ツールの定義と利用
エージェントがウェブ検索やコード実行などの操作を行えるツールを作成・統合する方法を学びます。
「ツールの定義と利用」はCoddyKit上の無料AI Agents with LangChain & Autonomous Workflowsレッスンです。 これはレッスン1/4です。 下記で完全なレッスンを無料で読むことができます。その後、ブラウザ内の組み込みコードエディタと24時間対応のAIチューターでハンズオン演習できます。 これはAI Agents with LangChain & Autonomous Workflows学習パスの一部であり、ウェブとCoddyKitアプリ全体で進捗が同期されます。 AI Agents with LangChain & Autonomous Workflowsコースには全4レッスンが含まれています。
このレッスンの一部はまだ翻訳されておらず、英語で表示されています。
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!
よくある質問
「ツールの定義と利用」レッスンは無料ですか？
はい。「ツールの定義と利用」の完全なテキストはこのウェブで無料で読めます。インタラクティブに演習し（組み込みコードエディタと24時間対応のAIチューター）、AI Agents with LangChain & Autonomous Workflowsコースの残りをアンロックするには、CoddyKit PROにアップグレードしてください。 AI Agents with LangChain & Autonomous Workflowsコースには全4レッスンが含まれています。
「ツールの定義と利用」で何を学びますか？
エージェントがウェブ検索やコード実行などの操作を行えるツールを作成・統合する方法を学びます。 ブラウザで直接実行するハンズオンコードでAI Agents with LangChain & Autonomous Workflowsを演習し、24時間対応のAIチューターがレッスンを進める中での質問に答えます。
AI Agents with LangChain & Autonomous Workflowsを始めるのに経験は必要ですか？
事前経験は必要ありません。CoddyKitのAI Agents with LangChain & Autonomous Workflowsは初級者から上級者向けに構成されているため、ここから始めるか最初から始めて、自分のペースで進むことができます。 これはレッスン1/4です。
「ツールの定義と利用」レッスンにはどのくらい時間がかかりますか？
ほとんどのCoddyKitレッスンは約5～10分かかります。各レッスンはコンパクトでインタラクティブなので、着実に進歩し、ウェブとアプリ全体で正確に前回の場所から再開できます。
このAI Agents with LangChain & Autonomous Workflowsレッスンでコードを書いて実行できますか？
はい。すべてのAI Agents with LangChain & Autonomous Workflowsレッスンに組み込みコードエディタが含まれているため、ブラウザでリアルコードを書いて実行し、即座のAIフィードバックを取得できます。ローカル設定は不要です。