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RAGFreeOpen Source
POSTGRESML
Postgres with GPUs for ML and AI applications
MIT
ABOUT
Building AI applications with RAG typically involves stitching together multiple systems — a database for storage, a separate ML framework for embeddings, a vector store for semantic search, and hand-coded glue to connect them. PostgresML adds GPU-accelerated ML training, embedding generation, and vector search directly inside PostgreSQL, letting developers build and deploy RAG pipelines entirely in SQL without moving data or managing separate infrastructure.
INSTALL
pip install pgml
INTEGRATION GUIDE
1. Generate vector embeddings and run RAG pipelines using SQL queries directly inside PostgreSQL with GPU acceleration
2. Train and deploy ML models on data that never leaves the database, eliminating data movement overhead for AI pipelines
3. Perform hybrid search combining full-text search, metadata filters, and vector similarity in a single SQL query
TAGS
postgresmachine-learningembeddingsvector-searchraggpusql