RAG-Based Document Q&A System
Highly concurrent vector retrieval pipeline packaged for instant deployment.
Problem
Querying large, unstructured document corpora requires an architecture capable of handling concurrent ingestion and retrieval requests without blocking system throughput.
Approach
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.
Outcome
Achieved consistent retrieval latency and relevance scores under high concurrent load in a production-like environment.
Technical Highlights
(6)- 01
FastAPI async endpoints handle concurrent ingestion and retrieval without blocking
- 02
Intelligent document chunking with overlap for context preservation
- 03
Optimized batch index writes to Pinecone Vector Database
- 04
Docker containers with configurable worker counts for horizontal scaling
- 05
Consistent retrieval latency under high concurrent load
- 06
Production-grade environment packaging for instant deployment