diff --git a/api/core/model_runtime/model_providers/_position.yaml b/api/core/model_runtime/model_providers/_position.yaml index 89fccef6598fdd..39a1c909c3eb32 100644 --- a/api/core/model_runtime/model_providers/_position.yaml +++ b/api/core/model_runtime/model_providers/_position.yaml @@ -41,3 +41,4 @@ - mixedbread - nomic - voyage +- ibm diff --git a/api/core/model_runtime/model_providers/ibm/__init__.py b/api/core/model_runtime/model_providers/ibm/__init__.py new file mode 100644 index 00000000000000..e69de29bb2d1d6 diff --git a/api/core/model_runtime/model_providers/ibm/_assets/icon_l_en.svg b/api/core/model_runtime/model_providers/ibm/_assets/icon_l_en.svg new file mode 100644 index 00000000000000..30c40d7d7a4705 --- /dev/null +++ b/api/core/model_runtime/model_providers/ibm/_assets/icon_l_en.svg @@ -0,0 +1,45 @@ + + + +Created with Fabric.js 5.2.4 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/api/core/model_runtime/model_providers/ibm/_assets/icon_s_en.svg b/api/core/model_runtime/model_providers/ibm/_assets/icon_s_en.svg new file mode 100644 index 00000000000000..f1fe82bc954e56 --- /dev/null +++ b/api/core/model_runtime/model_providers/ibm/_assets/icon_s_en.svg @@ -0,0 +1,16 @@ + + + +Created with Fabric.js 5.2.4 + + + + + + + + + + + + \ No newline at end of file diff --git a/api/core/model_runtime/model_providers/ibm/ibm.py b/api/core/model_runtime/model_providers/ibm/ibm.py new file mode 100644 index 00000000000000..3b3da3e6917598 --- /dev/null +++ b/api/core/model_runtime/model_providers/ibm/ibm.py @@ -0,0 +1,28 @@ +import logging + +from ibm_watsonx_ai import APIClient, Credentials + +from core.model_runtime.errors.validate import CredentialsValidateFailedError +from core.model_runtime.model_providers.__base.model_provider import ModelProvider + +logger = logging.getLogger(__name__) + + +class IbmProvider(ModelProvider): + def validate_provider_credentials(self, credentials: dict) -> None: + """ + Validate provider credentials + + if validate failed, raise exception + + :param credentials: provider credentials, credentials form defined in `provider_credential_schema`. + """ + try: + credentials = Credentials(url=credentials.get("base_url"), api_key=credentials.get("api_key")) + + client = APIClient(credentials) + except CredentialsValidateFailedError as ex: + raise ex + except Exception as ex: + logger.exception(f"{self.get_provider_schema().provider} credentials validate failed") + raise ex diff --git a/api/core/model_runtime/model_providers/ibm/ibm.yaml b/api/core/model_runtime/model_providers/ibm/ibm.yaml new file mode 100644 index 00000000000000..7d7f6c4ebbafad --- /dev/null +++ b/api/core/model_runtime/model_providers/ibm/ibm.yaml @@ -0,0 +1,133 @@ +provider: ibm +label: + zh_Hans: IBM + en_US: IBM +icon_small: + en_US: icon_s_en.svg +icon_large: + en_US: icon_l_en.svg +background: "#E3F0FF" +help: + title: + en_US: Get your API key from IBM + zh_Hans: 从 IBM 获取 API Key + url: + en_US: https://cloud.ibm.com/iam/apikeys +supported_model_types: + - llm +configurate_methods: + - customizable-model +provider_credential_schema: + credential_form_schemas: + - variable: api_key + label: + zh_Hans: API Key + 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 + show_on: [ ] + - variable: base_url + label: + zh_Hans: API Base URL + en_US: API Base URL + type: text-input + required: false + placeholder: + zh_Hans: 在此输入您的 API Base URL，如 https://eu-de.ml.cloud.ibm.com + en_US: Enter your API Base URL, e.g. https://eu-de.ml.cloud.ibm.com +model_credential_schema: + model: + 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: mode + show_on: + - variable: __model_type + value: llm + label: + en_US: Completion mode + type: select + required: false + default: chat + placeholder: + zh_Hans: 选择对话类型 + en_US: Select completion mode + options: + - value: completion + label: + en_US: Completion + zh_Hans: 补全 + - value: chat + label: + en_US: Chat + zh_Hans: 对话 + - variable: 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: base_url + label: + zh_Hans: API Base URL + en_US: API Base URL + type: text-input + required: true + placeholder: + zh_Hans: 在此输入您的 API Base