How to Build a RAG System From Scratch

Retrieval-Augmented Generation (RAG) is a powerful technique that enhances Large Language Models (LLMs) by providing them with external, up-to-date information.

1. Document Ingestion

The first step is gathering your documents. You can use loaders for PDFs, markdown files, or databases. The key is to extract clean text.

2. Text Splitting

LLMs have context windows, so you must split your text into manageable chunks. Use recursive character splitters to maintain paragraph context.

3. Embedding and Vector Stores

Convert your chunks into vector embeddings using models like text-embedding-3-small. Store these vectors in a database like Pinecone, Weaviate, or Chroma.

4. Retrieval and Generation

When a user asks a question, embed their query, search your vector store for the top-k most similar chunks, and pass those chunks as context to the LLM to generate an answer.