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Checkout the open source vector search engine Weaviate: https://github.com/semi-technologies/weaviate
It’s not a relational db, but it supports Graph-like connections between objects, which makes it really easy to model your relations.
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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 Approximate Nearest Neighbour search.
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Scout APM
Less time debugging, more time building. Scout APM allows you to find and fix performance issues with no hassle. Now with error monitoring and external services monitoring, Scout is a developer's best friend when it comes to application development.
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semantic-search-through-wikipedia-with-weaviate
Semantic search through a vectorized Wikipedia (SentenceBERT) with the Weaviate vector search engine
* Wikipedia demo dataset: https://github.com/semi-technologies/semantic-search-through...
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* Wikipedia demo dataset: https://github.com/semi-technologies/semantic-search-through...
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biggraph-wikidata-search-with-weaviate
Search through Facebook Research's PyTorch BigGraph Wikidata-dataset with the Weaviate vector search engine
* Wikidata dataset: https://github.com/semi-technologies/biggraph-wikidata-searc...
Last week there was also a feature on Techcrunch about vector search and Weaviate:
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* Wikidata dataset: https://github.com/semi-technologies/biggraph-wikidata-searc...
Last week there was also a feature on Techcrunch about vector search and Weaviate:
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Check out pgvector: https://github.com/ankane/pgvector (disclosure: am author)
It uses IVFFlat indexing, but could be extended to support product quantization / ScaNN.
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SonarLint
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Not the author, but at work we've had in the hundreds of millions. Faiss can certainly scale.
If you do have a tiny index and want to try Google's version of vector search (as an alternative to Faiss), you can easily run ScaNN locally [1] (linked in the article, that's the underlying tech). On small scale I had better perf with ScaNN
[1] https://github.com/google-research/google-research/tree/mast...
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I agree having a good vector is important to start with. However this is not very hard to make it work, you only need to finetune some of the clip models[1] to run it well.
Disclose: I have built a vector search engine to proof this idea[2]
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If anyone is interested, I maintain a list of open source vector search engine services[1].
Feel free to submit a new issues or merge request if you wish for new library added
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Vespa.ai supports combining dense vector search with keyword search and ranking, see https://docs.google.com/presentation/d/1vWKhSvFH-4MFcs4aNa9C...
There is also a Vespa sample application (open source, Apache 2) demonstrating multiple different retrieval and ranking strategies over at https://github.com/vespa-engine/sample-apps/blob/master/msma...
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