Personal
InsightDocs — RAG over your docs
A retrieval-augmented generation app that answers questions about your Markdown docs with inline, verifiable citations.
PythonRAGLLMStreamlitChromaDB
Overview
InsightDocs is a retrieval-augmented generation (RAG) application for querying Markdown documentation. You upload docs, ask questions in plain English, and get answers with inline citations that link back to the exact source chunks — so every answer is verifiable.
What it does
- Hybrid retrieval — combines dense (semantic) and sparse (BM25 keyword) search using Reciprocal Rank Fusion for better recall
- Header-aware chunking — preserves document hierarchy as metadata so retrieved context stays coherent
- Cross-encoder reranking —
BAAI/bge-reranker-basereorders candidates for relevance before they reach the model - Citations & refusal — an expandable source panel shows the retrieved text, and the system refuses to answer when context is insufficient
- Evaluation harness — an ablation setup compares four retrieval configurations
Stack
Python · Streamlit (UI) · ChromaDB (vector store) · BM25 · OpenRouter for embeddings and chat · deployed on Hugging Face Spaces.
Why it's interesting
The focus is on trustworthy retrieval: hybrid search plus reranking plus a refusal mechanism means the system prefers saying "I don't know" over hallucinating — and always shows its work.