mirror of
https://github.com/jxxghp/MoviePilot.git
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684 lines
26 KiB
Python
684 lines
26 KiB
Python
"""MoviePilot 自定义工具筛选中间件。"""
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from dataclasses import dataclass, replace
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import json
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from collections.abc import Awaitable, Callable
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from typing import Annotated, Any, NotRequired
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from langchain.agents.middleware.types import (
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AgentState,
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ContextT,
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ModelRequest,
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ModelResponse,
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ResponseT,
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)
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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 AIMessage, HumanMessage, SystemMessage
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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 typing_extensions import TypedDict # noqa
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from app.agent.llm import LLMHelper
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from app.agent.tools.tags import ToolTag
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from app.log import logger
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MIN_SELECTED_TOOL_COUNT = 4
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RECENT_SELECTION_CONTEXT_MESSAGE_LIMIT = 6
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RECENT_SELECTION_CONTEXT_MAX_CHARS = 6000
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RECENT_SELECTION_CONTEXT_TRUNCATION_PREFIX = "..."
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TOOL_GROUP_EXCLUDED_TAGS = frozenset(
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{
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ToolTag.AgentTool.value,
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ToolTag.Read.value,
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ToolTag.Write.value,
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ToolTag.Admin.value,
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ToolTag.Message.value,
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ToolTag.UserInteraction.value,
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ToolTag.TerminalResponse.value,
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}
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)
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MOVIEPILOT_TOOL_SELECTION_HINT = """
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MoviePilot tool-chain hints:
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- Tools with the same capability tag belong to the same functional group.
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- For multi-step MoviePilot tasks, keep same-tag tools together when relevant.
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- Prefer selecting likely next-step tools in the same capability group instead of selecting only the first tool.
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"""
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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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selected_tool_names: NotRequired[Annotated[list[str] | None, PrivateStateAttr]]
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"""当前这条用户请求首轮筛选得到的工具名列表。"""
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class ToolSelectionStateUpdate(TypedDict):
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"""工具筛选中间件状态更新项。"""
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selected_tool_names: list[str] | None
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@dataclass(frozen=True)
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class _ToolSelectionAttempt:
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"""工具筛选尝试结果，用于统一记录最终日志。"""
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request: ModelRequest
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selected_tool_names: list[str]
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status: str
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detail: str = ""
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class ToolSelectorMiddleware(LLMToolSelectorMiddleware):
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"""
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使用 provider-neutral JSON 提示执行工具筛选。
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LangChain 默认会通过 `with_structured_output()` 走 provider-specific 的
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结构化输出能力，不同 OpenAI/Anthropic 兼容端点对 `response_format`、
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JSON schema 和工具绑定的支持并不一致。工具筛选只是 Agent 执行前的
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辅助优化，失败时也会恢复使用全部工具，因此这里统一使用文本提示约束
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模型返回 `{"tools": [...]}` 并手动解析，避免在筛选阶段引入额外兼容分支。
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另外，LangChain 原生工具筛选挂在 `wrap_model_call` 上，会在同一条用户请求
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的每次“模型回合”前都重新筛选一次工具。对于会多轮调用工具的复杂任务，
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这会重复消耗一次额外的 LLM 调用。这里改成：
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- `abefore_agent()`：在本轮 Agent 执行开始时筛选一次；
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- `awrap_model_call()`：从 `request.state` 读取首轮筛选结果并复用。
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"""
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state_schema = ToolSelectionState
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def __init__(
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self,
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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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model=model,
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system_prompt=self._append_tool_selection_hint(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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@classmethod
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def _render_recent_conversation_context(
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cls,
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messages: list[Any],
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) -> tuple[str, int]:
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"""渲染最近对话上下文，供工具筛选模型理解多轮追问。"""
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rendered_messages = []
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for message in messages:
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if isinstance(message, HumanMessage):
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role = "User"
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elif isinstance(message, AIMessage):
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role = "Assistant"
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else:
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continue
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content = LLMHelper.extract_text_content(message.content).strip()
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if not content:
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continue
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rendered_messages.append(f"{role}: {content}")
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recent_messages = rendered_messages[-RECENT_SELECTION_CONTEXT_MESSAGE_LIMIT:]
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context = "\n\n".join(recent_messages)
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if len(context) > RECENT_SELECTION_CONTEXT_MAX_CHARS:
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context = (
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f"{RECENT_SELECTION_CONTEXT_TRUNCATION_PREFIX}"
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f"{context[-RECENT_SELECTION_CONTEXT_MAX_CHARS:]}"
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)
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return context, len(recent_messages)
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@classmethod
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def _build_contextual_user_message(
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cls,
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messages: list[Any],
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last_user_message: HumanMessage,
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) -> HumanMessage:
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"""根据最近对话构造工具筛选专用用户消息。"""
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context, message_count = cls._render_recent_conversation_context(messages)
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if message_count <= 1:
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return last_user_message
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return HumanMessage(
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content=(
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"Recent conversation context for tool selection:\n"
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f"{context}\n\n"
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"Select tools for the latest user instruction. Use prior assistant "
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"messages and earlier user requests when the latest user message "
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"depends on previous context."
