feat: enhance gemini models

api/core/file/file_manager.py

@@ -50,12 +50,12 @@ def to_prompt_message_content(
             else:
                 data = _to_base64_data_string(f)
 
-            return ImagePromptMessageContent(data=data, detail=image_detail_config)
+            return ImagePromptMessageContent(data=data, detail=image_detail_config, format=f.extension.lstrip("."))
         case FileType.AUDIO:
-            encoded_string = _get_encoded_string(f)
+            data = _to_base64_data_string(f)
             if f.extension is None:
                 raise ValueError("Missing file extension")
-            return AudioPromptMessageContent(data=encoded_string, format=f.extension.lstrip("."))
+            return AudioPromptMessageContent(data=data, format=f.extension.lstrip("."))
         case FileType.VIDEO:
             if dify_config.MULTIMODAL_SEND_VIDEO_FORMAT == "url":
                 data = _to_url(f)
@@ -65,14 +65,8 @@ def to_prompt_message_content(
                 raise ValueError("Missing file extension")
             return VideoPromptMessageContent(data=data, format=f.extension.lstrip("."))
         case FileType.DOCUMENT:
-            data = _get_encoded_string(f)
-            if f.mime_type is None:
-                raise ValueError("Missing file mime_type")
-            return DocumentPromptMessageContent(
-                encode_format="base64",
-                mime_type=f.mime_type,
-                data=data,
-            )
+            data = _to_base64_data_string(f)
+            return DocumentPromptMessageContent(encode_format="base64", data=data, format=f.extension.lstrip("."))
         case _:
             raise ValueError(f"file type {f.type} is not supported")
 

api/core/model_runtime/entities/message_entities.py

@@ -101,13 +101,14 @@ class DETAIL(StrEnum):
 
     type: PromptMessageContentType = PromptMessageContentType.IMAGE
     detail: DETAIL = DETAIL.LOW
+    format: str = Field("jpg", description="Image format")
 
 
 class DocumentPromptMessageContent(PromptMessageContent):
     type: PromptMessageContentType = PromptMessageContentType.DOCUMENT
     encode_format: Literal["base64"]
-    mime_type: str
     data: str
+    format: str = Field(..., description="Document format")
 
 
 class PromptMessage(ABC, BaseModel):

api/core/model_runtime/model_providers/anthropic/llm/llm.py

@@ -526,17 +526,19 @@ def _convert_prompt_messages(self, prompt_messages: Sequence[PromptMessage]) ->
                                 }
                                 sub_messages.append(sub_message_dict)
                             elif isinstance(message_content, DocumentPromptMessageContent):
-                                if message_content.mime_type != "application/pdf":
+                                data_split = message_content.data.split(";base64,")
+                                mime_type = data_split[0].replace("data:", "")
+                                base64_data = data_split[1]
+                                if mime_type != "application/pdf":
                                     raise ValueError(
-                                        f"Unsupported document type {message_content.mime_type}, "
-                                        "only support application/pdf"
+                                        f"Unsupported document type {mime_type}, " "only support application/pdf"
                                     )
                                 sub_message_dict = {
                                     "type": "document",
                                     "source": {
                                         "type": message_content.encode_format,
-                                        "media_type": message_content.mime_type,
-                                        "data": message_content.data,
+                                        "media_type": mime_type,
+                                        "data": base64_data,
                                     },
                                 }
                                 sub_messages.append(sub_message_dict)

api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-001.yaml

@@ -8,6 +8,8 @@ features:
   - tool-call
   - stream-tool-call
   - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 1048576

api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-002.yaml

@@ -8,6 +8,8 @@ features:
   - tool-call
   - stream-tool-call
   - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 1048576

api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-8b-exp-0827.yaml

@@ -8,6 +8,8 @@ features:
   - tool-call
   - stream-tool-call
   - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 1048576

api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-8b-exp-0924.yaml

@@ -8,6 +8,8 @@ features:
   - tool-call
   - stream-tool-call
   - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 1048576

api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-exp-0827.yaml

@@ -8,6 +8,8 @@ features:
   - tool-call
   - stream-tool-call
   - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 1048576

api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-latest.yaml

@@ -8,6 +8,8 @@ features:
   - tool-call
   - stream-tool-call
   - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 1048576

