Add integration for Timescale Vector(Postgres) #10650
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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.
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Ɑ: vector store
docs/extras/modules/ts_git_log.json
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@baskaryan it's a demo dataset that we use to illustrate the similarity search with time-based filtering. The dataset is a JSON of git commit entries. Each entry has a text component (describing the changes in that commit), but also metadata like the author and most importantly the timestamp that the entry was made. We use this dataset to illustrate to users how to use TimescaleVector's similarity search with time-filtering.
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would it be easy to host the file somewhere and load it in notebook? would be nice to avoid 30k new lines if it's easy 😅
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Can do. I'll update the notebook with a link to the dataset and update the file loading instructions as well. Standby.
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@baskaryan fixed in latest commit!
- Using the distance strategy from utils - Changing naming of embedding_function -> embedding - Fixing uses of ValueError when it should be ImportError
| @property | ||
| def distance_strategy(self) -> Any: | ||
| if self._distance_strategy == "l2": | ||
| return self.EmbeddingStore.embedding.l2_distance |
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where is self.EmbeddingStore set?
…rovided link to download file in timescale vector notebook
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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:pgvectorwith faster and more accurate similarity search on 1B+ vectors via DiskANN inspired indexing algorithm.Timescale Vector scales with you from POC to production:
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.