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)
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)
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def _prepare_selection_request(
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self,
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request: ModelRequest[ContextT],
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) -> Any | None:
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"""准备带最近对话上下文的工具筛选请求。"""
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selection_request = super()._prepare_selection_request(request)
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if selection_request is None:
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return None
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contextual_user_message = self._build_contextual_user_message(
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messages=request.messages,
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last_user_message=selection_request.last_user_message,
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)
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if contextual_user_message is selection_request.last_user_message:
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return selection_request
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return replace(selection_request, last_user_message=contextual_user_message)
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@staticmethod
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def _append_tool_selection_hint(system_prompt: str) -> str:
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"""追加 MoviePilot 工具组选择提示，避免复杂链路只选中首个工具。"""
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if "MoviePilot tool-chain hints:" in system_prompt:
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return system_prompt
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return f"{system_prompt.rstrip()}{MOVIEPILOT_TOOL_SELECTION_HINT}"
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def _get_tool_selection_limit(self, valid_tool_names: list[str]) -> int:
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"""计算补齐筛选结果时允许使用的工具数量上限。"""
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if self.max_tools:
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return min(self.max_tools, len(valid_tool_names))
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return len(valid_tool_names)
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@staticmethod
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def _normalize_tool_tags(tool: BaseTool) -> list[str]:
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"""读取工具的业务标签，过滤掉无法表达工具组的通用标签。"""
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tags = getattr(tool, "tags", None) or []
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if isinstance(tags, str):
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tags = [tags]
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normalized_tags = []
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for tag in tags:
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tag_value = getattr(tag, "value", tag)
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if not tag_value:
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continue
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tag_name = str(tag_value)
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if tag_name in TOOL_GROUP_EXCLUDED_TAGS or tag_name in normalized_tags:
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continue
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normalized_tags.append(tag_name)
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return normalized_tags
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@classmethod
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def _build_tool_groups(
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cls,
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available_tools: list[BaseTool],
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valid_tool_names: list[str],
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) -> list[tuple[str, list[str]]]:
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"""根据工具标签构造能力组，保留当前工具列表中的稳定顺序。"""
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valid_tool_set = set(valid_tool_names)
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tool_groups: dict[str, list[str]] = {}
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for tool in available_tools:
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tool_name = getattr(tool, "name", None)
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if not tool_name or tool_name not in valid_tool_set:
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continue
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for tag in cls._normalize_tool_tags(tool):
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group_tool_names = tool_groups.setdefault(tag, [])
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if tool_name not in group_tool_names:
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group_tool_names.append(tool_name)
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return [
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(tag, tool_names)
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for tag, tool_names in tool_groups.items()
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if len(tool_names) > 1
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]
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@classmethod
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def _get_matched_tool_groups(
