RAG Explained with Python: Build a Document‑QA in One Weekend

In this weekend project, you'll build a simple Retrieval‑Augmented Generation (RAG) app in Python that can answer questions from your own PDFs or notes. Perfect for students and beginners who want a practical AI project.

🧰 What You'll Use

• Python + Jupyter/Colab
• Embeddings (e.g., sentence-transformers)
• Vector DB (FAISS or Chroma)
• LLM API for generation

🪜 Steps

1. Collect small PDF/text files. Split into chunks.
2. Create embeddings for chunks. Store in FAISS/Chroma.
3. On a question: retrieve top‑k chunks.
4. Build a prompt with retrieved context + question.
5. Call LLM API. Show answer with sources.

⚠️ Pitfalls

• Chunk size too large — retrieval gets noisy.
• Missing text normalization — hurts recall.
• No source display — users can't verify answers.

🚀 Next Steps

• Add a minimal web UI
• Support multiple files and file types
• Deploy and share a public demo

📚 Official docs you should bookmark

• Sentence Transformers / embeddings: sbert.net (training and inference library used in countless RAG tutorials).
• FAISS (Facebook AI Similarity Search): github.com/facebookresearch/faiss.
• Chroma: docs.trychroma.com.
• LangChain retrieval concepts: LangChain documentation (abstractions change—pin versions in your repo).
• LlamaIndex: docs.llamaindex.ai.
• OpenAI API (generation): platform.openai.com/docs.

🧪 Level up: hybrid retrieval & evaluation

Weekend prototypes usually start with pure vector search. Production systems often add keyword/BM25 and merge rankings—see our hybrid search + RRF guide (with Elasticsearch, Pinecone, and Weaviate citations). Also read LLM evaluation basics so you test answers against a small golden set instead of guessing.