207 lines
7.3 KiB
Python
207 lines
7.3 KiB
Python
import os
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from pathlib import Path
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from typing import Annotated
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from langchain.tools import InjectedToolCallId
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from langchain_core.messages import ToolMessage
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from langgraph.prebuilt.tool_node import ToolRuntime
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from langgraph.types import Command, interrupt
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from pydantic import BaseModel, Field
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from yuxi.agents.toolkits.registry import ToolExtraMetadata, _all_tool_instances, _extra_registry, tool
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from yuxi.utils import logger
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from yuxi.utils.paths import VIRTUAL_PATH_OUTPUTS
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from yuxi.utils.question_utils import normalize_questions
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# Lazy initialization for TavilySearch (only when API key is available)
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_tavily_search_instance = None
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def _create_tavily_search():
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"""Create and register TavilySearch tool with metadata."""
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global _tavily_search_instance
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if _tavily_search_instance is None:
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from langchain_tavily import TavilySearch
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_tavily_search_instance = TavilySearch()
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return _tavily_search_instance
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# 注册 TavilySearch 工具（延迟初始化）
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def _register_tavily_tool():
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"""Register TavilySearch tool with extra metadata."""
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tavily_instance = _create_tavily_search()
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# 手动注册到全局注册表
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_extra_registry["tavily_search"] = ToolExtraMetadata(
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category="buildin",
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tags=["搜索"],
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display_name="Tavily 网页搜索",
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)
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# 添加到工具实例列表
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_all_tool_instances.append(tavily_instance)
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# 模块加载时注册
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if os.getenv("TAVILY_API_KEY"):
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try:
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_register_tavily_tool()
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except Exception as e:
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logger.warning(f"Failed to register TavilySearch tool: {e}")
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class PresentArtifactsInput(BaseModel):
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"""Expose artifact files to the frontend after the agent finishes."""
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filepaths: list[str] = Field(description=f"需要展示给用户的文件绝对路径列表，只允许位于 {VIRTUAL_PATH_OUTPUTS} 下")
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def _normalize_presented_artifact_path(filepath: str, runtime: ToolRuntime) -> str:
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from yuxi.agents.backends.sandbox.paths import (
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VIRTUAL_PATH_PREFIX,
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ensure_thread_dirs,
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resolve_virtual_path,
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sandbox_outputs_dir,
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)
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outputs_virtual_prefix = f"{VIRTUAL_PATH_PREFIX}/outputs"
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runtime_context = runtime.context
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thread_id = getattr(runtime_context, "file_thread_id", None) or getattr(runtime_context, "thread_id", None)
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if not thread_id:
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raise ValueError("当前运行时缺少 thread_id")
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uid = getattr(runtime_context, "uid", None)
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if not uid:
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raise ValueError("当前运行时缺少 uid")
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ensure_thread_dirs(thread_id, str(uid))
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outputs_dir = sandbox_outputs_dir(thread_id).resolve()
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normalized_input = str(filepath or "").strip()
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if not normalized_input:
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raise ValueError("文件路径不能为空")
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stripped = normalized_input.lstrip("/")
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virtual_prefix = VIRTUAL_PATH_PREFIX.lstrip("/")
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if stripped == virtual_prefix or stripped.startswith(f"{virtual_prefix}/"):
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actual_path = resolve_virtual_path(thread_id, normalized_input, uid=str(uid))
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else:
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actual_path = Path(normalized_input).expanduser().resolve()
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if not actual_path.exists() or not actual_path.is_file():
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raise ValueError(f"文件不存在或不是普通文件: {normalized_input}")
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try:
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relative_path = actual_path.relative_to(outputs_dir)
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except ValueError as exc:
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raise ValueError(f"只允许展示 {outputs_virtual_prefix}/ 下的文件: {normalized_input}") from exc
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return f"{outputs_virtual_prefix}/{relative_path.as_posix()}"
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PRESENT_ARTIFACTS_DESCRIPTION = f"""
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将已经生成好的结果文件展示给用户。
