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  "slug": "complete-guide-to-defining-parameterized-tools-in-langchain",
  "author": "goumang",
  "title": "LangChain 带参数 Tool 定义完整指南",
  "summary": "本文详细介绍 LangChain 中使用 @tool 装饰器定义带参数 Tool 的完整方法，包括参数类型定义、必填/可选参数设置、参数描述编写以及 Pydantic 模型验证。通过实际代码示例展示如何构建可被 LLM 正确调用的工具函数，并提供常见错误的排查指南。",
  "content": "# 概述\n\nLangChain 的 @tool 装饰器是将 Python 函数转换为 LLM 可调用工具的核心机制。通过为工具定义清晰的参数schema，LLM 能够理解如何正确调用工具并传递参数。本文详细介绍带参数 Tool 的定义方法、参数验证以及最佳实践。\n\n## 前置条件\n\n- Python 3.8+\n- LangChain >= 0.1.0\n- 安装命令：`pip install langchain-core langchain-community`\n\n## 核心内容\n\n### 1. 基础 @tool 装饰器用法\n\n@tool 装饰器会自动从函数签名中提取参数信息：\n\n```python\nfrom langchain_core.tools import tool\n\n@tool\ndef get_weather(location: str) -> str:\n    \"\"\"获取指定位置的天气信息\"\"\"\n    return f\"{location} 今天天气晴朗，温度 22°C\"\n```\n\n### 2. 带参数的 Tool 定义\n\n使用 Annotated 可以添加更详细的参数元数据：\n\n```python\nfrom typing import Annotated\nfrom langchain_core.tools import tool\n\n@tool\ndef search_code(\n    query: Annotated[str, \"搜索关键词，需简洁明确\"],\n    language: Annotated[str, \"编程语言，如 python、javascript\"] = \"python\",\n    max_results: Annotated[int, \"最大返回结果数\"] = 10\n) -> str:\n    \"\"\"在代码库中搜索相关代码\"\"\"\n    return f\"找到 {max_results} 条 {language} 相关结果: {query}\"\n```\n\n### 3. 使用 Pydantic 模型定义复杂参数\n\n对于复杂的参数结构，使用 Pydantic 模型：\n\n```python\nfrom pydantic import BaseModel, Field\nfrom langchain_core.tools import tool\n\nclass SearchConfig(BaseModel):\n    query: str = Field(description=\"搜索查询语句\")\n    language: str = Field(default=\"python\", description=\"目标编程语言\")\n    case_sensitive: bool = Field(default=False, description=\"是否区分大小写\")\n\n@tool(args_schema=SearchConfig)\ndef search_code_advanced(config: SearchConfig) -> str:\n    \"\"\"使用配置对象进行高级搜索\"\"\"\n    mode = \"case-sensitive\" if config.case_sensitive else \"case-insensitive\"\n    return f\"在 {config.language} 中搜索 '{config.query}' ({mode})\"\n```\n\n### 4. 返回值处理\n\nTool 可以返回字符串、字典或 Pydantic 对象：\n\n```python\nfrom pydantic import BaseModel\nfrom langchain_core.tools import tool\n\nclass SearchResult(BaseModel):\n    title: str\n    url: str\n    snippet: str\n\n@tool(response_format=\"content_and_artifact\")\ndef web_search(query: str) -> tuple[str, SearchResult]:\n    \"\"\"搜索网页并返回结构化结果\"\"\"\n    result = SearchResult(\n        title=f\"关于 {query} 的搜索结果\",\n        url=f\"https://example.com/search?q={query}\",\n        snippet=f\"这是 {query} 的相关结果摘要...\"\n    )\n    return f\"找到 1 个结果\", result\n```\n\n## 完整代码示例\n\n以下是一个完整的天气查询工具示例：\n\n```python\nfrom typing import Annotated\nfrom langchain_core.tools import tool\nfrom langchain_openai import ChatOpenAI\nfrom langgraph.prebuilt import create_react_agent\n\n@tool\ndef get_weather(\n    city: Annotated[str, \"城市名称，中文或英文\"],\n    unit: Annotated[str, \"温度单位，celsius 或 fahrenheit\"] = \"celsius\"\n) -> str:\n    \"\"\"获取指定城市的当前天气信息\"\"\"\n    weather_data = {\n        \"北京\": {\"celsius\": 18, \"fahrenheit\": 64},\n        \"上海\": {\"celsius\": 22, \"fahrenheit\": 72},\n        \"东京\": {\"celsius\": 20, \"fahrenheit\": 68}\n    }\n    if city not in weather_data:\n        return f\"抱歉，暂不支持查询 {city} 的天气\"\n    temp = weather_data[city][unit]\n    return f\"{city} 当前温度: {temp}°{'C' if unit == 'celsius' else 'F'}\"\n\n# 创建 Agent\nmodel = ChatOpenAI(model=\"gpt-4\")\nagent = create_react_agent(model, tools=[get_weather])\n\n# 调用\nresult = agent.invoke({\"messages\": [(\"human\", \"北京今天多少度？\")]})\nprint(result[\"messages\"][-1].content)\n# 输出: 北京当前温度: 18°C\n```\n\n## 常见问题\n\n**Q1: LLM 无法正确传递参数怎么办？**\n- 确保每个参数都有清晰的 description\n- description 应该说明参数的语义和格式要求\n- 检查参数类型是否与 LLM 理解的格式匹配\n\n**Q2: 必填参数和可选参数如何区分？**\n- 有默认值的参数被视为可选参数\n- 没有默认值的参数为必填参数\n- 使用 Annotated 添加更详细的描述\n\n**Q3: 如何处理参数验证失败？**\n- 使用 Pydantic BaseModel 的 Field 设置约束\n- 在工具函数内部添加验证逻辑\n- 返回有意义的错误信息给 LLM\n\n## 参考资料\n\n- [LangChain Tools 官方文档](https://docs.langchain.com/oss/python/langchain/overview)\n- [LangChain @tool 装饰器指南](https://langchain.cadn.net.cn/python/docs/how_to/custom_tools/index.html)\n- [Pydantic Field 文档](https://docs.pydantic.dev/latest/api/fields/)\n",
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