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https://gitee.com/wanwujie/deer-flow
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feat: add view_image tool and optimize web fetch tools
Add image viewing capability for vision-enabled models with ViewImageMiddleware and view_image_tool. Limit web_fetch tool output to 4096 characters to prevent excessive content. Update model config to support vision capability flag. Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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hetao
@@ -7,6 +7,7 @@ from src.agents.middlewares.clarification_middleware import ClarificationMiddlew
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from src.agents.middlewares.thread_data_middleware import ThreadDataMiddleware
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from src.agents.middlewares.title_middleware import TitleMiddleware
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from src.agents.middlewares.uploads_middleware import UploadsMiddleware
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from src.agents.middlewares.view_image_middleware import ViewImageMiddleware
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from src.agents.thread_state import ThreadState
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from src.config.summarization_config import get_summarization_config
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from src.models import create_chat_model
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@@ -174,6 +175,7 @@ Being proactive with task management demonstrates thoroughness and ensures all r
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# UploadsMiddleware should be after ThreadDataMiddleware to access thread_id
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# SummarizationMiddleware should be early to reduce context before other processing
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# TodoListMiddleware should be before ClarificationMiddleware to allow todo management
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# ViewImageMiddleware should be before ClarificationMiddleware to inject image details before LLM
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# ClarificationMiddleware should be last to intercept clarification requests after model calls
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def _build_middlewares(config: RunnableConfig):
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"""Build middleware chain based on runtime configuration.
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@@ -197,7 +199,24 @@ def _build_middlewares(config: RunnableConfig):
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if todo_list_middleware is not None:
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middlewares.append(todo_list_middleware)
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middlewares.extend([TitleMiddleware(), ClarificationMiddleware()])
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# Add TitleMiddleware
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middlewares.append(TitleMiddleware())
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# Add ViewImageMiddleware only if the current model supports vision
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model_name = config.get("configurable", {}).get("model_name") or config.get("configurable", {}).get("model")
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from src.config import get_app_config
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app_config = get_app_config()
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# If no model_name specified, use the first model (default)
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if model_name is None and app_config.models:
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model_name = app_config.models[0].name
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model_config = app_config.get_model_config(model_name) if model_name else None
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if model_config is not None and model_config.supports_vision:
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middlewares.append(ViewImageMiddleware())
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# ClarificationMiddleware should always be last
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middlewares.append(ClarificationMiddleware())
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return middlewares
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221
backend/src/agents/middlewares/view_image_middleware.py
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221
backend/src/agents/middlewares/view_image_middleware.py
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@@ -0,0 +1,221 @@
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"""Middleware for injecting image details into conversation before LLM call."""
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from typing import NotRequired, override
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from langchain.agents import AgentState
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from langchain.agents.middleware import AgentMiddleware
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from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
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from langgraph.runtime import Runtime
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from src.agents.thread_state import ViewedImageData
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class ViewImageMiddlewareState(AgentState):
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"""Compatible with the `ThreadState` schema."""
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viewed_images: NotRequired[dict[str, ViewedImageData] | None]
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class ViewImageMiddleware(AgentMiddleware[ViewImageMiddlewareState]):
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"""Injects image details as a human message before LLM calls when view_image tools have completed.
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This middleware:
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1. Runs before each LLM call
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2. Checks if the last assistant message contains view_image tool calls
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3. Verifies all tool calls in that message have been completed (have corresponding ToolMessages)
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4. If conditions are met, creates a human message with all viewed image details (including base64 data)
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5. Adds the message to state so the LLM can see and analyze the images
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This enables the LLM to automatically receive and analyze images that were loaded via view_image tool,
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without requiring explicit user prompts to describe the images.
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"""
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state_schema = ViewImageMiddlewareState
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def _get_last_assistant_message(self, messages: list) -> AIMessage | None:
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"""Get the last assistant message from the message list.
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Args:
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messages: List of messages
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Returns:
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Last AIMessage or None if not found
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"""
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for msg in reversed(messages):
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if isinstance(msg, AIMessage):
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return msg
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return None
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def _has_view_image_tool(self, message: AIMessage) -> bool:
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"""Check if the assistant message contains view_image tool calls.
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Args:
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message: Assistant message to check
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Returns:
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True if message contains view_image tool calls
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"""
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if not hasattr(message, "tool_calls") or not message.tool_calls:
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return False
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return any(tool_call.get("name") == "view_image" for tool_call in message.tool_calls)
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def _all_tools_completed(self, messages: list, assistant_msg: AIMessage) -> bool:
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"""Check if all tool calls in the assistant message have been completed.
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Args:
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messages: List of all messages
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assistant_msg: The assistant message containing tool calls
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Returns:
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True if all tool calls have corresponding ToolMessages
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"""
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if not hasattr(assistant_msg, "tool_calls") or not assistant_msg.tool_calls:
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return False
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# Get all tool call IDs from the assistant message
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tool_call_ids = {tool_call.get("id") for tool_call in assistant_msg.tool_calls if tool_call.get("id")}
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# Find the index of the assistant message
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try:
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assistant_idx = messages.index(assistant_msg)
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except ValueError:
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return False
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# Get all ToolMessages after the assistant message
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completed_tool_ids = set()
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for msg in messages[assistant_idx + 1 :]:
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if isinstance(msg, ToolMessage) and msg.tool_call_id:
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completed_tool_ids.add(msg.tool_call_id)
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# Check if all tool calls have been completed
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return tool_call_ids.issubset(completed_tool_ids)
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def _create_image_details_message(self, state: ViewImageMiddlewareState) -> list[str | dict]:
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"""Create a formatted message with all viewed image details.
