Vector database built for scalable similarity search
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Re: Vector database built for scalable similarity search
#2A good set of resources if you wanna check out some other ones, been reviewing some myself.
https://github.com/currentslab/awesome-vector-search
I was surprised to see Elastic actually has ok support for some of this stuff, though it appears slower for most of the tasks.
Re: Vector database built for scalable similarity search
#3A good set of resources if you wanna check out some other ones, been reviewing some myself. https://github.com/currentslab/awesome-vector-search I was surprised to see Elastic actually has ok support for some of this stuff, though it appears slower for most of the tasks.
We added HNSW-based vector search to Typesense as well recently: https://typesense.org/docs/0.24.0/api/vector-search.html
So you can combine attribute-based filters along with nearest-neighbor search.
Put together this semantic search + filtering demo just last week: https://github.com/typesense/typesense-instantsearch-semanti...
Re: Vector database built for scalable similarity search
#4I work at Zilliz (https://zilliz.com) and am a part of the Milvus community. Here are some other resources in case anybody's interested in learning more about embeddings, vector search, and vector databases:
1) Embedding crash course https://developers.google.com/machine-learning/crash-course/...
2) What is a vector database? https://zilliz.com/learn/what-is-vector-database
3) Introduction to vector similarity search https://zilliz.com/blog/vector-similarity-search
4) ANN benchmarks http://ann-benchmarks.com
5) Unstructured data ETL https://github.com/towhee-io/towhee
Re: Vector database built for scalable similarity search
#5Ah I see, they released Milvus 2.0 which is similar to Pinecone. What are the differences?
Re: Vector database built for scalable similarity search
#6Can these databases do fast averaging of nearest neighbors for regression or do you have to retrieve the neighbors and manually compute a mean across them?
Re: Vector database built for scalable similarity search
#7I work at Zilliz ( https://zilliz.com ) and am a part of the Milvus community. Here are some other resources in case anybody's interested in learning more about embeddings, vector search, and vector databases: 1) Embedding crash course https://developers.google.com/machine-learning/crash-course/... 2) What is a vector database? https://zilliz.com/learn/what-is-vector-database 3) Introduction to vector similarity sear…
Does Zilliz do BYOC?
Re: Vector database built for scalable similarity search
#8I work at Zilliz ( https://zilliz.com ) and am a part of the Milvus community. Here are some other resources in case anybody's interested in learning more about embeddings, vector search, and vector databases: 1) Embedding crash course https://developers.google.com/machine-learning/crash-course/... 2) What is a vector database? https://zilliz.com/learn/what-is-vector-database 3) Introduction to vector similarity sear…
Does Zilliz do BYOC?
Not yet, but this functionality should be coming soon. We're currently working on adding the capability to call third party embedding APIs directly from a Zilliz Cloud instance.
Re: Vector database built for scalable similarity search
#9A good set of resources if you wanna check out some other ones, been reviewing some myself. https://github.com/currentslab/awesome-vector-search I was surprised to see Elastic actually has ok support for some of this stuff, though it appears slower for most of the tasks.
Thanks for the link. Nice to see Marqo on there (disclaimer I am a co-founder of Marqo). For anyone that is interested it includes a really nice api for handling a lot of the manipulations and operations you want to do (adding, updating, patching documents, filtering, embeddings only a subset of fields, multi-modal querying, multi-modal document representations) which are absent from vector db's. It also takes care of inference https://github.com/marqo-ai/marqo
Re: Vector database built for scalable similarity search
#10Can these databases do fast averaging of nearest neighbors for regression or do you have to retrieve the neighbors and manually compute a mean across them?
You'll have to manually compute the mean after retrieving the nearest neighbors. Automatic fast averaging could be an interesting use case for clustering or using a vector database to generate training though.