644 index 07badcc31312ff..00000000000000 --- a/api/core/model_providers/rules/zhipuai.json +++ /dev/null @@ -1,54 +0,0 @@ -{ - "support_provider_types": [ - "system", - "custom" - ], - "system_config": { - "supported_quota_types": [ - "free" - ], - "quota_unit": "tokens" - }, - "model_flexibility": "fixed", - "supported_model_types": [ - "text-generation", - "embeddings" - ], - "price_config": { - "chatglm_turbo": { - "prompt": "0.005", - "completion": "0.005", - "unit": "0.001", - "currency": "RMB" - }, - "chatglm_pro": { - "prompt": "0.01", - "completion": "0.01", - "unit": "0.001", - "currency": "RMB" - }, - "chatglm_std": { - "prompt": "0.005", - "completion": "0.005", - "unit": "0.001", - "currency": "RMB" - }, - "chatglm_lite": { - "prompt": "0.002", - "completion": "0.002", - "unit": "0.001", - "currency": "RMB" - }, - "chatglm_lite_32k": { - "prompt": "0.0004", - "completion": "0.0004", - "unit": "0.001", - "currency": "RMB" - }, - "text_embedding": { - "completion": "0", - "unit": "0.001", - "currency": "RMB" - } - } -} \ No newline at end of file diff --git a/api/core/model_runtime/README.md b/api/core/model_runtime/README.md new file mode 100644 index 00000000000000..eba1f3f2d05c51 --- /dev/null +++ b/api/core/model_runtime/README.md @@ -0,0 +1,69 @@ +# Model Runtime + +This module provides the interface for invoking and authenticating various models, and offers Dify a unified information and credentials form rule for model providers. + +- On one hand, it decouples models from upstream and downstream processes, facilitating horizontal expansion for developers, +- On the other hand, it allows for direct display of providers and models in the frontend interface by simply defining them in the backend, eliminating the need to modify frontend logic. + +## Features + +- Supports capability invocation for 5 types of models + + - `LLM` - LLM text completion, dialogue, pre-computed tokens capability + - `Text Embedding Model` - Text Embedding, pre-computed tokens capability + - `Rerank Model` - Segment Rerank capability + - `Speech-to-text Model` - Speech to text capability + - `Moderation` - Moderation capability + +- Model provider display + + ![image-20231210143654461](./docs/en_US/images/index/image-20231210143654461.png) + + Displays a list of all supported providers, including provider names, icons, supported model types list, predefined model list, configuration method, and credentials form rules, etc. For detailed rule design, see: [Schema](./schema.md). + +- Selectable model list display + + ![image-20231210144229650](./docs/en_US/images/index/image-20231210144229650.png) + + After configuring provider/model credentials, the dropdown (application orchestration interface/default model) allows viewing of the available LLM list. Greyed out items represent predefined model lists from providers without configured credentials, facilitating user review of supported models. + + In addition, this list also returns configurable parameter information and rules for LLM, as shown below: + + ![image-20231210144814617](./docs/en_US/images/index/image-20231210144814617.png) + + These parameters are all defined in the backend, allowing different settings for various parameters supported by different models, as detailed in: [Schema](./docs/en_US/schema.md#ParameterRule). + +- Provider/model credential authentication + + ![image-20231210151548521](./docs/en_US/images/index/image-20231210151548521.png) + + ![image-20231210151628992](./docs/en_US/images/index/image-20231210151628992.png) + + The provider list returns configuration information for the credentials form, which can be authenticated through Runtime's interface. The first image above is a provider credential DEMO, and the second is a model credential DEMO. + +## Structure + +![](./docs/en_US/images/index/image-20231210165243632.png) + +Model Runtime is divided into three layers: + +- The outermost layer is the factory method + + It provides methods for obtaining all providers, all model lists, getting provider instances, and authenticating provider/model credentials. + +- The second layer is the provider layer + + It provides the current provider's model list, model instance obtaining, provider credential authentication, and provider configuration rule information, **allowing horizontal expansion** to support different providers. + +- The bottom layer is the model layer + + It offers direct invocation of various model types, predefined model configuration information, getting predefined/remote model lists, model credential authentication methods. Different models provide additional special methods, like LLM's pre-computed tokens method, cost information obtaining method, etc., **allowing horizontal expansion** for different models under the same provider (within supported model types). + + + +## Next Steps + +- Add new provider configuration: [Link](./docs/en_US/provider_scale_out.md) +- Add new models for existing providers: [Link](./docs/en_US/provider_scale_out.md#AddModel) +- View YAML configuration rules: [Link](./docs/en_US/schema.md) +- Implement interface methods: [Link](./docs/en_US/interfaces.md) diff --git a/api/core/model_runtime/README_CN.md b/api/core/model_runtime/README_CN.md new file mode 100644 index 00000000000000..d9000845c69c71 --- /dev/null +++ b/api/core/model_runtime/README_CN.md @@ -0,0 +1,88 @@ +# Model Runtime + +该模块提供了各模型的调用、鉴权接口，并为 Dify 提供了统一的模型供应商的信息和凭据表单规则。 + +- 一方面将模型和上下游解耦，方便开发者对模型横向扩展， +- 另一方面提供了只需在后端定义供应商和模型，即可在前端页面直接展示，无需修改前端逻辑。 + +## 功能介绍 + +- 支持 5 种模型类型的能力调用 + + - `LLM` - LLM 文本补全、对话，预计算 tokens 能力 + - `Text Embedidng Model` - 文本 Embedding ，预计算 tokens 能力 + - `Rerank Model` - 分段 Rerank 能力 + - `Speech-to-text Model` - 语音转文本能力 + - `Moderation` - Moderation 能力 + +- 模型供应商展示 + + ![image-20231210143654461](./docs/zh_Hans/images/index/image-20231210143654461.png) + +​ 展示所有已支持的供应商列表，除了返回供应商名称、图标之外，还提供了支持的模型类型列表，预定义模型列表、配置方式以及配置凭据的表单规则等等，规则设计详见：[Schema](./schema.md)。 + +- 可选择的模型列表展示 + + ![image-20231210144229650](./docs/zh_Hans/images/index/image-20231210144229650.png) + +​ 配置供应商/模型凭据后，可在此下拉（应用编排界面/默认模型）查看可用的 LLM 列表，其中灰色的为未配置凭据供应商的预定义模型列表，方便用户查看已支持的模型。 + +​ 除此之外，该列表还返回了 LLM 可配置的参数信息和规则，如下图： + +​ ![image-20231210144814617](./docs/zh_Hans/images/index/image-20231210144814617.png) + +​ 这里的参数均为后端定义，相比之前只有 5 种固定参数，这里可为不同模型设置所支持的各种参数，详见：[Schema](./docs/zh_Hans/schema.md#ParameterRule)。 + +- 供应商/模型凭据鉴权 + + ![image-20231210151548521](./docs/zh_Hans/images/index/image-20231210151548521.png) + +![image-20231210151628992](./docs/zh_Hans/images/index/image-20231210151628992.png) + +​ 供应商列表返回了凭据表单的配置信息，可通过 Runtime 提供的接口对凭据进行鉴权，上图 1 为供应商凭据 DEMO，上图 2 为模型凭据 DEMO。 + +## 结构 + +![](./docs/zh_Hans/images/index/image-20231210165243632.png) + +Model Runtime 分三层： + +- 最外层为工厂方法 + + 提供获取所有供应商、所有模型列表、获取供应商实例、供应商/模型凭据鉴权方法。 + +- 第二层为供应商层 + + 提供获取当前供应商模型列表、获取模型实例、供应商凭据鉴权、供应商配置规则信息，**可横向扩展**以支持不同的供应商。 + + 对于供应商/模型凭据，有两种情况 + - 如OpenAI这类中心化供应商，需要定义如**api_key**这类的鉴权凭据 + - 如[**Xinference**](https://github.com/xorbitsai/inference)这类本地部署的供应商，需要定义如**server_url**这类的地址凭据，有时候还需要定义**model_uid**之类的模型类型凭据，就像下面这样，当在供应商层定义了这些凭据后，就可以在前端页面上直接展示，无需修改前端逻辑。 + ![Alt text](docs/zh_Hans/images/index/image.png) + + 当配置好凭据后，就可以通过DifyRuntime的外部接口直接获取到对应供应商所需要的**Schema**（凭据表单规则），从而在可以在不修改前端逻辑的情况下，提供新的供应商/模型的支持。 + +- 最底层为模型层 + + 提供各种模型类型的直接调用、预定义模型配置信息、获取预定义/远程模型列表、模型凭据鉴权方法，不同模型额外提供了特殊方法，如 LLM 提供预计算 tokens 方法、获取费用信息方法等，**可横向扩展**同供应商下不同的模型（支持的模型类型下）。 + + 在这里我们需要先区分模型参数与模型凭据。 + + - 模型参数(**在本层定义**)：这是一类经常需要变动，随时调整的参数，如 LLM 的 **max_tokens**、**temperature** 等，这些参数是由用户在前端页面上进行调整的，因此需要在后端定义参数的规则，以便前端页面进行展示和调整。在DifyRuntime中，他们的参数名一般为**model_parameters: dict[str, any]**。 + + - 模型凭据(**在供应商层定义**)：这是一类不经常变动，一般在配置好后就不会再变动的参数，如 **api_key**、**server_url** 等。在DifyRuntime中，他们的参数名一般为**credentials: dict[str, any]**，Provider层的credentials会直接被传递到这一层，不需要再单独定义。 + +## 下一步 + +### [增加新的供应商配置 👈🏻](./docs/zh_Hans/provider_scale_out.md) +当添加后，这里将会出现一个新的供应商 + +![Alt text](docs/zh_Hans/images/index/image-1.png) + +### [为已存在的供应商新增模型 👈🏻](./docs/zh_Hans/provider_scale_out.md#增加模型) +当添加后，对应供应商的模型列表中将会出现一个新的预定义模型供用户选择，如GPT-3.5 GPT-4 ChatGLM3-6b等，而对于支持自定义模型的供应商，则不需要新增模型。 + +![Alt text](docs/zh_Hans/images/index/image-2.png) + +### [接口的具体实现 👈🏻](./docs/zh_Hans/interfaces.md) +你可以在这里找到你想要查看的接口的具体实现，以及接口的参数和返回值的具体含义。 \ No newline at end of file diff --git a/api/core/model_providers/models/reranking/__init__.py b/api/core/model_runtime/__init__.py similarity index 100% rename from api/core/model_providers/models/reranking/__init__.py rename to api/core/model_runtime/__init__.py diff --git a/api/core/model_providers/models/speech2text/__init__.py b/api/core/model_runtime/callbacks/__init__.py similarity index 100% rename from api/core/model_providers/models/speech2text/__init__.py rename to api/core/model_runtime/callbacks/__init__.py diff --git a/api/core/model_runtime/callbacks/base_callback.py b/api/core/model_runtime/callbacks/base_callback.py new file mode 100644 index 00000000000000..144b779540fc4a --- /dev/null +++ b/api/core/model_runtime/callbacks/base_callback.py @@ -0,0 +1,113 @@ +from abc import ABC +from typing import Optional, List + +from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk +from core.model_runtime.entities.message_entities import PromptMessageTool, PromptMessage +from core.model_runtime.model_providers.__base.ai_model import AIModel + +_TEXT_COLOR_MAPPING = { + "blue": "36;1", + "yellow": "33;1", + "pink": "38;5;200", + "green": "32;1", + "red": "31;1", +} + + +class Callback(ABC): + """ + Base class for callbacks. + Only for LLM. + """ + raise_error: bool = False + + def on_before_invoke(self, llm_instance: AIModel, model: str, credentials: dict, + prompt_messages: list[PromptMessage], model_parameters: dict, + tools: Optional[list[PromptMessageTool]] = None, stop: Optional[List[str]] = None, + stream: bool = True, user: Optional[str] = None) -> None: + """ + Before invoke callback + + :param llm_instance: LLM instance + :param model: model name + :param credentials: model credentials + :param prompt_messages: prompt messages + :param model_parameters: model parameters + :param tools: tools for tool calling + :param stop: stop words + :param stream: is stream response + :param user: unique user id + """ + raise NotImplementedError() + + def on_new_chunk(self, llm_instance: AIModel, chunk: LLMResultChunk, model: str, credentials: dict, + prompt_messages: list[PromptMessage], model_parameters: dict, + tools: Optional[list[PromptMessageTool]] = None, stop: Optional[List[str]] = None, + stream: bool = True, user: Optional[str] = None): + """ + On new chunk callback + + :param llm_instance: LLM instance + :param chunk: chunk + :param model: model name + :param credentials: model credentials + :param prompt_messages: prompt messages + :param model_parameters: model parameters + :param tools: tools for tool calling + :param stop: stop words + :param stream: is stream response + :param user: unique user id + """ + raise NotImplementedError() + + def on_after_invoke(self, llm_instance: AIModel, result: LLMResult, model: str, credentials: dict, + prompt_messages: list[PromptMessage], model_parameters: dict, + tools: Optional[list[PromptMessageTool]] = None, stop: Optional[List[str]] = None, + stream: bool = True, user: Optional[str] = None) -> None: + """ + After invoke callback + + :param llm_instance: LLM instance + :param result: result + :param model: model name + :param credentials: model credentials + :param prompt_messages: prompt messages + :param model_parameters: model parameters + :param tools: tools for tool calling + :param stop: stop words + :param stream: is stream response + :param user: unique user id + """ + raise NotImplementedError() + + def on_invoke_error(self, llm_instance: AIModel, ex: Exception, model: str, credentials: dict, + prompt_messages: list[PromptMessage], model_parameters: dict, + tools: Optional[list[PromptMessageTool]] = None, stop: Optional[List[str]] = None, + stream: bool = True, user: Optional[str] = None) -> None: + """ + Invoke error callback + + :param llm_instance: LLM instance + :param ex: exception + :param model: model name + :param credentials: model credentials + :param prompt_messages: prompt messages + :param model_parameters: model parameters + :param tools: tools for tool calling + :param stop: stop words + :param stream: is stream response + :param user: unique user id + """ + raise NotImplementedError() + + def print_text( + self, text: str, color: Optional[str] = None, end: str = "" + ) -> None: + """Print text with highlighting and no end characters.""" + text_to_print = self._get_colored_text(text, color) if color else text + print(text_to_print, end=end) + + def _get_colored_text(self, text: str, color: str) -> str: + """Get colored text.""" + color_str = _TEXT_COLOR_MAPPING[color] + return f"\u001b[{color_str}m\033[1;3m{text}\u001b[0m" diff --git a/api/core/model_runtime/callbacks/logging_callback.py b/api/core/model_runtime/callbacks/logging_callback.py new file mode 100644 index 00000000000000..4bd86e81dc4023 --- /dev/null +++ b/api/core/model_runtime/callbacks/logging_callback.py @@ -0,0 +1,133 @@ +import json +import logging +import sys +from typing import Optional, List + +from core.model_runtime.callbacks.base_callback import Callback +from core.model_runtime.entities.llm_entities import LLMResultChunk, LLMResult +from core.model_runtime.entities.message_entities import PromptMessageTool, PromptMessage +from core.model_runtime.model_providers.__base.ai_model import AIModel + +logger = logging.getLogger(__name__) + +class LoggingCallback(Callback): + def on_before_invoke(self, llm_instance: AIModel, model: str, credentials: dict, + prompt_messages: list[PromptMessage], model_parameters: dict, + tools: Optional[list[PromptMessageTool]] = None, stop: Optional[List[str]] = None, + stream: bool = True, user: Optional[str] = None) -> None: + """ + Before invoke callback + + :param llm_instance: LLM instance + :param model: model name + :param credentials: model credentials + :param prompt_messages: prompt messages + :param model_parameters: model parameters + :param tools: tools for tool calling + :param stop: stop words + :param stream: is stream response + :param user: unique user id + """ + self.print_text("\n[on_llm_before_invoke]\n", color='blue') + self.print_text(f"Model: {model}\n", color='blue') + self.print_text(f"Parameters:\n", color='blue') + for key, value in model_parameters.items(): + self.print_text(f"\t{key}: {value}\n", color='blue') + + if stop: + self.print_text(f"\tstop: {stop}\n", color='blue') + + if tools: + self.print_text(f"\tTools:\n", color='blue') + for tool in tools: + self.print_text(f"\t\t{tool.name}\n", color='blue') + + self.print_text(f"Stream: {stream}\n", color='blue') + + if user: + self.print_text(f"User: {user}\n", color='blue') + + self.print_text(f"Prompt messages:\n", color='blue') + for prompt_message in prompt_messages: + if prompt_message.name: + self.print_text(f"\tname: {prompt_message.name}\n", color='blue') + + self.print_text(f"\trole: {prompt_message.role.value}\n", color='blue') + self.print_text(f"\tcontent: {prompt_message.content}\n", color='blue') + + if stream: + self.print_text("\n[on_llm_new_chunk]") + + def on_new_chunk(self, llm_instance: AIModel, chunk: LLMResultChunk, model: str, credentials: dict, + prompt_messages: list[PromptMessage], model_parameters: dict, + tools: Optional[list[PromptMessageTool]] = None, stop: Optional[List[str]] = None, + stream: bool = True, user: Optional[str] = None): + """ + On new chunk callback + + :param llm_instance: LLM instance + :param chunk: chunk + :param model: model name + :param credentials: model credentials + :param prompt_messages: prompt messages + :param model_parameters: model parameters + :param tools: tools for tool calling + :param stop: stop words + :param stream: is stream response + :param user: unique user id + """ + sys.stdout.write(chunk.delta.message.content) + sys.stdout.flush() + + def on_after_invoke(self, llm_instance: AIModel, result: LLMResult, model: str, credentials: dict, + prompt_messages: list[PromptMessage], model_parameters: dict, + tools: Optional[list[PromptMessageTool]] = None, stop: Optional[List[str]] = None, + stream: bool = True, user: Optional[str] = None) -> None: + """ + After invoke callback + + :param llm_instance: LLM instance + :param result: result + :param model: model name + :param credentials: model credentials + :param prompt_messages: prompt messages + :param model_parameters: model parameters + :param tools: tools for tool calling + :param stop: stop words + :param stream: is stream response + :param user: unique user id + """ + self.print_text("\n[on_llm_after_invoke]\n", color='yellow') + self.print_text(f"Content: {result.message.content}\n", color='yellow') + + if result.message.tool_calls: + self.print_text(f"Tool calls:\n", color='yellow') + for tool_call in result.message.tool_calls: + self.print_text(f"\t{tool_call.id}\n", color='yellow') + self.print_text(f"\t{tool_call.function.name}\n", color='yellow') + self.print_text(f"\t{json.dumps(tool_call.function.arguments)}\n", color='yellow') + + self.print_text(f"Model: {result.model}\n", color='yellow') + self.print_text(f"Usage: {result.usage}\n", color='yellow') + self.print_text(f"System Fingerprint: {result.system_fingerprint}\n", color='yellow') + + def on_invoke_error(self, llm_instance: AIModel, ex: Exception, model: str, credentials: dict, + prompt_messages: list[PromptMessage], model_parameters: dict, + tools: Optional[list[PromptMessageTool]] = None, stop: Optional[List[str]] = None, + stream: bool = True, user: Optional[str] = None) -> None: + """ + Invoke error callback + + :param llm_instance: LLM instance + :param ex: exception + :param model: model name + :param credentials: model credentials + :param prompt_messages: prompt messages + :param model_parameters: model parameters + :param tools: tools for tool calling + :param stop: stop words + :param stream: is stream response + :param user: unique user id + """ + self.print_text("\n[on_llm_invoke_error]\n", color='red') + logger.exception(ex) diff --git a/api/core/model_runtime/docs/en_US/images/index/image-20231210143654461.png b/api/core/model_runtime/docs/en_US/images/index/image-20231210143654461.png new file mode 100644 index 00000000000000..2e234f6c21807e Binary files /dev/null and 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+1,668 @@ +# Interface Methods + +This section describes the interface methods and parameter explanations that need to be implemented by providers and various model types. + +## Provider + +Inherit the `__base.model_provider.ModelProvider` base class and implement the following interfaces: + +```python +def validate_provider_credentials(self, credentials: dict) -> None: + """ + Validate provider credentials + You can choose any validate_credentials method of model type or implement validate method by yourself, + such as: get model list api + + if validate failed, raise exception + + :param credentials: provider credentials, credentials form defined in `provider_credential_schema`. + """ +``` + +- `credentials` (object) Credential information + + The parameters of credential information are defined by the `provider_credential_schema` in the provider's YAML configuration file. Inputs such as `api_key` are included. + +If verification fails, throw the `errors.validate.CredentialsValidateFailedError` error. + +## Model + +Models are divided into 5 different types, each inheriting from different base classes and requiring the implementation of different methods. + +All models need to uniformly implement the following 2 methods: + +- Model Credential Verification + + Similar to provider credential verification, this step involves verification for an individual model. + + + ```python + def validate_credentials(self, model: str, credentials: dict) -> None: + """ + Validate model credentials + + :param model: model name + :param credentials: model credentials + :return: + """ + ``` + + Parameters: + + - `model` (string) Model