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Args:
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state: Current state containing viewed_images
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Returns:
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List of content blocks (text and images) for the HumanMessage
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"""
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viewed_images = state.get("viewed_images", {})
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if not viewed_images:
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return ["No images have been viewed."]
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# Build the message with image information
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content_blocks: list[str | dict] = [{"type": "text", "text": "Here are the images you've viewed:"}]
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for image_path, image_data in viewed_images.items():
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mime_type = image_data.get("mime_type", "unknown")
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base64_data = image_data.get("base64", "")
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# Add text description
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content_blocks.append({"type": "text", "text": f"\n- **{image_path}** ({mime_type})"})
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# Add the actual image data so LLM can "see" it
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if base64_data:
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content_blocks.append(
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{
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"type": "image_url",
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"image_url": {"url": f"data:{mime_type};base64,{base64_data}"},
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}
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)
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return content_blocks
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def _should_inject_image_message(self, state: ViewImageMiddlewareState) -> bool:
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"""Determine if we should inject an image details message.
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Args:
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state: Current state
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Returns:
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True if we should inject the message
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"""
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messages = state.get("messages", [])
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if not messages:
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return False
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# Get the last assistant message
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last_assistant_msg = self._get_last_assistant_message(messages)
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if not last_assistant_msg:
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return False
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# Check if it has view_image tool calls
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if not self._has_view_image_tool(last_assistant_msg):
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return False
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# Check if all tools have been completed
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if not self._all_tools_completed(messages, last_assistant_msg):
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return False
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# Check if we've already added an image details message
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# Look for a human message after the last assistant message that contains image details
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assistant_idx = messages.index(last_assistant_msg)
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for msg in messages[assistant_idx + 1 :]:
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if isinstance(msg, HumanMessage):
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content_str = str(msg.content)
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if "Here are the images you've viewed" in content_str or "Here are the details of the images you've viewed" in content_str:
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# Already added, don't add again
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return False
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return True
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def _inject_image_message(self, state: ViewImageMiddlewareState) -> dict | None:
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"""Internal helper to inject image details message.
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Args:
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state: Current state
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Returns:
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State update with additional human message, or None if no update needed
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"""
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if not self._should_inject_image_message(state):
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return None
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# Create the image details message with text and image content
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image_content = self._create_image_details_message(state)
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# Create a new human message with mixed content (text + images)
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human_msg = HumanMessage(content=image_content)
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print("[ViewImageMiddleware] Injecting image details message with images before LLM call")
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# Return state update with the new message
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return {"messages": [human_msg]}
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@override
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def before_model(self, state: ViewImageMiddlewareState, runtime: Runtime) -> dict | None:
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"""Inject image details message before LLM call if view_image tools have completed (sync version).
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This runs before each LLM call, checking if the previous turn included view_image
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tool calls that have all completed. If so, it injects a human message with the image
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details so the LLM can see and analyze the images.
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Args:
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state: Current state
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runtime: Runtime context (unused but required by interface)
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Returns:
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State update with additional human message, or None if no update needed
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"""
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return self._inject_image_message(state)
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@override
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async def abefore_model(self, state: ViewImageMiddlewareState, runtime: Runtime) -> dict | None:
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"""Inject image details message before LLM call if view_image tools have completed (async version).
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This runs before each LLM call, checking if the previous turn included view_image
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tool calls that have all completed. If so, it injects a human message with the image
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details so the LLM can see and analyze the images.
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Args:
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state: Current state
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runtime: Runtime context (unused but required by interface)
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Returns:
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State update with additional human message, or None if no update needed
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"""
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return self._inject_image_message(state)
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@@ -1,4 +1,4 @@
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from typing import NotRequired, TypedDict
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from typing import Annotated, NotRequired, TypedDict
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from langchain.agents import AgentState
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@@ -13,10 +13,43 @@ class ThreadDataState(TypedDict):
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outputs_path: NotRequired[str | None]
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class ViewedImageData(TypedDict):
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base64: str
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mime_type: str
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def merge_artifacts(existing: list[str] | None, new: list[str] | None) -> list[str]:
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"""Reducer for artifacts list - merges and deduplicates artifacts."""
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if existing is None:
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return new or []
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if new is None:
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return existing
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# Use dict.fromkeys to deduplicate while preserving order
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return list(dict.fromkeys(existing + new))
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def merge_viewed_images(existing: dict[str, ViewedImageData] | None, new: dict[str, ViewedImageData] | None) -> dict[str, ViewedImageData]:
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"""Reducer for viewed_images dict - merges image dictionaries.
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Special case: If new is an empty dict {}, it clears the existing images.
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This allows middlewares to clear the viewed_images state after processing.
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"""
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if existing is None:
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return new or {}
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if new is None:
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return existing
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# Special case: empty dict means clear all viewed images
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if len(new) == 0:
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return {}
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# Merge dictionaries, new values override existing ones for same keys
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return {**existing, **new}
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class ThreadState(AgentState):
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sandbox: NotRequired[SandboxState | None]
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thread_data: NotRequired[ThreadDataState | None]
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title: NotRequired[str | None]
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artifacts: NotRequired[list[str] | None]
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artifacts: Annotated[list[str], merge_artifacts]
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todos: NotRequired[list | None]
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uploaded_files: NotRequired[list[dict] | None]
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viewed_images: Annotated[dict[str, ViewedImageData], merge_viewed_images] # image_path -> {base64, mime_type}
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