Build Your First RAG System

Step 1: Set up your environment

Install LangChain, OpenAI SDK, and a vector database like Chroma or Pinecone. Configure your API keys securely.

pip install langchain openai chromadb

Step 2: Load and chunk your documents

Load your documents and split them into manageable chunks. Use text splitters that preserve context and meaning.

Step 3: Create embeddings

Generate vector embeddings for your document chunks using OpenAI’s embedding model. Store them in your vector database.

Step 4: Build the retrieval system

Implement semantic search to find relevant document chunks based on user queries. Use similarity search with your vector database.

Step 5: Integrate with LLM

Connect your retrieval system to an LLM (like GPT-4) to generate answers based on retrieved context. Add prompt engineering for better results.

Step 6: Evaluate your system

Test your RAG system with sample questions. Measure accuracy, relevance, and response quality. Iterate based on results.