How to Build a RAG Pipeline with Open-Source Tools

Retrieval-Augmented Generation (RAG) is the gold standard for providing LLMs with private, up-to-date data. Building one with open-source tools ensures you have full control over your data and costs.

Step 1: Document Ingestion and Chunking

The first step is to break your documents into smaller, manageable "chunks." Using libraries like LangChain or LlamaIndex, you can split PDFs, text files, or even websites into segments of 500-1000 tokens. This ensures that the context provided to the LLM is relevant and doesn't exceed the context window.

Step 2: Vectorization and Storage

Next, you need to convert these chunks into vector embeddings using a model like BGE or FastEmbed. Store these embeddings in an open-source vector database like Qdrant or Milvus. This allows you to perform "semantic search," finding the most relevant pieces of information based on the meaning of a user query rather than just keywords.