Timescale recently released Timescale Vector [0] a scalable search index (DiskANN) and efficient time-based vector search, in addition to all capabilities of pg

Timescale recently released Timescale Vector [0] a scalable search index (DiskANN) and efficient time-based vector search, in addition to all capabilities of pgvector and vanilla PostgreSQL. We plan to add the document processing and embedding creation capabilities you discuss into our Python client library [1] next, but Timescale Vector integrates with LangChain and LlamaIndex today [2], which both have document chunking and embedding creation capabilities. (I work on Timescale Vector) [0]: https://www.timescale.com/blog/how-we-made-postgresql-the-best-vector-database/ https://www.timescale.com/blog/how-we-made-postgresql-the-be... [1]: https://github.com/timescale/python-vector https://github.com/timescale/python-vector [2]: https://www.timescale.com/ai/#resources https://www.timescale.com/ai/#resources