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This is developer evangelism at its best at the behest of VCs to scale and "productionalize". There are reasons why this problem is a fundamentally difficult an
by kevinmm 4y ago
This is developer evangelism at its best at the behest of VCs to scale and "productionalize". There are reasons why this problem is a fundamentally difficult and coming out of the blue claiming to have found a 10x solution in a field that has attracted lots of research interest is highly sus. I would love to see a study that actually exposed their methodology, replication from independent parties and more importantly CODE.
- opisthenar84 4y agoIf code and methodology is what you're looking for, there's some great open-source vector databases out there. Milvus: https://github.com/milvus-io/milvus https://github.com/milvus-io/milvus Qdrant: https://github.com/qdrant/qdrant https://github.com/qdrant/qdrant Weaviate: https://github.com/semi-technologies/weaviate https://github.com/semi-technologies/weaviate Milvus seems to be the most advanced and best performing vector DB (https://www.farfetchtechblog.com/en/blog/post/powering-ai-with-vector-databases-a-benchmark-part-i/ https://www.farfetchtechblog.com/en/blog/post/powering-ai-wi...). Haven't seen Qdrant benchmarks yet but cool project nonetheless. FWIW, these open source projects are how I got into the area of vector similarity search to begin with.