Power your RAG and Agentic applications with PostgreSQL
As of February 2026, this project is no longer being maintained or supported.
A Python library that transforms PostgreSQL into a robust, production-ready retrieval engine for RAG and Agentic applications. - 🔄 **Automatically create and synchronize vector embeddings** from PostgreSQL data and S3 documents. Embeddings update automatically as data changes. - 🤖 **[Semantic Catalog](/docs/semantic_catalog/README.md): Enable natural language to SQL with AI**. Automatically generate database descriptions and power text-to-SQL for agentic applications. - 🔍 Powerful vector and semantic search with pgvector and pgvectorscale. - 🛡️ Production-ready out-of-the-box: Supports batch processing for efficient embedding generation, with built-in handling for model failures, rate limits, and latency spikes. - 🐘 Works with any PostgreSQL database, including Timescale Cloud, Amazon RDS, Supabase and more. **Basic Architecture**: The system consists of an application you write, a PostgreSQL database, and stateless vectorizer workers. The application defines a vectorizer configuration to embed data from sources like PostgreSQL or S3. The workers read this configuration, processes the data queue into embeddings and chunked text, and writes the results back. The application then queries this data to power RAG and semantic search. The key strength of this architecture lies in its resilience: data modifications made by the application are decoupled from the embedding process, ensuring that failures in the embedding service do not affect the core data operations.