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cls,
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selected_names: list[str],
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available_tools: list[BaseTool],
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valid_tool_names: list[str],
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) -> list[tuple[str, list[str]]]:
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"""返回已选工具命中的标签能力组。"""
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groups_by_tag = {
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tag: tool_names
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for tag, tool_names in cls._build_tool_groups(
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available_tools=available_tools,
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valid_tool_names=valid_tool_names,
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)
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}
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tools_by_name = {
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tool.name: tool
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for tool in available_tools
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if getattr(tool, "name", None)
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}
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matched_groups: list[tuple[str, list[str]]] = []
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seen_tags = set()
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for tool_name in selected_names:
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tool = tools_by_name.get(tool_name)
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if not tool:
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continue
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for tag in cls._normalize_tool_tags(tool):
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if tag in seen_tags or tag not in groups_by_tag:
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continue
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matched_groups.append((tag, groups_by_tag[tag]))
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seen_tags.add(tag)
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return matched_groups
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def _complete_low_count_selection(
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self,
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selected_tool_names: list[str],
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valid_tool_names: list[str],
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available_tools: list[BaseTool],
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) -> list[str]:
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"""
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当模型只选出极少工具时，按工具标签补齐同组工具。
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工具标签是工具自身声明的能力归属。这里只补齐已经命中的标签组，
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不会把所有工具组都展开。
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"""
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limit = self._get_tool_selection_limit(valid_tool_names)
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selected_names = [
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tool_name
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for tool_name in selected_tool_names
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if tool_name in valid_tool_names
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]
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selected_set = set(selected_names)
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valid_tool_set = set(valid_tool_names)
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completed_names = list(selected_names)
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matched_groups = self._get_matched_tool_groups(
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selected_names=selected_names,
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available_tools=available_tools,
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valid_tool_names=valid_tool_names,
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)
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if not matched_groups:
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return completed_names[:limit]
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matched_group_tool_names = {
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tool_name
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for _, group_tool_names in matched_groups
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for tool_name in group_tool_names
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}
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target_count = min(
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max(MIN_SELECTED_TOOL_COUNT, len(matched_group_tool_names)),
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limit,
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)
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if len(selected_names) >= target_count:
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return selected_names[:limit]
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for _, group_tool_names in matched_groups:
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for tool_name in group_tool_names:
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if tool_name in selected_set or tool_name not in valid_tool_set:
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continue
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completed_names.append(tool_name)
