Our great sponsors
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Weaviate
Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database.
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knowhere
Discontinued Knowhere is an open-source vector search engine, integrating FAISS, HNSW, etc.
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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nmslib
Non-Metric Space Library (NMSLIB): An efficient similarity search library and a toolkit for evaluation of k-NN methods for generic non-metric spaces.
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qdrant
Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
I'm affiliated with Weaviate, so maybe nice to get this out here for the record :)
We call Weaviate a "vector search engine" (i.e., we prefer "vector search engine" because it describes the type of database) since around Aug, 2020
Github: https://github.com/semi-technologies/weaviate/tree/a3967aff5...
The reason was simple; our community started to say that the mixed vector and scalar filter search capabilities were what they liked most.
Also, our benchmarks are available for quite some time here: https://weaviate.io/developers/weaviate/current/benchmarks/a...
They are based on ann-benchmarks.com but adjusted for full databases.
Just to clarify, Milvus is much more than a wrapper around FAISS. Our vector search component called Knowhere (https://github.com/milvus-io/knowhere) utilizes FAISS and Annoy and will soon include ScaNN, DiskANN, and in-house vector indexes as well. Milvus then uses Knowhere as the compute engine, and implements a variety of database functions such as horizontal scaling, caching, replication, failover, and object storage on top of Knowhere. If you're interested, I recommend checking out our architecture page (https://milvus.io/docs/architecture_overview.md).
There's one, but with some limitations (For example - only vectors of up to 1024 dimensions)
https://github.com/pgvector/pgvector