AI Data Retrieval System (RAG + pgvector)
Project Overview
Designed and implemented a production-ready AI-driven retrieval system combining structured data ingestion, vector embeddings, and semantic search. The solution enables efficient similarity-based retrieval and serves as a foundation for retrieval-augmented generation (RAG) workflows.
Business Context
Organizations require accurate, context-aware retrieval from semi-structured data sources. Traditional keyword search lacks semantic understanding and fails to scale for AI-powered applications.
Solution
Built a scalable retrieval architecture integrating OpenAI embeddings with PostgreSQL (pgvector) to enable efficient vector similarity search and structured response generation.
Architecture Highlights
- OpenAI embedding generation pipeline
- PostgreSQL with pgvector extension
- Indexed vector columns for cosine similarity search
- FastAPI endpoints for RAG workflows
- Modular ingestion → embedding → retrieval pipeline
Tech Stack
Results
- Production-ready semantic search layer
- Low-latency similarity queries via indexed vectors
- Clean separation between ingestion, storage, and retrieval layers
- Architecture ready for scaling and additional AI enrichment