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selected_set.add(tool_name)
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if len(completed_names) >= target_count:
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return completed_names[:limit]
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return completed_names[:limit]
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def _process_selection_response(
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self,
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response: dict[str, Any],
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available_tools: list[BaseTool],
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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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"""
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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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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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return request.override(
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tools=[*available_tools, *always_included_tools, *provider_tools]
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)
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response["tools"] = self._complete_low_count_selection(
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selected_tool_names=[
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tool_name
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for tool_name in response.get("tools", [])
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if isinstance(tool_name, str)
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],
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valid_tool_names=valid_tool_names,
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available_tools=available_tools,
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)
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modified_request = 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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return modified_request
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@staticmethod
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def _parse_json_object(text: str) -> dict[str, Any]:
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"""
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解析模型返回的 JSON。
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不同模型可能偶发输出 Markdown 围栏或前后说明文本，因此这里从
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响应中提取第一个 JSON 对象作为兜底。
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"""
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stripped_text = text.strip()
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if not stripped_text:
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raise ValueError("工具筛选返回了空响应")
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try:
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payload = json.loads(stripped_text)
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if isinstance(payload, dict):
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return payload
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except json.JSONDecodeError:
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pass
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start = stripped_text.find("{")
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end = stripped_text.rfind("}")
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if start == -1 or end == -1 or end <= start:
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raise ValueError(f"工具筛选返回的内容不是合法 JSON: {stripped_text}")
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payload = json.loads(stripped_text[start: end + 1])
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if not isinstance(payload, dict):
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raise ValueError("工具筛选 JSON 顶层必须是对象")
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return payload
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@classmethod
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def _render_tool_list(cls, available_tools: list[Any]) -> str:
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"""把工具名和描述渲染成稳定的文本列表。"""
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lines = []
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for tool in available_tools:
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tags = cls._normalize_tool_tags(tool)
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tag_text = f" [group tags: {', '.join(tags)}]" if tags else ""
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lines.append(f"- {tool.name}{tag_text}: {tool.description}")
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return "\n".join(lines)
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@classmethod
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def _render_tool_groups(cls, available_tools: list[BaseTool]) -> str:
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"""把当前可用工具按标签渲染成能力组提示。"""
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valid_tool_names = [
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tool.name
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for tool in available_tools
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if getattr(tool, "name", None)
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]
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groups = cls._build_tool_groups(
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available_tools=available_tools,
