RAG: Give Your Agent a Brain
Retrieval-Augmented Generation from scratch: embedding documents, vector stores, retrieval strategies, and integrating retrieval into LangGraph agents.
Quick Reference
- →RAG = Retrieve relevant documents, Augment the prompt with them, Generate an answer grounded in the retrieved context
- →Use a text splitter (RecursiveCharacterTextSplitter) to chunk documents into 500-1000 token pieces with overlap
- →Embed chunks with an embedding model (e.g., text-embedding-3-small) and store in a vector database (Pinecone, Chroma, pgvector)
- →Retrieve top-k documents (k=3-5) using similarity search and inject them into the system prompt as context
- →In LangGraph, implement RAG as a retrieve node → generate node pipeline with the retrieved docs passed via state
RAG Pipeline Overview
RAG gives agents access to private, up-to-date, or domain-specific knowledge that is not in the model's training data. Retrieve relevant documents from a knowledge base, augment the prompt with that context, and generate an answer grounded in the retrieved content.
RAG flow: query is embedded, similar docs are retrieved from a vector store, then fed to the LLM for grounded generation
The pipeline has two phases. Indexing (offline, done once): load documents, split into chunks, embed each chunk, store vectors. Retrieval + generation (online, per query): embed the user query, search for similar vectors, inject the top-k documents into the prompt, generate a grounded answer.