LLMs RAG ทำงานอย่างไร

Introduction

This tutorial explains how Retrieval-Augmented Generation (RAG) works, based on the insights from the VulturePrime video. RAG combines the power of large language models (LLMs) with retrieval systems to enhance the quality of responses generated by AI. This guide will walk you through the concepts, the architecture of RAG, and practical steps to implement it.

Step 1: Understand the Basics of RAG

• RAG is a framework that integrates LLMs with a retrieval mechanism.
• It improves the model's ability to provide accurate and relevant information by pulling data from external sources.
• Key components of RAG include:
  • Retrieval System: Fetches relevant documents or data.
  • Language Model: Generates coherent and contextually appropriate responses.

Step 2: Explore Frontend and Backend Architecture

• The RAG architecture consists of two main parts:
  • Frontend: Handles user interaction and displays results.
  • Backend: Manages data retrieval and processing using a language model.

Frontend Details

• The frontend can be built using web technologies like HTML, CSS, and JavaScript.
• It interacts with the backend through APIs to fetch data based on user queries.

Backend Details

• The backend is responsible for:
  • Document retrieval from a database.
  • Processing the retrieved documents with an LLM to generate responses.
• You can find examples of frontend and backend implementations in the provided GitHub repositories.

Step 3: Set Up Your Development Environment

• To start developing RAG applications, you need to set up your environment:
  • Install Required Tools: Ensure you have Node.js, a code editor, and Git installed.
  • Clone Repositories:
    • Frontend:

      git clone https://github.com/vultureprime/ai-web-interface/tree/main/next-rag-faqs
      

    • Backend:

      git clone https://github.com/vultureprime/ai-web-backend/tree/main/RAG-openai-langchain
      

Step 4: Implement the Frontend

• Create user interfaces for query input and results display.
• Use frameworks like React or Vue.js for a responsive design.
• Connect the frontend to the backend API endpoints to send user queries.

Step 5: Implement the Backend

• Set up a server using frameworks like Express.js.
• Implement routes to handle incoming requests and respond with data.
• Integrate the retrieval system to fetch documents based on user queries.

Step 6: Test and Deploy

• After implementation, thoroughly test your application:
  • Ensure the frontend communicates correctly with the backend.
  • Validate that the retrieval mechanism provides relevant documents.
• Deploy your application using platforms like Heroku or Vercel.

Conclusion

RAG enhances the capabilities of language models by integrating them with retrieval systems, enabling more accurate responses. By following this tutorial, you have learned the key steps to set up a RAG application, from understanding its components to deploying your project. For further learning, consider exploring the educational programs and resources provided by VulturePrime to deepen your understanding of AI development.