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One thing I can confirm from this code is that unlike what I previously thought vector search is not the main point of a vector database, rather it's read, write, update crud, just like normal SQL databases, with the exception that data are vector data instead of text and numbers.
What did you previously think a vector db was?
It's a stretch to call this building a vector database when this is just an API over Cloudflare's distributed database offerings. This also uses a fixed embedding which will not be compatible with all machine learning projects that people will want to use a vector database for. The chosen embedding only supports text so making an image search for example wouldn't be possible to do.
Glass half empty then. Most vector databases are using some local or external provider to get the embeddings and then using some storage engine to store and retreive the embeddings. Whether it's pgvector leaning on postgresql, or chromadb on sqlite, or pinecone originally being on rocksdb (I believe they've now built their own engine). This is no different, and is still in it's infancy, so one presumes they might add…
Curious at how people are using vectordbs at an enterprise level. Let's say you have a team of 5 data scientists/developers who are working on a collection of GenAI features/tooling. Does it make sense to have one single vectordb where all documentation is embedded and powers all the apps, or do you make a bunch of niche databases that are tailored to the service? Also, one of the things i've noticed is that these da…
Curious at how people are using vectordbs at an enterprise level. Let's say you have a team of 5 data scientists/developers who are working on a collection of GenAI features/tooling. Does it make sense to have one single vectordb where all documentation is embedded and powers all the apps, or do you make a bunch of niche databases that are tailored to the service? Also, one of the things i've noticed is that these da…
We have multiple teams kind of working in silos so we haven't really consolidated on a single enterprise solution yet. That said, the team I'm on has consolidated on using qdrant with different collections. We've also started using a sort of hungarian notation in collection names as we've just ran into the problem of multiple embedding models.
Earlier quoted context omitted.
What did you previously think a vector db was?
sounds like they thought it was something more akin to Elastisearch
Curious at how people are using vectordbs at an enterprise level. Let's say you have a team of 5 data scientists/developers who are working on a collection of GenAI features/tooling. Does it make sense to have one single vectordb where all documentation is embedded and powers all the apps, or do you make a bunch of niche databases that are tailored to the service? Also, one of the things i've noticed is that these da…
It's a stretch to call this building a vector database when this is just an API over Cloudflare's distributed database offerings. This also uses a fixed embedding which will not be compatible with all machine learning projects that people will want to use a vector database for. The chosen embedding only supports text so making an image search for example wouldn't be possible to do.
Glass half empty then. Most vector databases are using some local or external provider to get the embeddings and then using some storage engine to store and retreive the embeddings. Whether it's pgvector leaning on postgresql, or chromadb on sqlite, or pinecone originally being on rocksdb (I believe they've now built their own engine). This is no different, and is still in it's infancy, so one presumes they might add…
retrieve != query
this project is extremely simplistic in regards to its vector search tech. pgvector is an open source implementation of an _index_ (multiple algos actually), this uses Cloudflare's completely proprietary index with a single call.