Weaviate
nmslib
| Weaviate | nmslib | |
|---|---|---|
| 77 | 4 | |
| 9,587 | 3,290 | |
| 3.7% | 0.6% | |
| 10.0 | 0.0 | |
| 5 days ago | about 2 months ago | |
| Go | C++ | |
| BSD 3-clause "New" or "Revised" License | Apache License 2.0 |
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Weaviate
- Weaviate – A cloud-native, open-source vector database
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pgvecto.rs alternatives - qdrant and Weaviate
3 projects | 13 Mar 2024
- FLaNK Stack 29 Jan 2024
- Qdrant, the Vector Search Database, raised $28M in a Series A round
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How to use Weaviate to store and query vector embeddings
In this tutorial, I introduce Weaviate, an open-source vector database, with the thenlper/gte-base embedding model from Alibaba, through Hugging Face's transformers library.
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Choosing vector database: a side-by-side comparison
This will be solved in Weaviate https://github.com/weaviate/weaviate/issues/2424
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Who's hiring developer advocates? (October 2023)
Link to GitHub -->
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Do we think about vector dbs wrong?
Hey @rvrs, I work on Weaviate and we are doing some improvements around increasing write throughput:
1. gRPC. Using gRPC to write vectors has had a really nice performance boost. It is released in Weaviate core but here is still some work on do on the clients. Feel free to get in contact if you would like to try it out.
2. Parameter tuning. lowering `efConstruction` can speed up imports.
3. We are also working on async indexing https://github.com/weaviate/weaviate/issues/3463 which will further speed things up.
In comparison with pgvector, Weaviate has more flexible query options such as hybrid search and quantization to save memory on larger datasets.
- Weaviate vector database
- Weaviate 1.21: Support for ImageBind and GPT4all and more
nmslib
- Vector search just got up to 10x faster and vertically scalable
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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.
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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...
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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
What are some alternatives?
Milvus - A cloud-native vector database, storage for next generation AI applications
faiss - A library for efficient similarity search and clustering of dense vectors.
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/
jina - ☁️ Build multimodal AI applications with cloud-native stack
vald - Vald. A Highly Scalable Distributed Vector Search Engine
knowhere - Knowhere is an open-source vector search engine, integrating FAISS, HNSW, etc.