AI Agents with LangChain & Autonomous Workflows · 课时

定义与使用工具

学习如何创建和集成工具，使智能体能够执行搜索网页或运行代码等操作

定义与使用工具 是 CoddyKit 上的免费 AI Agents with LangChain & Autonomous Workflows 课时。这是第 1 节课，共 4 节。你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。这是 AI Agents with LangChain & Autonomous Workflows 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 AI Agents with LangChain & Autonomous Workflows 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

Agents Need Tools

Welcome! In this lesson, we'll explore how AI agents can go beyond just talking. While Large Language Models (LLMs) are great at understanding and generating text, they have limitations.

They can't access real-time information, perform calculations, or interact with external systems. This is where tools come in!

Extending Agent Capabilities

Think of tools as the agent's 'hands' and 'eyes' to the outside world. They allow an agent to:

Search the web: Get up-to-date information.
Execute code: Perform calculations or run scripts.
Access databases: Retrieve specific data.
Interact with APIs: Control smart devices, send emails, etc.

Tools transform a conversational LLM into an active, problem-solving agent.

Tool's Core Components

In LangChain, a tool is essentially a function that an agent can call. Every tool needs three core components:

Function (func): The actual Python code that performs the action.
Name (name): A unique string identifier for the tool.
Description (description): A clear, concise explanation of what the tool does and when it should be used. This helps the LLM decide if and when to use the tool.

Crafting a Tool Function

Let's start by defining a simple Python function. This function will simulate getting weather information. This is the 'action' part of our future tool.

Notice the location: str type hint. This helps define what input the function expects.

def get_current_weather(location: str) -> str:
    """Get the current weather in a given location."""
    # In a real application, this would call an external API.
    if location == "London":
        return "It's 15 degrees Celsius and cloudy."
    elif location == "New York":
        return "It's 22 degrees Celsius and sunny."
    else:
        return "Weather data not available for this location."

Making it a LangChain Tool

Now, let's wrap our get_current_weather function into a LangChain Tool object. We'll give it a name and a helpful description. You can run this snippet to see the tool's properties.

from langchain.tools import Tool

def get_current_weather(location: str) -> str:
    """Get the current weather in a given location."""
    if location == "London":
        return "It's 15 degrees Celsius and cloudy."
    elif location == "New York":
        return "It's 22 degrees Celsius and sunny."
    else:
        return "Weather data not available for this location."

# Create the Tool object
weather_tool = Tool(
    name="get_current_weather",
    func=get_current_weather,
    description="Useful for getting the current weather in a specific location."
)

if __name__ == "__main__":
    print(f"Tool name: {weather_tool.name}")
    print(f"Tool description: {weather_tool.description}")
    print(f"Weather in London (direct call): {weather_tool.func('London')}")

Agent Chooses Wisely

Once you've defined your Tool objects, you pass a list of them to your LangChain agent. The LLM within the agent then uses its reasoning capabilities to decide:

If a tool is needed for the current user query.
Which tool to use from the available list.
What arguments to pass to the chosen tool.

This decision is heavily influenced by the tool's description.

Equipping Your Agent (Conceptual)

While a full runnable agent requires an LLM API key, conceptually, this is how you'd equip an agent with our weather_tool. The agent is 'initialized' with a list of tools it can use.

When you ask the agent a question like "What's the weather in Paris?", it will read the description of weather_tool and decide to call its function with "Paris" as the argument.

from langchain.agents import initialize_agent, AgentType
from langchain.tools import Tool
# from langchain_openai import ChatOpenAI # Requires API key

# Assume weather_tool is defined as before
def get_current_weather(location: str) -> str:
    return "Weather data..." # Simplified for concept

weather_tool = Tool(
    name="get_current_weather",
    func=get_current_weather,
    description="Useful for getting the current weather."
)

# This part needs an actual LLM setup, e.g.:
# llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo")

# agent = initialize_agent(
#     [weather_tool],
#     llm,
#     agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
#     verbose=True
# )

# The agent then uses the tool based on query.

Clear Descriptions are Key

The description of your tool is paramount. It's the only way the LLM understands its purpose.

Be specific: Clearly state what the tool does.
Mention inputs: What information does it need?
State outputs: What kind of result does it return?
Guide usage: When should the agent consider using this tool?

A poorly described tool will either be ignored or used incorrectly by the agent.

Structured Tool Inputs (Pydantic)

For more complex tools, you can ensure the agent provides arguments in a specific format using Pydantic. This helps validate inputs and makes your tools robust.

We define a BaseModel that describes the expected inputs. LangChain uses this to guide the LLM's argument generation.

from langchain.tools import Tool
from pydantic import BaseModel, Field
from typing import Type

# Define a Pydantic model for the tool's input
class WeatherInput(BaseModel):
    location: str = Field(description="The city and state, e.g., San Francisco, CA")

def get_current_weather_with_schema(location: str) -> str:
    """Get the current weather in a given location."""
    if location == "London":
        return "It's 15 degrees Celsius and cloudy."
    elif location == "New York":
        return "It's 22 degrees Celsius and sunny."
    else:
        return "Weather data not available for this location."

weather_tool_schema = Tool(
    name="get_current_weather",
    func=get_current_weather_with_schema,
    description="Useful for getting the current weather in a specific location.",
    args_schema=WeatherInput # Link the Pydantic schema here
)

if __name__ == "__main__":
    print(f"Tool with schema: {weather_tool_schema.name}")
    print(f"Expected input fields: {weather_tool_schema.args_schema.schema()['properties']}")

Check Your Understanding

Consider the role of tools in LangChain agents.

Tools: Agents' Superpowers

You've successfully started your journey into equipping AI agents with tools! We learned that:

Tools are functions that extend an agent's capabilities beyond its inherent LLM knowledge.
Every tool needs a function, a unique name, and a clear description.
Good descriptions are vital for the LLM to choose and use tools correctly.
Pydantic schemas can provide structured input validation for tools.

Next, we'll explore different agent types and how they make decisions about using these tools!

常见问题解答

「定义与使用工具」课时是免费的吗？

是的 — 「定义与使用工具」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 AI Agents with LangChain & Autonomous Workflows 课程的其余内容，请升级到 CoddyKit PRO。 AI Agents with LangChain & Autonomous Workflows 课程共包含 4 节课。

「定义与使用工具」这节课中我会学到什么？

学习如何创建和集成工具，使智能体能够执行搜索网页或运行代码等操作你通过在浏览器中直接运行的动手代码来练习 AI Agents with LangChain & Autonomous Workflows，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

学习 AI Agents with LangChain & Autonomous Workflows 需要有经验吗？

无需任何先前经验。CoddyKit 上的 AI Agents with LangChain & Autonomous Workflows 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。这是第 1 节课，共 4 节。

「定义与使用工具」课时需要多长时间？

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。

我能在这节 AI Agents with LangChain & Autonomous Workflows 课中编写并运行代码吗？

能。每节 AI Agents with LangChain & Autonomous Workflows 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。

此课程中的所有课时

← 返回 AI Agents with LangChain & Autonomous Workflows