🤖 RAG Chatbot (Python + FAISS)

A minimal, production-ready Retrieval-Augmented Generation (RAG) chatbot written in Python. It embeds your FAQ/docs, stores vectors in FAISS, retrieves relevant chunks for each query, and generates answers with an LLM (OpenAI-compatible by default).

✅ Great for prototypes, local demos, and as a base to evolve into ElasticSearch/Milvus/Pinecone or Agentic AI.

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Step 1 – Add Your Documents

Place your content as plain text in: data/docs/

Sample files included:

• faq_getting_started.txt
• faq_account.txt
• faq_billing.txt

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Step 2 — Build the Vector Index

Generate the vector database for retrieval: python build_index.py

This will produce:

• data/vector.index
• data/chunks.npy

Step 3 — Run the Chatbot

Start the chatbot: python chatbot.py

Example dialogue: User: How do I reset my password? Bot: You can reset your password from the Account → Security → Change Password page.

To exit, type: exit

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How the RAG Pipeline Works

1. Load documents
2. Chunk content
3. Generate embeddings
4. Build FAISS index
5. Embed user query
6. Retrieve top-K chunks
7. Inject retrieved context
8. LLM generates the answer

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License

MIT — free for personal and commercial use