RAG-Based Document Q&A System
Highly concurrent vector retrieval pipeline packaged for instant deployment.
Querying large, unstructured document corpora requires an architecture capable of handling concurrent ingestion and retrieval requests without blocking system throughput.
Designed a highly concurrent retrieval-augmented generation pipeline using FastAPI and Python. Implemented document chunking, embedding, and optimized index writes to a Pinecone Vector Database, packaging the entire environment into Docker containers with configurable worker counts.
Achieved consistent retrieval latency and relevance scores under high concurrent load in a production-like environment.
Key Technical Highlights
FastAPI async endpoints handle concurrent ingestion and retrieval without blocking
Intelligent document chunking with overlap for context preservation
Optimized batch index writes to Pinecone Vector Database
Docker containers with configurable worker counts for horizontal scaling
Consistent retrieval latency under high concurrent load
Production-grade environment packaging for instant deployment