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https://github.com/jxxghp/MoviePilot.git
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chore: update text processing dependencies
This commit is contained in:
jxxghp
@@ -2,11 +2,9 @@
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import json
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from collections.abc import Awaitable, Callable
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from dataclasses import dataclass
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from typing import Annotated, Any, Literal, Union, NotRequired
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from typing import Annotated, Any, NotRequired
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from langchain.agents.middleware.types import (
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AgentMiddleware,
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AgentState,
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ContextT,
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ModelRequest,
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@@ -16,78 +14,18 @@ from langchain.agents.middleware.types import (
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from langchain.agents.middleware.types import (
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PrivateStateAttr, # noqa
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)
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from langchain.agents.middleware.tool_selection import (
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DEFAULT_SYSTEM_PROMPT,
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LLMToolSelectorMiddleware,
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)
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from langchain_core.language_models.chat_models import BaseChatModel
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from langchain_core.messages import HumanMessage
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from langchain_core.runnables import RunnableConfig
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from langchain_core.tools import BaseTool
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from langgraph.runtime import Runtime
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from pydantic import Field, TypeAdapter
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from typing_extensions import TypedDict # noqa
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from app.log import logger
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DEFAULT_SYSTEM_PROMPT = (
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"Your goal is to select the most relevant tools for answering the user's query."
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)
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@dataclass
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class _SelectionRequest:
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"""Prepared inputs for tool selection."""
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available_tools: list[BaseTool]
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system_message: str
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last_user_message: HumanMessage
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model: BaseChatModel
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valid_tool_names: list[str]
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def _create_tool_selection_response(tools: list[BaseTool]) -> TypeAdapter[Any]:
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"""Create a structured output schema for tool selection.
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Args:
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tools: Available tools to include in the schema.
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Returns:
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`TypeAdapter` for a schema where each tool name is a `Literal` with its
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description.
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Raises:
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AssertionError: If `tools` is empty.
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"""
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if not tools:
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msg = "Invalid usage: tools must be non-empty"
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raise AssertionError(msg)
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# Create a Union of Annotated Literal types for each tool name with description
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# For instance: Union[Annotated[Literal["tool1"], Field(description="...")], ...]
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literals = [
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Annotated[Literal[tool.name], Field(description=tool.description)]
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for tool in tools # noqa
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]
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selected_tool_type = Union[tuple(literals)] # type: ignore[valid-type] # noqa: UP007
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description = "Tools to use. Place the most relevant tools first."
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class ToolSelectionResponse(TypedDict):
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"""Use to select relevant tools."""
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tools: Annotated[list[selected_tool_type], Field(description=description)] # type: ignore[valid-type]
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return TypeAdapter(ToolSelectionResponse)
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def _render_tool_list(tools: list[BaseTool]) -> str:
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"""Format tools as markdown list.
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Args:
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tools: Tools to format.
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Returns:
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Markdown string with each tool on a new line.
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"""
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return "\n".join(f"- {tool.name}: {tool.description}" for tool in tools)
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class ToolSelectionState(AgentState):
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"""工具筛选中间件私有状态。"""
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@@ -102,9 +40,7 @@ class ToolSelectionStateUpdate(TypedDict):
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selected_tool_names: list[str] | None
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class ToolSelectorMiddleware(
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AgentMiddleware[AgentState[ResponseT], ContextT, ResponseT]
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):
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class ToolSelectorMiddleware(LLMToolSelectorMiddleware):
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"""
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为 DeepSeek 兼容端点提供更稳妥的工具筛选实现。
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@@ -129,94 +65,19 @@ class ToolSelectorMiddleware(
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def __init__(
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self,
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model: BaseChatModel,
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model: BaseChatModel | str | None = None,
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system_prompt: str = DEFAULT_SYSTEM_PROMPT,
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selection_tools: list[Any] | None = None,
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max_tools: int | None = None,
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always_include: list[str] | None = None,
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) -> None:
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super().__init__()
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self.model = model
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self.system_prompt = system_prompt
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self.max_tools = max_tools
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self.always_include = always_include or []
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self.selection_tools = selection_tools or []
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def _prepare_selection_request(
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self, request: ModelRequest[ContextT]
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) -> _SelectionRequest | None:
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"""Prepare inputs for tool selection.
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Args:
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request: the model request.
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Returns:
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`SelectionRequest` with prepared inputs, or `None` if no selection is
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needed.
