etiennedi

5 ms·

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by etiennedi 3y ago
We (at Weaviate) support separate BM25, vector, and combined hybrid search, so I can talk about the performance aspect of all of these a lot. The tl;dr is: It's complicated. What you say, may be true in some cases, but not in others. O
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by etiennedi 3y ago
Thanks, appreciate your perspective. This is much more insightful than just "affiliated==botnet". I agree that this can be a huge positive and - at least for me - that was the motivation for wanting to be active in the comments; t
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by etiennedi 3y ago
Fair point, edited my post. The fact that I love the visual design is my personal opinion though :-D
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by etiennedi 3y ago
Not sure if that alone is what made Go successful. But yes, having great networking tools in the stdlib is very refreshing! One of the things Go got right.
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by etiennedi 3y ago
Love the visual design, too. The generative/RAG/semantic search is the interesting part, but it's also just very pleasant to look at. Which can go a long way. EDIT: Disclaimer, I am affiliated with Weaviate.
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Cohere Multilingual ML Models with Weaviate(weaviate.io)
3 points by etiennedi 4y ago|0 comments
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Vamana vs. HNSW – Exploring ANN Vector Search Algorithms(weaviate.io)
7 points by etiennedi 4y ago|0 comments
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Gomemlimit (Go 1.19) is a game-changer for high-memory applications(weaviate.io)
8 points by etiennedi 4y ago|1 comments
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by etiennedi 4y ago
Hi, the author and long-time Go user here. Let me know if you have any questions. For me the introduction of GOMEMLIMIT finally makes Go a viable choice for high-heap applications. No longer do we have to worry about running OOM with 50%+1B
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by etiennedi 5y ago
I agree, but at the same time now is the easiest it's ever been to create great vectors. Sentence-Bert [1] by Nils Reimers is a collection of pre-trained models specficially trained to create good vectors. You can use them out of the b
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by etiennedi 5y ago
The models that create the vector embeddings are trained on either general or domain specific knowledge. So, to oversimplify it a bit: The model has learned - based on the training data it was presented with - that "Scandinavian"
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by etiennedi 5y ago
Spot on! Both of those were motivating factors when building Weaviate (Open Source Vector Search Engine). We really wanted it to feel like a full database or search engine. You should be able to do anything you would do with Elasticsearch,
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by etiennedi 5y ago
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 mode
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by etiennedi 5y ago
Exactly. See my reply on this comment thread comment thread to see how Weaviate solves the filter-issue by using an inverted index to produce a whitelist of IDs which is then passed to the vector index where non matching IDs are simply skip
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by etiennedi 5y ago
Main author and architect of Weaviate ( https://github.com/semi-technologies/weaviate ) here. This real-time requirement was one of the major design principles from the get-go in Weaviate. In Weaviate, any imported vecto
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by etiennedi 5y ago
You might be interested in checking out Weaviate ( https://github.com/semi-technologies/weaviate ) which supports combining vector search with boolean filters. In Weaviate, the vector index is stored alongside an inverte