pythonadvanced4 hr est.
RAG Document Q&A App
Build a complete Retrieval-Augmented Generation (RAG) system to ask questions against a private company handbook.
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Last updated: 2026-06-05
Before You Begin
- 1Python
- 2ChromaDB or FAISS
- 3LangChain
Project Architecture
Folder Structure
rag/ ├── ingest.py └── query.py
Data Flow
[Docs] -> [Chunks & Embeddings] -> [Vector DB] -> [Retrieve context] -> [LLM answers query]
Source Code Breakdown & Implementation
Install chromadb, langchain, sentence-transformers.
Use local embeddings (HuggingFace) to save costs, and OpenAI for the final answer generation.
CLI or simple Streamlit app.
Handle out-of-domain questions gracefully ('I don't know').
Complete Solution Code
Compare your approach
Testing Checklist
- • Ask a question contained in the docs
- • Ask a completely unrelated question
Common Bugs
Bug: Poor retrieval
Fix: Adjust chunk size and overlap.