api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash.yaml

@@ -8,6 +8,8 @@ features:
   - tool-call
   - stream-tool-call
   - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 1048576

api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-001.yaml

@@ -8,6 +8,8 @@ features:
   - tool-call
   - stream-tool-call
   - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 2097152

api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-002.yaml

@@ -8,6 +8,8 @@ features:
   - tool-call
   - stream-tool-call
   - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 2097152

api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-exp-0801.yaml

@@ -8,6 +8,8 @@ features:
   - tool-call
   - stream-tool-call
   - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 2097152

api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-exp-0827.yaml

@@ -8,6 +8,8 @@ features:
   - tool-call
   - stream-tool-call
   - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 2097152

api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-latest.yaml

@@ -8,6 +8,8 @@ features:
   - tool-call
   - stream-tool-call
   - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 2097152

api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro.yaml

@@ -8,6 +8,8 @@ features:
   - tool-call
   - stream-tool-call
   - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 2097152

api/core/model_runtime/model_providers/google/llm/gemini-exp-1114.yaml

@@ -8,6 +8,8 @@ features:
   - tool-call
   - stream-tool-call
   - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 32767

api/core/model_runtime/model_providers/google/llm/gemini-exp-1121.yaml

@@ -7,6 +7,9 @@ features:
   - vision
   - tool-call
   - stream-tool-call
+  - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 32767

api/core/model_runtime/model_providers/google/llm/llm.py

@@ -1,29 +1,30 @@
 import base64
-import io
 import json
+import os
+import tempfile
+import time
 from collections.abc import Generator
-from typing import Optional, Union, cast
+from typing import Optional, Union
 
 import google.ai.generativelanguage as glm
 import google.generativeai as genai
 import requests
 from google.api_core import exceptions
-from google.generativeai.client import _ClientManager
-from google.generativeai.types import ContentType, GenerateContentResponse
+from google.generativeai.types import ContentType, File, GenerateContentResponse
 from google.generativeai.types.content_types import to_part
-from PIL import Image
 
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta
 from core.model_runtime.entities.message_entities import (
     AssistantPromptMessage,
-    DocumentPromptMessageContent,
     ImagePromptMessageContent,
     PromptMessage,
+    PromptMessageContent,
     PromptMessageContentType,
     PromptMessageTool,
     SystemPromptMessage,
     ToolPromptMessage,
     UserPromptMessage,
+    VideoPromptMessageContent,
 )
 from core.model_runtime.errors.invoke import (
     InvokeAuthorizationError,
@@ -35,21 +36,7 @@
 )
 from core.model_runtime.errors.validate import CredentialsValidateFailedError
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
-
-GOOGLE_AVAILABLE_MIMETYPE = [
-    "application/pdf",
-    "application/x-javascript",
-    "text/javascript",
-    "application/x-python",
-    "text/x-python",
-    "text/plain",
-    "text/html",
-    "text/css",
-    "text/md",
-    "text/csv",
-    "text/xml",
-    "text/rtf",
-]
+from extensions.ext_redis import redis_client
 
 
 class GoogleLargeLanguageModel(LargeLanguageModel):
@@ -201,29 +188,17 @@ def _generate(
         if stop:
             config_kwargs["stop_sequences"] = stop
 
+        genai.configure(api_key=credentials["google_api_key"])
         google_model = genai.GenerativeModel(model_name=model)
 
         history = []
 
-        # hack for gemini-pro-vision, which currently does not support multi-turn chat
-        if model == "gemini-pro-vision":
-            last_msg = prompt_messages[-1]
-            content = self._format_message_to_glm_content(last_msg)
-            history.append(content)
-        else:
-            for msg in prompt_messages:  # makes message roles strictly alternating
-                content = self._format_message_to_glm_content(msg)
-                if history and history[-1]["role"] == content["role"]:
-                    history[-1]["parts"].extend(content["parts"])
-                else:
-                    history.append(content)
-
-        # Create a new ClientManager with tenant's API key
-        new_client_manager = _ClientManager()
-        new_client_manager.configure(api_key=credentials["google_api_key"])
-        new_custom_client = new_client_manager.make_client("generative")
-
-        google_model._client = new_custom_client
+        for msg in prompt_messages:  # makes message roles strictly alternating
+            content = self._format_message_to_glm_content(msg)
+            if history and history[-1]["role"] == content["role"]:
+                history[-1]["parts"].extend(content["parts"])
+            else:
+                history.append(content)
 