URL，如 https://eu-de.ml.cloud.ibm.com + en_US: Enter your API Base URL, e.g. https://eu-de.ml.cloud.ibm.com + - variable: project_id + label: + zh_Hans: Project ID + en_US: Project ID + type: text-input + required: true + placeholder: + zh_Hans: 在此输入您的 API Base + en_US: Enter your Project ID + - variable: function_calling_type + show_on: + - variable: __model_type + value: llm + label: + en_US: Function calling + type: select + required: false + default: no_call + options: + - value: tool_call + label: + en_US: Tool Call + zh_Hans: Tool Call + - value: no_call + label: + en_US: Not Support + zh_Hans: 不支持 + - variable: vision_support + show_on: + - variable: __model_type + value: llm + label: + zh_Hans: Vision 支持 + en_US: Vision Support + type: select + required: false + default: no_support + options: + - value: support + label: + en_US: Support + zh_Hans: 支持 + - value: no_support + label: + en_US: Not Support + zh_Hans: 不支持 diff --git a/api/core/model_runtime/model_providers/ibm/llm/__init__.py b/api/core/model_runtime/model_providers/ibm/llm/__init__.py new file mode 100644 index 00000000000000..e69de29bb2d1d6 diff --git a/api/core/model_runtime/model_providers/ibm/llm/llm.py b/api/core/model_runtime/model_providers/ibm/llm/llm.py new file mode 100644 index 00000000000000..eb63ec01b53931 --- /dev/null +++ b/api/core/model_runtime/model_providers/ibm/llm/llm.py @@ -0,0 +1,970 @@ +from collections.abc import Generator +from typing import Any, Optional, Union, cast + +import requests +from ibm_watsonx_ai import APIClient, Credentials +from ibm_watsonx_ai.foundation_models import ModelInference +from ibm_watsonx_ai.foundation_models.schema import TextChatParameters, TextGenParameters + +from core.model_runtime.entities.llm_entities import ( + LLMMode, + LLMResult, + LLMResultChunk, + LLMResultChunkDelta, +) +from core.model_runtime.entities.message_entities import ( + AssistantPromptMessage, + ImagePromptMessageContent, + PromptMessage, + PromptMessageContent, + PromptMessageContentType, + PromptMessageFunction, + PromptMessageTool, + SystemPromptMessage, + ToolPromptMessage, + UserPromptMessage, +) +from core.model_runtime.entities.model_entities import ( + AIModelEntity, + DefaultParameterName, + FetchFrom, + I18nObject, + ModelFeature, + ModelPropertyKey, + ModelType, + ParameterRule, + ParameterType, +) +from core.model_runtime.errors.invoke import ( + InvokeAuthorizationError, + InvokeBadRequestError, + InvokeConnectionError, + InvokeError, + InvokeRateLimitError, + InvokeServerUnavailableError, +) +from core.model_runtime.errors.validate import CredentialsValidateFailedError +from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel +from core.model_runtime.utils import helper + + +class IbmLargeLanguageModel(LargeLanguageModel): + """ + Model class for IBM large language model. + """ + + 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 + """ + + model_mode = self.get_model_mode(model, credentials) + + if model_mode == LLMMode.CHAT: + return self._chat_generate( + model=model, + credentials=credentials, + prompt_messages=prompt_messages, + model_parameters=model_parameters, + tools=tools, + stop=stop, + stream=stream, + user=user, + ) + else: + return self._generate( + model=model, + credentials=credentials, + prompt_messages=prompt_messages, + model_parameters=model_parameters, + stop=stop, + stream=stream, + user=user, + ) + + 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: + :param credentials: + :param prompt_messages: + :param tools: tools for tool calling + :return: + """ + return self._num_tokens_from_messages(model, prompt_messages, tools, credentials) + + def validate_credentials(self, model: str, credentials: dict) -> None: + """ + Validate model credentials + + :param model: model name + :param credentials: model credentials + :return: + """ + + try: + model_mode = self.get_model_mode(model) + + if model_mode == LLMMode.CHAT: + self._chat_generate( + model=model, + credentials=credentials, + prompt_messages=[UserPromptMessage(content="ping")], + model_parameters={ + "max_tokens": 20, + "temperature": 0, + }, + stream=False, + ) + else: + self._generate( + model=model, + credentials=credentials, + prompt_messages=[UserPromptMessage(content="ping")], + model_parameters={ + "max_new_tokens": 20, + "temperature": 0, + }, + stream=False, + ) + except Exception as ex: + raise CredentialsValidateFailedError(str(ex)) + + def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity: + """ + generate custom model entities from credentials + + :param model: model name + :param credentials: credentials + + :return: AIModelEntity + """ + + features = [] + + # tool_call support + function_calling_type = credentials.get("function_calling_type", "no_call") + if function_calling_type == "tool_call": + features.append(ModelFeature.MULTI_TOOL_CALL) + + # vision_support + vision_support = credentials.get("vision_support", "not_support") + if vision_support == "support": + features.append(ModelFeature.VISION) + + completion_model = None + if credentials.get("mode") == "chat": + completion_model = LLMMode.CHAT.value + elif credentials.get("mode") == "completion": + completion_model = LLMMode.COMPLETION.value + + model_properties = ( + { + ModelPropertyKey.MODE: completion_model, + } + if completion_model + else {} + ) + + model_parameters_rules = [ + ParameterRule( + name=DefaultParameterName.TEMPERATURE.value, + label=I18nObject(en_US="Temperature", zh_Hans="温度"), + type=ParameterType.FLOAT, + default=0.7, + min=0, + max=2, + precision=2, + ), + ParameterRule( + name=DefaultParameterName.TOP_P.value, + label=I18nObject(en_US="Top P", zh_Hans="Top P"), + type=ParameterType.FLOAT, + default=float(1), + min=0, + max=1, + precision=2, + ), + ] + + if completion_model == LLMMode.CHAT.value: + model_parameters_rules.append( + ParameterRule( + name=DefaultParameterName.FREQUENCY_PENALTY.value, + label=I18nObject(en_US="Frequency Penalty", zh_Hans="频率惩罚"), + type=ParameterType.FLOAT, + default=0.5, + min=-2, + max=2, + ) + ) + model_parameters_rules.append( + ParameterRule( + name=DefaultParameterName.PRESENCE_PENALTY.value, + label=I18nObject(en_US="Presence Penalty", zh_Hans="存在惩罚"), + type=ParameterType.FLOAT, + default=0.3, + min=-2, + max=2, + ) + ) + model_parameters_rules.append( + ParameterRule( + name=DefaultParameterName.MAX_TOKENS.value, + label=I18nObject(en_US="Max Tokens", zh_Hans="最大标记"), + type=ParameterType.INT, + default=4096, + min=1, + max=128000, + ) + ) + else: + model_parameters_rules.append( + ParameterRule( + name="max_new_tokens", + label=I18nObject(en_US="Max New Tokens", zh_Hans="最大新令牌数"), + type=ParameterType.INT, + default=4096, + min=1, + max=128000, + ) + ) + model_parameters_rules.append( + ParameterRule( + name="min_new_tokens", + label=I18nObject(en_US="Min New Tokens", zh_Hans="最小新标记数量"), + type=ParameterType.INT, + default=0, + min=0, + ) + ) + model_parameters_rules.append( + ParameterRule( + name="repetition_penalty", + label=I18nObject(en_US="Repetition Penalty", zh_Hans="重复惩罚"), + type=ParameterType.FLOAT, + default=float(1), + min=1, + max=2, + precision=2, + ) + ) + model_parameters_rules.append( + ParameterRule( + name=DefaultParameterName.TOP_K.value, + label=I18nObject(en_US="Top K", zh_Hans="顶部 K"), + type=ParameterType.INT, + default=50, + min=1, + max=100, + ) + ) + + entity = AIModelEntity( + model=model, + label=I18nObject(zh_Hans=model, en_US=model), + model_type=ModelType.LLM, + features=list(features), + fetch_from=FetchFrom.CUSTOMIZABLE_MODEL, + model_properties=dict(model_properties), + parameter_rules=list(model_parameters_rules), + ) + + return entity + + def _generate( + self, + model: str, + credentials: dict, + prompt_messages: list[PromptMessage], + model_parameters: dict, + stop: Optional[list[str]] = None, + stream: bool = True, + user: Optional[str] = None, + ) -> Union[LLMResult, Generator]: + """ + Generate llm model + + :param model: model name + :param credentials: credentials + :param prompt_messages: prompt messages + :param model_parameters: model parameters + :param stop: stop words + :param stream: is stream response + :param user: unique user id + :return: full response or stream response chunk generator result + """ + + # initialize credentials, client and model_inference + ibmCredentials = Credentials( + url=credentials.get("base_url"), + api_key=credentials.get("api_key"), + ) + client = APIClient(ibmCredentials, project_id=credentials.get("project_id")) + model_inference = ModelInference(model_id=model, api_client=client) + + params = TextGenParameters(**model_parameters) + if stop: + params.stop_sequences = stop + + if stream: + response = model_inference.generate_text_stream( + prompt=prompt_messages[0].content, params=params, raw_response=True + ) + + return self._handle_generate_stream_response(model, credentials, response, prompt_messages) + else: + response = model_inference.generate_text( + prompt=prompt_messages[0].content, params=params, raw_response=True + ) + + return self._handle_generate_response(model, credentials, response, prompt_messages) + + def _handle_generate_response( + self, model: str, credentials: dict, response: Any, prompt_messages: list[PromptMessage] + ) -> LLMResult: + """ + Handle llm generate response + + :param model: model name + :param credentials: credentials + :param response: response + :param prompt_messages: prompt messages + :return: llm response + """ + resultsList = response.get("results") + if not resultsList: + raise InvokeBadRequestError(f"Invalid response structure: missing or empty 'results': {response}") + results = resultsList[0] + + # get assistant_text and transform it to prompt message + assistant_text = results.get("generated_text") + assistant_prompt_message = AssistantPromptMessage(content=assistant_text) + + # calculate tokens and usage + prompt_tokens = results.get("input_token_count") + if prompt_tokens is None: + prompt_tokens = self._num_tokens_from_messages(model, credentials, prompt_messages) + completion_tokens = results.get("generated_token_count") + if completion_tokens is None: + completion_tokens = self._num_tokens_from_messages(model, credentials, [assistant_prompt_message]) + usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens) + + return LLMResult(model=model, prompt_messages=prompt_messages, message=assistant_prompt_message, usage=usage) + + def _handle_generate_stream_response( + self, + model: str, + credentials: dict, + response: Generator[Any], + prompt_messages: list[PromptMessage], + ) -> Generator: + """ + Handle llm generate stream response + + :param model: model name + :param credentials: credentials + :param response: response + :param prompt_messages: prompt messages + :return: llm response chunk generator + """ + chunk_index = 0 + full_assistant_content = "" + + for chunk in response: + resultsList = chunk.get("results") + if not resultsList: + raise InvokeBadRequestError(f"Invalid chunk structure: missing or empty 'results': {chunk}") + results = resultsList[0] + + finish_reason = results.get("stop_reason") + + if finish_reason == "not_finished": + # Process chunk's generated_text + generated_text = results.get("generated_text") + + if not generated_text: # Skip chunks with empty or None generated_text + continue + + # Create assistant message and append content + assistant_prompt_message = AssistantPromptMessage(content=generated_text) + full_assistant_content += generated_text + + yield LLMResultChunk( + model=model, + prompt_messages=prompt_messages, + delta=LLMResultChunkDelta( + index=chunk_index, + message=assistant_prompt_message, + ), + ) + + chunk_index += 1 + continue # Skip further processing for already handled chunk + + else: + # calculate tokens and usage + prompt_tokens = results.get("input_token_count") + if prompt_tokens is None: + prompt_tokens = self._num_tokens_from_messages(model, credentials, prompt_messages) + completion_tokens = results.get("generated_token_count") + if completion_tokens is None: + completion_tokens = self._num_tokens_from_messages( + model, credentials, [AssistantPromptMessage(content=full_assistant_content)] + ) + usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens) + + yield LLMResultChunk( + model=model, + prompt_messages=prompt_messages, + delta=LLMResultChunkDelta( + index=chunk_index, + message=AssistantPromptMessage(content=""), + finish_reason=finish_reason, + usage=usage, + ), + ) + break + + def _chat_generate( + 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 llm chat model + + :param model: model name + :param credentials: 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 + """ + + # initialize credentials, client and model_inference + ibmCredentials = Credentials( + url=credentials.get("base_url"), + api_key=credentials.get("api_key"), + ) + client = APIClient(ibmCredentials, project_id=credentials.get("project_id")) + model_inference = ModelInference(model_id=model, api_client=client) + + params = TextChatParameters(**model_parameters) + + messages = [self._convert_prompt_message_to_dict(m, credentials) for m in prompt_messages] + # Filter messages: IBM LLM invocation allow at most one image per request + messages = self.filter_prompt_image_messages(messages) + + function_calling_type = credentials.get("function_calling_type", "no_call") + formatted_tools = None + if tools and function_calling_type == "tool_call": + formatted_tools = [] + for tool in tools: + formatted_tools.append(helper.dump_model(PromptMessageFunction(function=tool))) + + if stream: + response = model_inference.chat_stream( + messages=messages, + params=model_parameters, + tools=formatted_tools, + ) + + return self._handle_chat_generate_stream_response(model, credentials, response, prompt_messages) + else: + response = model_inference.chat( + messages=messages, + params=model_parameters, + tools=formatted_tools, + ) + + return self._handle_chat_generate_response(model, credentials, response, prompt_messages) + + def _handle_chat_generate_response( + self, model: str, credentials: dict, response: dict, prompt_messages: list[PromptMessage] + ) -> LLMResult: + """ + Handle llm chat response + + :param model: model name + :param credentials: credentials + :param response: response + :param prompt_messages: prompt messages + :return: LLMResult - llm response + """ + + function_calling_type = credentials.get("function_calling_type", "no_call") + + output = response["choices"][0] + message_id = response.get("id") + + response_content = output.get("message", {}).get("content", None) + + tool_calls = None + if function_calling_type == "tool_call": + tool_calls = output.get("message", {}).get("tool_calls") + + assistant_message = AssistantPromptMessage(content=response_content, tool_calls=[]) + + if tool_calls and function_calling_type == "tool_call": + assistant_message.tool_calls = self._extract_response_tool_calls(tool_calls) + + # calculate tokens and usage + if response.get("usage"): + prompt_tokens = response["usage"]["prompt_tokens"] + completion_tokens = response["usage"]["completion_tokens"] + else: + prompt_tokens = self._num_tokens_from_string(model, prompt_messages[0].content) + completion_tokens = self._num_tokens_from_string(model, assistant_message.content) + usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens) + + return LLMResult( + id=message_id, model=model, prompt_messages=prompt_messages, message=assistant_message, usage=usage + ) + + def _handle_chat_generate_stream_response( + self, model: str, credentials: dict, response: Generator, prompt_messages: list[PromptMessage] + ) -> Generator: + """ + Handle llm stream response + + :param model: model name + :param credentials: model credentials + :param response: streamed response + :param prompt_messages: prompt messages + :return: llm response chunk generator + """ + chunk_index = 0 + full_assistant_content = "" + + def create_final_llm_result_chunk( + id: Optional[str], index: int, message: AssistantPromptMessage, finish_reason: str, usage: dict + ) -> LLMResultChunk: + # calculate tokens and usage + prompt_tokens = usage and usage.get("prompt_tokens") + if prompt_tokens is None: + prompt_tokens = self._num_tokens_from_string(model, prompt_messages[0].content) + completion_tokens = usage and usage.get("completion_tokens") + if completion_tokens is None: + completion_tokens = self._num_tokens_from_string(model, full_assistant_content) + usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens) + + return