도구 정의와 사용
에이전트가 웹 검색이나 코드 실행과 같은 작업을 수행할 수 있도록 도구를 만들고 통합하는 방법을 배웁니다.
도구 정의와 사용은(는) CoddyKit의 무료 AI Agents with LangChain & Autonomous Workflows 강의입니다. 이것은 4개 중 1번째 강의입니다. 아래에서 전체 강의를 무료로 읽을 수 있으며, 내장 코드 에디터와 24/7 AI 튜터와 함께 브라우저에서 직접 실습할 수 있습니다. 이 강의는 AI Agents with LangChain & Autonomous Workflows 학습 경로의 일부이며, 진행 상황이 웹과 CoddyKit 앱에 동기화됩니다. AI Agents with LangChain & Autonomous Workflows 강의에는 총 4개의 강의가 포함되어 있습니다.
이 강의의 일부는 아직 번역되지 않았으며 영어로 표시됩니다.
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!
자주 묻는 질문
“도구 정의와 사용” 강의는 무료인가요?
네 — “도구 정의와 사용” 전체 내용을 이 웹사이트에서 무료로 읽을 수 있습니다. 인터랙티브하게 실습하려면(내장 코드 에디터와 24/7 AI 튜터), CoddyKit PRO로 업그레이드하면 AI Agents with LangChain & Autonomous Workflows 강의 전체를 잠금 해제할 수 있습니다. AI Agents with LangChain & Autonomous Workflows 강의에는 총 4개의 강의가 포함되어 있습니다.
“도구 정의와 사용”에서 뭘 배우나요?
에이전트가 웹 검색이나 코드 실행과 같은 작업을 수행할 수 있도록 도구를 만들고 통합하는 방법을 배웁니다. 브라우저에서 직접 실행하는 실습 코드로 AI Agents with LangChain & Autonomous Workflows을(를) 배우며, 24/7 AI 튜터가 강의를 진행하면서 질문에 답변해줍니다.
AI Agents with LangChain & Autonomous Workflows을(를) 시작하는 데 경험이 필요한가요?
사전 경험은 필요하지 않습니다. CoddyKit의 AI Agents with LangChain & Autonomous Workflows은(는) 초급자부터 고급 학습자까지를 위해 구성되어 있으므로, 여기서 시작하거나 처음부터 시작할 수 있으며 자신의 속도대로 진행할 수 있습니다. 이것은 4개 중 1번째 강의입니다.
“도구 정의와 사용” 강의는 얼마나 걸리나요?
대부분의 CoddyKit 강의는 약 5~10분이 소요됩니다. 각 강의는 간결하고 인터랙티브하여 꾸준한 진행이 가능하며, 웹과 앱에서 중단한 부분부터 바로 시작할 수 있습니다.
이 AI Agents with LangChain & Autonomous Workflows 강의에서 코드를 작성하고 실행할 수 있나요?
네. 모든 AI Agents with LangChain & Autonomous Workflows 강의에는 내장 코드 에디터가 포함되어 있으므로, 브라우저에서 바로 실제 코드를 작성하고 실행한 후 즉시 AI 피드백을 받을 수 있습니다 — 로컬 설정이 필요 없습니다.