RAG Document Q&A App
Build a complete Retrieval-Augmented Generation (RAG) system to ask questions against a private company handbook.
The Problem
Build a complete Retrieval-Augmented Generation (RAG) system to ask questions against a private company handbook.
Real-World Use Case
Build a complete Retrieval-Augmented Generation (RAG) system to ask questions against a private company handbook.
Technology Stack
Python
Prerequisite
ChromaDB or FAISS
Prerequisite
LangChain
Prerequisite
Architecture & Design
Folder Structure
rag/
├── ingest.py
└── query.pyStep-by-Step Implementation
Ingest text files
Install chromadb, langchain, sentence-transformers.
# Full RAG pipelineCode Explanation
Implementation step
Create Vector DB locally
Use local embeddings (HuggingFace) to save costs, and OpenAI for the final answer generation.
# Full RAG pipelineCode Explanation
Implementation step
Embed user query
CLI or simple Streamlit app.
# Full RAG pipelineCode Explanation
Implementation step
Fetch top 3 chunks
Handle out-of-domain questions gracefully ('I don't know').
# Full RAG pipelineCode Explanation
Implementation step
Inject into LLM prompt
Handle out-of-domain questions gracefully ('I don't know').
# Full RAG pipelineCode Explanation
Implementation step
Common Errors
Adjust chunk size and overlap.
Security & Performance
Ask a question contained in the docs
Ask a completely unrelated question
Add source citations to the answer
Interview Questions
Q: Why not just paste the doc into ChatGPT?
A: RAG scales to thousands of documents that wouldn't fit in a prompt.