name + + - `credentials` (object) Credential information + + The parameters of credential information are defined by either the `provider_credential_schema` or `model_credential_schema` in the provider's YAML configuration file. Inputs such as `api_key` are included. + + If verification fails, throw the `errors.validate.CredentialsValidateFailedError` error. + +- Invocation Error Mapping Table + + When there is an exception in model invocation, it needs to be mapped to the `InvokeError` type specified by Runtime. This facilitates Dify's ability to handle different errors with appropriate follow-up actions. + + Runtime Errors: + + - `InvokeConnectionError` Invocation connection error + - `InvokeServerUnavailableError` Invocation service provider unavailable + - `InvokeRateLimitError` Invocation reached rate limit + - `InvokeAuthorizationError` Invocation authorization failure + - `InvokeBadRequestError` Invocation parameter error + + ```python + @property + def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]: + """ + Map model invoke error to unified error + The key is the error type thrown to the caller + The value is the error type thrown by the model, + which needs to be converted into a unified error type for the caller. + + :return: Invoke error mapping + """ + ``` + +​ You can refer to OpenAI's `_invoke_error_mapping` for an example. + +### LLM + +Inherit the `__base.large_language_model.LargeLanguageModel` base class and implement the following interfaces: + +- LLM Invocation + + Implement the core method for LLM invocation, which can support both streaming and synchronous returns. + + + ```python + def _invoke(self, model: str, credentials: dict, + prompt_messages: list[PromptMessage], model_parameters: dict, + tools: Optional[list[PromptMessageTool]] = None, stop: Optional[List[str]] = None, + stream: bool = True, user: Optional[str] = None) \ + -> Union[LLMResult, Generator]: + """ + Invoke large language model + + :param model: model name + :param credentials: model credentials + :param prompt_messages: prompt messages + :param model_parameters: model parameters + :param tools: tools for tool calling + :param stop: stop words + :param stream: is stream response + :param user: unique user id + :return: full response or stream response chunk generator result + """ + ``` + + - Parameters: + + - `model` (string) Model name + + - `credentials` (object) Credential information + + The parameters of credential information are defined by either the `provider_credential_schema` or `model_credential_schema` in the provider's YAML configuration file. Inputs such as `api_key` are included. + + - `prompt_messages` (array[[PromptMessage](#PromptMessage)]) List of prompts + + If the model is of the `Completion` type, the list only needs to include one [UserPromptMessage](#UserPromptMessage) element; + + If the model is of the `Chat` type, it requires a list of elements such as [SystemPromptMessage](#SystemPromptMessage), [UserPromptMessage](#UserPromptMessage), [AssistantPromptMessage](#AssistantPromptMessage), [ToolPromptMessage](#ToolPromptMessage) depending on the message. + + - `model_parameters` (object) Model parameters + + The model parameters are defined by the `parameter_rules` in the model's YAML configuration. + + - `tools` (array[[PromptMessageTool](#PromptMessageTool)]) [optional] List of tools, equivalent to the `function` in `function calling`. + + That is, the tool list for tool calling. + + - `stop` (array[string]) [optional] Stop sequences + + The model output will stop before the string defined by the stop sequence. + + - `stream` (bool) Whether to output in a streaming manner, default is True + + Streaming output returns Generator[[LLMResultChunk](#LLMResultChunk)], non-streaming output returns [LLMResult](#LLMResult). + + - `user` (string) [optional] Unique identifier of the user + + This can help the provider monitor and detect abusive behavior. + + - Returns + + Streaming output returns Generator[[LLMResultChunk](#LLMResultChunk)], non-streaming output returns [LLMResult](#LLMResult). + +- Pre-calculating Input Tokens + + If the model does not provide a pre-calculated tokens interface, you can directly return 0. + + ```python + def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage], + tools: Optional[list[PromptMessageTool]] = None) -> int: + """ + Get number of tokens for given prompt messages + + :param model: model name + :param credentials: model credentials + :param prompt_messages: prompt messages + :param tools: tools for tool calling + :return: + """ + ``` + + For parameter explanations, refer to the above section on `LLM Invocation`. + +- Fetch Custom Model Schema [Optional] + + ```python + def get_customizable_model_schema(self, model: str, credentials: dict) -> Optional[AIModelEntity]: + """ + Get customizable model schema + + :param model: model name + :param credentials: model credentials + :return: model schema + """ + ``` + + When the provider supports adding custom LLMs, this method can be implemented to allow custom models to fetch model schema. The default return null. + + +### TextEmbedding + +Inherit the `__base.text_embedding_model.TextEmbeddingModel` base class and implement the following interfaces: + +- Embedding Invocation + + ```python + def _invoke(self, model: str, credentials: dict, + texts: list[str], user: Optional[str] = None) \ + -> TextEmbeddingResult: + """ + Invoke large language model + + :param model: model name + :param credentials: model credentials + :param texts: texts to embed + :param user: unique user id + :return: embeddings result + """ + ``` + + - Parameters: + + - `model` (string) Model name + + - `credentials` (object) Credential information + + The parameters of credential information are defined by either the `provider_credential_schema` or `model_credential_schema` in the provider's YAML configuration file. Inputs such as `api_key` are included. + + - `texts` (array[string]) List of texts, capable of batch processing + + - `user` (string) [optional] Unique identifier of the user + + This can help the provider monitor and detect abusive behavior. + + - Returns: + + [TextEmbeddingResult](#TextEmbeddingResult) entity. + +- Pre-calculating Tokens + + ```python + def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int: + """ + Get number of tokens for given prompt messages + + :param model: model name + :param credentials: model credentials + :param texts: texts to embed + :return: + """ + ``` + + For parameter explanations, refer to the above section on `Embedding Invocation`. + +### Rerank + +Inherit the `__base.rerank_model.RerankModel` base class and implement the following interfaces: + +- Rerank Invocation + + ```python + def _invoke(self, model: str, credentials: dict, + query: str, docs: list[str], score_threshold: Optional[float] = None, top_n: Optional[int] = None, + user: Optional[str] = None) \ + -> RerankResult: + """ + Invoke rerank model + + :param model: model name + :param credentials: model credentials + :param query: search query + :param docs: docs for reranking + :param score_threshold: score threshold + :param top_n: top n + :param user: unique user id + :return: rerank result + """ + ``` + + - Parameters: + + - `model` (string) Model name + + - `credentials` (object) Credential information + + The parameters of credential information are defined by either the `provider_credential_schema` or `model_credential_schema` in the provider's YAML configuration file. Inputs such as `api_key` are included. + + - `query` (string) Query request content + + - `docs` (array[string]) List of segments to be reranked + + - `score_threshold` (float) [optional] Score threshold + + - `top_n` (int) [optional] Select the top n segments + + - `user` (string) [optional] Unique identifier of the user + + This can help the provider monitor and detect abusive behavior. + + - Returns: + + [RerankResult](#RerankResult) entity. + +### Speech2text + +Inherit the `__base.speech2text_model.Speech2TextModel` base class and implement the following interfaces: + +- Invoke Invocation + + ```python + def _invoke(self, model: str, credentials: dict, + file: IO[bytes], user: Optional[str] = None) \ + -> str: + """ + Invoke large language model + + :param model: model name + :param credentials: model credentials + :param file: audio file + :param user: unique user id + :return: text for given audio file + """ + ``` + + - Parameters: + + - `model` (string) Model name + + - `credentials` (object) Credential information + + The parameters of credential information are defined by either the `provider_credential_schema` or `model_credential_schema` in the provider's YAML configuration file. Inputs such as `api_key` are included. + + - `file` (File) File stream + + - `user` (string) [optional] Unique identifier of the user + + This can help the provider monitor and detect abusive behavior. + + - Returns: + + The string after speech-to-text conversion. + +### Moderation + +Inherit the `__base.moderation_model.ModerationModel` base class and implement the following interfaces: + +- Invoke Invocation + + ```python + def _invoke(self, model: str, credentials: dict, + text: str, user: Optional[str] = None) \ + -> bool: + """ + Invoke large language model + + :param model: model name + :param credentials: model credentials + :param text: text to moderate + :param user: unique user id + :return: false if text is safe, true otherwise + """ + ``` + + - Parameters: + + - `model` (string) Model name + + - `credentials` (object) Credential information + + The parameters of credential information are defined by either the `provider_credential_schema` or `model_credential_schema` in the provider's YAML configuration file. Inputs such as `api_key` are included. + + - `text` (string) Text content + + - `user` (string) [optional] Unique identifier of the user + + This can help the provider monitor and detect abusive behavior. + + - Returns: + + False indicates that the input text is safe, True indicates otherwise. + + + +## Entities + +### PromptMessageRole + +Message role + +```python +class PromptMessageRole(Enum): + """ + Enum class for prompt message. + """ + SYSTEM = "system" + USER = "user" + ASSISTANT = "assistant" + TOOL = "tool" +``` + +### PromptMessageContentType + +Message content types, divided into text and image. + +```python +class PromptMessageContentType(Enum): + """ + Enum class for prompt message content type. + """ + TEXT = 'text' + IMAGE = 'image' +``` + +### PromptMessageContent + +Message content base class, used only for parameter declaration and cannot be initialized. + +```python +class PromptMessageContent(BaseModel): + """ + Model class for prompt message content. + """ + type: PromptMessageContentType + data: str +``` + +Currently, two types are supported: text and image. It's possible to simultaneously input text and multiple images. + +You need to initialize `TextPromptMessageContent` and `ImagePromptMessageContent` separately for input. + +### TextPromptMessageContent + +```python +class TextPromptMessageContent(PromptMessageContent): + """ + Model class for text prompt message content. + """ + type: PromptMessageContentType = PromptMessageContentType.TEXT +``` + +If inputting a combination of text and images, the text needs to be constructed into this entity as part of the `content` list. + +### ImagePromptMessageContent + +```python +class ImagePromptMessageContent(PromptMessageContent): + """ + Model class for image prompt message content. + """ + class DETAIL(Enum): + LOW = 'low' + HIGH = 'high' + + type: PromptMessageContentType = PromptMessageContentType.IMAGE + detail: DETAIL = DETAIL.LOW # Resolution +``` + +If inputting a combination of text and images, the images need to be constructed into this entity as part of the `content` list. + +`data` can be either a `url` or a `base64` encoded string of the image. + +### PromptMessage + +The base class for all Role message bodies, used only for parameter declaration and cannot be initialized. + +```python +class PromptMessage(ABC, BaseModel): + """ + Model class for prompt message. + """ + role: PromptMessageRole + content: Optional[str | list[PromptMessageContent]] = None # Supports two types: string and content list. The content list is designed to meet the needs of multimodal inputs. For more details, see the PromptMessageContent explanation. + name: Optional[str] = None +``` + +### UserPromptMessage + +UserMessage message body, representing a user's message. + +```python +class UserPromptMessage(PromptMessage): + """ + Model class for user prompt message. + """ + role: PromptMessageRole = PromptMessageRole.USER +``` + +### AssistantPromptMessage + +Represents a message returned by the model, typically used for `few-shots` or inputting chat history. + +```python +class AssistantPromptMessage(PromptMessage): + """ + Model class for assistant prompt message. + """ + class ToolCall(BaseModel): + """ + Model class for assistant prompt message tool call. + """ + class ToolCallFunction(BaseModel): + """ + Model class for assistant prompt message tool call function. + """ + name: str # tool name + arguments: str # tool arguments + + id: str # Tool ID, effective only in OpenAI tool calls. It's the unique ID for tool invocation and the same tool can be called multiple times. + type: str # default: function + function: ToolCallFunction # tool call information + + role: PromptMessageRole = PromptMessageRole.ASSISTANT + tool_calls: list[ToolCall] = [] # The result of tool invocation in response from the model (returned only when tools are input and the model deems it necessary to invoke a tool). +``` + +Where `tool_calls` are the list of `tool calls` returned by the model after invoking the model with the `tools` input. + +### SystemPromptMessage + +Represents system messages, usually used for setting system commands given to the model. + +```python +class SystemPromptMessage(PromptMessage): + """ + Model class for system prompt message. + """ + role: PromptMessageRole = PromptMessageRole.SYSTEM +``` + +### ToolPromptMessage + +Represents tool messages, used for conveying the results of a tool execution to the model for the next step of processing. + +```python +class ToolPromptMessage(PromptMessage): + """ + Model class for tool prompt message. + """ + role: PromptMessageRole = PromptMessageRole.TOOL + tool_call_id: str # Tool invocation ID. If OpenAI tool call is not supported, the name of the tool can also be inputted. +``` + +The base class's `content` takes in the results of tool execution. + +### PromptMessageTool + +```python +class PromptMessageTool(BaseModel): + """ + Model class for prompt message tool. + """ + name: str + description: str + parameters: dict +``` + +--- + +### LLMResult + +```python +class LLMResult(BaseModel): + """ + Model class for llm result. + """ + model: str # Actual used modele + prompt_messages: list[PromptMessage] # prompt messages + message: AssistantPromptMessage # response message + usage: LLMUsage # usage info + system_fingerprint: Optional[str] = None # request fingerprint, refer to OpenAI definition +``` + +### LLMResultChunkDelta + +In streaming returns, each iteration contains the `delta` entity. + +```python +class LLMResultChunkDelta(BaseModel): + """ + Model class for llm result chunk delta. + """ + index: int + message: AssistantPromptMessage # response message + usage: Optional[LLMUsage] = None # usage info + finish_reason: Optional[str] = None # finish reason, only the last one returns +``` + +### LLMResultChunk + +Each iteration entity in streaming returns. + +```python +class LLMResultChunk(BaseModel): + """ + Model class for llm result chunk. + """ + model: str # Actual used modele + prompt_messages: list[PromptMessage] # prompt messages + system_fingerprint: Optional[str] = None # request fingerprint, refer to OpenAI definition + delta: LLMResultChunkDelta +``` + +### LLMUsage + +```python +class LLMUsage(ModelUsage): + """ + Model class for LLM usage. + """ + prompt_tokens: int # Tokens used for prompt + prompt_unit_price: Decimal # Unit price for prompt + prompt_price_unit: Decimal # Price unit for prompt, i.e., the unit price based on how many tokens + prompt_price: Decimal # Cost for prompt + completion_tokens: int # Tokens used for response + completion_unit_price: Decimal # Unit price for response + completion_price_unit: Decimal # Price unit for response, i.e., the unit price based on how many tokens + completion_price: Decimal # Cost for response + total_tokens: int # Total number of tokens used + total_price: Decimal # Total cost + currency: str # Currency unit + latency: float # Request latency (s) +``` + +--- + +### TextEmbeddingResult + +```python +class TextEmbeddingResult(BaseModel): + """ + Model class for text embedding result. + """ + model: str # Actual model used + embeddings: list[list[float]] # List of embedding vectors, corresponding to the input texts list + usage: EmbeddingUsage # Usage information +``` + +### EmbeddingUsage + +```python +class EmbeddingUsage(ModelUsage): + """ + Model class for embedding usage. + """ + tokens: int # Number of tokens used + total_tokens: int # Total number of tokens used + unit_price: Decimal # Unit price + price_unit: Decimal # Price unit, i.e., the unit price based on how many tokens + total_price: Decimal # Total cost + currency: str # Currency unit + latency: float # Request latency (s) +``` + +--- + +### RerankResult + +```python +class RerankResult(BaseModel): + """ + Model class for rerank result. + """ + model: str # Actual model used + docs: list[RerankDocument] # Reranked document list +``` + +### RerankDocument + +```python +class RerankDocument(BaseModel): + """ + Model class for rerank document. + """ + index: int # original index + text: str + score: float +``` diff --git a/api/core/model_runtime/docs/en_US/provider_scale_out.md b/api/core/model_runtime/docs/en_US/provider_scale_out.md new file mode 100644 index 00000000000000..1d012c5a29660d --- /dev/null +++ b/api/core/model_runtime/docs/en_US/provider_scale_out.md @@ -0,0 +1,264 @@ +## Adding a New Provider + +Providers support three types of model configuration methods: + +- `predefined-model` Predefined model + + This indicates that users only need to configure the unified provider credentials to use the predefined models under the provider. + +- `customizable-model` Customizable model + + Users need to add credential configurations for each model. + +- `fetch-from-remote` Fetch from remote + + This is consistent with the `predefined-model` configuration method. Only unified provider credentials need to be configured, and models are obtained from the provider through credential information. + +These three configuration methods **can coexist**, meaning a provider can support `predefined-model` + `customizable-model` or `predefined-model` + `fetch-from-remote`, etc. In other words, configuring the unified provider credentials allows the use of predefined and remotely fetched models, and if new models are added, they can be used in addition to the custom models. + +## Getting Started + +Adding a new provider starts with determining the English identifier of the provider, such as `anthropic`, and using this identifier to create a `module` in `model_providers`. + +Under this `module`, we first need to prepare the provider's YAML configuration. + +### Preparing Provider YAML + +Here, using `Anthropic` as an example, we preset the provider's basic information, supported model types, configuration methods, and credential rules. + +```YAML +provider: anthropic # Provider identifier +label: # Provider display name, can be set in en_US English and zh_Hans Chinese, zh_Hans will default to en_US if not set. + en_US: Anthropic +icon_small: # Small provider icon, stored in the _assets directory under the corresponding provider implementation directory, same language strategy as label + en_US: icon_s_en.png +icon_large: # Large provider icon, stored in the _assets directory under the corresponding