Google Cloud machine learning integration extension for registering model endpoints, generating embeddings, and invoking predictions from SQL.
| Package | google_ml_integration |
|---|---|
| Version | 1.4.3 |
| PostgreSQL versions | 12 · 13 · 14 · 15 · 16 · 17 · 18 |
| Repository | Unspecified |
| Extension type | Standard |
| Project status | Active |
| Distribution | google-cloud-sql-postgresql |
| Vendor | Google Cloud |
| Requires CREATE EXTENSION | Yes |
| Requires preload | No |
| Schema | google_ml, public |
| Latest release | 2025-09-09 |
| Catalogue updated | 2026-07-30 |
| vectorize | The simplest way to do vector search on Postgres |
| pg_deeplake | Deeplake is AI Data Runtime for Agents. It provides serverless postgres with a multimodal datalake, enabling scalable retrieval and training. |
| pg_gembed | Generate embeddings inside PostgreSQL |
| avocado | AvocadoDB PostgreSQL extension for deterministic context compilation for AI agents. |
| ecaz | Rust PostgreSQL extension for high-performance vector storage with broad quantization and index support. |
| pgmnemo | Single-plan multimodal agent memory for PostgreSQL |
| vector | vector data type and ivfflat and hnsw access methods |
| rds_ai | Alibaba Cloud RDS extension for calling LLMs, generating embeddings, semantic retrieval, and end-to-end RAG from SQL |
| lantern_extras | Convenience functions for working with vector embeddings |
| pgedge_vectorizer | Asynchronous text chunking and embedding generation for PostgreSQL using background workers and pgvector. |
| rag_bge_small_en_v15 | rag_bge_small_en_v15: local bge-small-en-v1.5 embeddings in PostgreSQL |
| pgcontext | Vector, filter-aware HNSW, and hybrid retrieval over authoritative PostgreSQL tables. |