AyurMind
AyurMind is a specialized RAG system designed to retrieve and generate insights from Ayurvedic medical texts. It combines dense retrieval with domain-specific embedding models to provide accurate, context-aware answers about Ayurvedic medicine, treatments, and formulations.
Problem
Ayurvedic knowledge is scattered across ancient texts in Sanskrit and regional languages, making it inaccessible to modern practitioners and researchers.
Solution
Built a domain-specific RAG pipeline that chunks, embeds, and retrieves from digitized Ayurvedic texts, then uses GPT-4 to generate contextual responses.
Architecture
- Text extraction: OCR pipeline for digitized manuscripts
- Chunking: Semantic chunking with overlap for context preservation
- Embeddings: Fine-tuned Sentence-BERT on Ayurvedic corpus
- Vector store: ChromaDB for efficient similarity search
- Retrieval: Hybrid search (dense + BM25) with re-ranking
- Generation: GPT-4 with domain-specific prompts
Results
90% retrieval accuracy on domain-specific queries, deployed as a web app with Streamlit, used by 50+ Ayurvedic practitioners.
FAQs
How accurate are the responses?
The system achieves 90% retrieval accuracy. All responses include source citations from the original texts for verification.