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**Description:** This commit adds a vector store for the Postgres-based vector database (`TimescaleVector`). Timescale Vector(https://www.timescale.com/ai) is PostgreSQL++ for AI applications. It enables you to efficiently store and query billions of vector embeddings in `PostgreSQL`: - Enhances `pgvector` with faster and more accurate similarity search on 1B+ vectors via DiskANN inspired indexing algorithm. - Enables fast time-based vector search via automatic time-based partitioning and indexing. - Provides a familiar SQL interface for querying vector embeddings and relational data. Timescale Vector scales with you from POC to production: - Simplifies operations by enabling you to store relational metadata, vector embeddings, and time-series data in a single database. - Benefits from rock-solid PostgreSQL foundation with enterprise-grade feature liked streaming backups and replication, high-availability and row-level security. - Enables a worry-free experience with enterprise-grade security and compliance. Timescale Vector is available on Timescale, the cloud PostgreSQL platform. (There is no self-hosted version at this time.) LangChain users get a 90-day free trial for Timescale Vector. --------- Co-authored-by: Bagatur <baskaryan@gmail.com> Co-authored-by: Avthar Sewrathan <avthar@timescale.com> |
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| activeloop_deeplake_self_query.ipynb | ||
| chroma_self_query.ipynb | ||
| dashvector.ipynb | ||
| elasticsearch_self_query.ipynb | ||
| milvus_self_query.ipynb | ||
| myscale_self_query.ipynb | ||
| pinecone.ipynb | ||
| qdrant_self_query.ipynb | ||
| redis_self_query.ipynb | ||
| supabase_self_query.ipynb | ||
| timescalevector_self_query.ipynb | ||
| vectara_self_query.ipynb | ||
| weaviate_self_query.ipynb | ||