Convenience functions for working with vector embeddings
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| Package | lantern_extras |
|---|---|
| Version | 0.6.0 |
| PostgreSQL versions | 12 · 13 · 14 · 15 · 16 · 17 |
| Repository | Unspecified |
| Extension type | Preloaded |
| Project status | Active |
| Requires CREATE EXTENSION | Yes |
| Requires preload | Yes |
| Trusted | No |
| Relocatable | No |
| Shared libraries | lantern_extras |
| Schema | lantern_extras |
| Stars | 890 |
| Latest commit | 2024-12-12 |
| Latest release | 2024-11-15 |
| Catalogue updated | 2026-08-30 |
| vectorize | The simplest way to do vector search on Postgres |
| pgcontext | Vector, filter-aware HNSW, and hybrid retrieval over authoritative PostgreSQL tables. |
| vector | vector data type and ivfflat and hnsw access methods |
| avocado | AvocadoDB PostgreSQL extension for deterministic context compilation for AI agents. |
| 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. |
| rds_ai | Alibaba Cloud RDS extension for calling LLMs, generating embeddings, semantic retrieval, and end-to-end RAG from SQL |
| 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 |
| ruvector | SIMD-accelerated vector type and indexes with graph, solver, TDA, local embedding, and AI SQL functions. |