LLMResultChunk( + id=id, + model=model, + prompt_messages=prompt_messages, + delta=LLMResultChunkDelta(index=index, message=message, finish_reason=finish_reason, usage=usage), + ) + + tools_calls: list[AssistantPromptMessage.ToolCall] = [] + + def increase_tool_call(new_tool_calls: list[AssistantPromptMessage.ToolCall]): + def get_tool_call(tool_call_id: str): + if not tool_call_id: + return tools_calls[-1] + + tool_call = next((tool_call for tool_call in tools_calls if tool_call.id == tool_call_id), None) + if tool_call is None: + tool_call = AssistantPromptMessage.ToolCall( + id=tool_call_id, + type="function", + function=AssistantPromptMessage.ToolCall.ToolCallFunction(name="", arguments=""), + ) + tools_calls.append(tool_call) + + return tool_call + + for new_tool_call in new_tool_calls: + # get tool call + tool_call = get_tool_call(new_tool_call.function.name) + # update tool call + if new_tool_call.id: + tool_call.id = new_tool_call.id + if new_tool_call.type: + tool_call.type = new_tool_call.type + if new_tool_call.function.name: + tool_call.function.name = new_tool_call.function.name + if new_tool_call.function.arguments: + tool_call.function.arguments += new_tool_call.function.arguments + + finish_reason = None # The default value of finish_reason is None + message_id, usage = None, None + + for chunk in response: + if chunk: + if chunk: + if u := chunk.get("usage"): + usage = u + if not chunk or len(chunk["choices"]) == 0: + continue + + choice = chunk["choices"][0] + finish_reason = chunk["choices"][0].get("finish_reason") + message_id = chunk.get("id") + chunk_index += 1 + + if "delta" in choice: + delta = choice["delta"] + delta_content = delta.get("content") + + assistant_message_tool_calls = None + + if "tool_calls" in delta and credentials.get("function_calling_type", "no_call") == "tool_call": + assistant_message_tool_calls = delta.get("tool_calls", None) + + # extract tool calls from response + if assistant_message_tool_calls: + tool_calls = self._extract_response_tool_calls(assistant_message_tool_calls) + increase_tool_call(tool_calls) + + if delta_content is None or delta_content == "": + continue + + # transform assistant message to prompt message + assistant_prompt_message = AssistantPromptMessage( + content=delta_content, + ) + + # reset tool calls + tool_calls = [] + full_assistant_content += delta_content + elif "text" in choice: + choice_text = choice.get("text", "") + if choice_text == "": + continue + + # transform assistant message to prompt message + assistant_prompt_message = AssistantPromptMessage(content=choice_text) + full_assistant_content += choice_text + else: + continue + + yield LLMResultChunk( + id=message_id, + model=model, + prompt_messages=prompt_messages, + delta=LLMResultChunkDelta( + index=chunk_index, + message=assistant_prompt_message, + ), + ) + + chunk_index += 1 + + if tools_calls: + yield LLMResultChunk( + id=message_id, + model=model, + prompt_messages=prompt_messages, + delta=LLMResultChunkDelta( + index=chunk_index, + message=AssistantPromptMessage(tool_calls=tools_calls, content=""), + ), + ) + + yield create_final_llm_result_chunk( + id=message_id, + index=chunk_index, + message=AssistantPromptMessage(content=""), + finish_reason=finish_reason, + usage=usage, + ) + + def _convert_prompt_message_to_dict(self, message: PromptMessage, credentials: Optional[dict] = None) -> dict: + """ + Convert PromptMessage to dict for OpenAI API format + """ + if isinstance(message, UserPromptMessage): + message = cast(UserPromptMessage, message) + if isinstance(message.content, str): + message_dict = {"role": "user", "content": message.content} + else: + sub_messages = [] + for message_content in message.content: + if message_content.type == PromptMessageContentType.TEXT: + message_content = cast(PromptMessageContent, message_content) + sub_message_dict = {"type": "text", "text": message_content.data} + sub_messages.append(sub_message_dict) + elif message_content.type == PromptMessageContentType.IMAGE: + message_content = cast(ImagePromptMessageContent, message_content) + sub_message_dict = { + "type": "image_url", + "image_url": {"url": message_content.data, "detail": message_content.detail.value}, + } + sub_messages.append(sub_message_dict) + + message_dict = {"role": "user", "content": sub_messages} + elif isinstance(message, AssistantPromptMessage): + message = cast(AssistantPromptMessage, message) + message_dict = {"role": "assistant", "content": message.content} + if message.tool_calls: + function_calling_type = credentials.get("function_calling_type", "no_call") + if function_calling_type == "tool_call": + message_dict["tool_calls"] = [tool_call.dict() for tool_call in message.tool_calls] + elif function_calling_type == "function_call": + function_call = message.tool_calls[0] + message_dict["function_call"] = { + "name": function_call.function.name, + "arguments": function_call.function.arguments, + } + elif isinstance(message, SystemPromptMessage): + message = cast(SystemPromptMessage, message) + message_dict = {"role": "system", "content": message.content} + elif isinstance(message, ToolPromptMessage): + message = cast(ToolPromptMessage, message) + function_calling_type = credentials.get("function_calling_type", "no_call") + if function_calling_type == "tool_call": + message_dict = {"role": "tool", "content": message.content, "tool_call_id": message.tool_call_id} + elif function_calling_type == "function_call": + message_dict = {"role": "function", "content": message.content, "name": message.tool_call_id} + else: + raise ValueError(f"Got unknown type {message}") + + if message.name and message_dict.get("role", "") != "tool": + message_dict["name"] = message.name + + return message_dict + + def _num_tokens_from_string( + self, model: str, text: Union[str, list[PromptMessageContent]], tools: Optional[list[PromptMessageTool]] = None + ) -> int: + """ + Approximate num tokens for model with gpt2 tokenizer. + + :param model: model name + :param text: prompt text + :param tools: tools for tool calling + :return: number of tokens + """ + if isinstance(text, str): + full_text = text + else: + full_text = "" + for message_content in text: + if message_content.type == PromptMessageContentType.TEXT: + message_content = cast(PromptMessageContent, message_content) + full_text += message_content.data + + num_tokens = self._get_num_tokens_by_gpt2(full_text) + + if tools: + num_tokens += self._num_tokens_for_tools(tools) + + return num_tokens + + def _num_tokens_from_messages( + self, + model: str, + messages: list[PromptMessage], + tools: Optional[list[PromptMessageTool]] = None, + credentials: Optional[dict] = None, + ) -> int: + """ + Approximate num tokens with GPT2 tokenizer. + """ + + tokens_per_message = 3 + tokens_per_name = 1 + + num_tokens = 0 + messages_dict = [self._convert_prompt_message_to_dict(m, credentials) for m in messages] + for message in messages_dict: + num_tokens += tokens_per_message + for key, value in message.items(): + # Cast str(value) in case the message value is not a string + # This occurs with function messages + # TODO: The current token calculation method for the image type is not implemented, + # which need to download the image and then get the resolution for calculation, + # and will increase the request delay + if isinstance(value, list): + text = "" + for item in value: + if isinstance(item, dict) and item["type"] == "text": + text += item["text"] + + value = text + + if key == "tool_calls": + for tool_call in value: + for t_key, t_value in tool_call.items(): + num_tokens += self._get_num_tokens_by_gpt2(t_key) + if t_key == "function": + for f_key, f_value in t_value.items(): + num_tokens += self._get_num_tokens_by_gpt2(f_key) + num_tokens += self._get_num_tokens_by_gpt2(f_value) + else: + num_tokens += self._get_num_tokens_by_gpt2(t_key) + num_tokens += self._get_num_tokens_by_gpt2(t_value) + else: + num_tokens += self._get_num_tokens_by_gpt2(str(value)) + + if key == "name": + num_tokens += tokens_per_name + + # every reply is primed with assistant + num_tokens += 3 + + if tools: + num_tokens += self._num_tokens_for_tools(tools) + + return num_tokens + + def _num_tokens_for_tools(self, tools: list[PromptMessageTool]) -> int: + """ + Calculate num tokens for tool calling with tiktoken package. + + :param tools: tools for tool calling + :return: number of