mirror of
https://github.com/jxxghp/MoviePilot.git
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577 lines
22 KiB
Python
577 lines
22 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
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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.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.log import logger
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MIN_SELECTED_TOOL_COUNT = 4
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MOVIEPILOT_TOOL_SELECTION_HINT = """
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MoviePilot tool-chain hints:
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- For media search and download tasks, keep related steps together when relevant:
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search_media, search_torrents, get_search_results, add_download_tasks, query_download_tasks.
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- For file organization and library transfer tasks, keep related steps together when relevant:
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list_directory, query_directory_settings, recognize_media, query_library_exists, transfer_file, query_transfer_history, scrape_metadata.
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- For subscription tasks, keep related steps together when relevant:
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search_subscribe, add_subscribe, query_subscribes, update_subscribe, query_subscribe_history, query_popular_subscribes.
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- For download management tasks, keep related steps together when relevant:
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query_download_tasks, update_download_tasks, delete_download_tasks, query_downloaders.
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- For site diagnostics or maintenance tasks, keep related steps together when relevant:
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query_sites, query_site_userdata, test_site, update_site, update_site_cookie.
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- For scheduler and workflow tasks, keep related steps together when relevant:
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query_schedulers, run_scheduler, query_workflows, run_workflow, query_episode_schedule.
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- For plugin tasks, keep related steps together when relevant:
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query_installed_plugins, query_market_plugins, query_plugin_capabilities, query_plugin_config, update_plugin_config, query_plugin_data, install_plugin, uninstall_plugin, reload_plugin.
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- For rule, identifier, or system setting tasks, keep related steps together when relevant:
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query_rule_groups, query_builtin_filter_rules, query_custom_filter_rules, add_custom_filter_rule, update_custom_filter_rule, delete_custom_filter_rule, add_rule_group, update_rule_group, delete_rule_group, query_custom_identifiers, update_custom_identifiers, query_system_settings, update_system_settings.
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- Prefer including the likely next-step tools in the same workflow instead of selecting only the first tool.
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"""
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TOOL_CHAIN_GROUPS = (
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(
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"media_download",
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(
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"search_media",
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"search_torrents",
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"get_search_results",
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"add_download_tasks",
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"query_download_tasks",
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"query_downloaders",
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),
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),
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(
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"library_transfer",
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(
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"list_directory",
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"query_directory_settings",
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"recognize_media",
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"query_library_exists",
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"transfer_file",
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"query_transfer_history",
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"scrape_metadata",
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),
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),
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(
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"subscription",
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(
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"search_subscribe",
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"add_subscribe",
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"query_subscribes",
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"update_subscribe",
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"delete_subscribe",
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"query_subscribe_history",
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"query_popular_subscribes",
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"query_subscribe_shares",
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),
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),
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(
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"download_management",
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(
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"query_download_tasks",
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"update_download_tasks",
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"delete_download_tasks",
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"query_downloaders",
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),
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),
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(
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"site_management",
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(
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"query_sites",
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"query_site_userdata",
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"test_site",
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"update_site",
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"update_site_cookie",
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),
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),
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(
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"workflow_scheduler",
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(
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"query_schedulers",
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"run_scheduler",
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"query_workflows",
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"run_workflow",
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"query_episode_schedule",
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),
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),
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(
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"plugin_management",
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(
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"query_installed_plugins",
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"query_market_plugins",
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"query_plugin_capabilities",
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"query_plugin_config",
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"update_plugin_config",
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"query_plugin_data",
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"install_plugin",
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"uninstall_plugin",
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"reload_plugin",
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),
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),
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(
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"rule_settings",
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(
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"query_rule_groups",
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"query_builtin_filter_rules",
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"query_custom_filter_rules",
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"add_custom_filter_rule",
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"update_custom_filter_rule",
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"delete_custom_filter_rule",
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"add_rule_group",
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"update_rule_group",
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"delete_rule_group",
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"query_custom_identifiers",
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"update_custom_identifiers",
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"query_system_settings",
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"update_system_settings",
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),
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),
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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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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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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__(
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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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@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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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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) -> list[str]:
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"""
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当模型只选出极少工具时，按 MoviePilot 常见工具链补齐相邻工具。
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这只补齐已经命中的工具组，不会把所有工具组都展开，因此能降低
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“选了搜索工具但漏了结果/下载工具”这类链式任务失败概率。
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"""
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limit = self._get_tool_selection_limit(valid_tool_names)
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target_count = min(MIN_SELECTED_TOOL_COUNT, limit)
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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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if len(selected_names) >= target_count:
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return selected_names[:limit]
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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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for _, group_tool_names in TOOL_CHAIN_GROUPS:
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if not selected_set.intersection(group_tool_names):
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continue
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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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logger.warning("工具筛选结果为空，将恢复使用所有工具。")
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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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)
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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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"""
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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 = (
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str(getattr(model, "model_name", "") or getattr(model, "model", ""))
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.strip()
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.lower()
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)
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base_url = (
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str(getattr(model, "openai_api_base", "") or getattr(model, "api_base", ""))
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.strip()
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.lower()
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)
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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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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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f"{limit_instruction}\n"
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"- Do not add explanations, markdown, or extra keys.\n\n"
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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 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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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_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(selection_request),
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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 [tool.name for tool in request.tools if not isinstance(tool, dict)]
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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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|
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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
|
||
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。
|
||
"""
|
||
selection_request = self._prepare_selection_request(request)
|
||
if selection_request is None:
|
||
return request
|
||
|
||
if not self._is_deepseek_compatible_model(selection_request.model):
|
||
captured_request: ModelRequest[ContextT] = request
|
||
|
||
async def _capture_handler(
|
||
updated_request: ModelRequest[ContextT],
|
||
) -> ModelRequest[ContextT]:
|
||
nonlocal captured_request
|
||
captured_request = updated_request
|
||
return updated_request
|
||
|
||
await super().awrap_model_call(request, _capture_handler)
|
||
return captured_request
|
||
|
||
response = await self._aselect_tools_with_deepseek(selection_request)
|
||
return self._process_selection_response(
|
||
response,
|
||
selection_request.available_tools,
|
||
selection_request.valid_tool_names,
|
||
request,
|
||
)
|
||
|
||
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:
|
||
return None
|
||
|
||
if not self.selection_tools or self.model is None:
|
||
return ToolSelectionStateUpdate(selected_tool_names=None)
|
||
|
||
selection_request = ModelRequest(
|
||
model=self.model,
|
||
tools=list(self.selection_tools),
|
||
messages=state["messages"],
|
||
state=state,
|
||
runtime=runtime,
|
||
)
|
||
modified_request = await self._aselect_request_once(selection_request)
|
||
selected_tool_names = self._extract_selected_tool_names(modified_request)
|
||
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
|
||
):
|
||
request = await self._aselect_request_once(request)
|
||
selected_tool_names = self._extract_selected_tool_names(request) 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)
|