andre-z

13 ms·

121.
by andre-z 4y ago
Qdrant | https://qdrant.tech | Full-time | Remote (global) | Rust Engineers Qdrant is an advanced Vector Search Engine. It is open-source and written in Rust. Check it out on GitHub. https://github.com/qdrant&#x2
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by andre-z 4y ago
Well, it depends on your needs. Do you have any requirements for performance, latency, etc? You should check https://qdrant.tech/benchmarks/ The feature set is also pretty different. You should choose the one fitting
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by andre-z 4y ago
https://github.com/qdrant/qdrant
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by andre-z 4y ago
Right, it's a separate category. There are also Vald, Milvus and Qdrant https://github.com/qdrant/qdrant
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by andre-z 4y ago
Exactly, filtering is vital. That is why Qdrant offers extended filtering, which happens at the same stage as retrieval, not as pre- or post-filtering, with two stages and an impact on performance and quality. It is not trivial to implem
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by andre-z 4y ago
It was to expect after recent ES releases. However, dedicated vector search engines offer better performance and more advanced features. Qdrant https://github.com/qdrant/qdrant is written in Rust. Fast, stable, and sup
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by andre-z 4y ago
There is also an open-source framework for benchmarking https://github.com/qdrant/vector-db-benchmark
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by andre-z 4y ago
Good work!
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Qdrant vector search engine v0.8 release with distributed deployment(github.com)
1 points by andre-z 4y ago|0 comments
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by andre-z 4y ago
The online triplet mining strategy is more efficient than the offline one. It implies "getting a batch of n samples and their associated labels, and form triplets on the fly." About Triplet vs. Contrastive Loss comparison and its
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Triplet vs. Contrastive Loss(qdrant.tech)
1 points by andre-z 4y ago|1 comments
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Qdrant raises €2M to build open source vector search(ibbventures.de)
3 points by andre-z 5y ago|0 comments
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by andre-z 5y ago
We are developing open-source vector search technology. https://github.com/qdrant/qdrant It is a neural search engine with extended filtering support that implements a custom modification of the HNSW algorithm for Appr