nmslib
knowhere
| nmslib | knowhere | |
|---|---|---|
| 4 | 1 | |
| 3,281 | 181 | |
| 0.4% | - | |
| 0.0 | 3.5 | |
| about 1 month ago | 9 months ago | |
| C++ | C++ | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
nmslib
- Vector search just got up to 10x faster and vertically scalable
-
The Missing ”WHERE” Clause for Vector Search
Amazon's Opensearch (fork of Elasticsearch) natively supports vector-based approximate KNN (using https://github.com/nmslib/nmslib/) which is integrated with Opensearch's native filtering functionality. Elasticsearch also has similar functionality, but I don't know if their KNN code scales quite as well.
-
Vector Search Indexes
nmslib (https://github.com/nmslib/nmslib) supports sparse vectors for some of its spaces. It has fewer indexing methods than faiss, though.
https://github.com/nmslib/nmslib/blob/master/manual/spaces.m...
-
Are there more practical tools for KNN searches and storing documents/embeddings?
I also needed to build a similar system and I used nmslib, maybe check it out - https://github.com/nmslib/nmslib
knowhere
-
Vector search just got up to 10x faster and vertically scalable
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).
What are some alternatives?
Milvus - A cloud-native vector database, storage for next generation AI applications
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.
pgvector - Open-source vector similarity search for Postgres
TorchPQ - Approximate nearest neighbor search with product quantization on GPU in pytorch and cuda
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
vald - Vald. A Highly Scalable Distributed Vector Search Engine