RAG with LangChain and OpenAI

Retrieval-Augmented Generation (RAG) grounds an LLM in your data: you retrieve the most relevant chunks and feed them to the model as context, so answers cite your documents instead of the model's memory.

The pipeline in four steps

1. Load & split your documents into chunks.
2. Embed the chunks and store them in a vector database.
3. Retrieve the top-k chunks for a user query.
4. Generate an answer with the query + retrieved context.

Minimal LangChain sketch

from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain_community.vectorstores import FAISS

store = FAISS.from_documents(docs, OpenAIEmbeddings())
retriever = store.as_retriever(search_kwargs={"k": 4})
# feed retriever results + question into a ChatOpenAI prompt

What makes RAG good

The quality is mostly in the retrieval: sensible chunking, good embeddings, and the right k. Get those right and the generation step almost takes care of itself.