Curso COMPLETO Langchain - ChatGPT para desenvolvedores

Introduction

This tutorial provides a comprehensive guide to using Langchain with ChatGPT, suitable for both beginners and advanced developers. You'll learn how to create integrated applications with ChatGPT by following a structured approach that covers essential concepts, tools, and practical implementations.

Step 1: Understanding OpenAI

• Familiarize yourself with OpenAI’s offerings and capabilities.
• Sign up for an API key on the OpenAI website to get started.
• Explore the documentation to understand how to leverage the API for your applications.

Step 2: Introduction to Langchain

• Learn what Langchain is and its role in developing applications using language models.
• Understand how Langchain simplifies the process of building applications by providing tools and abstractions.
• Review the core components of Langchain, including models, templates, parsers, and chains.

Step 3: Working with Models

• Explore the different models available through Langchain.
• Understand which model to choose based on your application needs.

Key Points

• Models vary in terms of capabilities and performance; select one that aligns with your goals.
• Use the following code snippet to load a model in your application:

  from langchain import load_model
  
  model = load_model("gpt-3.5-turbo")
  

Step 4: Utilizing Templates

• Learn how to create templates for generating prompts.
• Templates help in structuring interactions with the model effectively.

Practical Advice

• Use placeholders in your templates to make them dynamic.
• Example of a simple template:

  template = "Hello, {name}! How can I assist you today?"
  

Step 5: Implementing Parsers

• Understand the purpose of parsers in processing input and output data.
• Learn how to create a parser that formats the model’s responses for easier consumption.

Example Code

from langchain.parsers import SimpleParser

parser = SimpleParser()
response = parser.parse("Your model's response here")

Step 6: Building Chains

• Chains are sequences of actions that facilitate complex logic in your application.
• Discover how to create and manage chains effectively.

Steps to Create a Chain

1. Define the actions (e.g., model calls, data processing).
2. Connect actions in a logical sequence.
3. Execute the chain and handle the output.

Step 7: Developing Chat Applications

• Learn how to build a chat application using Langchain.
• Use the chain functionality to manage conversation flows.

Implementation Tips

• Maintain context in conversations by storing previous interactions.

Use the following code to handle user input and generate responses:

def chat(input_text)

response = model.generate(input_text) return response

Step 8: Creating a Q&A Application

• Use Langchain to develop a question-and-answer application that retrieves information based on user queries.
• Implement search and retrieval logic using chains.

Example of Q&A Logic

def answer_question(question)

Step 9: Exploring Agents

• Understand what agents are and how they differ from chains.
• Learn to create agents that can perform tasks based on user intentions.

Key Considerations

• Agents can autonomously make decisions based on input data.
• Consider using agents for more interactive and dynamic applications.

Conclusion

In this tutorial, you’ve learned the foundational aspects of integrating Langchain with ChatGPT to create various applications. Key takeaways include understanding OpenAI's API, leveraging models and templates, implementing parsers and chains, and developing chat and Q&A applications. As next steps, consider experimenting with your own projects using the concepts covered, and explore the official Langchain documentation for more advanced features.