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Raises:
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ValueError: If tools in `always_include` are not found in the request.
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AssertionError: If no user message is found in the request messages.
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"""
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# If no tools available, return None
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if not request.tools or len(request.tools) == 0:
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return None
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# Filter to only BaseTool instances (exclude provider-specific tool dicts)
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base_tools = [tool for tool in request.tools if not isinstance(tool, dict)]
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# Validate that always_include tools exist
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if self.always_include:
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available_tool_names = {tool.name for tool in base_tools}
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missing_tools = [
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name for name in self.always_include if name not in available_tool_names
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]
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if missing_tools:
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msg = (
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f"Tools in always_include not found in request: {missing_tools}. "
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f"Available tools: {sorted(available_tool_names)}"
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)
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raise ValueError(msg)
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# Separate tools that are always included from those available for selection
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available_tools = [
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tool for tool in base_tools if tool.name not in self.always_include
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]
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# If no tools available for selection, return None
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if not available_tools:
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return None
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system_message = self.system_prompt
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# If there's a max_tools limit, append instructions to the system prompt
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if self.max_tools is not None:
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system_message += (
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f"\nIMPORTANT: List the tool names in order of relevance, "
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f"with the most relevant first. "
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f"If you exceed the maximum number of tools, "
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f"only the first {self.max_tools} will be used."
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)
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# Get the last user message from the conversation history
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last_user_message: HumanMessage
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for message in reversed(request.messages):
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if isinstance(message, HumanMessage):
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last_user_message = message
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break
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else:
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msg = "No user message found in request messages"
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raise AssertionError(msg)
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model = self.model or request.model
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valid_tool_names = [tool.name for tool in available_tools]
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return _SelectionRequest(
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available_tools=available_tools,
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system_message=system_message,
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last_user_message=last_user_message,
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super().__init__(
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model=model,
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valid_tool_names=valid_tool_names,
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system_prompt=system_prompt,
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max_tools=max_tools,
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always_include=always_include,
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)
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self.selection_tools = selection_tools or []
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def _process_selection_response(
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self,
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@@ -225,46 +86,29 @@ class ToolSelectorMiddleware(
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valid_tool_names: list[str],
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request: ModelRequest[ContextT],
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) -> ModelRequest[ContextT]:
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"""Process the selection response and return filtered `ModelRequest`."""
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selected_tool_names: list[str] = []
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invalid_tool_selections = []
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for tool_name in response["tools"]:
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if tool_name not in valid_tool_names:
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invalid_tool_selections.append(tool_name)
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continue
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# Only add if not already selected and within max_tools limit
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if tool_name not in selected_tool_names and (
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self.max_tools is None or len(selected_tool_names) < self.max_tools
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):
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selected_tool_names.append(tool_name)
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if invalid_tool_selections:
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msg = f"Model selected invalid tools: {invalid_tool_selections}"
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raise ValueError(msg)
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# Filter tools based on selection and append always-included tools
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if selected_tool_names:
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selected_tools: list[BaseTool] = [
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tool for tool in available_tools if tool.name in selected_tool_names
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]
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else:
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# 如果模型筛选结果为空，则不对工具进行裁剪，使用所有可用工具
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"""
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处理工具筛选响应，并保留空结果回退所有工具的 MoviePilot 策略。
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"""
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if response.get("tools") == []:
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logger.warning("工具筛选结果为空，将恢复使用所有工具。")
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selected_tools = available_tools
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always_included_tools: list[BaseTool] = [
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tool
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for tool in request.tools
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if not isinstance(tool, dict) and tool.name in self.always_include
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]
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selected_tools.extend(always_included_tools)
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always_included_tools: list[BaseTool] = [
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tool
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for tool in request.tools
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if not isinstance(tool, dict) and tool.name in self.always_include
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]
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provider_tools = [tool for tool in request.tools if isinstance(tool, dict)]
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# Also preserve any provider-specific tool dicts from the original request
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provider_tools = [tool for tool in request.tools if isinstance(tool, dict)]
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return request.override(
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tools=[*available_tools, *always_included_tools, *provider_tools]
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)
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return request.override(tools=[*selected_tools, *provider_tools])
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return super()._process_selection_response(
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response,
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available_tools,
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valid_tool_names,
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request,
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)
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@staticmethod
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def _is_deepseek_compatible_model(model: BaseChatModel) -> bool:
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