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AI Agents with LangChain & Autonomous Workflows · Aula

Aproveitando conjuntos de ferramentas prontos

Descubra e implemente conjuntos de ferramentas pré-configurados fornecidos pelo LangChain para adicionar rapidamente recursos aos seus agentes.

Aproveitando conjuntos de ferramentas prontos é uma aula grátis de AI Agents with LangChain & Autonomous Workflows no CoddyKit. Esta é a aula 3 de 4. Você pode ler a aula completa abaixo gratuitamente — depois pratica ao vivo no navegador com um editor de código integrado e um tutor de IA 24/7. Faz parte do caminho de aprendizado de AI Agents with LangChain & Autonomous Workflows, e seu progresso é sincronizado entre a web e o app CoddyKit. O curso de AI Agents with LangChain & Autonomous Workflows inclui 4 aulas no total.

Partes desta aula ainda não foram traduzidas e aparecem em inglês.

Intro to Pre-built Toolkits

Welcome to leveraging pre-built toolkits in LangChain! So far, you've learned to define and use individual tools. But what if you need a set of related tools?

Toolkits are collections of pre-configured tools designed for specific purposes. They bundle common functionalities, making it much easier to add powerful capabilities to your AI agents.

Why Use Toolkits?

Using toolkits offers several key advantages:

  • Time-saving: No need to define common tools from scratch.
  • Reduced Boilerplate: Less code to write and manage.
  • Consistency: Ensures tools are implemented correctly and consistently.
  • Expanded Capabilities: Instantly equip your agent with complex functionalities like web search, database interaction, or advanced math.

Example: The Math Toolkit

Let's start with a simple yet powerful example: the Math Toolkit. This toolkit provides basic arithmetic operations, allowing your agent to perform calculations.

It's a great way to see how a collection of tools can be integrated and used by an agent to solve problems that require numerical processing.

Initializing a Toolkit

First, you need to import and initialize the toolkit. Here's how you can set up the MathToolkit and inspect the tools it provides:

from langchain_community.agent_toolkits import MathToolkit

def main():
    # Initialize the Math Toolkit
    toolkit = MathToolkit()
    print("MathToolkit initialized!")
    print("\nTools available in this toolkit:")
    # Iterate and print details of each tool
    for tool in toolkit.get_tools():
        print(f"- {tool.name}: {tool.description}")

if __name__ == "__main__":
    main()

Agents Using Toolkits

Once a toolkit is initialized, you pass its collection of tools to your agent, just like you would with individual tools.

The agent's reasoning engine will then intelligently decide which tool from the toolkit is best suited to answer a given query or complete a task.

Agent Solving Math Problems

Watch this agent use the MathToolkit to perform a multiplication. Notice how the agent 'thinks' about the problem and selects the correct tool.

(Remember to replace YOUR_API_KEY with your actual OpenAI API key for the code to run.)

from langchain_community.llms import OpenAI
from langchain.agents import initialize_agent, AgentType
from langchain_community.agent_toolkits import MathToolkit
import os

def main():
    # Set your OpenAI API key here
    # os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY" # Uncomment and replace

    if "OPENAI_API_KEY" not in os.environ:
        print("Error: OPENAI_API_KEY environment variable not set.")
        print("Please set it to run this example.")
        return

    llm = OpenAI(temperature=0) # Using a simple LLM

    toolkit = MathToolkit()
    tools = toolkit.get_tools()

    # Initialize the agent with the LLM and the toolkit's tools
    agent = initialize_agent(
        tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
    )

    print("\nAgent at work (verbose output shows thinking process):\n")
    agent.run("What is 12345 * 6789?")

if __name__ == "__main__":
    main()

Exploring Other Powerful Toolkits

LangChain offers many other pre-built toolkits for diverse tasks:

  • Wikipedia Toolkit: For agents needing to search and retrieve information from Wikipedia.
  • OpenAPI Toolkit: Allows agents to interact with any API described by an OpenAPI spec.
  • SQL Database Toolkit: Enables agents to query and interact with SQL databases.
  • Python Agent Toolkit: Allows agents to write and execute Python code.

Each toolkit significantly expands your agent's capabilities!

Discovering More Toolkits

Want to find more toolkits? Here's how:

  • LangChain Documentation: The official docs are the best resource.
  • Source Code: Explore the langchain_community.agent_toolkits module directly.
  • Community Examples: Look at how others are using LangChain in GitHub repos or tutorials.

New toolkits are constantly being developed and added!

Toolkit Quick Check

What is the primary benefit of using pre-built toolkits in LangChain?

Recap: Toolkit Power-Up!

Great job! In this lesson, you learned about:

  • What pre-built toolkits are in LangChain.
  • The significant benefits they offer, like saving time and expanding capabilities.
  • How to initialize and integrate toolkits (like the MathToolkit) with your agents.
  • Where to discover more powerful toolkits for various tasks.

Toolkits are a fantastic way to quickly supercharge your agents!

Perguntas Frequentes

A aula “Aproveitando conjuntos de ferramentas prontos” é grátis?

Sim — o texto completo de “Aproveitando conjuntos de ferramentas prontos” é grátis para ler aqui na web. Para praticá-la interativamente (um editor de código integrado e um tutor de IA 24/7) e desbloquear o restante do curso de AI Agents with LangChain & Autonomous Workflows, atualize para CoddyKit PRO. O curso de AI Agents with LangChain & Autonomous Workflows inclui 4 aulas no total.

O que vou aprender em “Aproveitando conjuntos de ferramentas prontos”?

Descubra e implemente conjuntos de ferramentas pré-configurados fornecidos pelo LangChain para adicionar rapidamente recursos aos seus agentes. Você pratica AI Agents with LangChain & Autonomous Workflows com código prático que executa diretamente no navegador, e um tutor de IA 24/7 responde suas dúvidas enquanto trabalha na aula.

Preciso ter experiência prévia para começar AI Agents with LangChain & Autonomous Workflows?

Nenhuma experiência prévia é necessária. AI Agents with LangChain & Autonomous Workflows no CoddyKit é estruturado para alunos iniciantes até avançados, então você pode começar aqui ou desde o início e aprender no seu ritmo. Esta é a aula 3 de 4.

Quanto tempo leva a aula “Aproveitando conjuntos de ferramentas prontos”?

A maioria das aulas CoddyKit leva cerca de 5–10 minutos. Cada uma é compacta e interativa, então você faz progresso constante e retoma exatamente de onde parou entre web e app.

Posso escrever e executar código nesta aula de AI Agents with LangChain & Autonomous Workflows?

Sim. Cada aula de AI Agents with LangChain & Autonomous Workflows inclui um editor de código integrado, então você escreve e executa código real direto no navegador e recebe feedback de IA instantaneamente — nenhuma configuração local necessária.

Todas as aulas deste curso

  1. Definindo e usando ferramentas
  2. Tipos de agentes e tomada de decisões
  3. Aproveitando conjuntos de ferramentas prontos
  4. Tratamento de erros e execução segura de ferramentas
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