Build a RAG Q&A Bot over Your Own Documents

A general chatbot does not know your internal docs, and pasting everything into a prompt does not scale or stay current.

Retrieval augmented generation fetches only the relevant chunks and grounds the model on them, so answers cite your real content and stay affordable.

Use this for support knowledge bases, internal wikis, product docs, or any Q&A over a document set that changes.

Python, an embeddings-capable LLM API key (kept in an env var, never in code), and a local vector store such as Chroma or FAISS.

Index then answer

from chromadb import Client
# 1) chunk your docs (~800 tokens, small overlap)
# 2) embed each chunk and add to the store
col = Client().create_collection('docs')
col.add(ids=ids, documents=chunks, embeddings=embeds)
# 3) at query time: embed the question, fetch top-k, and prompt the LLM
hits = col.query(query_embeddings=[q_embed], n_results=4)
answer = llm(f"Answer using only this context:\n{hits}\n\nQ: {question}")

Chunk and embed your documents into the vector store once. At query time, embed the question, retrieve the closest chunks, and ask the model to answer using only that context. Return the sources with the answer.

You have a grounded Q&A bot over your own content that stays cheap and current. Re-index when docs change and it keeps up.