| title | description | ms.author | author | ms.service | ms.subservice | ms.custom | ms.topic | ms.date |
|---|---|---|---|---|---|---|---|---|
How to optimize performance when using pgvector - Azure Cosmos DB for PostgreSQL |
How to optimize performance when using pgvector - Azure Cosmos DB for PostgreSQL |
adamwolk |
mulander |
cosmos-db |
postgresql |
build-2023 |
how-to |
05/10/2023 |
[!INCLUDE PostgreSQL]
The pgvector extension adds an open-source vector similarity search to PostgreSQL.
This article explores the limitations and tradeoffs of pgvector and shows how to use partitioning, indexing and search settings to improve performance.
For more on the extension itself, see basics of pgvector. You may also want to refer to the official README of the project.
[!INCLUDE Performance]
Congratulations, you just learned the tradeoffs, limitations and best practices to achieve the best performance with pgvector.