AI Retrieval-Augmented Generation Platform

Summary

A Retrieval-Augmented Generation (RAG) architecture that combines vector similarity search for semantic document retrieval with relational metadata filtering, using PostgreSQL with pgvector as the unified store for both embeddings and structured data. Redis provides a semantic cache to avoid redundant embedding model inference and reduce vector index query load for repeated or similar queries. Kafka manages the asynchronous embedding generation pipeline that keeps the vector index current as source documents are added or updated.

Problem Statement

LLM-based applications require retrieval of semantically relevant context before generation. Storing embeddings in a dedicated vector database while relational metadata lives in a separate OLTP store creates synchronization complexity and cross-store query latency. Using PostgreSQL with pgvector consolidates both concerns in one operational unit while supporting hybrid queries (vector similarity + SQL predicate filtering). The embedding generation pipeline must be asynchronous to prevent document ingestion latency from blocking the write path.