Building a RAG application from scratch using Python, LangChain, and the OpenAI API

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AI Summary

Summary: Building Retrieval-Augmented Generation Systems

Introduction

• Skill: Building retrieval-augmented generation systems with Python and OpenAI API.
• Focus: GPT-3.5 Turbo model and Lan chain framework.
• Goal: Understand the code and reasoning behind each component.

Setup

• Tools: Visual Studio Code, Jupyter Notebook extension.
• Repository with code and setup instructions provided.
• Steps:
  1. Install Python virtual environment and required libraries.
  2. Create Pinecone account, copy API key.
  3. Create .env file with OpenAI and Pinecone API keys.

Application Overview

• Create an application to ask questions about a YouTube video’s content.
• Example: Interview with Andrew Karpathy and Lex Fridman.
• Process: Transcribe video, ask model questions, receive answers based on the transcript.

Building the Solution

• Load environment variables for OpenAI API key.
• Set up the GPT-3.5 Turbo model using Lan chain.
• Test model with sample questions.
• Use Lan chain’s string output parser to format model responses.
• Create a chat prompt template for the model to answer questions based on context.
• Introduce Lan chain’s concept of chaining components for complex tasks.
• Split the video transcript into manageable chunks.
• Use embeddings to determine the relevance of transcript chunks to a question.
• Implement a vector store for efficient similarity search among chunks.
• Create a retriever to fetch relevant chunks from the vector store.
• Chain the retriever, prompt, model, and parser to answer questions from the video content.
• Load transcript chunks into Pinecone, a vector database, for persistent storage and retrieval.

Conclusion

• Demonstrated the use of Lan chain to build a system that answers questions from a video transcript.
• Highlighted the importance of embeddings and vector stores in handling large text data.
• Provided a practical example with a YouTube video transcript and Pinecone vector store.

GitHub - svpino/youtube-rag