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valid_tool_names=valid_tool_names,
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)
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if not groups:
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return ""
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rendered_groups = "\n".join(
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f"- {tag}: {', '.join(tool_names)}"
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for tag, tool_names in groups
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)
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return f"Capability groups from tool tags:\n{rendered_groups}\n\n"
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def _build_json_selection_prompt(self, selection_request: Any) -> str:
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"""
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生成显式 JSON 输出提示。
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使用纯提示约束可覆盖更多兼容端点，避免在工具筛选阶段依赖某个
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provider 专属的 `response_format` 或 schema 能力。
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"""
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limit_instruction = ""
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if self.max_tools:
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limit_instruction = f"- Select up to {self.max_tools} tools. IF NO TOOLS ARE RELEVANT, DO NOT RETURN AN EMPTY ARRAY. SELECT THE MOST APPLICABLE ONES TO ENSURE THE REQUEST IS HANDLED."
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return (
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f"{selection_request.system_message}\n\n"
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"Return the answer in JSON only.\n"
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'Use exactly this shape: {"tools": ["tool_name_1", "tool_name_2"]}\n'
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"Rules:\n"
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"- The `tools` field must be a JSON array of strings.\n"
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"- Only use tool names from the allowed list below.\n"
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"- Order tools by relevance, with the most relevant first.\n"
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"- Tools sharing the same capability tag are in the same group; include same-group tools together when relevant.\n"
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f"{limit_instruction}\n"
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"- Do not add explanations, markdown, or extra keys.\n\n"
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f"{self._render_tool_groups(selection_request.available_tools)}"
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"Allowed tools:\n"
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f"{self._render_tool_list(selection_request.available_tools)}"
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)
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def _normalize_selection_response(self, response: Any) -> dict[str, list[str]]:
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"""
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解析并标准化显式 JSON 模式的工具筛选结果。
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"""
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content = getattr(response, "content", response)
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text = LLMHelper.extract_text_content(content)
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logger.debug(f"工具筛选原始响应: {text}")
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payload = self._parse_json_object(text)
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tools = payload.get("tools")
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if not isinstance(tools, list):
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raise ValueError(f"工具筛选 JSON 缺少 `tools` 数组: {payload}")
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normalized_tools = [
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tool_name for tool_name in tools if isinstance(tool_name, str)
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]
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logger.debug(f"工具筛选标准化结果: {normalized_tools}")
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return {"tools": normalized_tools}
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async def _aselect_tools_with_json_prompt(
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self, selection_request: Any
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||
) -> dict[str, list[str]]:
|
||
"""
|
||
使用 JSON 提示执行异步工具筛选。
|
||
|
||
:param selection_request: LangChain 工具筛选请求
|
||
:return: 标准化后的工具名列表
|
||
"""
|
||
logger.debug("工具筛选走 JSON 提示分支")
|
||
response = await selection_request.model.ainvoke(
|
||
[
|
||
SystemMessage(
|
||
content=self._build_json_selection_prompt(selection_request)
|
||
),
|
||
selection_request.last_user_message,
|
||
]
|
||
)
|
||
return self._normalize_selection_response(response)
|
||
|
||
@staticmethod
|
||
def _extract_selected_tool_names(request: ModelRequest) -> list[str]:
|
||
"""从已筛选后的请求中提取最终工具名，保留原有顺序。"""
|
||
return [tool.name for tool in request.tools if not isinstance(tool, dict)]
|
||
|
||
@staticmethod
|
||
def _count_request_tools(request: ModelRequest) -> int:
|
||
"""统计当前请求中的 LangChain 工具数量，不包含 provider 原生工具字典。"""
|
||
return len([tool for tool in request.tools if not isinstance(tool, dict)])
|
||
|
||
@classmethod
|
||
def _log_selection_attempt(cls, attempt: _ToolSelectionAttempt) -> None:
|
||
"""按工具筛选最终状态记录稳定日志。"""
|
||
