พัฒนาแอพพลิเคชั่น LLM ด้วย Python & LangChain | สำหรับผู้เริ่มต้น [FULL COURSE]

Introduction

This tutorial will guide you through developing applications using Large Language Models (LLMs) with Python and LangChain. It is designed for beginners and covers everything from setting up your environment to implementing advanced features such as retrieval-augmented generation (RAG). By following these steps, you'll gain a solid understanding of how to leverage LLMs in your applications.

Step 1: Understand LangChain

• Familiarize yourself with LangChain, a framework that simplifies the creation of applications powered by LLMs.
• Explore its capabilities, including integrating different tools and managing interactions with language models.

Step 2: Sign Up for OpenAI API

• Go to the OpenAI API signup page.
• Create an account to obtain your API key.
• Keep your API key secure as it will be used to authenticate your application.

Step 3: Set Up Your Development Environment

• Install Python on your machine if you haven't already.
• Use a virtual environment to manage dependencies:

  python -m venv myenv
  source myenv/bin/activate  # On Windows use myenv\Scripts\activate
  

• Install essential packages, including LangChain:

  pip install langchain openai
  

Step 4: Configure Environment Variables

• Store your OpenAI API key as an environment variable for security:

  export OPENAI_API_KEY='your_api_key_here'  # On Windows use set OPENAI_API_KEY='your_api_key_here'
  

Step 5: Learn About Chat Models

• Understand the different chat models available through OpenAI.
• Explore how to interact with these models using Python code.

Step 6: Experiment with Temperature Settings

• Learn about the temperature parameter, which controls the randomness of the model’s output.
• Adjust the temperature for different use cases:
  • Low temperature (0.2-0.5) for more deterministic outputs.
  • High temperature (0.6-1.0) for more creative responses.

Step 7: Use Prompt Templates and Chains

• Create prompt templates to standardize inputs for your model.
• Implement chains to manage sequences of operations or interactions within your app.

Step 8: Understand Message Types

• Differentiate between various message types that can be sent to and from the model.
• Implement these message types to enhance user interaction.

Step 9: Implement Output Parsers

• Use output parsers to format the model’s responses in a usable way.
• This step ensures that the output is structured according to your application’s needs.

Step 10: Utilize Built-in Tools for Web Search

• Explore LangChain’s built-in tools for integrating web search capabilities.
• This feature allows your application to fetch real-time information from the web.

Step 11: Explore Retrieval-Augmented Generation

• Learn about RAG, a technique that combines retrieval and generation for improved responses.
• Implement RAG in your application to enhance the quality of output based on context.

Step 12: Work with Document Loaders

• Use document loaders to manage and load data into your application.
• This is crucial for applications that rely on external documents or datasets.

Step 13: Split Text into Chunks

• Implement text splitters to break down large texts into manageable chunks.
• This helps in processing large documents and improves response accuracy.

Step 14: Understand Embeddings and Vector Stores

• Learn about embeddings, which convert text into numerical representations.
• Use vector stores to efficiently manage and retrieve these embeddings.

Step 15: Set Up Retrievers and Chains

• Implement retrievers to fetch relevant data based on user queries.
• Create chains to connect different components of your application seamlessly.

Conclusion

In this tutorial, you have learned how to develop applications using LLMs with Python and LangChain. From setting up your environment to implementing advanced features like RAG and document loaders, you now have the tools necessary to create powerful language-based applications. To continue your learning, explore the LangChain documentation and experiment with various models and features. Happy coding!