What do people think about MongoDB's search offering and its pivot into vectors?
Choosing vector database: a side-by-side comparison
91–100 of 125 posts
Re: Choosing vector database: a side-by-side comparison
#92Everyone I talk to who is building some vector db based thing sooner or later realizes they also care about the features of a full-text search engine. They care about filtering, they care to some degree about direct lexical matches, they care about paging, getting groups / facet counts, etc. Vectors, IMO, are just one feature that a regular search engine should have. IMO currently Vespa does the best job of this, tho…
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Re: Choosing vector database: a side-by-side comparison
#93I'm interested to try some of these others next time around, but I've used qdrant self-hosted in two projects and been pleased. Milvus was recommended so I gave that a try but found it over complicated. Pgvector seems like an obvious choice if you are already using postgres and if that performance is ok.
Re: Choosing vector database: a side-by-side comparison
#94Everyone I talk to who is building some vector db based thing sooner or later realizes they also care about the features of a full-text search engine. They care about filtering, they care to some degree about direct lexical matches, they care about paging, getting groups / facet counts, etc. Vectors, IMO, are just one feature that a regular search engine should have. IMO currently Vespa does the best job of this, tho…
Agreed, vector search is great but it's only one of many tools you can use to create a great search solution. We recently did a bunch of evaluation work to quantify the differences between keyword search, vector search, hybrid, reranking, etc. across a few datasets. We shared the results here: https://techcommunity.microsoft.com/t5/azure-ai-services-blo... Disclosure - I work in the Azure Search team.
Check out FeatureBase, when you get a chance. Vectors and super fast operations on sets. I'm using it for managing keyterms extracted from the text and stored along with the vectors.
Re: Choosing vector database: a side-by-side comparison
#95I'd love to know how vector databases compare in their ability to do hybrid queries, vector similarity filtered by metadata values. For example, find the 100 items with the closest cosine similarity where genre = jazz and publication date between 1990 and 2000. Can the vector index operate on a subset of records? Or when searching for 100 closest matches does the database have to find 1000 matches and then apply the…
What you’re describing is easily done in Pinecone, and in other solutions as well. See: https://docs.pinecone.io/docs/metadata-filtering
Re: Choosing vector database: a side-by-side comparison
#96And soon, on MySQL/Vitess as well: https://planetscale.com/ai
Re: Choosing vector database: a side-by-side comparison
#97What advantage are vector databases providing above using an index in conjunction with a mature database? I’m not sold on this as a separate technology. Vector search is useful, but I don’t understand why I would go out of my way when I could implement FAISS or HNSWlib as an adjunct to postgres or a document store.
The thing is if you need a vector _database_ there is no reason why it can't be a pg extensions. And if you project is only small scale there is probably some HNSW pg extension library you could use. But what is most times needed instead of a vector database is a efficient fast responsive vectore approximate KNN search system with fast attribute filtering which overlaps with a fast an efficient text search system (e.…
Why is text search so related to vector search by your opinion?
Re: Choosing vector database: a side-by-side comparison
#98Strongly disagree about the Pinecone developer experience. Not that they don't have SDKs, but last I checked they didn't have documentation on how to approach local dev environments.
The implication being that you spin up a separate index for $70/mo, and then you have to upsert any relevant data yourself. Sure that's not difficult, but why do you have to do it at all? Why doesn't Pinecone make it easy to replicate data to another index for use in dev/staging?
Re: Choosing vector database: a side-by-side comparison
#99Everyone I talk to who is building some vector db based thing sooner or later realizes they also care about the features of a full-text search engine. They care about filtering, they care to some degree about direct lexical matches, they care about paging, getting groups / facet counts, etc. Vectors, IMO, are just one feature that a regular search engine should have. IMO currently Vespa does the best job of this, tho…
How does Vespa even make money? Just cloud stuff? That's nuts if search is already built in.
Re: Choosing vector database: a side-by-side comparison
#100Everyone I talk to who is building some vector db based thing sooner or later realizes they also care about the features of a full-text search engine. They care about filtering, they care to some degree about direct lexical matches, they care about paging, getting groups / facet counts, etc. Vectors, IMO, are just one feature that a regular search engine should have. IMO currently Vespa does the best job of this, tho…
We are using Typesense to fill this exact need, its been a welcome breath of fresh air over the typical ElasticSearch headaches.