provider implementation directory, same language strategy as label + en_US: icon_l_en.png +supported_model_types: # Supported model types, Anthropic only supports LLM +- llm +configurate_methods: # Supported configuration methods, Anthropic only supports predefined models +- predefined-model +provider_credential_schema: # Provider credential rules, as Anthropic only supports predefined models, unified provider credential rules need to be defined + credential_form_schemas: # List of credential form items + - variable: anthropic_api_key # Credential parameter variable name + label: # Display name + en_US: API Key + type: secret-input # Form type, here secret-input represents an encrypted information input box, showing masked information when editing. + required: true # Whether required + placeholder: # Placeholder information + zh_Hans: Enter your API Key here + en_US: Enter your API Key + - variable: anthropic_api_url + label: + en_US: API URL + type: text-input # Form type, here text-input represents a text input box + required: false + placeholder: + zh_Hans: Enter your API URL here + en_US: Enter your API URL +``` + +You can also refer to the YAML configuration information under other provider directories in `model_providers`. The complete YAML rules are available at: [Schema](schema.md#Provider). + +### Implementing Provider Code + +Providers need to inherit the `__base.model_provider.ModelProvider` base class and implement the `validate_provider_credentials` method for unified provider credential verification. For reference, see [AnthropicProvider](https://github.com/langgenius/dify-runtime/blob/main/lib/model_providers/anthropic/anthropic.py). +> If the provider is the type of `customizable-model`, there is no need to implement the `validate_provider_credentials` method. + +```python +def validate_provider_credentials(self, credentials: dict) -> None: + """ + Validate provider credentials + You can choose any validate_credentials method of model type or implement validate method by yourself, + such as: get model list api + + if validate failed, raise exception + + :param credentials: provider credentials, credentials form defined in `provider_credential_schema`. + """ +``` + +Of course, you can also preliminarily reserve the implementation of `validate_provider_credentials` and directly reuse it after the model credential verification method is implemented. + +--- + +### Adding Models + +After the provider integration is complete, the next step is to integrate models under the provider. + +First, we need to determine the type of the model to be integrated and create a `module` for the corresponding model type in the provider's directory. + +The currently supported model types are as follows: + +- `llm` Text generation model +- `text_embedding` Text Embedding model +- `rerank` Rerank model +- `speech2text` Speech to text +- `moderation` Moderation + +Continuing with `Anthropic` as an example, since `Anthropic` only supports LLM, we create a `module` named `llm` in `model_providers.anthropic`. + +For predefined models, we first need to create a YAML file named after the model, such as `claude-2.1.yaml`, under the `llm` `module`. + +#### Preparing Model YAML + +```yaml +model: claude-2.1 # Model identifier +# Model display name, can be set in en_US English and zh_Hans Chinese, zh_Hans will default to en_US if not set. +# Alternatively, if the label is not set, use the model identifier content. +label: + en_US: claude-2.1 +model_type: llm # Model type, claude-2.1 is an LLM +features: # Supported features, agent-thought for Agent reasoning, vision for image understanding +- agent-thought +model_properties: # Model properties + mode: chat # LLM mode, complete for text completion model, chat for dialogue model + context_size: 200000 # Maximum supported context size +parameter_rules: # Model invocation parameter rules, only required for LLM +- name: temperature # Invocation parameter variable name + # Default preset with 5 variable content configuration templates: temperature/top_p/max_tokens/presence_penalty/frequency_penalty + # Directly set the template variable name in use_template, which will use the default configuration in entities.defaults.PARAMETER_RULE_TEMPLATE + # If additional configuration parameters are set, they will override the default configuration + use_template: temperature +- name: top_p + use_template: top_p +- name: top_k + label: # Invocation parameter display name + zh_Hans: Sampling quantity + en_US: Top k + type: int # Parameter type, supports float/int/string/boolean + help: # Help information, describing the role of the parameter + zh_Hans: Only sample from the top K options for each subsequent token. + en_US: Only sample from the top K options for each subsequent token. + required: false # Whether required, can be left unset +- name: max_tokens_to_sample + use_template: max_tokens + default: 4096 # Default parameter value + min: 1 # Minimum parameter value, only applicable for float/int + max: 4096 # Maximum parameter value, only applicable for float/int +pricing: # Pricing information + input: '8.00' # Input price, i.e., Prompt price + output: '24.00' # Output price, i.e., returned content price + unit: '0.000001' # Pricing unit, i.e., the above prices are per 100K + currency: USD # Currency +``` + +It is recommended to prepare all model configurations before starting the implementation of the model code. + +Similarly, you can also refer to the YAML configuration information for corresponding model types of other providers in the `model_providers` directory. The complete YAML rules can be found at: [Schema](schema.md#AIModel). + +#### Implementing Model Invocation Code + +Next, you need to create a python file named `llm.py` under the `llm` `module` to write the implementation code. + +In `llm.py`, create an Anthropic LLM class, which we name `AnthropicLargeLanguageModel` (arbitrarily), inheriting the `__base.large_language_model.LargeLanguageModel` base class, and implement the following methods: + +- LLM Invocation + + Implement the core method for LLM invocation, which can support both streaming and synchronous returns. + + ```python + def _invoke(self, model: str, credentials: dict, + prompt_messages: list[PromptMessage], model_parameters: dict, + tools: Optional[list[PromptMessageTool]] = None, stop: Optional[List[str]] = None, + stream: bool = True, user: Optional[str] = None) \ + -> Union[LLMResult, Generator]: + """ + Invoke large language model + + :param model: model name + :param credentials: model credentials + :param prompt_messages: prompt messages + :param model_parameters: model parameters + :param tools: tools for tool calling + :param stop: stop words + :param stream: is stream response + :param user: unique user id + :return: full response or stream response chunk generator result + """ + ``` + +- Pre-calculating Input Tokens + + If the model does not provide a pre-calculated tokens interface, you can directly return 0. + + ```python + def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage], + tools: Optional[list[PromptMessageTool]] = None) -> int: + """ + Get number of tokens for given prompt messages + + :param model: model name + :param credentials: model credentials + :param prompt_messages: prompt messages + :param tools: tools for tool calling + :return: + """ + ``` + +- Model Credential Verification + + Similar to provider credential verification, this step involves verification for an individual model. + + ```python + def validate_credentials(self, model: str, credentials: dict) -> None: + """ + Validate model credentials + + :param model: model name + :param credentials: model credentials + :return: + """ + ``` + +- Invocation Error Mapping Table + + When there is an exception in model invocation, it needs to be mapped to the `InvokeError` type specified by Runtime. This facilitates Dify's ability to handle different errors with appropriate follow-up actions. + + Runtime Errors: + + - `InvokeConnectionError` Invocation connection error + - `InvokeServerUnavailableError` Invocation service provider unavailable + - `InvokeRateLimitError` Invocation reached rate limit + - `InvokeAuthorizationError` Invocation authorization failure + - `InvokeBadRequestError` Invocation parameter error + + ```python + @property + def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]: + """ + Map model invoke error to unified error + The key is the error type thrown to the caller + The value is the error type thrown by the model, + which needs to be converted into a unified error type for the caller. + + :return: Invoke error mapping + """ + ``` + +For details on the interface methods, see: [Interfaces](interfaces.md). For specific implementations, refer to: [llm.py](https://github.com/langgenius/dify-runtime/blob/main/lib/model_providers/anthropic/llm/llm.py). + +### Testing + +To ensure the availability of integrated providers/models, each method written needs corresponding integration test code in the `tests` directory. + +Continuing with `Anthropic` as an example: + +Before writing test code, you need to first add the necessary credential environment variables for the test provider in `.env.example`, such as: `ANTHROPIC_API_KEY`. + +Before execution, copy `.env.example` to `.env` and then execute. + +#### Writing Test Code + +Create a `module` with the same name as the provider in the `tests` directory: `anthropic`, and continue to create `test_provider.py` and test py files for the corresponding model types within this module, as shown below: + +```shell +. +├── __init__.py +├── anthropic +│   ├── __init__.py +│   ├── test_llm.py # LLM Testing +│   └── test_provider.py # Provider Testing +``` + +Write test code for all the various cases implemented above and submit the code after passing the tests. diff --git a/api/core/model_runtime/docs/en_US/schema.md b/api/core/model_runtime/docs/en_US/schema.md new file mode 100644 index 00000000000000..1dfc93c3560f87 --- /dev/null +++ b/api/core/model_runtime/docs/en_US/schema.md @@ -0,0 +1,194 @@ +# Configuration Rules + +- Provider rules are based on the [Provider](#Provider) entity. +- Model rules are based on the [AIModelEntity](#AIModelEntity) entity. + +> All entities mentioned below are based on `Pydantic BaseModel` and can be found in the `entities` module. + +### Provider + +- `provider` (string) Provider identifier, e.g., `openai` +- `label` (object) Provider display name, i18n, with `en_US` English and `zh_Hans` Chinese language settings + - `zh_Hans` (string) [optional] Chinese label name, if `zh_Hans` is not set, `en_US` will be used by default. + - `en_US` (string) English label name +- `description` (object) Provider description, i18n + - `zh_Hans` (string) [optional] Chinese description + - `en_US` (string) English description +- `icon_small` (string) [optional] Small provider ICON, stored in the `_assets` directory under the corresponding provider implementation directory, with the same language strategy as `label` + - `zh_Hans` (string) Chinese ICON + - `en_US` (string) English ICON +- `icon_large` (string) [optional] Large provider ICON, stored in the `_assets` directory under the corresponding provider implementation directory, with the same language strategy as `label` + - `zh_Hans` (string) Chinese ICON + - `en_US` (string) English ICON +- `background` (string) [optional] Background color value, e.g., #FFFFFF, if empty, the default frontend color value will be displayed. +- `help` (object) [optional] help information + - `title` (object) help title, i18n + - `zh_Hans` (string) [optional] Chinese title + - `en_US` (string) English title + - `url` (object) help link, i18n + - `zh_Hans` (string) [optional] Chinese link + - `en_US` (string) English link +- `supported_model_types` (array[[ModelType](#ModelType)]) Supported model types +- `configurate_methods` (array[[ConfigurateMethod](#ConfigurateMethod)]) Configuration methods +- `provider_credential_schema` ([ProviderCredentialSchema](#ProviderCredentialSchema)) Provider credential specification +- `model_credential_schema` ([ModelCredentialSchema](#ModelCredentialSchema)) Model credential specification + +### AIModelEntity + +- `model` (string) Model identifier, e.g., `gpt-3.5-turbo` +- `label` (object) [optional] Model display name, i18n, with `en_US` English and `zh_Hans` Chinese language settings + - `zh_Hans` (string) [optional] Chinese label name + - `en_US` (string) English label name +- `model_type` ([ModelType](#ModelType)) Model type +- `features` (array[[ModelFeature](#ModelFeature)]) [optional] Supported feature list +- `model_properties` (object) Model properties + - `mode` ([LLMMode](#LLMMode)) Mode (available for model type `llm`) + - `context_size` (int) Context size (available for model types `llm`, `text-embedding`) + - `max_chunks` (int) Maximum number of chunks (available for model types `text-embedding`, `moderation`) + - `file_upload_limit` (int) Maximum file upload limit, in MB (available for model type `speech2text`) + - `supported_file_extensions` (string) Supported file extension formats, e.g., mp3, mp4 (available for model type `speech2text`) + - `max_characters_per_chunk` (int) Maximum characters per chunk (available for model type `moderation`) +- `parameter_rules` (array[[ParameterRule](#ParameterRule)]) [optional] Model invocation parameter rules +- `pricing` ([PriceConfig](#PriceConfig)) [optional] Pricing information +- `deprecated` (bool) Whether deprecated. If deprecated, the model will no longer be displayed in the list, but those already configured can continue to be used. Default False. + +### ModelType + +- `llm` Text generation model +- `text-embedding` Text Embedding model +- `rerank` Rerank model +- `speech2text` Speech to text +- `moderation` Moderation + +### ConfigurateMethod + +- `predefined-model` Predefined model + + Indicates that users can use the predefined models under the provider by configuring the unified provider credentials. +- `customizable-model` Customizable model + + Users need to add credential configuration for each model. + +- `fetch-from-remote` Fetch from remote + + Consistent with the `predefined-model` configuration method, only unified provider credentials need to be configured, and models are obtained from the provider through credential information. + +### ModelFeature + +- `agent-thought` Agent reasoning, generally over 70B with thought chain capability. +- `vision` Vision, i.e., image understanding. + +### FetchFrom + +- `predefined-model` Predefined model +- `fetch-from-remote` Remote model + +### LLMMode + +- `complete` Text completion +- `chat` Dialogue + +### ParameterRule + +- `name` (string) Actual model invocation parameter name +- `use_template` (string) [optional] Using template + + By default, 5 variable content configuration templates are preset: + + - `temperature` + - `top_p` + - `frequency_penalty` + - `presence_penalty` + - `max_tokens` + + In use_template, you can directly set the template variable name, which will use the default configuration in entities.defaults.PARAMETER_RULE_TEMPLATE + No need to set any parameters other than `name` and `use_template`. If additional configuration parameters are set, they will override the default configuration. + Refer to `openai/llm/gpt-3.5-turbo.yaml`. + +- `label` (object) [optional] Label, i18n + + - `zh_Hans`(string) [optional] Chinese label name + - `en_US` (string) English label name + +- `type`(string) [optional] Parameter type + + - `int` Integer + - `float` Float + - `string` String + - `boolean` Boolean + +- `help` (string) [optional] Help information + + - `zh_Hans` (string) [optional] Chinese help information + - `en_US` (string) English help information + +- `required` (bool) Required, default False. + +- `default`(int/float/string/bool) [optional] Default value + +- `min`(int/float) [optional] Minimum value, applicable only to numeric types + +- `max`(int/float) [optional] Maximum value, applicable only to numeric types + +- `precision`(int) [optional] Precision, number of decimal places to keep, applicable only to numeric types + +- `options` (array[string]) [optional] Dropdown option values, applicable only when `type` is `string`, if not set or null, option values are not restricted + +### PriceConfig + +- `input` (float) Input price, i.e., Prompt price +- `output` (float) Output price, i.e., returned content price +- `unit` (float) Pricing unit, e.g., per 100K price is `0.000001` +- `currency` (string) Currency unit + +### ProviderCredentialSchema + +- `credential_form_schemas` (array[[CredentialFormSchema](#CredentialFormSchema)]) Credential form standard + +### ModelCredentialSchema + +- `model` (object) Model identifier, variable name defaults to `model` + - `label` (object) Model form item display name + - `en_US` (string) English + - `zh_Hans`(string) [optional] Chinese + - `placeholder` (object) Model prompt content + - `en_US`(string) English + - `zh_Hans`(string) [optional] Chinese +- `credential_form_schemas` (array[[CredentialFormSchema](#CredentialFormSchema)]) Credential form standard + +### CredentialFormSchema + +- `variable` (string) Form item variable name +- `label` (object) Form item label name + - `en_US`(string) English + - `zh_Hans` (string) [optional] Chinese +- `type` ([FormType](#FormType)) Form item type +- `required` (bool) Whether required +- `default`(string) Default value +- `options` (array[[FormOption](#FormOption)]) Specific property of form items of type `select` or `radio`, defining dropdown content +- `placeholder`(object) Specific property of form items of type `text-input`, placeholder content + - `en_US`(string) English + - `zh_Hans` (string) [optional] Chinese +- `max_length` (int) Specific property of form items of type `text-input`, defining maximum input length, 0 for no limit. +- `show_on` (array[[FormShowOnObject](#FormShowOnObject)]) Displayed when other form item values meet certain conditions, displayed always if empty. + +### FormType + +- `text-input` Text input component +- `secret-input` Password input component +- `select` Single-choice dropdown +- `radio` Radio component +- `switch` Switch component, only supports `true` and `false` values + +### FormOption + +- `label` (object) Label + - `en_US`(string) English + - `zh_Hans`(string) [optional] Chinese +- `value` (string) Dropdown option value +- `show_on` (array[[FormShowOnObject](#FormShowOnObject)]) Displayed when other form item values meet certain conditions, displayed always if empty. + +### FormShowOnObject + +- `variable` (string) Variable name of other form items +- `value` (string) Variable value of other form items diff --git a/api/core/model_runtime/docs/zh_Hans/customizable_model_scale_out.md b/api/core/model_runtime/docs/zh_Hans/customizable_model_scale_out.md new file mode 100644 index 00000000000000..daab6cedb9fe9d --- /dev/null +++ b/api/core/model_runtime/docs/zh_Hans/customizable_model_scale_out.md @@ -0,0 +1,296 @@ +## 自定义预定义模型接入 + +### 介绍 + +供应商集成完成后，接下来为供应商下模型的接入，为了帮助理解整个接入过程，我们以`Xinference`为例，逐步完成一个完整的供应商接入。 + +需要注意的是，对于自定义模型，每一个模型的接入都需要填写一个完整的供应商凭据。 + +而不同于预定义模型，自定义供应商接入时永远会拥有如下两个参数，不需要在供应商yaml中定义。 + +![Alt text](images/index/image-3.png) + + +在前文中，我们已经知道了供应商无需实现`validate_provider_credential`，Runtime会自行根据用户在此选择的模型类型和模型名称调用对应的模型层的`validate_credentials`来进行验证。 + +### 编写供应商yaml + +我们首先要确定，接入的这个供应商支持哪些类型的模型。 + +当前支持模型类型如下： + +- `llm` 文本生成模型 +- `text_embedding` 文本 Embedding 模型 +- `rerank` Rerank 模型 +- `speech2text` 语音转文字 +- `moderation` 审查 + +`Xinference`支持`LLM`和`Text Embedding`和Rerank，那么我们开始编写`xinference.yaml`。 + +```yaml +provider: xinference #确定供应商标识 +label: # 供应商展示名称，可设置 en_US 英文、zh_Hans 中文两种语言，zh_Hans 不设置将默认使用 en_US。 + en_US: Xorbots Inference +icon_small: # 小图标，可以参考其他供应商的图标，存储在对应供应商实现目录下的 _assets 目录，中英文策略同 label + en_US: icon_s_en.svg +icon_large: # 大图标 + en_US: icon_l_en.svg +help: # 帮助 + title: + en_US: How to deploy Xinference + zh_Hans: 如何部署 Xinference + url: + en_US: https://github.com/xorbitsai/inference +supported_model_types: # 支持的模型类型，Xinference同时支持LLM/Text Embedding/Rerank +- llm +- text-embedding +- rerank +configurate_methods: # 因为Xinference为本地部署的供应商，并且没有预定义模型，需要用什么模型需要根据Xinference的文档自己部署，所以这里只支持自定义模型 +- customizable-model +provider_credential_schema: + credential_form_schemas: +``` + +随后，我们需要思考在Xinference中定义一个模型需要哪些凭据 + +- 它支持三种不同的模型，因此，我们需要有`model_type`来指定这个模型的类型，它有三种类型，所以我们这么编写 +```yaml +provider_credential_schema: + credential_form_schemas: + - variable: model_type + type: select + label: + en_US: Model type + zh_Hans: 模型类型 + required: true + options: + - value: text-generation + label: + en_US: Language Model + zh_Hans: 语言模型 + - value: embeddings + label: + en_US: Text Embedding + - value: reranking + label: + en_US: Rerank +``` +- 每一个模型都有自己的名称`model_name`，因此需要在这里定义 +```yaml + - variable: model_name + type: text-input + label: + en_US: Model name + zh_Hans: 模型名称 + required: true + placeholder: + zh_Hans: 填写模型名称 + en_US: Input model name +``` +- 填写Xinference本地部署的地址 +```yaml + - variable: server_url + label: + zh_Hans: 服务器URL + en_US: Server url + type: text-input + required: true + placeholder: + zh_Hans: 在此输入Xinference的服务器地址，如 https://example.com/xxx + en_US: Enter the url of your Xinference, for example https://example.com/xxx +``` +- 每个模型都有唯一的model_uid，因此需要在这里定义 +```yaml + - variable: model_uid + label: + zh_Hans: 模型UID + en_US: Model uid + type: text-input + required: true + placeholder: + zh_Hans: 在此输入您的Model UID + en_US: Enter the model uid +``` +现在，我们就完成了供应商的基础定义。 + +### 编写模型代码 + +然后我们以`llm`类型为例，编写`xinference.llm.llm.py` + +在 `llm.py` 中创建一个 Xinference LLM 类，我们取名为 `XinferenceAILargeLanguageModel`（随意），继承 `__base.large_language_model.LargeLanguageModel` 基类，实现以下几个方法： + +- LLM 调用 + + 实现 LLM 调用的核心方法，可同时支持流式和同步返回。 + + ```python + def _invoke(self, model: str, credentials: dict, + prompt_messages: list[PromptMessage], model_parameters: dict, + tools: Optional[list[PromptMessageTool]] = None, stop: Optional[List[str]] = None, + stream: bool = True, user: Optional[str] = None) \ + -> Union[LLMResult, Generator]: + """ + Invoke large language model + + :param model: model name + :param credentials: model credentials + :param prompt_messages: prompt messages + :param model_parameters: model parameters + :param tools: tools for tool calling + :param stop: stop words + :param stream: is stream response + :param user: unique user id + :return: full response or stream response chunk generator result + """ + ``` + + 在实现时，需要注意使用两个函数来返回数据，分别用于处理同步返回和流式返回，因为Python会将函数中包含 `yield` 关键字的函数识别为生成器函数，返回的数据类型固定为 `Generator`，因此同步和流式返回需要分别实现，就像下面这样（注意下面例子使用了简化参数，实际实现时需要按照上面的参数列表进行实现）： + + ```python + def _invoke(self, stream: bool, **kwargs) \ + -> Union[LLMResult, Generator]: + if stream: + return self._handle_stream_response(**kwargs) + return self._handle_sync_response(**kwargs) + + def _handle_stream_response(self, **kwargs) -> Generator: + for chunk in response: + yield chunk + def _handle_sync_response(self, **kwargs) -> LLMResult: + return LLMResult(**response) + ``` + +- 预计算输入 tokens + + 若模型未提供预计算 tokens 接口，可直接返回 0。 + + ```python + def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage], + tools: Optional[list[PromptMessageTool]] = None) -> int: + """ + Get number of tokens for given prompt messages + + :param model: model name + :param credentials: model credentials + :param prompt_messages: prompt messages + :param tools: tools for tool calling + :return: + """ + ``` + + 有时候，也许你不需要直接返回0，所以你可以使用`self._get_num_tokens_by_gpt2(text: str)`来获取预计算的tokens，这个方法位于`AIModel`基类中，它会使用GPT2的Tokenizer进行计算，但是只能作为替代方法，并不完全准确。 + +- 模型凭据校验 + + 与供应商凭据校验类似，这里针对单个模型进行校验。 + + ```python + def validate_credentials(self, model: str, credentials: dict) -> None: + """ + Validate model credentials + + :param model: model name + :param credentials: model credentials + :return: + """ + ``` + +- 模型参数Schema + + 与自定义类型不同，由于没有在yaml文件中定义一个模型支持哪些参数，因此，我们需要动态时间模型参数的Schema。 + + 如Xinference支持`max_tokens` `temperature` `top_p` 这三个模型参数。 + + 但是有的供应商根据不同的模型支持不同的参数，如供应商`OpenLLM`支持`top_k`，但是并不是这个供应商提供的所有模型都支持`top_k`，我们这里举例A模型支持`top_k`，B模型不支持`top_k`，那么我们需要在这里动态生成模型参数的Schema，如下所示： + + ```python + def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity | None: + """ + used to define customizable model schema + """ + rules = [ + ParameterRule( + name='temperature', type=ParameterType.FLOAT, + use_template='temperature', + label=I18nObject( + zh_Hans='温度', en_US='Temperature' + ) + ), + ParameterRule( + name='top_p', type=ParameterType.FLOAT, + use_template='top_p', + label=I18nObject( + zh_Hans='Top P', en_US='Top P' + ) + ), + ParameterRule( + name='max_tokens', type=ParameterType.INT, + use_template='max_tokens', + min=1, + default=512, + label=I18nObject( + zh_Hans='最大生成长度', en_US='Max Tokens' + ) + ) + ] + + # if model is A, add top_k to rules + if model == 'A': + rules.append( + ParameterRule( + name='top_k', type=ParameterType.INT, + use_template='top_k', + min=1, + default=50, + label=I18nObject( + zh_Hans='Top K', en_US='Top K' + ) + ) + ) + + """ + some NOT IMPORTANT code here + """ + + entity = AIModelEntity( + model=model, + label=I18nObject( + en_US=model + ), + fetch_from=FetchFrom.CUSTOMIZABLE_MODEL, + model_type=model_type, + model_properties={ + 'mode': ModelType.LLM, + }, + parameter_rules=rules + ) + + return entity + ``` + +- 调用异常错误映射表 + + 当模型调用异常时需要映射到 Runtime 指定的 `InvokeError` 类型，方便 Dify 针对不同错误做不同后续处理。 + + Runtime Errors: + + - `InvokeConnectionError` 调用连接错误 + - `InvokeServerUnavailableError ` 调用服务方不可用 + - `InvokeRateLimitError ` 调用达到限额 + - `InvokeAuthorizationError` 调用鉴权失败 + - `InvokeBadRequestError ` 调用传参有误 + + ```python + @property + def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]: + """ + Map model invoke error to unified error + The key is the error type thrown to the caller + The value is the error type thrown by the model, + which needs to be converted into a unified error type for the caller. + + :return: Invoke error mapping + """ + ``` + +接口方法说明见：[Interfaces](./interfaces.md)，具体实现可参考：[llm.py](https://github.com/langgenius/dify-runtime/blob/main/lib/model_providers/anthropic/llm/llm.py)。 \ No newline at end of file diff --git a/api/core/model_runtime/docs/zh_Hans/images/index/image-1.png b/api/core/model_runtime/docs/zh_Hans/images/index/image-1.png new file mode 100644 index 00000000000000..b158d44b29dcc2 Binary files /dev/null and b/api/core/model_runtime/docs/zh_Hans/images/index/image-1.png differ diff --git a/api/core/model_runtime/docs/zh_Hans/images/index/image-2.png b/api/core/model_runtime/docs/zh_Hans/images/index/image-2.png new file mode 100644 index 00000000000000..c70cd3da5eea19 Binary files /dev/null and b/api/core/model_runtime/docs/zh_Hans/images/index/image-2.png differ diff --git a/api/core/model_runtime/docs/zh_Hans/images/index/image-20231210143654461.png b/api/core/model_runtime/docs/zh_Hans/images/index/image-20231210143654461.png new file mode 100644 index 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method by yourself, + such as: get model list api + + if validate failed, raise exception + + :param credentials: provider credentials, credentials form defined in `provider_credential_schema`. + """ +``` + +- `credentials` (object) 凭据信息 + + 凭据信息的参数由供应商 YAML 配置文件的 `provider_credential_schema` 定义，传入如：`api_key` 等。 + +验证失败请抛出 `errors.validate.CredentialsValidateFailedError` 错误。 + +**注：预定义模型需完整实现该接口，自定义模型供应商只需要如下简单实现即可** + +```python +class XinferenceProvider(Provider): + def validate_provider_credentials(self, credentials: dict) -> None: + pass +``` + +## 模型 + +模型分为 5 种不同的模型类型，不同模型类型继承的基类不同，需要实现的方法也不同。 + +### 通用接口 + +所有模型均需要统一实现下面 2 个方法： + +- 模型凭据校验 + + 与供应商凭据校验类似，这里针对单个模型进行校验。 + + ```python + def validate_credentials(self, model: str, credentials: dict) -> None: + """ + Validate model credentials + + :param model: model name + :param credentials: model credentials + :return: + """ + ``` + + 参数： + + - `model` (string) 模型名称 + + - `credentials` (object) 凭据信息 + + 凭据信息的参数由供应商 YAML 配置文件的 `provider_credential_schema` 或 `model_credential_schema` 定义，传入如：`api_key` 等。 + + 验证失败请抛出 `errors.validate.CredentialsValidateFailedError` 错误。 + +- 调用异常错误映射表 + + 当模型调用异常时需要映射到 Runtime 指定的 `InvokeError` 类型，方便 Dify 针对不同错误做不同后续处理。 + + Runtime Errors: + + - `InvokeConnectionError` 调用连接错误 + - `InvokeServerUnavailableError ` 调用服务方不可用 + - `InvokeRateLimitError ` 调用达到限额 + - `InvokeAuthorizationError` 调用鉴权失败 + - `InvokeBadRequestError ` 调用传参有误 + + ```python + @property + def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]: + """ + Map model invoke error to unified error + The key is the error type thrown to the caller + The value is the error type thrown by the model, + which needs to be converted into a unified error type for the caller. + + :return: Invoke error mapping + """ + ``` + + 也可以直接抛出对应Erros，并做如下定义，这样在之后的调用中可以直接抛出`InvokeConnectionError`等异常。 + + ```python + @property + def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]: + return { + InvokeConnectionError: [ + InvokeConnectionError + ], + InvokeServerUnavailableError: [ + InvokeServerUnavailableError + ], + InvokeRateLimitError: [ + InvokeRateLimitError + ], + InvokeAuthorizationError: [ + InvokeAuthorizationError + ], + InvokeBadRequestError: [ + InvokeBadRequestError + ], + } + ``` + +​ 可参考 OpenAI `_invoke_error_mapping`。 + +### LLM + +继承 `__base.large_language_model.LargeLanguageModel` 基类，实现以下接口： + +- LLM 调用 + + 实现 LLM 调用的核心方法，可同时支持流式和同步返回。 + + ```python + def _invoke(self, model: str, credentials: dict, + prompt_messages: list[PromptMessage], model_parameters: dict, + tools: Optional[list[PromptMessageTool]] = None, stop: Optional[List[str]] = None, + stream: bool = True, user: Optional[str] = None) \ + -> Union[LLMResult, Generator]: + """ + Invoke large language model + + :param model: model name + :param credentials: model credentials + :param prompt_messages: prompt messages + :param model_parameters: model parameters + :param tools: tools for tool calling + :param stop: stop words + :param stream: is stream response + :param user: unique user id + :return: full response or stream response chunk generator result + """ + ``` + + - 参数： + + - `model` (string) 模型名称 + + - `credentials` (object) 凭据信息 + + 凭据信息的参数由供应商 YAML 配置文件的 `provider_credential_schema` 或 `model_credential_schema` 定义，传入如：`api_key` 等。 + + - `prompt_messages` (array[[PromptMessage](#PromptMessage)]) Prompt 列表 + + 若模型为 `Completion` 类型，则列表只需要传入一个 [UserPromptMessage](#UserPromptMessage) 元素即可； + + 若模型为 `Chat` 类型，需要根据消息不同传入 [SystemPromptMessage](#SystemPromptMessage), [UserPromptMessage](#UserPromptMessage), [AssistantPromptMessage](#AssistantPromptMessage), [ToolPromptMessage](#ToolPromptMessage) 元素列表 + + - `model_parameters` (object) 模型参数 + + 模型参数由模型 YAML 配置的 `parameter_rules` 定义。 + + - `tools` (array[[PromptMessageTool](#PromptMessageTool)]) [optional] 工具列表，等同于 `function calling` 中的 `function`。 + + 即传入 tool calling 的工具列表。 + + - `stop` (array[string]) [optional] 停止序列 + + 模型返回将在停止序列定义的字符串之前停止输出。 + + - `stream` (bool) 是否流式输出，默认 True + + 流式输出返回 Generator[[LLMResultChunk](#LLMResultChunk)]，非流式输出返回 [LLMResult](#LLMResult)。 + + - `user` (string) [optional] 用户的唯一标识符 + + 可以帮助供应商监控和检测滥用行为。 + + - 返回 + + 流式输出返回 Generator[[LLMResultChunk](#LLMResultChunk)]，非流式输出返回 [LLMResult](#LLMResult)。 + +- 预计算输入 tokens + + 若模型未提供预计算 tokens 接口，可直接返回 0。 + + ```python + def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage], + tools: Optional[list[PromptMessageTool]] = None) -> int: + """ + Get number of tokens for given prompt messages + + :param model: model name + :param credentials: model credentials + :param prompt_messages: prompt messages + :param tools: tools for tool calling + :return: + """ + ``` + + 参数说明见上述 `LLM 调用`。 + + 该接口需要根据对应`model`选择合适的`tokenizer`进行计算，如果对应模型没有提供`tokenizer`，可以使用`AIModel`基类中的`_get_num_tokens_by_gpt2(text: str)`方法进行计算。 + +- 获取自定义模型规则 [可选] + + ```python + def get_customizable_model_schema(self, model: str, credentials: dict) -> Optional[AIModelEntity]: + """ + Get customizable model schema + + :param model: model name + :param credentials: model credentials + :return: model schema + """ + ``` + +​当供应商支持增加自定义 LLM 时，可实现此方法让自定义模型可获取模型规则，默认返回 None。 + +对于`OpenAI`供应商下的大部分微调模型，可以通过其微调模型名称获取到其基类模型，如`gpt-3.5-turbo-1106`，然后返回基类模型的预定义参数规则，参考[openai](https://github.com/langgenius/dify/blob/feat/model-runtime/api/core/model_runtime/model_providers/openai/llm/llm.py#L801) +的具体实现 + +### TextEmbedding + +继承 `__base.text_embedding_model.TextEmbeddingModel` 基类，实现以下接口： + +- Embedding 调用 + + ```python + def _invoke(self, model: str, credentials: dict, + texts: list[str], user: Optional[str] = None) \ + -> TextEmbeddingResult: + """ + Invoke large language model + + :param model: model name + :param credentials: model credentials + :param texts: texts to embed + :param user: unique user id + :return: embeddings result + """ + ``` + + - 参数： + + - `model` (string) 模型名称 + + - `credentials` (object) 凭据信息 + + 凭据信息的参数由供应商 YAML 配置文件的 `provider_credential_schema` 或 `model_credential_schema` 定义，传入如：`api_key` 等。 + + - `texts` (array[string]) 文本列表，可批量处理 + + - `user` (string) [optional] 用户的唯一标识符 + + 可以帮助供应商监控和检测滥用行为。 + + - 返回： + + [TextEmbeddingResult](#TextEmbeddingResult) 实体。 + +- 预计算 tokens + + ```python + def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int: + """ + Get number of tokens for given prompt messages + + :param model: model name + :param credentials: model credentials + :param texts: texts to embed + :return: + """ + ``` + + 参数说明见上述 `Embedding 调用`。 + + 同上述`LargeLanguageModel`，该接口需要根据对应`model`选择合适的`tokenizer`进行计算，如果对应模型没有提供`tokenizer`，可以使用`AIModel`基类中的`_get_num_tokens_by_gpt2(text: str)`方法进行计算。 + +### Rerank + +继承 `__base.rerank_model.RerankModel` 基类，实现以下接口： + +- rerank 调用 + + ```python + def _invoke(self, model: str, credentials: dict, + query: str, docs: list[str], score_threshold: Optional[float] = None, top_n: Optional[int] = None, + user: Optional[str] = None) \ + -> RerankResult: + """ + Invoke rerank model + + :param model: model name + :param credentials: model credentials + :param query: search query + :param docs: docs for reranking + :param score_threshold: score threshold + :param top_n: top n + :param user: unique user id + :return: rerank result + """ + ``` + + - 参数： + + - `model` (string) 模型名称 + + - `credentials` (object) 凭据信息 + + 凭据信息的参数由供应商 YAML 配置文件的 `provider_credential_schema` 或 `model_credential_schema` 定义，传入如：`api_key` 等。 + + - `query` (string) 查询请求内容 + + - `docs` (array[string]) 需要重排的分段列表 + + - `score_threshold` (float) [optional] Score 阈值 + + - `top_n` (int) [optional] 取前 n 个分段 + + - `user` (string) [optional] 用户的唯一标识符 + + 可以帮助供应商监控和检测滥用行为。 + + - 返回： + + [RerankResult](#RerankResult) 实体。 + +### Speech2text + +继承 `__base.speech2text_model.Speech2TextModel` 基类，实现以下接口： + +- Invoke 调用 + + ```python + def _invoke(self, model: str, credentials: dict, + file: IO[bytes], user: Optional[str] = None) \ + -> str: + """ + Invoke large language model + + :param model: model name + :param credentials: model credentials + :param file: audio file + :param user: unique user id + :return: text for given audio file + """ + ``` + + - 参数： + + - `model` (string) 模型名称 + + - `credentials` (object) 凭据信息 + + 凭据信息的参数由供应商 YAML 配置文件的 `provider_credential_schema` 或 `model_credential_schema` 定义，传入如：`api_key` 等。 + + - `file` (File) 文件流 + + - `user` (string) [optional] 用户的唯一标识符 + + 可以帮助供应商监控和检测滥用行为。 + + - 返回： + + 语音转换后的字符串。 + +### Moderation + +继承 `__base.moderation_model.ModerationModel` 基类，实现以下接口： + +- Invoke 调用 + + ```python + def _invoke(self, model: str, credentials: dict, + text: str, user: Optional[str] = None) \ + -> bool: + """ + Invoke large language model + + :param model: model name + :param credentials: model credentials + :param text: text to moderate + :param user: unique user id + :return: false if text is safe, true otherwise + """ + ``` + + - 参数： + + - `model` (string) 模型名称 + + - `credentials` (object) 凭据信息 + + 凭据信息的参数由供应商 YAML 配置文件的 `provider_credential_schema` 或 `model_credential_schema` 定义，传入如：`api_key` 等。 + + - `text` (string) 文本内容 + + - `user` (string) [optional] 用户的唯一标识符 + + 可以帮助供应商监控和检测滥用行为。 + + - 返回： + + False 代表传入的文本安全，True 则反之。 + + + +## 实体 + +### PromptMessageRole + +消息角色 + +```python +class PromptMessageRole(Enum): + """ + Enum class for prompt message. + """ + SYSTEM = "system" + USER = "user" + ASSISTANT = "assistant" + TOOL = "tool" +``` + +### PromptMessageContentType + +消息内容类型，分为纯文本和图片。 + +```python +class PromptMessageContentType(Enum): + """ + Enum class for prompt message content type. + """ + TEXT = 'text' + IMAGE = 'image' +``` + +### PromptMessageContent + +消息内容基类，仅作为参数声明用，不可初始化。 + +```python +class PromptMessageContent(BaseModel): + """ + Model class for prompt message content. + """ + type: PromptMessageContentType + data: str # 内容数据 +``` + +当前支持文本和图片两种类型，可支持同时传入文本和多图。 + +需要分别初始化 `TextPromptMessageContent` 和 `ImagePromptMessageContent` 传入。 + +### TextPromptMessageContent + +```python +class TextPromptMessageContent(PromptMessageContent): + """ + Model class for text prompt message content. + """ + type: PromptMessageContentType = PromptMessageContentType.TEXT +``` + +若传入图文，其中文字需要构造此实体作为 `content` 列表中的一部分。 + +### ImagePromptMessageContent + +```python +class ImagePromptMessageContent(PromptMessageContent): + """ + Model class for image prompt message content. + """ + class DETAIL(Enum): + LOW = 'low' + HIGH = 'high' + + type: PromptMessageContentType = PromptMessageContentType.IMAGE + detail: DETAIL = DETAIL.LOW # 分辨率 +``` + +若传入图文，其中图片需要构造此实体作为 `content` 列表中的一部分 + +`data` 可以为 `url` 或者图片 `base64` 加密后的字符串。 + +### PromptMessage + +所有 Role 消息体的基类，仅作为参数声明用，不可初始化。 + +```python +class PromptMessage(ABC, BaseModel): + """ + Model class for prompt message. + """ + role: PromptMessageRole # 消息角色 + content: Optional[str | list[PromptMessageContent]] = None # 支持两种类型，字符串和内容列表，内容列表是为了满足多模态的需要，可详见 PromptMessageContent 说明。 + name: Optional[str] = None # 名称，可选。 +``` + +### UserPromptMessage + +UserMessage 消息体，代表用户消息。 + +```python +class UserPromptMessage(PromptMessage): + """ + Model class for user prompt message. + """ + role: PromptMessageRole = PromptMessageRole.USER +``` + +### AssistantPromptMessage + +代表模型返回消息，通常用于 `few-shots` 或聊天历史传入。 + +```python +class AssistantPromptMessage(PromptMessage): + """ + Model class for assistant prompt message. + """ + class ToolCall(BaseModel): + """ + Model class for assistant prompt message tool call. + """ + class ToolCallFunction(BaseModel): + """ + Model class for assistant prompt message tool call function. + """ + name: str # 工具名称 + arguments: str # 工具参数 + + id: str # 工具 ID，仅在 OpenAI tool call 生效，为工具调用的唯一 ID，同一个工具可以调用多次 + type: str # 默认 function + function: ToolCallFunction # 工具调用信息 + + role: PromptMessageRole = PromptMessageRole.ASSISTANT + tool_calls: list[ToolCall] = [] # 模型回复的工具调用结果（仅当传入 tools，并且模型认为需要调用工具时返回） +``` + +其中 `tool_calls` 为调用模型传入 `tools` 后，由模型返回的 `tool call` 列表。 + +### SystemPromptMessage + +代表系统消息，通常用于设定给模型的系统指令。 + +```python +class SystemPromptMessage(PromptMessage): + """ + Model class for system prompt message. + """ + role: PromptMessageRole = PromptMessageRole.SYSTEM +``` + +### ToolPromptMessage + +代表工具消息，用于工具执行后将结果交给模型进行下一步计划。 + +```python +class ToolPromptMessage(PromptMessage): + """ + Model class for tool prompt message. + """ + role: PromptMessageRole = PromptMessageRole.TOOL + tool_call_id: str # 工具调用 ID，若不支持 OpenAI tool call，也可传入工具名称 +``` + +基类的 `content` 传入工具执行结果。 + +### PromptMessageTool + +```python +class PromptMessageTool(BaseModel): + """ + Model class for prompt message tool. + """ + name: str # 工具名称 + description: str # 工具描述 + parameters: dict # 工具参数 dict +``` + +--- + +### LLMResult + +```python +class LLMResult(BaseModel): + """ + Model class for llm result. + """ + model: str # 实际使用模型 + prompt_messages: list[PromptMessage] # prompt 消息列表 + message: AssistantPromptMessage # 回复消息 + usage: LLMUsage # 使用的 tokens 及费用信息 + system_fingerprint: Optional[str] = None # 请求指纹，可参考 OpenAI 该参数定义 +``` + +### LLMResultChunkDelta + +流式返回中每个迭代内部 `delta` 实体 + +```python +class LLMResultChunkDelta(BaseModel): + """ + Model class for llm result chunk delta. + """ + index: int # 序号 + message: AssistantPromptMessage # 回复消息 + usage: Optional[LLMUsage] = None # 使用的 tokens 及费用信息，仅最后一条返回 + finish_reason: Optional[str] = None # 结束原因，仅最后一条返回 +``` + +### LLMResultChunk + +流式返回中每个迭代实体 + +```python +class LLMResultChunk(BaseModel): + """ + Model class for llm result chunk. + """ + model: str # 实际使用模型 + prompt_messages: list[PromptMessage] # prompt 消息列表 + system_fingerprint: Optional[str] = None # 请求指纹，可参考 OpenAI 该参数定义 + delta: LLMResultChunkDelta # 每个迭代存在变化的内容 +``` + +### LLMUsage + +```python +class LLMUsage(ModelUsage): + """ + Model class for llm usage. + """ + prompt_tokens: int # prompt 使用 tokens + prompt_unit_price: Decimal # prompt 单价 + prompt_price_unit: Decimal # prompt 价格单位，即单价基于多少 tokens + prompt_price: Decimal # prompt 费用 + completion_tokens: int # 回复使用 tokens + completion_unit_price: Decimal # 回复单价 + completion_price_unit: Decimal # 回复价格单位，即单价基于多少 tokens + completion_price: Decimal # 回复费用 + total_tokens: int # 总使用 token 数 + total_price: Decimal # 总费用 + currency: str # 货币单位 + latency: float # 请求耗时(s) +``` + +--- + +### TextEmbeddingResult + +```python +class TextEmbeddingResult(BaseModel): + """ + Model class for text embedding result. + """ + model: str # 实际使用模型 + embeddings: list[list[float]] # embedding 向量列表，对应传入的 texts 列表 + usage: EmbeddingUsage # 使用信息 +``` + +### EmbeddingUsage + +```python +class EmbeddingUsage(ModelUsage): + """ + Model class for embedding usage. + """ + tokens: int # 使用 token 数 + total_tokens: int # 总使用 token 数 + unit_price: Decimal # 单价 + price_unit: Decimal # 价格单位，即单价基于多少 tokens + total_price: Decimal # 总费用 + currency: str # 货币单位 + latency: float # 请求耗时(s) +``` + +--- + +### RerankResult + +```python +class RerankResult(BaseModel): + """ + Model class for rerank result. + """ + model: str # 实际使用模型 + docs: list[RerankDocument] # 重排后的分段列表 +``` + +### RerankDocument + +```python +class RerankDocument(BaseModel): + """ + Model class for rerank document. + """ + index: int # 原序号 + text: str # 分段文本内容 + score: float # 分数 +``` diff --git a/api/core/model_runtime/docs/zh_Hans/predefined_model_scale_out.md b/api/core/model_runtime/docs/zh_Hans/predefined_model_scale_out.md new file mode 100644 index 00000000000000..5f4e84680da72a --- /dev/null +++ b/api/core/model_runtime/docs/zh_Hans/predefined_model_scale_out.md @@ -0,0 +1,171 @@ +## 预定义模型接入 + +供应商集成完成后，接下来为供应商下模型的接入。 + +我们首先需要确定接入模型的类型，并在对应供应商的目录下创建对应模型类型的 `module`。 + +当前支持模型类型如下： + +- `llm` 文本生成模型 +- `text_embedding` 文本 Embedding 模型 +- `rerank` Rerank 模型 +- `speech2text` 语音转文字 +- `moderation` 审查 + +依旧以 `Anthropic` 为例，`Anthropic` 仅支持 LLM，因此在 `model_providers.anthropic` 创建一个 `llm` 为名称的 `module`。 + +对于预定义的模型，我们首先需要在 `llm` `module` 下创建以模型名为文件名称的 YAML 文件，如：`claude-2.1.yaml`。 + +### 准备模型 YAML + +```yaml +model: claude-2.1 # 模型标识 +# 模型展示名称，可设置 en_US 英文、zh_Hans 中文两种语言，zh_Hans 不设置将默认使用 en_US。 +# 也可不设置 label，则使用 model 标识内容。 +label: + en_US: claude-2.1 +model_type: llm # 模型类型，claude-2.1 为 LLM +features: # 支持功能，agent-thought 为支持 Agent 推理，vision 为支持图片理解 +- agent-thought +model_properties: # 模型属性 + mode: chat # LLM 模式，complete 文本补全模型，chat 对话模型 + context_size: 200000 # 支持最大上下文大小 +parameter_rules: # 模型调用参数规则，仅 LLM 需要提供 +- name: temperature # 调用参数变量名 + # 默认预置了 5 种变量内容配置模板，temperature/top_p/max_tokens/presence_penalty/frequency_penalty + # 可在 use_template 中直接设置模板变量名，将会使用 entities.defaults.PARAMETER_RULE_TEMPLATE 中的默认配置 + # 若设置了额外的配置参数，将覆盖默认配置 + use_template: temperature +- name: top_p + use_template: top_p +- name: top_k + label: # 调用参数展示名称 + zh_Hans: 取样数量 + en_US: Top k + type: int # 参数类型，支持 float/int/string/boolean + help: # 帮助信息，描述参数作用 + zh_Hans: 仅从每个后续标记的前 K 个选项中采样。 + en_US: Only sample from the top K options for each subsequent token. + required: false # 是否必填，可不设置 +- name: max_tokens_to_sample + use_template: max_tokens + default: 4096 # 参数默认值 + min: 1 # 参数最小值，仅 float/int 可用 + max: 4096 # 参数最大值，仅 float/int 可用 +pricing: # 价格信息 + input: '8.00' # 输入单价，即 Prompt 单价 + output: '24.00' # 输出单价，即返回内容单价 + unit: '0.000001' # 价格单位，即上述价格为每 100K 的单价 + currency: USD # 价格货币 +``` + +建议将所有模型配置都准备完毕后再开始模型代码的实现。 + +同样，也可以参考 `model_providers` 目录下其他供应商对应模型类型目录下的 YAML 配置信息，完整的 YAML 规则见：[Schema](schema.md#AIModel)。 + +### 实现模型调用代码 + +接下来需要在 `llm` `module` 下创建一个同名的 python 文件 `llm.py` 来编写代码实现。 + +在 `llm.py` 中创建一个 Anthropic LLM 类，我们取名为 `AnthropicLargeLanguageModel`（随意），继承 `__base.large_language_model.LargeLanguageModel` 基类，实现以下几个方法： + +- LLM 调用 + + 实现 LLM 调用的核心方法，可同时支持流式和同步返回。 + + ```python + def _invoke(self, model: str, credentials: dict, + prompt_messages: list[PromptMessage], model_parameters: dict, + tools: Optional[list[PromptMessageTool]] = None, stop: Optional[List[str]] = None, + stream: bool = True, user: Optional[str] = None) \ + -> Union[LLMResult, Generator]: + """ + Invoke large language model + + :param model: model name + :param credentials: model credentials + :param prompt_messages: prompt messages + :param model_parameters: model parameters + :param tools: tools for tool calling + :param stop: stop words + :param stream: is stream response + :param user: unique user id + :return: full response or stream response chunk generator result + """ + ``` + + 在实现时，需要注意使用两个函数来返回数据，分别用于处理同步返回和流式返回，因为Python会将函数中包含 `yield` 关键字的函数识别为生成器函数，返回的数据类型固定为 `Generator`，因此同步和流式返回需要分别实现，就像下面这样（注意下面例子使用了简化参数，实际实现时需要按照上面的参数列表进行实现）： + + ```python + def _invoke(self, stream: bool, **kwargs) \ + -> Union[LLMResult, Generator]: + if stream: + return self._handle_stream_response(**kwargs) + return self._handle_sync_response(**kwargs) + + def _handle_stream_response(self, **kwargs) -> Generator: + for chunk in response: + yield chunk + def _handle_sync_response(self, **kwargs) -> LLMResult: + return LLMResult(**response) + ``` + +- 预计算输入 tokens + + 若模型未提供预计算 tokens 接口，可直接返回 0。 + + ```python + def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage], + tools: Optional[list[PromptMessageTool]] = None) -> int: + """ + Get number of tokens for given prompt messages + + :param model: model name + :param credentials: model credentials + :param prompt_messages: prompt messages + :param tools: tools for tool calling + :return: + """ + ``` + +- 模型凭据校验 + + 与供应商凭据校验类似，这里针对单个模型进行校验。 + + ```python + def validate_credentials(self, model: str, credentials: dict) -> None: + """ + Validate model credentials + + :param model: model name + :param credentials: model credentials + :return: + """ + ``` + +- 调用异常错误映射表 + + 当模型调用异常时需要映射到 Runtime 指定的 `InvokeError` 类型，方便 Dify 针对不同错误做不同后续处理。 + + Runtime Errors: + + - `InvokeConnectionError` 调用连接错误 + - `InvokeServerUnavailableError ` 调用服务方不可用 + - `InvokeRateLimitError ` 调用达到限额 + - `InvokeAuthorizationError` 调用鉴权失败 + - `InvokeBadRequestError ` 调用传参有误 + + ```python + @property + def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]: + """ + Map model invoke error to unified error + The key is the error type thrown to the caller + The value is the error type thrown by the model, + which needs to be converted into a unified error type for the caller. + + :return: Invoke error mapping + """ + ``` + +接口方法说明见：[Interfaces](./interfaces.md)，具体实现可参考：[llm.py](https://github.com/langgenius/dify-runtime/blob/main/lib/model_providers/anthropic/llm/llm.py)。 \ No newline at end of file diff --git a/api/core/model_runtime/docs/zh_Hans/provider_scale_out.md b/api/core/model_runtime/docs/zh_Hans/provider_scale_out.md new file mode 100644 index 00000000000000..b34544c789fa76 --- /dev/null +++ b/api/core/model_runtime/docs/zh_Hans/provider_scale_out.md @@ -0,0 +1,188 @@ +## 增加新供应商 + +供应商支持三种模型配置方式： + +- `predefined-model ` 预定义模型 + + 表示用户只需要配置统一的供应商凭据即可使用供应商下的预定义模型。 + +- `customizable-model` 自定义模型 + + 用户需要新增每个模型的凭据配置，如Xinference，它同时支持 LLM 和 Text Embedding，但是每个模型都有唯一的**model_uid**，如果想要将两者同时接入，就需要为每个模型配置一个**model_uid**。 + +- `fetch-from-remote` 从远程获取 + + 与 `predefined-model` 配置方式一致，只需要配置统一的供应商凭据即可，模型通过凭据信息从供应商获取。 + + 如OpenAI，我们可以基于gpt-turbo-3.5来Fine Tune多个模型，而他们都位于同一个**api_key**下，当配置为 `fetch-from-remote` 时，开发者只需要配置统一的**api_key**即可让DifyRuntime获取到开发者所有的微调模型并接入Dify。 + +这三种配置方式**支持共存**，即存在供应商支持 `predefined-model` + `customizable-model` 或 `predefined-model` + `fetch-from-remote` 等，也就是配置了供应商统一凭据可以使用预定义模型和从远程获取的模型，若新增了模型，则可以在此基础上额外使用自定义的模型。 + +## 开始 + +### 介绍 + +#### 名词解释 + - `module`: 一个`module`即为一个Python Package，或者通俗一点，称为一个文件夹，里面包含了一个`__init__.py`文件，以及其他的`.py`文件。 + +#### 步骤 +新增一个供应商主要分为几步，这里简单列出，帮助大家有一个大概的认识，具体的步骤会在下面详细介绍。 + +- 创建供应商yaml文件，根据[ProviderSchema](./schema.md#provider)编写 +- 创建供应商代码，实现一个`class`。 +- 根据模型类型，在供应商`module`下创建对应的模型类型 `module`，如`llm`或`text_embedding`。 +- 根据模型类型，在对应的模型`module`下创建同名的代码文件，如`llm.py`，并实现一个`class`。 +- 如果有预定义模型，根据模型名称创建同名的yaml文件在模型`module`下，如`claude-2.1.yaml`，根据[AIModelEntity](./schema.md#aimodelentity)编写。 +- 编写测试代码，确保功能可用。 + +### 开始吧 + +增加一个新的供应商需要先确定供应商的英文标识，如 `anthropic`，使用该标识在 `model_providers` 创建以此为名称的 `module`。 + +在此 `module` 下，我们需要先准备供应商的 YAML 配置。 + +#### 准备供应商 YAML + +此处以 `Anthropic` 为例，预设了供应商基础信息、支持的模型类型、配置方式、凭据规则。 + +```YAML +provider: anthropic # 供应商标识 +label: # 供应商展示名称，可设置 en_US 英文、zh_Hans 中文两种语言，zh_Hans 不设置将默认使用 en_US。 + en_US: Anthropic +icon_small: # 供应商小图标，存储在对应供应商实现目录下的 _assets 目录，中英文策略同 label + en_US: icon_s_en.png +icon_large: # 供应商大图标，存储在对应供应商实现目录下的 _assets 目录，中英文策略同 label + en_US: icon_l_en.png +supported_model_types: # 支持的模型类型，Anthropic 仅支持 LLM +- llm +configurate_methods: # 支持的配置方式，Anthropic 仅支持预定义模型 +- predefined-model +provider_credential_schema: # 供应商凭据规则，由于 Anthropic 仅支持预定义模型，则需要定义统一供应商凭据规则 + credential_form_schemas: # 凭据表单项列表 + - variable: anthropic_api_key # 凭据参数变量名 + label: # 展示名称 + en_US: API Key + type: secret-input # 表单类型，此处 secret-input 代表加密信息输入框，编辑时只展示屏蔽后的信息。 + required: true # 是否必填 + placeholder: # PlaceHolder 信息 + zh_Hans: 在此输入您的 API Key + en_US: Enter your API Key + - variable: anthropic_api_url + label: + en_US: API URL + type: text-input # 表单类型，此处 text-input 代表文本输入框 + required: false + placeholder: + zh_Hans: 在此输入您的 API URL + en_US: Enter your API URL +``` + +如果接入的供应商提供自定义模型，比如`OpenAI`提供微调模型，那么我们就需要添加[`model_credential_schema`](./schema.md#modelcredentialschema)，以`OpenAI`为例： + +```yaml +model_credential_schema: + model: # 微调模型名称 + label: + en_US: Model Name + zh_Hans: 模型名称 + placeholder: + en_US: Enter your model name + zh_Hans: 输入模型名称 + credential_form_schemas: + - variable: openai_api_key + label: + en_US: API Key + type: secret-input + required: true + placeholder: + zh_Hans: 在此输入您的 API Key + en_US: Enter your API Key + - variable: openai_organization + label: + zh_Hans: 组织 ID + en_US: Organization + type: text-input + required: false + placeholder: + zh_Hans: 在此输入您的组织 ID + en_US: Enter your Organization ID + - variable: openai_api_base + label: + zh_Hans: API Base + en_US: API Base + type: text-input + required: false + placeholder: + zh_Hans: 在此输入您的 API Base + en_US: Enter your API Base +``` + +也可以参考 `model_providers` 目录下其他供应商目录下的 YAML 配置信息，完整的 YAML 规则见：[Schema](schema.md#Provider)。 + +#### 实现供应商代码 + +我们需要在`model_providers`下创建一个同名的python文件，如`anthropic.py`，并实现一个`class`，继承`__base.provider.Provider`基类，如`AnthropicProvider`。 + +##### 自定义模型供应商 + +当供应商为Xinference等自定义模型供应商时，可跳过该步骤，仅创建一个空的`XinferenceProvider`类即可，并实现一个空的`validate_provider_credentials`方法，该方法并不会被实际使用，仅用作避免抽象类无法实例化。 + +```python +class XinferenceProvider(Provider): + def validate_provider_credentials(self, credentials: dict) -> None: + pass +``` + +##### 预定义模型供应商 + +供应商需要继承 `__base.model_provider.ModelProvider` 基类，实现 `validate_provider_credentials` 供应商统一凭据校验方法即可，可参考 [AnthropicProvider](https://github.com/langgenius/dify-runtime/blob/main/lib/model_providers/anthropic/anthropic.py)。 + +```python +def validate_provider_credentials(self, credentials: dict) -> None: + """ + Validate provider credentials + You can choose any validate_credentials method of model type or implement validate method by yourself, + such as: get model list api + + if validate failed, raise exception + + :param credentials: provider credentials, credentials form defined in `provider_credential_schema`. + """ +``` + +当然也可以先预留 `validate_provider_credentials` 实现，在模型凭据校验方法实现后直接复用。 + +#### 增加模型 + +#### [增加预定义模型 👈🏻](./predefined_model_scale_out.md) +对于预定义模型，我们可以通过简单定义一个yaml，并通过实现调用代码来接入。 + +#### [增加自定义模型 👈🏻](./customizable_model_scale_out.md) +对于自定义模型，我们只需要实现调用代码即可接入，但是它需要处理的参数可能会更加复杂。 + +--- + +### 测试 + +为了保证接入供应商/模型的可用性，编写后的每个方法均需要在 `tests` 目录中编写对应的集成测试代码。 + +依旧以 `Anthropic` 为例。 + +在编写测试代码前，需要先在 `.env.example` 新增测试供应商所需要的凭据环境变量，如：`ANTHROPIC_API_KEY`。 + +在执行前需要将 `.env.example` 复制为 `.env` 再执行。 + +#### 编写测试代码 + +在 `tests` 目录下创建供应商同名的 `module`: `anthropic`，继续在此模块中创建 `test_provider.py` 以及对应模型类型的 test py 文件，如下所示： + +```shell +. +├── __init__.py +├── anthropic +│   ├── __init__.py +│   ├── test_llm.py # LLM 测试 +│   └── test_provider.py # 供应商测试 +``` + +针对上面实现的代码的各种情况进行测试代码编写，并测试通过后提交代码。 diff --git a/api/core/model_runtime/docs/zh_Hans/schema.md b/api/core/model_runtime/docs/zh_Hans/schema.md new file mode 100644 index 00000000000000..155750c0675403 --- /dev/null +++ b/api/core/model_runtime/docs/zh_Hans/schema.md @@ -0,0 +1,196 @@ +# 配置规则 + +- 供应商规则基于 [Provider](#Provider) 实体。 + +- 模型规则基于 [AIModelEntity](#AIModelEntity) 实体。 + +> 以下所有实体均基于 `Pydantic BaseModel`，可在 `entities` 模块中找到对应实体。 + +### Provider + +- `provider` (string) 供应商标识，如：`openai` +- `label` (object) 供应商展示名称，i18n，可设置 `en_US` 英文、`zh_Hans` 中文两种语言 + - `zh_Hans ` (string) [optional] 中文标签名，`zh_Hans` 不设置将默认使用 `en_US`。 + - `en_US` (string) 英文标签名 +- `description` (object) [optional] 供应商描述，i18n + - `zh_Hans` (string) [optional] 中文描述 + - `en_US` (string) 英文描述 +- `icon_small` (string) [optional] 供应商小 ICON，存储在对应供应商实现目录下的 `_assets` 目录，中英文策略同 `label` + - `zh_Hans` (string) [optional] 中文 ICON + - `en_US` (string) 英文 ICON +- `icon_large` (string) [optional] 供应商大 ICON，存储在对应供应商实现目录下的 _assets 目录，中英文策略同 label + - `zh_Hans `(string) [optional] 中文 ICON + - `en_US` (string) 英文 ICON +- `background` (string) [optional] 背景颜色色值，例：#FFFFFF，为空则展示前端默认色值。 +- `help` (object) [optional] 帮助信息 + - `title` (object) 帮助标题，i18n + - `zh_Hans` (string) [optional] 中文标题 + - `en_US` (string) 英文标题 + - `url` (object) 帮助链接，i18n + - `zh_Hans` (string) [optional] 中文链接 + - `en_US` (string) 英文链接 +- `supported_model_types` (array[[ModelType](#ModelType)]) 支持的模型类型 +- `configurate_methods` (array[[ConfigurateMethod](#ConfigurateMethod)]) 配置方式 +- `provider_credential_schema` ([ProviderCredentialSchema](#ProviderCredentialSchema)) 供应商凭据规格 +- `model_credential_schema` ([ModelCredentialSchema](#ModelCredentialSchema)) 模型凭据规格 + +### AIModelEntity + +- `model` (string) 模型标识，如：`gpt-3.5-turbo` +- `label` (object) [optional] 模型展示名称，i18n，可设置 `en_US` 英文、`zh_Hans` 中文两种语言 + - `zh_Hans `(string) [optional] 中文标签名 + - `en_US` (string) 英文标签名 +- `model_type` ([ModelType](#ModelType)) 模型类型 +- `features` (array[[ModelFeature](#ModelFeature)]) [optional] 支持功能列表 +- `model_properties` (object) 模型属性 + - `mode` ([LLMMode](#LLMMode)) 模式 (模型类型 `llm` 可用) + - `context_size` (int) 上下文大小 (模型类型 `llm` `text-embedding` 可用) + - `max_chunks` (int) 最大分块数量 (模型类型 `text-embedding ` `moderation` 可用) + - `file_upload_limit` (int) 文件最大上传限制，单位：MB。（模型类型 `speech2text` 可用） + - `supported_file_extensions` (string) 支持文件扩展格式，如：mp3,mp4（模型类型 `speech2text` 可用） + - `max_characters_per_chunk` (int) 每块最大字符数 (模型类型 `moderation` 可用) +- `parameter_rules` (array[[ParameterRule](#ParameterRule)]) [optional] 模型调用参数规则 +- `pricing` ([PriceConfig](#PriceConfig)) [optional] 价格信息 +- `deprecated` (bool) 是否废弃。若废弃，模型列表将不再展示，但已经配置的可以继续使用，默认 False。 + +### ModelType + +- `llm` 文本生成模型 +- `text-embedding` 文本 Embedding 模型 +- `rerank` Rerank 模型 +- `speech2text` 语音转文字 +- `moderation` 审查 + +### ConfigurateMethod + +- `predefined-model ` 预定义模型 + + 表示用户只需要配置统一的供应商凭据即可使用供应商下的预定义模型。 +- `customizable-model` 自定义模型 + + 用户需要新增每个模型的凭据配置。 + +- `fetch-from-remote` 从远程获取 + + 与 `predefined-model` 配置方式一致，只需要配置统一的供应商凭据即可，模型通过凭据信息从供应商获取。 + +### ModelFeature + +- `agent-thought` Agent 推理，一般超过 70B 有思维链能力。 +- `vision` 视觉，即：图像理解。 + +### FetchFrom + +- `predefined-model` 预定义模型 +- `fetch-from-remote` 远程模型 + +### LLMMode + +- `completion` 文本补全 +- `chat` 对话 + +### ParameterRule + +- `name` (string) 调用模型实际参数名 + +- `use_template` (string) [optional] 使用模板 + + 默认预置了 5 种变量内容配置模板： + + - `temperature` + - `top_p` + - `frequency_penalty` + - `presence_penalty` + - `max_tokens` + + 可在 use_template 中直接设置模板变量名，将会使用 entities.defaults.PARAMETER_RULE_TEMPLATE 中的默认配置 + 不用设置除 `name` 和 `use_template` 之外的所有参数，若设置了额外的配置参数，将覆盖默认配置。 + 可参考 `openai/llm/gpt-3.5-turbo.yaml`。 + +- `label` (object) [optional] 标签，i18n + + - `zh_Hans`(string) [optional] 中文标签名 + - `en_US` (string) 英文标签名 + +- `type`(string) [optional] 参数类型 + + - `int` 整数 + - `float` 浮点数 + - `string` 字符串 + - `boolean` 布尔型 + +- `help` (string) [optional] 帮助信息 + + - `zh_Hans` (string) [optional] 中文帮助信息 + - `en_US` (string) 英文帮助信息 + +- `required` (bool) 是否必填，默认 False。 + +- `default`(int/float/string/bool) [optional] 默认值 + +- `min`(int/float) [optional] 最小值，仅数字类型适用 + +- `max`(int/float) [optional] 最大值，仅数字类型适用 + +- `precision`(int) [optional] 精度，保留小数位数，仅数字类型适用 + +- `options` (array[string]) [optional] 下拉选项值，仅当 `type` 为 `string` 时适用，若不设置或为 null 则不限制选项值 + +### PriceConfig + +- `input` (float) 输入单价，即 Prompt 单价 +- `output` (float) 输出单价，即返回内容单价 +- `unit` (float) 价格单位，如：每 100K 的单价为 `0.000001` +- `currency` (string) 货币单位 + +### ProviderCredentialSchema + +- `credential_form_schemas` (array[[CredentialFormSchema](#CredentialFormSchema)]) 凭据表单规范 + +### ModelCredentialSchema + +- `model` (object) 模型标识，变量名默认 `model` + - `label` (object) 模型表单项展示名称 + - `en_US` (string) 英文 + - `zh_Hans`(string) [optional] 中文 + - `placeholder` (object) 模型提示内容 + - `en_US`(string) 英文 + - `zh_Hans`(string) [optional] 中文 +- `credential_form_schemas` (array[[CredentialFormSchema](#CredentialFormSchema)]) 凭据表单规范 + +### CredentialFormSchema + +- `variable` (string) 表单项变量名 +- `label` (object) 表单项标签名 + - `en_US`(string) 英文 + - `zh_Hans` (string) [optional] 中文 +- `type` ([FormType](#FormType)) 表单项类型 +- `required` (bool) 是否必填 +- `default`(string) 默认值 +- `options` (array[[FormOption](#FormOption)]) 表单项为 `select` 或 `radio` 专有属性，定义下拉内容 +- `placeholder`(object) 表单项为 `text-input `专有属性，表单项 PlaceHolder + - `en_US`(string) 英文 + - `zh_Hans` (string) [optional] 中文 +- `max_length` (int) 表单项为`text-input`专有属性，定义输入最大长度，0 为不限制。 +- `show_on` (array[[FormShowOnObject](#FormShowOnObject)]) 当其他表单项值符合条件时显示，为空则始终显示。 + +### FormType + +- `text-input` 文本输入组件 +- `secret-input` 密码输入组件 +- `select` 单选下拉 +- `radio` Radio 组件 +- `switch` 开关组件，仅支持 `true` 和 `false` + +### FormOption + +- `label` (object) 标签 + - `en_US`(string) 英文 + - `zh_Hans`(string) [optional] 中文 +- `value` (string) 下拉选项值 +- `show_on` (array[[FormShowOnObject](#FormShowOnObject)]) 当其他表单项值符合条件时显示，为空则始终显示。 + +### FormShowOnObject + +- `variable` (string) 其他表单项变量名 +- `value` (string) 其他表单项变量值 diff --git a/api/core/model_providers/providers/__init__.py b/api/core/model_runtime/entities/__init__.py similarity index 100% rename from api/core/model_providers/providers/__init__.py rename to api/core/model_runtime/entities/__init__.py diff --git a/api/core/model_runtime/entities/common_entities.py b/api/core/model_runtime/entities/common_entities.py new file mode 100644 index 00000000000000..175c13cfdcc04c --- /dev/null +++ b/api/core/model_runtime/entities/common_entities.py @@ -0,0 +1,16 @@ +from typing import Optional + +from pydantic import BaseModel + + +class I18nObject(BaseModel): + """ + Model class for i18n object. + """ + zh_Hans: Optional[str] = None + en_US: str + + def __init__(self, **data): + super().__init__(**data) + if not self.zh_Hans: + self.zh_Hans = self.en_US diff --git a/api/core/model_runtime/entities/defaults.py b/api/core/model_runtime/entities/defaults.py new file mode 100644 index 00000000000000..02929710dc55bd --- /dev/null +++ b/api/core/model_runtime/entities/defaults.py @@ -0,0 +1,87 @@ +from typing import Dict + +from core.model_runtime.entities.model_entities import DefaultParameterName + + +PARAMETER_RULE_TEMPLATE: Dict[DefaultParameterName, dict] = { + DefaultParameterName.TEMPERATURE: { + 'label': { + 'en_US': 'Temperature', + 'zh_Hans': '温度', + }, + 'type': 'float', + 'help': { + 'en_US': 'Controls randomness. Lower temperature results in less random completions. As the temperature approaches zero, the model will become deterministic and repetitive. Higher temperature results in more random completions.', + 'zh_Hans': '温度控制随机性。