mirror of
https://github.com/jxxghp/MoviePilot.git
synced 2026-05-07 05:42:40 +08:00
afcc071d07
- Refactor MoviePilotToolSelectorMiddleware to perform tool selection once per agent execution and cache the result in state, avoiding redundant LLM calls for each model round. - Add abefore_agent to select tools at the start of agent execution and store selected tool names in state. - Update awrap_model_call to reuse cached tool selection from state for subsequent model calls. - Enhance test coverage for tool selection caching and reuse logic. - Improve error logging in skill version extraction.
337 lines
13 KiB
Python
337 lines
13 KiB
Python
"""MoviePilot 自定义工具筛选中间件。"""
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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, TypedDict
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from langchain.agents.middleware import LLMToolSelectorMiddleware
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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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PrivateStateAttr, # noqa
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ResponseT,
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)
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from langchain_core.language_models.chat_models import BaseChatModel
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from langchain_core.runnables import RunnableConfig
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from langgraph.runtime import Runtime
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from app.log import logger
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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[
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Annotated[list[str] | None, PrivateStateAttr]
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]
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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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class MoviePilotToolSelectorMiddleware(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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LangChain 默认会通过 `with_structured_output()` 走 OpenAI 的
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`response_format=json_schema` 路径，但 DeepSeek 官方 OpenAI 兼容端点公开文档
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仅保证 `json_object` 模式可用。对于 `deepseek-reasoner`，这会在工具筛选阶段
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提前触发 400，导致 Agent 还没真正开始执行工具就失败。
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因此这里仅在识别到 DeepSeek 模型/端点时，退回到显式 JSON 输出模式：
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1. 使用 `response_format={"type": "json_object"}`；
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2. 在提示词中明确约束返回 JSON 结构；
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3. 手动解析 `{"tools": [...]}`，其余模型继续沿用 LangChain 默认实现。
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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__(self, *args, selection_tools: list[Any] | None = None, **kwargs) -> None:
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super().__init__(*args, **kwargs)
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# `abefore_agent()` 无法直接拿到 ModelRequest，因此把首次可见的工具集
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# 通过初始化参数传入，后续在进入模型循环前完成一次真实筛选。
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self._selection_tools = selection_tools or []
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@staticmethod
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def _is_deepseek_compatible_model(model: BaseChatModel) -> bool:
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"""
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判断当前模型是否应当走 DeepSeek JSON 兼容分支。
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除了官方 `langchain_deepseek`，用户也可能通过 OpenAI-compatible
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配置把 DeepSeek 端点接到 `ChatOpenAI`。因此这里同时检查模块名、模型名
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和 Base URL，避免只靠单一条件漏判。
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"""
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module_name = type(model).__module__.lower()
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model_name = str(
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getattr(model, "model_name", "") or getattr(model, "model", "")
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).strip().lower()
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base_url = str(
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getattr(model, "openai_api_base", "") or getattr(model, "api_base", "")
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).strip().lower()
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return (
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"deepseek" in module_name
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or model_name.startswith("deepseek-")
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or "api.deepseek.com" in base_url
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)
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@staticmethod
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def _extract_text_content(content: Any) -> str:
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"""
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从模型响应中提取纯文本。
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这里不依赖上层 LLMHelper，避免中间件与 LLM 构造逻辑互相耦合。
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"""
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if content is None:
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return ""
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if isinstance(content, str):
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return content
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if isinstance(content, list):
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text_parts: list[str] = []
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for block in content:
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if isinstance(block, str):
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text_parts.append(block)
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continue
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if isinstance(block, dict):
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if block.get("type") == "text" and isinstance(
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block.get("text"), str
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):
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text_parts.append(block["text"])
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continue
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if not block.get("type") and isinstance(block.get("text"), str):
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text_parts.append(block["text"])
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return "".join(text_parts)
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if isinstance(content, dict):
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if content.get("type") == "text" and isinstance(content.get("text"), str):
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return content["text"]
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if not content.get("type") and isinstance(content.get("text"), str):
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return content["text"]
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return ""
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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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DeepSeek 在 JSON 模式下通常会返回纯 JSON，但这里仍做一层兜底，
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兼容模型偶发输出围栏或前后说明文本的情况。
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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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@staticmethod
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def _render_tool_list(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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return "\n".join(
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f"- {tool.name}: {tool.description}" for tool in available_tools
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)
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def _build_deepseek_selection_prompt(self, selection_request: Any) -> str:
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"""
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为 DeepSeek 生成显式 JSON 输出提示。
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DeepSeek 官方文档要求在 JSON 输出模式下，提示词中必须明确包含 JSON
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约束，否则兼容端点可能返回空内容或无意义输出。
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"""
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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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"- Do not add explanations, markdown, or extra keys.\n\n"
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f"Allowed tools:\n{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 DeepSeek 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 = self._extract_text_content(content)
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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 = [tool_name for tool_name in tools if isinstance(tool_name, str)]
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return {"tools": normalized_tools}
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async def _aselect_tools_with_deepseek(
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self, selection_request: Any
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) -> dict[str, list[str]]:
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"""
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使用 DeepSeek 兼容的 JSON 输出模式执行异步工具筛选。
