How to Build a Simple RAG Knowledge Base Chatbot with Open-Source Tools

A RAG (Retrieval-Augmented Generation) chatbot answers questions by combining two systems: retrieval (find the best knowledge-base passages) and generation (use an LLM to write a response grounded in those passages). If retrieval is good, the model has evidence—so hallucinations drop and answers become auditable.

This guide walks you through a minimal, practical RAG setup with open-source tools: document ingestion, chunking, embeddings, vector storage, retrieval, citations, follow-ups, and evaluation.

1. What RAG Is (And Why It Works)

• RAG retrieves: semantic search finds relevant chunks by meaning, not exact keywords.
• RAG grounds: the LLM is instructed to answer using only the provided context.
• RAG adds traceability: citations point to the exact sources used.
• RAG updates easily: you can refresh the index without retraining a model.

2. Minimal RAG Architecture

Documents → Clean + Chunk → Embeddings → Vector Store
User question → Embed query → Retrieve top-k chunks (+ optional rerank)
LLM prompt (question + retrieved chunks + rules) → Grounded answer + citations

3. Open-Source Stack Options

• LLM (local): Ollama (simple local model runner)
• Embeddings: SentenceTransformers (lightweight, reliable)
• Vector store: Qdrant (service), Chroma (simple), FAISS (embedded index)
• App layer: Python + FastAPI/Flask; UI via simple web page or Streamlit

4. Step 1: Ingest and Normalize Documents

Good ingestion is mostly about consistency:

• Extract text cleanly: remove navigation, repeated headers/footers.
• Normalize: whitespace, bullets, and broken lines.
• Preserve structure: headings, section titles, and source URLs.
• Track metadata: doc_id, title, section, path, updated_at.

5. Step 2: Chunking Strategy (Settings That Matter)

Chunking is the #1 RAG lever. Start with:

• Chunk size: ~300–600 tokens
• Overlap: ~10–20%
• Split rules: prefer heading/paragraph boundaries over raw character splits
• Keep IDs stable: chunk_id should be deterministic for updates

6. Step 3: Create Embeddings

• Use one embedding model consistently for documents and queries.
• Store raw text + metadata alongside the vector.
• Batch processing improves speed for large ingests.

7. Step 4: Store Vectors + Metadata

Your vector store record should include:

• vector: embedding
• payload: chunk_text, doc_title, section, source_url, tags, updated_at
• ids: doc_id, chunk_id

8. Step 5: Retrieval (Top-K, Filters, Rerank)

• Top-k: start with 5–10 chunks retrieved
• Filters: restrict by tag, product, date, or doc source
• Reranking: optional but high impact for precision (especially in noisy corpora)
• Diversity: avoid returning 10 near-duplicate chunks from one section

9. Step 6: Grounded Answer Prompt + Citations

Prompt rules that materially reduce hallucinations:

• Use only provided context; if insufficient, say so.
• Cite sources next to claims (chunk IDs or titles/sections).
• Prefer quotes for definitions when accuracy matters.
• Ask clarifying questions when the query is ambiguous.

Answer format example (conceptual)
- Short answer
- Steps / details
- Citations: [doc_title §section] or [chunk_id]

10. Step 7: Follow-Ups and Conversation Memory

• Short-term memory: keep the last few user turns in the prompt.
• Question rewriting: convert follow-ups into standalone queries for retrieval.
• Keep retrieval fresh: retrieve again on every turn (don’t rely only on prior context).

11. Step 8: Evaluate and Improve Quality

Evaluate both retrieval and generation:

• Retrieval: does the top-k include the answer-bearing chunk?
• Grounding: does the answer stick to provided evidence?
• Helpfulness: does it give actionable steps and handle ambiguity?
• Safety: does it avoid leaking secrets or making risky claims?

12. Deployment Checklist (Local to Production)

• Index refresh: scheduled re-ingest for changed docs.
• Observability: log query, retrieved chunk IDs, latency, and errors.
• Access control: restrict sources by user/team where needed.
• PII handling: avoid indexing sensitive data without policy and controls.
• Fallback behavior: “I don’t know” + escalate path.
• Rate limiting: protect the service and the model runtime.

13. FAQ: RAG Chatbots

Can I run RAG fully locally?

Yes. You can run a local LLM (via Ollama), local embeddings, and a local vector index. Performance depends on your hardware and corpus size.

Do I need reranking?

Not for every dataset, but reranking often improves precision—especially when many chunks are semantically similar.

How do I reduce hallucinations the most?

Improve retrieval quality and enforce “answer only from context” prompting, plus citations and an explicit “insufficient context” fallback.

How do I handle PDFs and messy docs?

Extract and normalize text carefully, remove repeated boilerplate, and chunk by headings where possible. Keep source metadata so you can debug retrieval quickly.

What should I store in metadata?

At minimum: doc title, section heading, source URL/path, updated time, doc_id, and chunk_id. Metadata enables filters and trustworthy citations.

Key RAG terms (quick glossary)

RAG

Retrieval-Augmented Generation: retrieve relevant passages, then generate an answer grounded in them.

Embedding

A vector representation of text that captures semantic meaning for search.

Vector database

A system optimized for storing vectors and running similarity search with metadata filters.

Chunk

A smaller segment of a document (with metadata) used for retrieval and prompting.

Top-k

The number of retrieved chunks returned by the vector search step.

Reranker

A model that re-sorts retrieved candidates to improve relevance/precision.

Grounding

Constraining the model to answer using provided evidence (and cite it).

Query rewriting

Converting follow-up questions into standalone queries to improve retrieval.