Vector database: Difference between revisions
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link to Cloudflare learning center; plus describe what pgvector does for Postgres |
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Postgres offers similar (free) open source capabilities. MySQL's implementation is reserved for their enterprise version customers. | Postgres offers similar (free) open source capabilities. MySQL's implementation is reserved for their enterprise version customers. | ||
https://mariadb.org/amazon-mariadb-vector/</ref> and [[Postgres]]<ref>https://github.com/pgvector/pgvector</ref> offer vector capability now.) | https://mariadb.org/amazon-mariadb-vector/</ref> and [[Postgres]]<ref>https://github.com/pgvector/pgvector | ||
Open-source vector similarity search for Postgres | |||
Store your vectors with the rest of your data. Supports: | |||
exact and approximate nearest neighbor search | |||
single-precision, half-precision, binary, and sparse vectors | |||
L2 distance, inner product, cosine distance, L1 distance, Hamming distance, and Jaccard distance | |||
any language with a Postgres client</ref> offer vector capability now.) Cloudflare has [https://www.cloudflare.com/learning/ai/what-is-vector-database/ information about vector database technology] in their learning center (including a glossary) which describes embeddings, and uses for vector databases.<ref>because they offer '''Vectorize''' their vector database product. https://developers.cloudflare.com/vectorize/ </ref> | |||
== Commercial == | == Commercial == | ||