较低的温度会导致较少的随机完成。随着温度接近零，模型将变得确定性和重复性。较高的温度会导致更多的随机完成。', + }, + 'required': False, + 'default': 0.0, + 'min': 0.0, + 'max': 1.0, + 'precision': 1, + }, + DefaultParameterName.TOP_P: { + 'label': { + 'en_US': 'Top P', + 'zh_Hans': 'Top P', + }, + 'type': 'float', + 'help': { + 'en_US': 'Controls diversity via nucleus sampling: 0.5 means half of all likelihood-weighted options are considered.', + 'zh_Hans': '通过核心采样控制多样性：0.5表示考虑了一半的所有可能性加权选项。', + }, + 'required': False, + 'default': 1.0, + 'min': 0.0, + 'max': 1.0, + 'precision': 1, + }, + DefaultParameterName.PRESENCE_PENALTY: { + 'label': { + 'en_US': 'Presence Penalty', + 'zh_Hans': '存在惩罚', + }, + 'type': 'float', + 'help': { + 'en_US': 'Applies a penalty to the log-probability of tokens already in the text.', + 'zh_Hans': '对文本中已有的标记的对数概率施加惩罚。', + }, + 'required': False, + 'default': 0.0, + 'min': 0.0, + 'max': 1.0, + 'precision': 1, + }, + DefaultParameterName.FREQUENCY_PENALTY: { + 'label': { + 'en_US': 'Frequency Penalty', + 'zh_Hans': '频率惩罚', + }, + 'type': 'float', + 'help': { + 'en_US': 'Applies a penalty to the log-probability of tokens that appear in the text.', + 'zh_Hans': '对文本中出现的标记的对数概率施加惩罚。', + }, + 'required': False, + 'default': 0.0, + 'min': 0.0, + 'max': 1.0, + 'precision': 1, + }, + DefaultParameterName.MAX_TOKENS: { + 'label': { + 'en_US': 'Max Tokens', + 'zh_Hans': '最大标记', + }, + 'type': 'int', + 'help': { + 'en_US': 'The maximum number of tokens to generate. Requests can use up to 2048 tokens shared between prompt and completion.', + 'zh_Hans': '要生成的标记的最大数量。请求可以使用最多2048个标记，这些标记在提示和完成之间共享。', + }, + 'required': False, + 'default': 64, + 'min': 1, + 'max': 2048, + 'precision': 0, + } +} \ No newline at end of file diff --git a/api/core/model_runtime/entities/llm_entities.py b/api/core/model_runtime/entities/llm_entities.py new file mode 100644 index 00000000000000..b5bd9e267a0573 --- /dev/null +++ b/api/core/model_runtime/entities/llm_entities.py @@ -0,0 +1,102 @@ +from decimal import Decimal +from enum import Enum +from typing import Optional + +from pydantic import BaseModel + +from core.model_runtime.entities.message_entities import AssistantPromptMessage, PromptMessage +from core.model_runtime.entities.model_entities import ModelUsage, PriceInfo + + +class LLMMode(Enum): + """ + Enum class for large language model mode. + """ + COMPLETION = "completion" + CHAT = "chat" + + @classmethod + def value_of(cls, value: str) -> 'LLMMode': + """ + Get value of given mode. + + :param value: mode value + :return: mode + """ + for mode in cls: + if mode.value == value: + return mode + raise ValueError(f'invalid mode value {value}') + + +class LLMUsage(ModelUsage): + """ + Model class for llm usage. + """ + prompt_tokens: int + prompt_unit_price: Decimal + prompt_price_unit: Decimal + prompt_price: Decimal + completion_tokens: int + completion_unit_price: Decimal + completion_price_unit: Decimal + completion_price: Decimal + total_tokens: int + total_price: Decimal + currency: str + latency: float + + @classmethod + def empty_usage(cls): + return cls( + prompt_tokens=0, + prompt_unit_price=Decimal('0.0'), + prompt_price_unit=Decimal('0.0'), + prompt_price=Decimal('0.0'), + completion_tokens=0, + completion_unit_price=Decimal('0.0'), + completion_price_unit=Decimal('0.0'), + completion_price=Decimal('0.0'), + total_tokens=0, + total_price=Decimal('0.0'), + currency='USD', + latency=0.0 + ) + + +class LLMResult(BaseModel): + """ + Model class for llm result. + """ + model: str + prompt_messages: list[PromptMessage] + message: AssistantPromptMessage + usage: LLMUsage + system_fingerprint: Optional[str] = None + + +class LLMResultChunkDelta(BaseModel): + """ + Model class for llm result chunk delta. + """ + index: int + message: AssistantPromptMessage + usage: Optional[LLMUsage] = None + finish_reason: Optional[str] = None + + +class LLMResultChunk(BaseModel): + """ + Model class for llm result chunk. + """ + model: str + prompt_messages: list[PromptMessage] + system_fingerprint: Optional[str] = None + delta: LLMResultChunkDelta + + +class NumTokensResult(PriceInfo): + """ + Model class for number of tokens result. + """ + tokens: int diff --git a/api/core/model_runtime/entities/message_entities.py b/api/core/model_runtime/entities/message_entities.py new file mode 100644 index 00000000000000..83b12082b28525 --- /dev/null +++ b/api/core/model_runtime/entities/message_entities.py @@ -0,0 +1,134 @@ +from abc import ABC +from enum import Enum +from typing import Optional + +from pydantic import BaseModel + + +class PromptMessageRole(Enum): + """ + Enum class for prompt message. + """ + SYSTEM = "system" + USER = "user" + ASSISTANT = "assistant" + TOOL = "tool" + + @classmethod + def value_of(cls, value: str) -> 'PromptMessageRole': + """ + Get value of given mode. + + :param value: mode value + :return: mode + """ + for mode in cls: + if mode.value == value: + return mode + raise ValueError(f'invalid prompt message type value {value}') + + +class PromptMessageTool(BaseModel): + """ + Model class for prompt message tool. + """ + name: str + description: str + parameters: dict + + +class PromptMessageFunction(BaseModel): + """ + Model class for prompt message function. + """ + type: str = 'function' + function: PromptMessageTool + + +class PromptMessageContentType(Enum): + """ + Enum class for prompt message content type. + """ + TEXT = 'text' + IMAGE = 'image' + + +class PromptMessageContent(BaseModel): + """ + Model class for prompt message content. + """ + type: PromptMessageContentType + data: str + + +class TextPromptMessageContent(PromptMessageContent): + """ + Model class for text prompt message content. + """ + type: PromptMessageContentType = PromptMessageContentType.TEXT + + +class ImagePromptMessageContent(PromptMessageContent): + """ + Model class for image prompt message content. + """ + class DETAIL(Enum): + LOW = 'low' + HIGH = 'high' + + type: PromptMessageContentType = PromptMessageContentType.IMAGE + detail: DETAIL = DETAIL.LOW + + +class PromptMessage(ABC, BaseModel): + """ + Model class for prompt message. + """ + role: PromptMessageRole + content: Optional[str | list[PromptMessageContent]] = None + name: Optional[str] = None + + +class UserPromptMessage(PromptMessage): + """ + Model class for user prompt message. + """ + role: PromptMessageRole = PromptMessageRole.USER + + +class AssistantPromptMessage(PromptMessage): + """ + Model class for assistant prompt message. + """ + class ToolCall(BaseModel): + """ + Model class for assistant prompt message tool call. + """ + class ToolCallFunction(BaseModel): + """ + Model class for assistant prompt message tool call function. + """ + name: str + arguments: str + + id: str + type: str + function: ToolCallFunction + + role: PromptMessageRole = PromptMessageRole.ASSISTANT + tool_calls: list[ToolCall] = [] + + +class SystemPromptMessage(PromptMessage): + """ + Model class for system prompt message. + """ + role: PromptMessageRole = PromptMessageRole.SYSTEM + + +class ToolPromptMessage(PromptMessage): + """ + Model class for tool prompt message. + """ + role: PromptMessageRole = PromptMessageRole.TOOL + tool_call_id: str diff --git a/api/core/model_runtime/entities/model_entities.py b/api/core/model_runtime/entities/model_entities.py new file mode 100644 index 00000000000000..b9393071a9268c --- /dev/null +++ b/api/core/model_runtime/entities/model_entities.py @@ -0,0 +1,196 @@ +from decimal import Decimal +from enum import Enum +from typing import Any, Optional + +from pydantic import BaseModel + +from core.model_runtime.entities.common_entities import I18nObject + + +class ModelType(Enum): + """ + Enum class for model type. + """ + LLM = "llm" + TEXT_EMBEDDING = "text-embedding" + RERANK = "rerank" + SPEECH2TEXT = "speech2text" + MODERATION = "moderation" + # TTS = "tts" + # TEXT2IMG = "text2img" + + @classmethod + def value_of(cls, origin_model_type: str) -> "ModelType": + """ + Get model type from origin model type. + + :return: model type + """ + if origin_model_type == 'text-generation' or origin_model_type == cls.LLM.value: + return cls.LLM + elif origin_model_type == 'embeddings' or origin_model_type == cls.TEXT_EMBEDDING.value: + return cls.TEXT_EMBEDDING + elif origin_model_type == 'reranking' or origin_model_type == cls.RERANK.value: + return cls.RERANK + elif origin_model_type == cls.SPEECH2TEXT.value: + return cls.SPEECH2TEXT + elif origin_model_type == cls.MODERATION.value: + return cls.MODERATION + else: + raise ValueError(f'invalid origin model type {origin_model_type}') + + def to_origin_model_type(self) -> str: + """ + Get origin model type from model type. + + :return: origin model type + """ + if self == self.LLM: + return 'text-generation' + elif self == self.TEXT_EMBEDDING: + return 'embeddings' + elif self == self.RERANK: + return 'reranking' + elif self == self.SPEECH2TEXT: + return 'speech2text' + elif self == self.MODERATION: + return 'moderation' + else: + raise ValueError(f'invalid model type {self}') + + +class FetchFrom(Enum): + """ + Enum class for fetch from. + """ + PREDEFINED_MODEL = "predefined-model" + CUSTOMIZABLE_MODEL = "customizable-model" + + +class ModelFeature(Enum): + """ + Enum class for llm feature. + """ + TOOL_CALL = "tool-call" + MULTI_TOOL_CALL = "multi-tool-call" + AGENT_THOUGHT = "agent-thought" + VISION = "vision" + + +class DefaultParameterName(Enum): + """ + Enum class for parameter template variable. + """ + TEMPERATURE = "temperature" + TOP_P = "top_p" + PRESENCE_PENALTY = "presence_penalty" + FREQUENCY_PENALTY = "frequency_penalty" + MAX_TOKENS = "max_tokens" + + @classmethod + def value_of(cls, value: Any) -> 'DefaultParameterName': + """ + Get parameter name from value. + + :param value: parameter value + :return: parameter name + """ + for name in cls: + if name.value == value: + return name + raise ValueError(f'invalid parameter name {value}') + + +class ParameterType(Enum): + """ + Enum class for parameter type. + """ + FLOAT = "float" + INT = "int" + STRING = "string" + BOOLEAN = "boolean" + + +class ModelPropertyKey(Enum): + """ + Enum class for model property key. + """ + MODE = "mode" + CONTEXT_SIZE = "context_size" + MAX_CHUNKS = "max_chunks" + FILE_UPLOAD_LIMIT = "file_upload_limit" + SUPPORTED_FILE_EXTENSIONS = "supported_file_extensions" + MAX_CHARACTERS_PER_CHUNK = "max_characters_per_chunk" + + +class ProviderModel(BaseModel): + """ + Model class for provider model. + """ + model: str + label: I18nObject + model_type: ModelType + features: Optional[list[ModelFeature]] = None + fetch_from: FetchFrom + model_properties: dict[ModelPropertyKey, Any] + deprecated: bool = False + + class Config: + protected_namespaces = () + + +class ParameterRule(BaseModel): + """ + Model class for parameter rule. + """ + name: str + use_template: Optional[str] = None + label: I18nObject + type: ParameterType + help: Optional[I18nObject] = None + required: bool = False + default: Optional[Any] = None + min: Optional[float | int] = None + max: Optional[float | int] = None + precision: Optional[int] = None + options: list[str] = [] + + +class PriceConfig(BaseModel): + """ + Model class for pricing info. + """ + input: Decimal + output: Optional[Decimal] = None + unit: Decimal + currency: str + + +class AIModelEntity(ProviderModel): + """ + Model class for AI model. + """ + parameter_rules: list[ParameterRule] = [] + pricing: Optional[PriceConfig] = None + + +class ModelUsage(BaseModel): + pass + + +class PriceType(Enum): + """ + Enum class for price type. + """ + INPUT = "input" + OUTPUT = "output" + + +class PriceInfo(BaseModel): + """ + Model class for price info. + """ + unit_price: Decimal + unit: Decimal + total_amount: Decimal + currency: str diff --git a/api/core/model_runtime/entities/provider_entities.py b/api/core/model_runtime/entities/provider_entities.py new file mode 100644 index 00000000000000..bf3fe0878fa695 --- /dev/null +++ b/api/core/model_runtime/entities/provider_entities.py @@ -0,0 +1,149 @@ +from enum import Enum +from typing import Optional + +from pydantic import BaseModel + +from core.model_runtime.entities.common_entities import I18nObject +from core.model_runtime.entities.model_entities import ModelType, ProviderModel, AIModelEntity + + +class ConfigurateMethod(Enum): + """ + Enum class for configurate method of provider model. + """ + PREDEFINED_MODEL = "predefined-model" + CUSTOMIZABLE_MODEL = "customizable-model" + + +class FormType(Enum): + """ + Enum class for form type. + """ + TEXT_INPUT = "text-input" + SECRET_INPUT = "secret-input" + SELECT = "select" + RADIO = "radio" + SWITCH = "switch" + + +class FormShowOnObject(BaseModel): + """ + Model class for form show on. + """ + variable: str + value: str + + +class FormOption(BaseModel): + """ + Model class for form option. + """ + label: I18nObject + value: str + show_on: list[FormShowOnObject] = [] + + def __init__(self, **data): + super().__init__(**data) + if not self.label: + self.label = I18nObject( + en_US=self.value + ) + + +class CredentialFormSchema(BaseModel): + """ + Model class for credential form schema. + """ + variable: str + label: I18nObject + type: FormType + required: bool = True + default: Optional[str] = None + options: Optional[list[FormOption]] = None + placeholder: Optional[I18nObject] = None + max_length: int = 0 + show_on: list[FormShowOnObject] = [] + + +class ProviderCredentialSchema(BaseModel): + """ + Model class for provider credential schema. + """ + credential_form_schemas: list[CredentialFormSchema] + + +class FieldModelSchema(BaseModel): + label: I18nObject + placeholder: Optional[I18nObject] = None + + +class ModelCredentialSchema(BaseModel): + """ + Model class for model credential schema. + """ + model: FieldModelSchema + credential_form_schemas: list[CredentialFormSchema] + + +class SimpleProviderEntity(BaseModel): + """ + Simple model class for provider. + """ + provider: str + label: I18nObject + icon_small: Optional[I18nObject] = None + icon_large: Optional[I18nObject] = None + supported_model_types: list[ModelType] + models: list[AIModelEntity] = [] + + +class ProviderHelpEntity(BaseModel): + """ + Model class for provider help. + """ + title: I18nObject + url: I18nObject + + +class ProviderEntity(BaseModel): + """ + Model class for provider. + """ + provider: str + label: I18nObject + description: Optional[I18nObject] = None + icon_small: Optional[I18nObject] = None + icon_large: Optional[I18nObject] = None + background: Optional[str] = None + help: Optional[ProviderHelpEntity] = None + supported_model_types: list[ModelType] + configurate_methods: list[ConfigurateMethod] + models: list[ProviderModel] = [] + provider_credential_schema: Optional[ProviderCredentialSchema] = None + model_credential_schema: Optional[ModelCredentialSchema] = None + + class Config: + protected_namespaces = () + + def to_simple_provider(self) -> SimpleProviderEntity: + """ + Convert to simple provider. + + :return: simple provider + """ + return SimpleProviderEntity( + provider=self.provider, + label=self.label, + icon_small=self.icon_small, + icon_large=self.icon_large, + supported_model_types=self.supported_model_types, + models=self.models + ) + + +class ProviderConfig(BaseModel): + """ + Model class for provider config. + """ + provider: str + credentials: dict diff --git a/api/core/model_runtime/entities/rerank_entities.py b/api/core/model_runtime/entities/rerank_entities.py new file mode 100644 index 00000000000000..d51efd2b3be133 --- /dev/null +++ b/api/core/model_runtime/entities/rerank_entities.py @@ -0,0 +1,18 @@ +from pydantic import BaseModel + + +class RerankDocument(BaseModel): + """ + Model class for rerank document. + """ + index: int + text: str + score: float + + +class RerankResult(BaseModel): + """ + Model class for rerank result. + """ + model: str + docs: list[RerankDocument] diff --git a/api/core/model_runtime/entities/text_embedding_entities.py b/api/core/model_runtime/entities/text_embedding_entities.py new file mode 100644 index 00000000000000..7be3def3791333 --- /dev/null +++ b/api/core/model_runtime/entities/text_embedding_entities.py @@ -0,0 +1,28 @@ +from decimal import Decimal + +from pydantic import BaseModel + +from core.model_runtime.entities.model_entities import ModelUsage + + +class EmbeddingUsage(ModelUsage): + """ + Model class for embedding usage. + """ + tokens: int + total_tokens: int + unit_price: Decimal + price_unit: Decimal + total_price: Decimal + currency: str + latency: float + + +class TextEmbeddingResult(BaseModel): + """ + Model class for text embedding result. + """ + model: str + embeddings: list[list[float]] + usage: EmbeddingUsage + diff --git a/api/core/third_party/langchain/embeddings/__init__.py b/api/core/model_runtime/errors/__init__.py similarity index 100% rename from api/core/third_party/langchain/embeddings/__init__.py rename to api/core/model_runtime/errors/__init__.py diff --git a/api/core/model_runtime/errors/invoke.py b/api/core/model_runtime/errors/invoke.py new file mode 100644 index 00000000000000..fc51f5c748e66f --- /dev/null +++ b/api/core/model_runtime/errors/invoke.py @@ -0,0 +1,34 @@ +from typing import Optional + + +class InvokeError(Exception): + """Base class for all LLM exceptions.""" + description: Optional[str] = None + + def __init__(self, description: Optional[str] = None) -> None: + self.description = description + + +class InvokeConnectionError(InvokeError): + """Raised when the Invoke returns connection error.""" + description = "Connection Error" + + +class InvokeServerUnavailableError(InvokeError): + """Raised when the Invoke returns server unavailable error.""" + description = "Server Unavailable Error" + + +class InvokeRateLimitError(InvokeError): + """Raised when the Invoke returns rate limit error.""" + description = "Rate Limit Error" + + +class InvokeAuthorizationError(InvokeError): + """Raised when the Invoke returns authorization error.""" + description = "Incorrect model credentials provided, please check and try again. " + + +class InvokeBadRequestError(InvokeError): + """Raised when the Invoke returns bad request.""" + description = "Bad Request Error" diff --git a/api/core/model_runtime/errors/validate.py b/api/core/model_runtime/errors/validate.py new file mode 100644 index 00000000000000..8db79a52bb612a --- /dev/null +++ b/api/core/model_runtime/errors/validate.py @@ -0,0 +1,5 @@ +class CredentialsValidateFailedError(Exception): + """ + Credentials validate failed error + """ + pass diff --git a/api/tests/integration_tests/models/__init__.py b/api/core/model_runtime/model_providers/__base/__init__.py similarity index 100% rename from api/tests/integration_tests/models/__init__.py rename to api/core/model_runtime/model_providers/__base/__init__.py diff --git a/api/core/model_runtime/model_providers/__base/ai_model.py b/api/core/model_runtime/model_providers/__base/ai_model.py new file mode 100644 index 00000000000000..e0b1796da73548 --- /dev/null +++ b/api/core/model_runtime/model_providers/__base/ai_model.py @@ -0,0 +1,328 @@ +import decimal +import json +import logging +import os +from abc import ABC, abstractmethod +from typing import Optional + +import yaml +from pydantic import ValidationError + +from core.model_runtime.entities.defaults import PARAMETER_RULE_TEMPLATE +from core.model_runtime.entities.model_entities import PriceInfo, AIModelEntity, PriceType, PriceConfig, \ + DefaultParameterName, FetchFrom, ModelType +from core.model_runtime.entities.common_entities import I18nObject +from core.model_runtime.errors.invoke import InvokeError, InvokeAuthorizationError +from core.model_runtime.model_providers.