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"""
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logger.debug("工具筛选走 DeepSeek JSON 兼容分支")
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structured_model = selection_request.model.bind(
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response_format={"type": "json_object"}
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)
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response = await structured_model.ainvoke(
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[
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{
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"role": "system",
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"content": self._build_deepseek_selection_prompt(
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selection_request
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),
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},
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selection_request.last_user_message,
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]
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)
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return self._normalize_selection_response(response)
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@staticmethod
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def _extract_selected_tool_names(request: ModelRequest) -> list[str]:
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"""从已筛选后的请求中提取最终工具名，保留原有顺序。"""
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return [
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tool.name for tool in request.tools if not isinstance(tool, dict)
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]
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@staticmethod
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def _apply_selected_tools(
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request: ModelRequest[ContextT],
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selected_tool_names: list[str],
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) -> ModelRequest[ContextT]:
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"""
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将已筛选出的工具集应用到当前模型请求。
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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
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原样保留，避免破坏 LangChain/provider 自身的工具绑定约定。
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"""
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if not selected_tool_names:
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return request
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current_tools_by_name = {
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tool.name: tool
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for tool in request.tools
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if not isinstance(tool, dict)
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}
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selected_tools = [
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current_tools_by_name[tool_name]
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for tool_name in selected_tool_names
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if tool_name in current_tools_by_name
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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(tools=[*selected_tools, *provider_tools])
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async def _aselect_request_once(
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self, request: ModelRequest[ContextT]
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) -> ModelRequest[ContextT]:
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"""
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执行一次真实工具筛选，并返回筛选后的请求对象。
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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
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“首轮筛选，后续复用”的行为。
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"""
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selection_request = self._prepare_selection_request(request)
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if selection_request is None:
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return request
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if not self._is_deepseek_compatible_model(selection_request.model):
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captured_request: ModelRequest[ContextT] = request
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async def _capture_handler(
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updated_request: ModelRequest[ContextT],
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) -> ModelRequest[ContextT]:
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nonlocal captured_request
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captured_request = updated_request
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return updated_request
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await super().awrap_model_call(request, _capture_handler)
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return captured_request
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response = await self._aselect_tools_with_deepseek(selection_request)
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return self._process_selection_response(
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response,
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selection_request.available_tools,
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selection_request.valid_tool_names,
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request,
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)
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async def abefore_agent( # noqa
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self,
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state: ToolSelectionState,
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runtime: Runtime, # noqa
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config: RunnableConfig,
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) -> ToolSelectionStateUpdate | None: # ty: ignore[invalid-method-override]
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"""
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在本轮 Agent 执行开始前完成一次真实工具筛选。
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这样后续多轮 `model -> tools -> model` 循环都只复用这一次结果，
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不会为每次模型回合重复追加一笔 selector LLM 开销。
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"""
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if "selected_tool_names" in state:
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return None
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if not self._selection_tools or self.model is None:
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return ToolSelectionStateUpdate(selected_tool_names=None)
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selection_request = ModelRequest(
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model=self.model,
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tools=list(self._selection_tools),
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messages=state["messages"],
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state=state,
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runtime=runtime,
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)
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modified_request = await self._aselect_request_once(selection_request)
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selected_tool_names = self._extract_selected_tool_names(modified_request)
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return ToolSelectionStateUpdate(
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selected_tool_names=selected_tool_names or None
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)
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async def awrap_model_call(
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self,
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request: ModelRequest[ContextT],
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handler: Callable[
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[ModelRequest[ContextT]], Awaitable[ModelResponse[ResponseT]]
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],
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) -> ModelResponse[ResponseT]:
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"""
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从 state 中读取首次筛选结果，并应用到每次模型回合。
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"""
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selected_tool_names = request.state.get("selected_tool_names") # noqa
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# 正常路径下，`abefore_agent()` 已经提前写入状态；这里只保留一层兜底，
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# 兼容直接单测或未来某些绕过 before_agent 的调用场景。
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if selected_tool_names is None and self._selection_tools and self.model is not None:
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request = await self._aselect_request_once(request)
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selected_tool_names = self._extract_selected_tool_names(request) or None
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request.state["selected_tool_names"] = selected_tool_names # noqa
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if selected_tool_names:
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request = self._apply_selected_tools(request, selected_tool_names)
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return await handler(request)
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