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使用场景：
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1. 你已经在 `{VIRTUAL_PATH_OUTPUTS}` 下写好了最终结果文件
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2. 你希望前端在对话结束后显示这些结果文件卡片
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3. 这些文件需要支持下载或预览
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注意事项：
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1. 只能传入 `{VIRTUAL_PATH_OUTPUTS}` 下的文件
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2. 不要传入中间过程文件，只有真正需要给用户看的结果文件才调用
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3. 可以一次传多个文件
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"""
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@tool(
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category="buildin",
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tags=["文件", "交付物"],
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display_name="展示交付物",
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description=PRESENT_ARTIFACTS_DESCRIPTION,
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args_schema=PresentArtifactsInput,
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)
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def present_artifacts(
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filepaths: list[str],
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runtime: ToolRuntime,
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tool_call_id: Annotated[str, InjectedToolCallId],
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) -> Command:
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"""登记当前线程 outputs 目录下的交付物文件，使前端在对话结束后展示给用户。"""
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try:
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normalized_paths = [_normalize_presented_artifact_path(filepath, runtime) for filepath in filepaths]
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except ValueError as exc:
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return Command(update={"messages": [ToolMessage(content=f"Error: {exc}", tool_call_id=tool_call_id)]})
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return Command(
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update={
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"artifacts": normalized_paths,
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"messages": [ToolMessage(content="已将交付物展示给用户", tool_call_id=tool_call_id)],
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}
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)
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ASK_USER_QUESTION_DESCRIPTION = """
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在执行过程中，当你需要用户做决定或补充需求时，使用这个工具向用户提问。
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适用场景：
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1. 收集用户偏好或需求（例如风格、范围、优先级）
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2. 澄清模糊指令（存在多种合理解释时）
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3. 在实现过程中让用户选择方案方向
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4. 在有明显权衡时让用户做取舍
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使用规范：
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1. questions 提供 1-5 个问题，每项包含：question、options、multi_select、allow_other
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2. 每个问题的 options 提供 2-5 个有区分度的选项，每项包含 label 和 value
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3. 若有推荐选项：把推荐项放在第一位，并在 label 末尾加 "(Recommended)"
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4. 若需要多选：将该问题的 multi_select 设为 true
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5. allow_other 通常保持 true，用户可通过 Other 输入自定义答案
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注意事项：
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1. 不要用这个工具询问“是否继续执行”“计划是否准备好”这类流程控制问题
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2. 不要在信息已充分、无需用户决策时滥用该工具
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3. 先基于现有上下文自行决策，只有关键不确定性时才提问
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返回结果：
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answer 为 object，格式为 {question_id: answer}。
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其中 answer 可能是 string（单选）、list（多选）或 object（Other 文本）。
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"""
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@tool(
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category="buildin",
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tags=["交互"],
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display_name="向用户提问",
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description=ASK_USER_QUESTION_DESCRIPTION,
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)
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def ask_user_question(
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questions: Annotated[
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list[dict] | str | None,
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"问题列表，每项格式 {question, options, multi_select, allow_other, question_id(optional)}",
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] = None,
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) -> dict:
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"""向用户发起问题并等待回答。"""
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# 解析 questions 参数：如果是字符串，尝试解析为 JSON
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if isinstance(questions, str):
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try:
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import json
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questions = json.loads(questions)
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logger.debug(f"Parsed string questions to list: {questions}")
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except Exception as e:
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logger.error(f"Failed to parse questions string: {e}, using None")
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questions = None
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normalized_questions = normalize_questions(questions or [])
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if not normalized_questions:
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raise ValueError("questions 至少需要包含一个有效问题")
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interrupt_payload = {
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"questions": normalized_questions,
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"source": "ask_user_question",
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}
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answer = interrupt(interrupt_payload)
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return {
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"questions": normalized_questions,
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"answer": answer,
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}
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