tokens + """ + num_tokens = 0 + for tool in tools: + num_tokens += self._get_num_tokens_by_gpt2("type") + num_tokens += self._get_num_tokens_by_gpt2("function") + num_tokens += self._get_num_tokens_by_gpt2("function") + + # calculate num tokens for function object + num_tokens += self._get_num_tokens_by_gpt2("name") + num_tokens += self._get_num_tokens_by_gpt2(tool.name) + num_tokens += self._get_num_tokens_by_gpt2("description") + num_tokens += self._get_num_tokens_by_gpt2(tool.description) + parameters = tool.parameters + num_tokens += self._get_num_tokens_by_gpt2("parameters") + if "title" in parameters: + num_tokens += self._get_num_tokens_by_gpt2("title") + num_tokens += self._get_num_tokens_by_gpt2(parameters.get("title")) + num_tokens += self._get_num_tokens_by_gpt2("type") + num_tokens += self._get_num_tokens_by_gpt2(parameters.get("type")) + if "properties" in parameters: + num_tokens += self._get_num_tokens_by_gpt2("properties") + for key, value in parameters.get("properties").items(): + num_tokens += self._get_num_tokens_by_gpt2(key) + for field_key, field_value in value.items(): + num_tokens += self._get_num_tokens_by_gpt2(field_key) + if field_key == "enum": + for enum_field in field_value: + num_tokens += 3 + num_tokens += self._get_num_tokens_by_gpt2(enum_field) + else: + num_tokens += self._get_num_tokens_by_gpt2(field_key) + num_tokens += self._get_num_tokens_by_gpt2(str(field_value)) + if "required" in parameters: + num_tokens += self._get_num_tokens_by_gpt2("required") + for required_field in parameters["required"]: + num_tokens += 3 + num_tokens += self._get_num_tokens_by_gpt2(required_field) + + return num_tokens + + def _extract_response_tool_calls(self, response_tool_calls: list[dict]) -> list[AssistantPromptMessage.ToolCall]: + """ + Extract tool calls from response + + :param response_tool_calls: response tool calls + :return: list of tool calls + """ + tool_calls = [] + if response_tool_calls: + for response_tool_call in response_tool_calls: + function = AssistantPromptMessage.ToolCall.ToolCallFunction( + name=response_tool_call.get("function", {}).get("name", ""), + arguments=response_tool_call.get("function", {}).get("arguments", ""), + ) + + tool_call = AssistantPromptMessage.ToolCall( + id=response_tool_call.get("id", ""), type=response_tool_call.get("type", ""), function=function + ) + tool_calls.append(tool_call) + + return tool_calls + + def filter_prompt_image_messages(self, messages: list[dict]) -> list[dict]: + prompt_user_messages_with_images = [ + message + for message in messages + if message["role"] == "user" + and not isinstance(message["content"], str) + and any(content.get("type") == "image_url" for content in message.get("content", [])) + ] + + if prompt_user_messages_with_images: + last_prompt_user_message_with_image = prompt_user_messages_with_images[-1] + messages = [ + message + for message in messages + if not ( + message["role"] == "user" + and not isinstance(message["content"], str) + and any(content.get("type") == "image_url" for content in message.get("content", [])) + ) + ] + messages.append(last_prompt_user_message_with_image) + + return messages + + @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 + """ + return { + Exception: [], + InvokeAuthorizationError: [ + requests.exceptions.InvalidHeader, # Missing or Invalid API Key + ], + InvokeBadRequestError: [ + requests.exceptions.HTTPError, # Invalid Endpoint URL or model name + requests.exceptions.InvalidURL, # Misconfigured request or other API error + ], + InvokeRateLimitError: [ + requests.exceptions.RetryError # Too many requests sent in a short period of time + ], + InvokeServerUnavailableError: [ + requests.exceptions.ConnectionError, # Engine Overloaded + requests.exceptions.HTTPError, # Server Error + ], + InvokeConnectionError: [ + requests.exceptions.ConnectTimeout, # Timeout + requests.exceptions.ReadTimeout, # Timeout + ], + } diff --git a/api/poetry.lock b/api/poetry.lock index 958673a00bf947..f35d0143ca5ca7 100644 --- a/api/poetry.lock +++ b/api/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. 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