         response = google_model.generate_content(
             contents=history,
@@ -346,7 +321,7 @@ def _convert_one_message_to_text(self, message: PromptMessage) -> str:
 
         content = message.content
         if isinstance(content, list):
-            content = "".join(c.data for c in content if c.type != PromptMessageContentType.IMAGE)
+            content = "".join(c.data for c in content if c.type == PromptMessageContentType.TEXT)
 
         if isinstance(message, UserPromptMessage):
             message_text = f"{human_prompt} {content}"
@@ -359,6 +334,44 @@ def _convert_one_message_to_text(self, message: PromptMessage) -> str:
 
         return message_text
 
+    def _upload_file_content_to_google(self, message_content: PromptMessageContent) -> File:
+        key = f"{message_content.type.value}:{hash(message_content.data)}"
+        if redis_client.exists(key):
+            try:
+                return genai.get_file(redis_client.get(key).decode())
+            except:
+                pass
+        with tempfile.NamedTemporaryFile(delete=False) as temp_file:
+            if message_content.data.startswith("data:"):
+                metadata, base64_data = message_content.data.split(",", 1)
+                file_content = base64.b64decode(base64_data)
+                mime_type = metadata.split(";", 1)[0].split(":")[1]
+                temp_file.write(file_content)
+            else:
+                # only ImagePromptMessageContent and VideoPromptMessageContent has url
+                try:
+                    response = requests.get(message_content.data)
+                    response.raise_for_status()
+                    if message_content.type is ImagePromptMessageContent:
+                        prefix = "image/"
+                    elif message_content.type is VideoPromptMessageContent:
+                        prefix = "video/"
+                    mime_type = prefix + message_content.format
+                    temp_file.write(response.content)
+                except Exception as ex:
+                    raise ValueError(f"Failed to fetch data from url {message_content.data}, {ex}")
+            temp_file.flush()
+        try:
+            file = genai.upload_file(path=temp_file.name, mime_type=mime_type)
+            while file.state.name == "PROCESSING":
+                time.sleep(5)
+                file = genai.get_file(file.name)
+            # google will delete your upload files in 2 days.
+            redis_client.setex(key, 47 * 60 * 60, file.name)
+            return file
+        finally:
+            os.unlink(temp_file.name)
+
     def _format_message_to_glm_content(self, message: PromptMessage) -> ContentType:
         """
         Format a single message into glm.Content for Google API
@@ -374,28 +387,8 @@ def _format_message_to_glm_content(self, message: PromptMessage) -> ContentType:
                 for c in message.content:
                     if c.type == PromptMessageContentType.TEXT:
                         glm_content["parts"].append(to_part(c.data))
-                    elif c.type == PromptMessageContentType.IMAGE:
-                        message_content = cast(ImagePromptMessageContent, c)
-                        if message_content.data.startswith("data:"):
-                            metadata, base64_data = c.data.split(",", 1)
-                            mime_type = metadata.split(";", 1)[0].split(":")[1]
-                        else:
-                            # fetch image data from url
-                            try:
-                                image_content = requests.get(message_content.data).content
-                                with Image.open(io.BytesIO(image_content)) as img:
-                                    mime_type = f"image/{img.format.lower()}"
-                                base64_data = base64.b64encode(image_content).decode("utf-8")
-                            except Exception as ex:
-                                raise ValueError(f"Failed to fetch image data from url {message_content.data}, {ex}")
-                        blob = {"inline_data": {"mime_type": mime_type, "data": base64_data}}
-                        glm_content["parts"].append(blob)
-                    elif c.type == PromptMessageContentType.DOCUMENT:
-                        message_content = cast(DocumentPromptMessageContent, c)
-                        if message_content.mime_type not in GOOGLE_AVAILABLE_MIMETYPE:
-                            raise ValueError(f"Unsupported mime type {message_content.mime_type}")
-                        blob = {"inline_data": {"mime_type": message_content.mime_type, "data": message_content.data}}
-                        glm_content["parts"].append(blob)
+                    else:
+                        glm_content["parts"].append(self._upload_file_content_to_google(c))
 
             return glm_content
         elif isinstance(message, AssistantPromptMessage):