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1 change: 1 addition & 0 deletions README.md
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Expand Up @@ -16,6 +16,7 @@ This list includes projects, extensions or demos showcasing how to use PostgreSQ
* [pgai](https://github.com/timescale/pgai): Enables you to generate embeddings within the PostgreSQL database. Developed by Timescale
* [pgvectorscale](https://github.com/timescale/pgvectorscale): Works in conjunction with pgvector and adds the DiskANN indexing type. Developed by Timescale
* [google_ml_integration](https://cloud.google.com/alloydb/docs/ai): It provides the AI query engine feature, which includes functions for generating embeddings, semantic ranking, and capacity to invoke predictions from models.
* [koldstore](https://github.com/kalamdb/koldstore): PostgreSQL tiered-storage that moves historical rows to Parquet while keeping the original table fully queryable and supporting updates and deletes.


## 💡 Applications
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