__base.tokenizers.gpt2_tokenzier import GPT2Tokenizer + + +class AIModel(ABC): + """ + Base class for all models. + """ + model_type: ModelType + model_schemas: list[AIModelEntity] = None + started_at: float = 0 + + @abstractmethod + def validate_credentials(self, model: str, credentials: dict) -> None: + """ + Validate model credentials + + :param model: model name + :param credentials: model credentials + :return: + """ + raise NotImplementedError + + @property + @abstractmethod + def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]: + """ + Map model invoke error to unified error + The key is the error type thrown to the caller + The value is the error type thrown by the model, + which needs to be converted into a unified error type for the caller. + + :return: Invoke error mapping + """ + raise NotImplementedError + + def _transform_invoke_error(self, error: Exception) -> InvokeError: + """ + Transform invoke error to unified error + + :param error: model invoke error + :return: unified error + """ + for invoke_error, model_errors in self._invoke_error_mapping.items(): + if isinstance(error, tuple(model_errors)): + if invoke_error == InvokeAuthorizationError: + return invoke_error(description="Incorrect model credentials provided, please check and try again. ") + + return invoke_error(description=f"{invoke_error.description}: {str(error)}") + + return InvokeError(description=f"Error: {str(error)}") + + def get_price(self, model: str, credentials: dict, price_type: PriceType, tokens: int) -> PriceInfo: + """ + Get price for given model and tokens + + :param model: model name + :param credentials: model credentials + :param price_type: price type + :param tokens: number of tokens + :return: price info + """ + # get model schema + model_schema = self.get_model_schema(model, credentials) + + # get price info from predefined model schema + price_config: Optional[PriceConfig] = None + if model_schema: + price_config: PriceConfig = model_schema.pricing + + # get unit price + unit_price = None + if price_config: + if price_type == PriceType.INPUT: + unit_price = price_config.input + elif price_type == PriceType.OUTPUT and price_config.output is not None: + unit_price = price_config.output + + if unit_price is None: + return PriceInfo( + unit_price=decimal.Decimal('0.0'), + unit=decimal.Decimal('0.0'), + total_amount=decimal.Decimal('0.0'), + currency="USD", + ) + + # calculate total amount + total_amount = tokens * unit_price * price_config.unit + total_amount = total_amount.quantize(decimal.Decimal('0.0000001'), rounding=decimal.ROUND_HALF_UP) + + return PriceInfo( + unit_price=unit_price, + unit=price_config.unit, + total_amount=total_amount, + currency=price_config.currency, + ) + + def predefined_models(self) -> list[AIModelEntity]: + """ + Get all predefined models for given provider. + + :return: + """ + if self.model_schemas: + return self.model_schemas + + model_schemas = [] + + # get module name + model_type = self.__class__.__module__.split('.')[-1] + + # get provider name + provider_name = self.__class__.__module__.split('.')[-3] + + # get the path of current classes + current_path = os.path.abspath(__file__) + # get parent path of the current path + provider_model_type_path = os.path.join(os.path.dirname(os.path.dirname(current_path)), provider_name, model_type) + + # get all yaml files path under provider_model_type_path that do not start with __ + model_schema_yaml_paths = [ + os.path.join(provider_model_type_path, model_schema_yaml) + for model_schema_yaml in os.listdir(provider_model_type_path) + if not model_schema_yaml.startswith('__') + and not model_schema_yaml.startswith('_') + and os.path.isfile(os.path.join(provider_model_type_path, model_schema_yaml)) + and model_schema_yaml.endswith('.yaml') + ] + + # get _position.yaml file path + position_file_path = os.path.join(provider_model_type_path, '_position.yaml') + + # read _position.yaml file + position_map = {} + if os.path.exists(position_file_path): + with open(position_file_path, 'r') as f: + position_map = yaml.safe_load(f) + + # traverse all model_schema_yaml_paths + for model_schema_yaml_path in model_schema_yaml_paths: + # read yaml data from yaml file + with open(model_schema_yaml_path, 'r') as f: + yaml_data = yaml.safe_load(f) + + new_parameter_rules = [] + for parameter_rule in yaml_data.get('parameter_rules', []): + if 'use_template' in parameter_rule: + try: + default_parameter_name = DefaultParameterName.value_of(parameter_rule['use_template']) + default_parameter_rule = self._get_default_parameter_rule_variable_map(default_parameter_name) + copy_default_parameter_rule = default_parameter_rule.copy() + copy_default_parameter_rule.update(parameter_rule) + parameter_rule = copy_default_parameter_rule + except ValueError: + pass + + if 'label' not in parameter_rule: + parameter_rule['label'] = { + 'zh_Hans': parameter_rule['name'], + 'en_US': parameter_rule['name'] + } + + new_parameter_rules.append(parameter_rule) + + yaml_data['parameter_rules'] = new_parameter_rules + + if 'label' not in yaml_data: + yaml_data['label'] = { + 'zh_Hans': yaml_data['model'], + 'en_US': yaml_data['model'] + } + + yaml_data['fetch_from'] = FetchFrom.PREDEFINED_MODEL.value + + try: + # yaml_data to entity + model_schema = AIModelEntity(**yaml_data) + except Exception as e: + model_schema_yaml_file_name = os.path.basename(model_schema_yaml_path).rstrip(".yaml") + raise Exception(f'Invalid model schema for {provider_name}.{model_type}.{model_schema_yaml_file_name}:' + f' {str(e)}') + + # cache model schema + model_schemas.append(model_schema) + + # resort model schemas by position + if position_map: + model_schemas.sort(key=lambda x: position_map.get(x.model, 999)) + + # cache model schemas + self.model_schemas = model_schemas + + return model_schemas + + def get_model_schema(self, model: str, credentials: Optional[dict] = None) -> Optional[AIModelEntity]: + """ + Get model schema by model name and credentials + + :param model: model name + :param credentials: model credentials + :return: model schema + """ + # get predefined models (predefined_models) + models = self.predefined_models() + + model_map = {model.model: model for model in models} + if model in model_map: + return model_map[model] + + if credentials: + model_schema = self.get_customizable_model_schema_from_credentials(model, credentials) + if model_schema: + return model_schema + + return None + + def get_customizable_model_schema_from_credentials(self, model: str, credentials: dict) -> Optional[AIModelEntity]: + """ + Get customizable model schema from credentials + + :param model: model name + :param credentials: model credentials + :return: model schema + """ + if 'schema' in credentials: + schema_dict = json.loads(credentials['schema']) + + try: + model_instance = AIModelEntity.parse_obj(schema_dict) + return model_instance + except ValidationError as e: + logging.exception(f"Invalid model schema for {model}") + return self._get_customizable_model_schema(model, credentials) + + return self._get_customizable_model_schema(model, credentials) + + def _get_customizable_model_schema(self, model: str, credentials: dict) -> Optional[AIModelEntity]: + """ + Get customizable model schema and fill in the template + """ + schema = self.get_customizable_model_schema(model, credentials) + + if not schema: + return None + + # fill in the template + new_parameter_rules = [] + for parameter_rule in schema.parameter_rules: + if parameter_rule.use_template: + try: + default_parameter_name = DefaultParameterName.value_of(parameter_rule.use_template) + default_parameter_rule = self._get_default_parameter_rule_variable_map(default_parameter_name) + if not parameter_rule.max: + parameter_rule.max = default_parameter_rule['max'] + if not parameter_rule.min: + parameter_rule.min = default_parameter_rule['min'] + if not parameter_rule.precision: + parameter_rule.default = default_parameter_rule['default'] + if not parameter_rule.precision: + parameter_rule.precision = default_parameter_rule['precision'] + if not parameter_rule.required: + parameter_rule.required = default_parameter_rule['required'] + if not parameter_rule.help: + parameter_rule.help = I18nObject( + en_US=default_parameter_rule['help']['en_US'], + ) + if not parameter_rule.help.en_US: + parameter_rule.help.en_US = default_parameter_rule['help']['en_US'] + if not parameter_rule.help.zh_Hans: + parameter_rule.help.zh_Hans = default_parameter_rule['help'].get('zh_Hans', default_parameter_rule['help']['en_US']) + except ValueError: + pass + + new_parameter_rules.append(parameter_rule) + + schema.parameter_rules = new_parameter_rules + + return schema + + def get_customizable_model_schema(self, model: str, credentials: dict) -> Optional[AIModelEntity]: + """ + Get customizable model schema + + :param model: model name + :param credentials: model credentials + :return: model schema + """ + return None + + def _get_default_parameter_rule_variable_map(self, name: DefaultParameterName) -> dict: + """ + Get default parameter rule for given name + + :param name: parameter name + :return: parameter rule + """ + default_parameter_rule = PARAMETER_RULE_TEMPLATE.get(name) + + if not default_parameter_rule: + raise Exception(f'Invalid model parameter rule name {name}') + + return default_parameter_rule + + def _get_num_tokens_by_gpt2(self, text: str) -> int: + """ + Get number of tokens for given prompt messages by gpt2 + Some provider models do not provide an interface for obtaining the number of tokens. + Here, the gpt2 tokenizer is used to calculate the number of tokens. + This method can be executed offline, and the gpt2 tokenizer has been cached in the project. + + :param text: plain text of prompt. You need to convert the original message to plain text + :return: number of tokens + """ + return GPT2Tokenizer.get_num_tokens(text) \ No newline at end of file diff --git a/api/tests/integration_tests/models/speech2text/audio.mp3 b/api/core/model_runtime/model_providers/__base/audio.mp3 similarity index 100% rename from api/tests/integration_tests/models/speech2text/audio.mp3 rename to api/core/model_runtime/model_providers/__base/audio.mp3 diff --git a/api/core/model_runtime/model_providers/__base/large_language_model.py b/api/core/model_runtime/model_providers/__base/large_language_model.py new file mode 100644 index 00000000000000..6b5ea88d40a090 --- /dev/null +++ b/api/core/model_runtime/model_providers/__base/large_language_model.py @@ -0,0 +1,557 @@ +import logging +import os +import time +from abc import abstractmethod +from typing import Optional, Generator, Union, List + +from core.model_runtime.callbacks.base_callback import Callback +from core.model_runtime.callbacks.logging_callback import LoggingCallback +from core.model_runtime.entities.message_entities import PromptMessage, PromptMessageTool, AssistantPromptMessage +from core.model_runtime.entities.model_entities import ModelPropertyKey, PriceType, ParameterType, ParameterRule, \ + ModelType +from core.model_runtime.entities.llm_entities import LLMResult, LLMMode, LLMUsage, \ + LLMResultChunk, LLMResultChunkDelta +from core.model_runtime.model_providers.__base.ai_model import AIModel + +logger = logging.getLogger(__name__) + + +class LargeLanguageModel(AIModel): + """ + Model class for large language model. + """ + model_type: ModelType = ModelType.LLM + + def invoke(self, model: str, credentials: dict, + prompt_messages: list[PromptMessage], model_parameters: Optional[dict] = None, + tools: Optional[list[PromptMessageTool]] = None, stop: Optional[List[str]] = None, + stream: bool = True, user: Optional[str] = None, callbacks: list[Callback] = None) \ + -> Union[LLMResult, Generator]: + """ + Invoke large language model + + :param model: model name + :param credentials: model credentials + :param prompt_messages: prompt messages + :param model_parameters: model parameters + :param tools: tools for tool calling + :param stop: stop words + :param stream: is stream response + :param user: unique user id + :param callbacks: callbacks + :return: full response or stream response chunk generator result + """ + # validate and filter model parameters + if model_parameters is None: + model_parameters = {} + + model_parameters = self._validate_and_filter_model_parameters(model, model_parameters, credentials) + + self.started_at = time.perf_counter() + + callbacks = callbacks or [] + + if bool(os.environ.get("DEBUG")): + callbacks.append(LoggingCallback()) + + # trigger before invoke callbacks + self._trigger_before_invoke_callbacks( + model=model, + credentials=credentials, + prompt_messages=prompt_messages, + model_parameters=model_parameters, + tools=tools, + stop=stop, + stream=stream, + user=user, + callbacks=callbacks + ) + + try: + result = self._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user) + except Exception as e: + self._trigger_invoke_error_callbacks( + model=model, + ex=e, + credentials=credentials, + prompt_messages=prompt_messages, + model_parameters=model_parameters, + tools=tools, + stop=stop, + stream=stream, + user=user, + callbacks=callbacks + ) + + raise self._transform_invoke_error(e) + + if stream and isinstance(result, Generator): + return self._invoke_result_generator( + model=model, + result=result, + credentials=credentials, + prompt_messages=prompt_messages, + model_parameters=model_parameters, + tools=tools, + stop=stop, + stream=stream, + user=user, + callbacks=callbacks + ) + else: + self._trigger_after_invoke_callbacks( + model=model, + result=result, + credentials=credentials, + prompt_messages=prompt_messages, + model_parameters=model_parameters, + tools=tools, + stop=stop, + stream=stream, + user=user, + callbacks=callbacks + ) + + return result + + def _invoke_result_generator(self, model: str, result: Generator, credentials: dict, + prompt_messages: list[PromptMessage], model_parameters: dict, + tools: Optional[list[PromptMessageTool]] = None, + stop: Optional[List[str]] = None, stream: bool = True, + user: Optional[str] = None, callbacks: list[Callback] = None) -> Generator: + """ + Invoke result generator + + :param result: result generator + :return: result generator + """ + prompt_message = AssistantPromptMessage( + content="" + ) + usage = None + system_fingerprint = None + real_model = model + + for chunk in result: + try: + yield chunk + + self._trigger_new_chunk_callbacks( + chunk=chunk, + model=model, + credentials=credentials, + prompt_messages=prompt_messages, + model_parameters=model_parameters, + tools=tools, + stop=stop, + stream=stream, + user=user, + callbacks=callbacks + ) + + prompt_message.content += chunk.delta.message.content + real_model = chunk.model + if chunk.delta.usage: + usage = chunk.delta.usage + + if chunk.system_fingerprint: + system_fingerprint = chunk.system_fingerprint + except Exception as e: + raise self._transform_invoke_error(e) + + self._trigger_after_invoke_callbacks( + model=model, + result=LLMResult( + model=real_model, + prompt_messages=prompt_messages, + message=prompt_message, + usage=usage, + system_fingerprint=system_fingerprint + ), + credentials=credentials, + prompt_messages=prompt_messages, + model_parameters=model_parameters, + tools=tools, + stop=stop, + stream=stream, + user=user, + callbacks=callbacks + ) + + @abstractmethod + def _invoke(self, model: str, credentials: dict, + prompt_messages: list[PromptMessage], model_parameters: dict, + tools: Optional[list[PromptMessageTool]] = None, stop: Optional[List[str]] = None, + stream: bool = True, user: Optional[str] = None) \ + -> Union[LLMResult, Generator]: + """ + Invoke large language model + + :param model: model name + :param credentials: model credentials + :param prompt_messages: prompt messages + :param model_parameters: model parameters + :param tools: tools for tool calling + :param stop: stop words + :param stream: is stream response + :param user: unique user id + :return: full response or stream response chunk generator result + """ + raise NotImplementedError + + @abstractmethod + def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage], + tools: Optional[list[PromptMessageTool]] = None) -> int: + """ + Get number of tokens for given prompt messages + + :param model: model name + :param credentials: model credentials + :param prompt_messages: prompt messages + :param tools: tools for tool calling + :return: + """ + raise NotImplementedError + + def _llm_result_to_stream(self, result: LLMResult) -> Generator: + """ + Transform llm result to stream + + :param result: llm result + :return: stream + """ + index = 0 + + tool_calls = result.message.tool_calls + + for word in result.message.content: + assistant_prompt_message = AssistantPromptMessage( + content=word, + tool_calls=tool_calls if index == (len(result.message.content) - 1) else [] + ) + + yield LLMResultChunk( + model=result.model, + prompt_messages=result.prompt_messages, + system_fingerprint=result.system_fingerprint, + delta=LLMResultChunkDelta( + index=index, + message=assistant_prompt_message, + ) + ) + + index += 1 + time.sleep(0.01) + + def get_parameter_rules(self, model: str, credentials: dict) -> list[ParameterRule]: + """ + Get parameter rules + + :param model: model name + :param credentials: model credentials + :return: parameter rules + """ + model_schema = self.get_model_schema(model, credentials) + if model_schema: + return model_schema.parameter_rules + + return [] + + def get_model_mode(self, model: str, credentials: Optional[dict] = None) -> LLMMode: + """ + Get model mode + + :param model: model name + :param credentials: model credentials + :return: model mode + """ + model_schema = self.get_model_schema(model, credentials) + + mode = LLMMode.CHAT + if model_schema and model_schema.model_properties.get(ModelPropertyKey.MODE): + mode = LLMMode.value_of(model_schema.model_properties[ModelPropertyKey.MODE]) + + return mode + + def _calc_response_usage(self, model: str, credentials: dict, prompt_tokens: int, completion_tokens: int) -> LLMUsage: + """ + Calculate response usage + + :param model: model name + :param credentials: model credentials + :param prompt_tokens: prompt tokens + :param completion_tokens: completion tokens + :return: usage + """ + # get prompt price info + prompt_price_info = self.get_price( + model=model, + credentials=credentials, + price_type=PriceType.INPUT, + tokens=prompt_tokens, + ) + + # get completion price info + completion_price_info = self.get_price( + model=model, + credentials=credentials, + price_type=PriceType.OUTPUT, + tokens=completion_tokens + ) + + # transform usage + usage = LLMUsage( + prompt_tokens=prompt_tokens, + prompt_unit_price=prompt_price_info.unit_price, + prompt_price_unit=prompt_price_info.unit, + prompt_price=prompt_price_info.total_amount, + completion_tokens=completion_tokens, + completion_unit_price=completion_price_info.unit_price, + completion_price_unit=completion_price_info.unit, + completion_price=completion_price_info.total_amount, + total_tokens=prompt_tokens + completion_tokens, + total_price=prompt_price_info.total_amount + completion_price_info.total_amount, + currency=prompt_price_info.currency, + latency=time.perf_counter() - self.started_at + ) + + return usage + + def _trigger_before_invoke_callbacks(self, model: str, credentials: dict, + prompt_messages: list[PromptMessage], model_parameters: dict, + tools: Optional[list[PromptMessageTool]] = None, + stop: Optional[List[str]] = None, stream: bool = True, + user: Optional[str] = None, callbacks: list[Callback] = None) -> None: + """ + Trigger before invoke callbacks + + :param model: model name + :param credentials: model credentials + :param prompt_messages: prompt messages + :param model_parameters: model parameters + :param tools: tools for tool calling + :param stop: stop words + :param stream: is stream response + :param user: unique user id + :param callbacks: callbacks + """ + if callbacks: + for callback in callbacks: + try: + callback.on_before_invoke( + llm_instance=self, + model=model, + credentials=credentials, + prompt_messages=prompt_messages, + model_parameters=model_parameters, + tools=tools, + stop=stop, + stream=stream, + user=user + ) + except Exception as e: + if callback.raise_error: + raise e + else: + logger.warning(f"Callback {callback.__class__.