PostgreSQL extension for deep learning model inference and vector storage inside the database.
pgext.cloud · Source code · Documentation · Control file
| Package | pgdl |
|---|---|
| Version | 1.3.0 |
| PostgreSQL versions | 12 |
| Repository | Unspecified |
| Extension type | Standard |
| Project status | Active |
| Requires CREATE EXTENSION | Yes |
| Requires preload | No |
| Trusted | No |
| Relocatable | Yes |
| Stars | 63 |
| Latest commit | 2025-07-30 |
| Catalogue updated | 2026-08-30 |
| vector | vector data type and ivfflat and hnsw access methods |
| pgcontext | Vector, filter-aware HNSW, and hybrid retrieval over authoritative PostgreSQL tables. |
| vectorize | The simplest way to do vector search on Postgres |
| pgmnemo | Single-plan multimodal agent memory for PostgreSQL |
| google_ml_integration | Google Cloud machine learning integration extension for registering model endpoints, generating embeddings, and invoking predictions from SQL. |
| ecaz | Rust PostgreSQL extension for high-performance vector storage with broad quantization and index support. |
| pg_bestmatch | Generate BM25 sparse vector inside PostgreSQL |
| pg_gembed | Generate embeddings inside PostgreSQL |
| pg_deeplake | Deeplake is AI Data Runtime for Agents. It provides serverless postgres with a multimodal datalake, enabling scalable retrieval and training. |
| pg4ml | Machine learning framework for PostgreSQL |