RAG for Medical equipment marketplace

Overview

Knowledge base system for medical device documentation with semantic search capabilities. Pipeline: Web scraping of manufacturer manuals for specified medical devices → chunking → indexing with metadata → storage in vector database. Core Functionality: On query, retrieves relevant documentation and specifications for a given medical device.

Achievements

• Built a comprehensive knowledge base covering manufacturer manuals and specifications for medical devices. • Achieved high retrieval accuracy through optimized chunking strategies and metadata enrichment. • Reduced query response time to sub-second levels through embedding model selection and vector store optimization (as mixed search via cosine similarity and metadata usage).

Responsibilities

• Designed and implemented web scraping pipelines to collect manufacturer manuals and device documentation.
• Developed chunking and indexing strategies with metadata tagging for accurate retrieval.
• Configured ChromaDB as vector store with metadata filtering for device-specific queries.
• Integrated all-MiniLM-L6-v2 embedding model for semantic search capabilities.
• Built RAG pipeline using LangChain with Llama 2 as the generation model.
• Set up distributed processing with Ray.io for scalable document ingestion.