__name__} on_before_invoke failed with error {e}") + + def _trigger_new_chunk_callbacks(self, chunk: LLMResultChunk, model: str, credentials: dict, + prompt_messages: list[PromptMessage], model_parameters: dict, + tools: Optional[list[PromptMessageTool]] = None, + stop: Optional[List[str]] = None, stream: bool = True, + user: Optional[str] = None, callbacks: list[Callback] = None) -> None: + """ + Trigger new chunk callbacks + + :param chunk: chunk + :param model: model name + :param credentials: model credentials + :param prompt_messages: prompt messages + :param model_parameters: model parameters + :param tools: tools for tool calling + :param stop: stop words + :param stream: is stream response + :param user: unique user id + """ + if callbacks: + for callback in callbacks: + try: + callback.on_new_chunk( + llm_instance=self, + chunk=chunk, + model=model, + credentials=credentials, + prompt_messages=prompt_messages, + model_parameters=model_parameters, + tools=tools, + stop=stop, + stream=stream, + user=user + ) + except Exception as e: + if callback.raise_error: + raise e + else: + logger.warning(f"Callback {callback.__class__.__name__} on_new_chunk failed with error {e}") + + def _trigger_after_invoke_callbacks(self, model: str, result: LLMResult, credentials: dict, + prompt_messages: list[PromptMessage], model_parameters: dict, + tools: Optional[list[PromptMessageTool]] = None, + stop: Optional[List[str]] = None, stream: bool = True, + user: Optional[str] = None, callbacks: list[Callback] = None) -> None: + """ + Trigger after invoke callbacks + + :param model: model name + :param result: result + :param credentials: model credentials + :param prompt_messages: prompt messages + :param model_parameters: model parameters + :param tools: tools for tool calling + :param stop: stop words + :param stream: is stream response + :param user: unique user id + :param callbacks: callbacks + """ + if callbacks: + for callback in callbacks: + try: + callback.on_after_invoke( + llm_instance=self, + result=result, + model=model, + credentials=credentials, + prompt_messages=prompt_messages, + model_parameters=model_parameters, + tools=tools, + stop=stop, + stream=stream, + user=user + ) + except Exception as e: + if callback.raise_error: + raise e + else: + logger.warning(f"Callback {callback.__class__.__name__} on_after_invoke failed with error {e}") + + def _trigger_invoke_error_callbacks(self, model: str, ex: Exception, credentials: dict, + prompt_messages: list[PromptMessage], model_parameters: dict, + tools: Optional[list[PromptMessageTool]] = None, + stop: Optional[List[str]] = None, stream: bool = True, + user: Optional[str] = None, callbacks: list[Callback] = None) -> None: + """ + Trigger invoke error callbacks + + :param model: model name + :param ex: exception + :param credentials: model credentials + :param prompt_messages: prompt messages + :param model_parameters: model parameters + :param tools: tools for tool calling + :param stop: stop words + :param stream: is stream response + :param user: unique user id + :param callbacks: callbacks + """ + if callbacks: + for callback in callbacks: + try: + callback.on_invoke_error( + llm_instance=self, + ex=ex, + model=model, + credentials=credentials, + prompt_messages=prompt_messages, + model_parameters=model_parameters, + tools=tools, + stop=stop, + stream=stream, + user=user + ) + except Exception as e: + if callback.raise_error: + raise e + else: + logger.warning(f"Callback {callback.__class__.__name__} on_invoke_error failed with error {e}") + + def _validate_and_filter_model_parameters(self, model: str, model_parameters: dict, credentials: dict) -> dict: + """ + Validate model parameters + + :param model: model name + :param model_parameters: model parameters + :param credentials: model credentials + :return: + """ + parameter_rules = self.get_parameter_rules(model, credentials) + + # validate model parameters + filtered_model_parameters = {} + for parameter_rule in parameter_rules: + parameter_name = parameter_rule.name + parameter_value = model_parameters.get(parameter_name) + if parameter_value is None: + if parameter_rule.use_template and parameter_rule.use_template in model_parameters: + # if parameter value is None, use template value variable name instead + parameter_value = model_parameters[parameter_rule.use_template] + else: + if parameter_rule.required: + if parameter_rule.default is not None: + filtered_model_parameters[parameter_name] = parameter_rule.default + continue + else: + raise ValueError(f"Model Parameter {parameter_name} is required.") + else: + continue + + # validate parameter value type + if parameter_rule.type == ParameterType.INT: + if not isinstance(parameter_value, int): + raise ValueError(f"Model Parameter {parameter_name} should be int.") + + # validate parameter value range + if parameter_rule.min is not None and parameter_value < parameter_rule.min: + raise ValueError( + f"Model Parameter {parameter_name} should be greater than or equal to {parameter_rule.min}.") + + if parameter_rule.max is not None and parameter_value > parameter_rule.max: + raise ValueError( + f"Model Parameter {parameter_name} should be less than or equal to {parameter_rule.max}.") + elif parameter_rule.type == ParameterType.FLOAT: + if not isinstance(parameter_value, (float, int)): + raise ValueError(f"Model Parameter {parameter_name} should be float.") + + # validate parameter value precision + if parameter_rule.precision is not None: + if parameter_rule.precision == 0: + if parameter_value != int(parameter_value): + raise ValueError(f"Model Parameter {parameter_name} should be int.") + else: + if parameter_value != round(parameter_value, parameter_rule.precision): + raise ValueError( + f"Model Parameter {parameter_name} should be round to {parameter_rule.precision} decimal places.") + + # validate parameter value range + if parameter_rule.min is not None and parameter_value < parameter_rule.min: + raise ValueError( + f"Model Parameter {parameter_name} should be greater than or equal to {parameter_rule.min}.") + + if parameter_rule.max is not None and parameter_value > parameter_rule.max: + raise ValueError( + f"Model Parameter {parameter_name} should be less than or equal to {parameter_rule.max}.") + elif parameter_rule.type == ParameterType.BOOLEAN: + if not isinstance(parameter_value, bool): + raise ValueError(f"Model Parameter {parameter_name} should be bool.") + elif parameter_rule.type == ParameterType.STRING: + if not isinstance(parameter_value, str): + raise ValueError(f"Model Parameter {parameter_name} should be string.") + + # validate options + if parameter_rule.options and parameter_value not in parameter_rule.options: + raise ValueError(f"Model Parameter {parameter_name} should be one of {parameter_rule.options}.") + else: + raise ValueError(f"Model Parameter {parameter_name} type {parameter_rule.type} is not supported.") + + filtered_model_parameters[parameter_name] = parameter_value + + return filtered_model_parameters diff --git a/api/core/model_runtime/model_providers/__base/model_provider.py b/api/core/model_runtime/model_providers/__base/model_provider.py new file mode 100644 index 00000000000000..71d4a0c5d6bbf6 --- /dev/null +++ b/api/core/model_runtime/model_providers/__base/model_provider.py @@ -0,0 +1,125 @@ +import importlib +import os +from abc import ABC, abstractmethod +from typing import Optional, Dict + +import yaml + +from core.model_runtime.entities.model_entities import ModelType, AIModelEntity +from core.model_runtime.entities.provider_entities import ProviderEntity +from core.model_runtime.model_providers.__base.ai_model import AIModel + + +class ModelProvider(ABC): + provider_schema: ProviderEntity = None + model_instance_map: Dict[str, AIModel] = {} + + @abstractmethod + def validate_provider_credentials(self, credentials: dict) -> None: + """ + Validate provider credentials + You can choose any validate_credentials method of model type or implement validate method by yourself, + such as: get model list api + + if validate failed, raise exception + + :param credentials: provider credentials, credentials form defined in `provider_credential_schema`. + """ + raise NotImplementedError + + def get_provider_schema(self) -> ProviderEntity: + """ + Get provider schema + + :return: provider schema + """ + if self.provider_schema: + return self.provider_schema + + # get dirname of the current path + provider_name = self.__class__.__module__.split('.')[-1] + + # get the path of the model_provider classes + base_path = os.path.abspath(__file__) + current_path = os.path.join(os.path.dirname(os.path.dirname(base_path)), provider_name) + + # read provider schema from yaml file + yaml_path = os.path.join(current_path, f'{provider_name}.yaml') + yaml_data = {} + if os.path.exists(yaml_path): + with open(yaml_path, 'r') as f: + yaml_data = yaml.safe_load(f) + + try: + # yaml_data to entity + provider_schema = ProviderEntity(**yaml_data) + except Exception as e: + raise Exception(f'Invalid provider schema for {provider_name}: {str(e)}') + + # cache schema + self.provider_schema = provider_schema + + return provider_schema + + def models(self, model_type: ModelType) -> list[AIModelEntity]: + """ + Get all models for given model type + + :param model_type: model type defined in `ModelType` + :return: list of models + """ + provider_schema = self.get_provider_schema() + if model_type not in provider_schema.supported_model_types: + return [] + + # get model instance of the model type + model_instance = self.get_model_instance(model_type) + + # get predefined models (predefined_models) + models = model_instance.predefined_models() + + # return models + return models + + def get_model_instance(self, model_type: ModelType) -> AIModel: + """ + Get model instance + + :param model_type: model type defined in `ModelType` + :return: + """ + # get dirname of the current path + provider_name = self.__class__.__module__.split('.')[-1] + + if f"{provider_name}.{model_type.value}" in self.model_instance_map: + return self.model_instance_map[f"{provider_name}.{model_type.value}"] + + # get the path of the model type classes + base_path = os.path.abspath(__file__) + model_type_name = model_type.value.replace('-', '_') + model_type_path = os.path.join(os.path.dirname(os.path.dirname(base_path)), provider_name, model_type_name) + model_type_py_path = os.path.join(model_type_path, f'{model_type_name}.py') + + if not os.path.isdir(model_type_path) or not os.path.exists(model_type_py_path): + raise Exception(f'Invalid model type {model_type} for provider {provider_name}') + + # Dynamic loading {model_type_name}.py file and find the subclass of AIModel + parent_module = '.'.join(self.__class__.__module__.split('.')[:-1]) + spec = importlib.util.spec_from_file_location(f"{parent_module}.{model_type_name}.{model_type_name}", model_type_py_path) + mod = importlib.util.module_from_spec(spec) + spec.loader.exec_module(mod) + + model_class = None + for name, obj in vars(mod).items(): + if (isinstance(obj, type) and issubclass(obj, AIModel) and not obj.__abstractmethods__ + and obj != AIModel): + model_class = obj + break + + if not model_class: + raise Exception(f'Missing AIModel Class for model type {model_type} in {model_type_py_path}') + + model_instance_map = model_class() + self.model_instance_map[f"{provider_name}.{model_type.value}"] = model_instance_map + + return model_instance_map diff --git a/api/core/model_runtime/model_providers/__base/moderation_model.py b/api/core/model_runtime/model_providers/__base/moderation_model.py new file mode 100644 index 00000000000000..00cb1d6cc31624 --- /dev/null +++ b/api/core/model_runtime/model_providers/__base/moderation_model.py @@ -0,0 +1,48 @@ +import time +from abc import abstractmethod +from typing import Optional + +from core.model_runtime.entities.model_entities import ModelType +from core.model_runtime.model_providers.__base.ai_model import AIModel + + +class ModerationModel(AIModel): + """ + Model class for moderation model. + """ + model_type: ModelType = ModelType.MODERATION + + def invoke(self, model: str, credentials: dict, + text: str, user: Optional[str] = None) \ + -> bool: + """ + Invoke moderation model + + :param model: model name + :param credentials: model credentials + :param text: text to moderate + :param user: unique user id + :return: false if text is safe, true otherwise + """ + self.started_at = time.perf_counter() + + try: + return self._invoke(model, credentials, text, user) + except Exception as e: + raise self._transform_invoke_error(e) + + @abstractmethod + def _invoke(self, model: str, credentials: dict, + text: str, user: Optional[str] = None) \ + -> bool: + """ + Invoke large language model + + :param model: model name + :param credentials: model credentials + :param text: text to moderate + :param user: unique user id + :return: false if text is safe, true otherwise + """ + raise NotImplementedError + diff --git a/api/core/model_runtime/model_providers/__base/rerank_model.py b/api/core/model_runtime/model_providers/__base/rerank_model.py new file mode 100644 index 00000000000000..2c86f25180eab8 --- /dev/null +++ b/api/core/model_runtime/model_providers/__base/rerank_model.py @@ -0,0 +1,56 @@ +import time +from abc import abstractmethod +from typing import Optional + +from core.model_runtime.entities.model_entities import ModelType +from core.model_runtime.entities.rerank_entities import RerankResult +from core.model_runtime.model_providers.__base.ai_model import AIModel + + +class RerankModel(AIModel): + """ + Base Model class for rerank model. + """ + model_type: ModelType = ModelType.RERANK + + def invoke(self, model: str, credentials: dict, + query: str, docs: list[str], score_threshold: Optional[float] = None, top_n: Optional[int] = None, + user: Optional[str] = None) \ + -> RerankResult: + """ + Invoke rerank model + + :param model: model name + :param credentials: model credentials + :param query: search query + :param docs: docs for reranking + :param score_threshold: score threshold + :param top_n: top n + :param user: unique user id + :return: rerank result + """ + self.started_at = time.perf_counter() + + try: + return self._invoke(model, credentials, query, docs, score_threshold, top_n, user) + except Exception as e: + raise self._transform_invoke_error(e) + + @abstractmethod + def _invoke(self, model: str, credentials: dict, + query: str, docs: list[str], score_threshold: Optional[float] = None, top_n: Optional[int] = None, + user: Optional[str] = None) \ + -> RerankResult: + """ + Invoke rerank model + + :param model: model name + :param credentials: model credentials + :param query: search query + :param docs: docs for reranking + :param score_threshold: score threshold + :param top_n: top n + :param user: unique user id + :return: rerank result + """ + raise NotImplementedError diff --git a/api/core/model_runtime/model_providers/__base/speech2text_model.py b/api/core/model_runtime/model_providers/__base/speech2text_model.py new file mode 100644 index 00000000000000..151bccc0748fd3 --- /dev/null +++ b/api/core/model_runtime/model_providers/__base/speech2text_model.py @@ -0,0 +1,57 @@ +import os +from abc import abstractmethod +from typing import Optional, IO + +from core.model_runtime.entities.model_entities import ModelType +from core.model_runtime.model_providers.__base.ai_model import AIModel + + +class Speech2TextModel(AIModel): + """ + Model class for speech2text model. + """ + model_type: ModelType = ModelType.SPEECH2TEXT + + def invoke(self, model: str, credentials: dict, + file: IO[bytes], user: Optional[str] = None) \ + -> str: + """ + Invoke large language model + + :param model: model name + :param credentials: model credentials + :param file: audio file + :param user: unique user id + :return: text for given audio file + """ + try: + return self._invoke(model, credentials, file, user) + except Exception as e: + raise self._transform_invoke_error(e) + + @abstractmethod + def _invoke(self, model: str, credentials: dict, + file: IO[bytes], user: Optional[str] = None) \ + -> str: + """ + Invoke large language model + + :param model: model name + :param credentials: model credentials + :param file: audio file + :param user: unique user id + :return: text for given audio file + """ + raise NotImplementedError + + def _get_demo_file_path(self) -> str: + """ + Get demo file for given model + + :return: demo file + """ + # Get the directory of the current file + current_dir = os.path.dirname(os.path.abspath(__file__)) + + # Construct the path to the audio file + return os.path.join(current_dir, 'audio.mp3') diff --git a/api/core/model_runtime/model_providers/__base/text_embedding_model.py b/api/core/model_runtime/model_providers/__base/text_embedding_model.py new file mode 100644 index 00000000000000..1da4dcef9182fe --- /dev/null +++ b/api/core/model_runtime/model_providers/__base/text_embedding_model.py @@ -0,0 +1,90 @@ +import time +from abc import abstractmethod +from typing import Optional + +from core.model_runtime.entities.model_entities import ModelPropertyKey, ModelType +from core.model_runtime.entities.text_embedding_entities import TextEmbeddingResult +from core.model_runtime.model_providers.__base.ai_model import AIModel + + +class TextEmbeddingModel(AIModel): + """ + Model class for text embedding model. + """ + model_type: ModelType = ModelType.TEXT_EMBEDDING + + def invoke(self, model: str, credentials: dict, + texts: list[str], user: Optional[str] = None) \ + -> TextEmbeddingResult: + """ + Invoke large language model + + :param model: model name + :param credentials: model credentials + :param texts: texts to embed + :param user: unique user id + :return: embeddings result + """ + self.started_at = time.perf_counter() + + try: + return self._invoke(model, credentials, texts, user) + except Exception as e: + raise self._transform_invoke_error(e) + + @abstractmethod + def _invoke(self, model: str, credentials: dict, + texts: list[str], user: Optional[str] = None) \ + -> TextEmbeddingResult: + """ + Invoke large language model + + :param model: model name + :param credentials: model credentials + :param texts: texts to embed + :param user: unique user id + :return: embeddings result + """ + raise NotImplementedError + + @abstractmethod + def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int: + """ + Get number of tokens for given prompt messages + + :param model: model name + :param credentials: model credentials + :param texts: texts to embed + :return: + """ + raise NotImplementedError + + def _get_context_size(self, model: str, credentials: dict) -> int: + """ + Get context size for given embedding model + + :param model: model name + :param credentials: model credentials + :return: context size + """ + model_schema = self.get_model_schema(model, credentials) + + if model_schema and ModelPropertyKey.CONTEXT_SIZE in model_schema.model_properties: + return model_schema.model_properties[ModelPropertyKey.CONTEXT_SIZE] + + return 1000 + + def _get_max_chunks(self, model: str, credentials: dict) -> int: + """ + Get max chunks for given embedding model + + :param model: model name + :param credentials: model credentials + :return: max chunks + """ + model_schema = self.get_model_schema(model, credentials) + + if model_schema and ModelPropertyKey.MAX_CHUNKS in model_schema.model_properties: + return model_schema.model_properties[ModelPropertyKey.MAX_CHUNKS] + + return 1 diff --git a/api/core/model_runtime/model_providers/__base/tokenizers/gpt2/merges.txt b/api/core/model_runtime/model_providers/__base/tokenizers/gpt2/merges.txt new file mode 100644 index 00000000000000..226b0752cac778 --- /dev/null +++ b/api/core/model_runtime/model_providers/__base/tokenizers/gpt2/merges.txt @@ -0,0 +1,50001 @@ +#version: 0.2 +Ġ t +Ġ a +h e +i n +r e +o n +Ġt he +e r +Ġ s +a t +Ġ w +Ġ o +e n +Ġ c +i t +i s +a n +o r +e s +Ġ b +e d +Ġ f +in g +Ġ p +o u +Ġa n +a l +a r +Ġt o +Ġ m +Ġo f +Ġ in +Ġ d +Ġ h +Ġan d +i c +a s +l e +Ġt h +i on +o m +l l +en t +Ġ n +Ġ l +s t 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