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  "slug": "complete-guide-to-defining-parameterized-tools-in-langchain",
  "author": "goumang",
  "title": "Complete Guide to Defining Parameterized Tools in LangChain",
  "summary": "This article provides a comprehensive guide to defining parameterized tools in LangChain using the @tool decorator, including parameter type definitions, required/optional parameter settings, parameter descriptions, and Pydantic model validation. Includes practical code examples and troubleshooting for common errors.",
  "content": "# Overview\n\nThe @tool decorator in LangChain is the core mechanism for converting Python functions into LLM-callable tools. By defining a clear parameter schema for tools, LLMs can understand how to correctly invoke tools and pass parameters. This article provides a detailed guide on defining parameterized tools, parameter validation, and best practices.\n\n## Prerequisites\n\n- Python 3.8+\n- LangChain >= 0.1.0\n- Installation: `pip install langchain-core langchain-community`\n\n## Core Content\n\n### 1. Basic @tool Decorator Usage\n\nThe @tool decorator automatically extracts parameter information from function signatures:\n\n```python\nfrom langchain_core.tools import tool\n\n@tool\ndef get_weather(location: str) -> str:\n    \"\"\"Get weather information for a specified location\"\"\"\n    return f\"The weather in {location} is sunny, 22°C\"\n```\n\n### 2. Parameterized Tool Definition\n\nUsing Annotated allows adding detailed parameter metadata:\n\n```python\nfrom typing import Annotated\nfrom langchain_core.tools import tool\n\n@tool\ndef search_code(\n    query: Annotated[str, \"Search keyword, should be concise and clear\"],\n    language: Annotated[str, \"Programming language, e.g. python, javascript\"] = \"python\",\n    max_results: Annotated[int, \"Maximum number of results to return\"] = 10\n) -> str:\n    \"\"\"Search for relevant code in the codebase\"\"\"\n    return f\"Found {max_results} {language} results for: {query}\"\n```\n\n### 3. Using Pydantic Models for Complex Parameters\n\nFor complex parameter structures, use Pydantic models:\n\n```python\nfrom pydantic import BaseModel, Field\nfrom langchain_core.tools import tool\n\nclass SearchConfig(BaseModel):\n    query: str = Field(description=\"Search query string\")\n    language: str = Field(default=\"python\", description=\"Target programming language\")\n    case_sensitive: bool = Field(default=False, description=\"Whether to match case\")\n\n@tool(args_schema=SearchConfig)\ndef search_code_advanced(config: SearchConfig) -> str:\n    \"\"\"Perform advanced search using a configuration object\"\"\"\n    mode = \"case-sensitive\" if config.case_sensitive else \"case-insensitive\"\n    return f\"Searching '{config.query}' in {config.language} ({mode})\"\n```\n\n### 4. Return Value Handling\n\nTools can return strings, dictionaries, or Pydantic objects:\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    \"\"\"Search the web and return structured results\"\"\"\n    result = SearchResult(\n        title=f\"Search results for {query}\",\n        url=f\"https://example.com/search?q={query}\",\n        snippet=f\"This is a relevant result for {query}...\"\n    )\n    return f\"Found 1 result\", result\n```\n\n## Complete Code Example\n\nA complete weather query tool example:\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, \"City name, Chinese or English\"],\n    unit: Annotated[str, \"Temperature unit, celsius or fahrenheit\"] = \"celsius\"\n) -> str:\n    \"\"\"Get current weather information for a specified city\"\"\"\n    weather_data = {\n        \"Beijing\": {\"celsius\": 18, \"fahrenheit\": 64},\n        \"Shanghai\": {\"celsius\": 22, \"fahrenheit\": 72},\n        \"Tokyo\": {\"celsius\": 20, \"fahrenheit\": 68}\n    }\n    if city not in weather_data:\n        return f\"Sorry, weather data for {city} is not available\"\n    temp = weather_data[city][unit]\n    return f\"Current temperature in {city}: {temp}°{'C' if unit == 'celsius' else 'F'}\"\n\n# Create Agent\nmodel = ChatOpenAI(model=\"gpt-4\")\nagent = create_react_agent(model, tools=[get_weather])\n\n# Invoke\nresult = agent.invoke({\"messages\": [(\"human\", \"What's the weather in Beijing today?\")]})\nprint(result[\"messages\"][-1].content)\n# Output: Current temperature in Beijing: 18°C\n```\n\n## Common Questions\n\n**Q1: What to do if LLM cannot pass parameters correctly?**\n- Ensure every parameter has a clear description\n- Descriptions should explain the parameter's semantics and format requirements\n- Check if parameter types match what the LLM understands\n\n**Q2: How to distinguish required and optional parameters?**\n- Parameters with default values are considered optional\n- Parameters without defaults are required\n- Use Annotated for more detailed descriptions\n\n**Q3: How to handle parameter validation failures?**\n- Use Pydantic BaseModel's Field to set constraints\n- Add validation logic inside the tool function\n- Return meaningful error messages to the LLM\n\n## References\n\n- [LangChain Tools Official Documentation](https://docs.langchain.com/oss/python/langchain/overview)\n- [LangChain @tool Decorator Guide](https://langchain.cadn.net.cn/python/docs/how_to/custom_tools/index.html)\n- [Pydantic Field Documentation](https://docs.pydantic.dev/latest/api/fields/)\n",
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