tool_count = cls._count_request_tools(attempt.request)
|
||
if attempt.status == "selected":
|
||
selected_text = ", ".join(attempt.selected_tool_names) or "无有效工具"
|
||
logger.info(f"工具筛选结果: {selected_text}")
|
||
return
|
||
if attempt.status == "empty_fallback":
|
||
logger.info(f"工具筛选结果为空，将恢复使用所有工具（共 {tool_count} 个）。")
|
||
return
|
||
if attempt.status == "failed_fallback":
|
||
logger.warning(
|
||
f"工具筛选失败，将恢复使用所有工具（共 {tool_count} 个）: {attempt.detail}"
|
||
)
|
||
return
|
||
if attempt.status == "skipped":
|
||
logger.info(f"工具筛选跳过: {attempt.detail}。")
|
||
return
|
||
if attempt.status == "reused":
|
||
selected_text = ", ".join(attempt.selected_tool_names) or "无有效工具"
|
||
logger.info(f"工具筛选复用已有结果: {selected_text}")
|
||
|
||
@staticmethod
|
||
def _apply_selected_tools(
|
||
request: ModelRequest[ContextT],
|
||
selected_tool_names: list[str],
|
||
) -> ModelRequest[ContextT]:
|
||
"""
|
||
将已筛选出的工具集应用到当前模型请求。
|
||
|
||
这里只复用首次筛选出的客户端工具名；provider-specific 的 dict 工具仍然
|
||
原样保留，避免破坏 LangChain/provider 自身的工具绑定约定。
|
||
"""
|
||
if not selected_tool_names:
|
||
return request
|
||
|
||
current_tools_by_name = {
|
||
tool.name: tool for tool in request.tools if not isinstance(tool, dict)
|
||
}
|
||
selected_tools = [
|
||
current_tools_by_name[tool_name]
|
||
for tool_name in selected_tool_names
|
||
if tool_name in current_tools_by_name
|
||
]
|
||
provider_tools = [tool for tool in request.tools if isinstance(tool, dict)]
|
||
return request.override(tools=[*selected_tools, *provider_tools])
|
||
|
||
async def _aselect_request_once(
|
||
self, request: ModelRequest[ContextT]
|
||
) -> ModelRequest[ContextT]:
|
||
"""
|
||
执行一次真实工具筛选，并返回筛选后的请求对象。
|
||
|
||
这里单独抽成 helper，便于首次筛选后缓存结果，也便于测试覆盖
|
||
“首轮筛选，后续复用”的行为。
|
||
"""
|
||
return (await self._aselect_request_once_with_status(request)).request
|
||
|
||
async def _aselect_request_once_with_status(
|
||
self, request: ModelRequest[ContextT]
|
||
) -> _ToolSelectionAttempt:
|
||
"""
|
||
执行一次真实工具筛选，并携带最终状态供调用方统一记录日志。
|
||
"""
|
||
selection_request = self._prepare_selection_request(request)
|
||
if selection_request is None:
|
||
return _ToolSelectionAttempt(
|
||
request=request,
|
||
selected_tool_names=self._extract_selected_tool_names(request),
|
||
status="skipped",
|
||
detail="没有需要筛选的工具",
|
||
)
|
||
|
||
try:
|
||
response = await self._aselect_tools_with_json_prompt(selection_request)
|
||
modified_request = self._process_selection_response(
|
||
response,
|
||
selection_request.available_tools,
|
||
selection_request.valid_tool_names,
|
||
request,
|
||
)
|
||
status = (
|
||
"empty_fallback"
|
||
if response.get("tools") == []
|
||
else "selected"
|
||
)
|
||
return _ToolSelectionAttempt(
|
||
request=modified_request,
|
||
selected_tool_names=self._extract_selected_tool_names(modified_request),
|
||
status=status,
|
||
)
|
||
except Exception as err:
|
||
return _ToolSelectionAttempt(
|
||
request=request,
|
||
selected_tool_names=self._extract_selected_tool_names(request),
|
||
status="failed_fallback",
|
||
detail=str(err),
|
||
)
|
||
|
||
async def abefore_agent( # noqa
|
||
self,
|
||
state: ToolSelectionState,
|
||
runtime: Runtime, # noqa
|
||
config: RunnableConfig,
|
||
) -> ToolSelectionStateUpdate | None: # ty: ignore[invalid-method-override]
|
||
"""
|
||
在本轮 Agent 执行开始前完成一次真实工具筛选。
|
||
|
||
这样后续多轮 `model -> tools -> model` 循环都只复用这一次结果，
|
||
不会为每次模型回合重复追加一笔 selector LLM 开销。
|
||
"""
|
||
if "selected_tool_names" in state:
|
||
self._log_selection_attempt(
|
||
_ToolSelectionAttempt(
|
||
request=ModelRequest(
|
||
model=self.model,
|
||
tools=list(self.selection_tools),
|
||
messages=state["messages"],
|
||
state=state,
|
||
runtime=runtime,
|
||
),
|
||
selected_tool_names=state.get("selected_tool_names") or [],
|
||
status="reused",
|
||
)
|
||
)
|
||
return None
|
||
|
||
if not self.selection_tools or self.model is None:
|
||
detail = "没有可筛选工具" if not self.selection_tools else "未配置筛选模型"
|
||
self._log_selection_attempt(
|
||
_ToolSelectionAttempt(
|
||
request=ModelRequest(
|
||
model=self.model,
|
||
tools=list(self.selection_tools),
|
||
messages=state["messages"],
|
||
state=state,
|
||
runtime=runtime,
|
||
),
|
||
selected_tool_names=[],
|
||
status="skipped",
|
||
detail=detail,
|
||
)
|
||
)
|
||
return ToolSelectionStateUpdate(selected_tool_names=None)
|
||
|
||
selection_request = ModelRequest(
|
||
model=self.model,
|
||
tools=list(self.selection_tools),
|
||
messages=state["messages"],
|
||
state=state,
|
||
runtime=runtime,
|
||
)
|
||
attempt = await self._aselect_request_once_with_status(selection_request)
|
||
self._log_selection_attempt(attempt)
|
||
selected_tool_names = attempt.selected_tool_names
|
||
return ToolSelectionStateUpdate(selected_tool_names=selected_tool_names or None)
|
||
|
||
async def awrap_model_call(
|
||
self,
|
||
request: ModelRequest[ContextT],
|
||
handler: Callable[
|
||
[ModelRequest[ContextT]], Awaitable[ModelResponse[ResponseT]]
|
||
],
|
||
) -> ModelResponse[ResponseT]:
|
||
"""
|
||
从 state 中读取首次筛选结果，并应用到每次模型回合。
|
||
"""
|
||
selected_tool_names = request.state.get("selected_tool_names") # noqa
|
||
|
||
# 正常路径下，`abefore_agent()` 已经提前写入状态；这里只保留一层兜底，
|
||
# 兼容直接单测或未来某些绕过 before_agent 的调用场景。
|
||
if (
|
||
selected_tool_names is None
|
||
and self.selection_tools
|
||
and self.model is not None
|
||
):
|
||
attempt = await self._aselect_request_once_with_status(request)
|
||
self._log_selection_attempt(attempt)
|
||
request = attempt.request
|
||
selected_tool_names = attempt.selected_tool_names or None
|
||
request.state["selected_tool_names"] = selected_tool_names # noqa
|
||
|
||
if selected_tool_names:
|
||
request = self._apply_selected_tools(request, selected_tool_names)
|
||
|
||
return await handler(request)
|