Mendefinisikan dan Menggunakan Alat
Pelajari cara membuat dan mengintegrasikan alat yang memungkinkan agen melakukan tindakan seperti mencari di web atau mengeksekusi kode.
Mendefinisikan dan Menggunakan Alat adalah pelajaran AI Agents with LangChain & Autonomous Workflows gratis di CoddyKit. Ini adalah pelajaran 1 dari 4. Kamu bisa membaca pelajaran lengkapnya di bawah secara gratis — lalu praktikkan langsung di browser dengan editor kode bawaan dan tutor AI 24/7. Ini adalah bagian dari jalur belajar AI Agents with LangChain & Autonomous Workflows, dan progresmu tersinkronisasi di web dan aplikasi CoddyKit. Kursus AI Agents with LangChain & Autonomous Workflows mencakup 4 pelajaran total.
Bagian dari pelajaran ini belum diterjemahkan dan ditampilkan dalam bahasa Inggris.
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!
Belajar AI Agents with LangChain & Autonomous Workflows dengan tutor AI — gratis
Tulis dan jalankan kode asli di browser kamu, dapatkan bantuan instan dari tutor AI 24/7, dan lanjutkan di mana kamu tinggalkan di web atau aplikasi.
- Kursus
- 12
- Pelajaran
- 50
Pertanyaan yang Sering Diajukan
Apakah pelajaran “Mendefinisikan dan Menggunakan Alat” gratis?
Ya — teks lengkap “Mendefinisikan dan Menggunakan Alat” gratis dibaca di sini di web. Untuk praktiknya secara interaktif (editor kode bawaan dan tutor AI 24/7) dan buka sisa kursus AI Agents with LangChain & Autonomous Workflows, upgrade ke CoddyKit PRO. Kursus AI Agents with LangChain & Autonomous Workflows mencakup 4 pelajaran total.
Apa yang akan aku pelajari di “Mendefinisikan dan Menggunakan Alat”?
Pelajari cara membuat dan mengintegrasikan alat yang memungkinkan agen melakukan tindakan seperti mencari di web atau mengeksekusi kode. Kamu berlatih AI Agents with LangChain & Autonomous Workflows dengan kode praktik yang langsung kamu jalankan di browser, dan tutor AI 24/7 menjawab pertanyaanmu saat kamu mengerjakan pelajaran ini.
Apakah aku perlu pengalaman untuk memulai AI Agents with LangChain & Autonomous Workflows?
Tidak diperlukan pengalaman sebelumnya. AI Agents with LangChain & Autonomous Workflows di CoddyKit dirancang untuk pemula hingga pelajar tingkat lanjut, jadi kamu bisa memulai di sini atau dari awal dan belajar sesuai kecepatan kamu sendiri. Ini adalah pelajaran 1 dari 4.
Berapa lama pelajaran “Mendefinisikan dan Menggunakan Alat” memakan waktu?
Sebagian besar pelajaran CoddyKit memakan waktu sekitar 5–10 menit. Setiap pelajaran ringkas dan interaktif, jadi kamu membuat kemajuan stabil dan melanjutkan dari tempat kamu tinggalkan di web dan aplikasi.
Bisakah aku menulis dan menjalankan kode dalam pelajaran AI Agents with LangChain & Autonomous Workflows ini?
Ya. Setiap pelajaran AI Agents with LangChain & Autonomous Workflows menyertakan editor kode bawaan, jadi kamu menulis dan menjalankan kode nyata langsung di browser dan mendapatkan umpan balik AI instan — tidak diperlukan penyiapan lokal.
Semua pelajaran dalam kursus ini
- Mendefinisikan dan Menggunakan Alat
- Jenis Agen dan Pengambilan Keputusan
- Memanfaatkan Perangkat Alat Siap Pakai
- Penanganan Kesalahan dan Eksekusi Alat yang Aman