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Vector databases are the wrong abstraction

timescale.com

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Re: Vector databases are the wrong abstraction

#11

Post by hhdhdbdb »

avthar wrote: post #2

Hey HN! Post co-author here, excited to share our new open-source PostgreSQL tool that re-imagines vector embeddings as database indexes. It's not literally an index but it functions like one to update embeddings as source data gets added, deleted or changed. Right now the system only supports OpenAI as an embedding provider, but we plan to extend with local and OSS model support soon. Eager to hear your feedback and…

Pretty smart. Why is the DB api the abstraction layer though? Why not two columns and a microservice. I assume you are making async calls to get the embeddings?

I say that because it seems n unsual. Index would suit sync better. But async things like embeddings, geo for an address, is this email considered a spammer etc. feel like app level stuff.

Re: Vector databases are the wrong abstraction

#12

Post by jdthedisciple »

choilive wrote: post #5
dinobones wrote: post #4

Wow, actually a good point I haven't seen anyone make. Taking raw embeddings and then storing them into vector databases, would be like if you took raw n-grams of your text and put them into a database for search. Storing documents makes much more sense.

Been using pgvector for a while, and to me it was kind of obvious that the source document and the embeddings are fundamentally linked so we always stored them "together". Basically anyone doing embeddings at scale is doing something similar to what Pgai Vectorizer is doing and is certainly a nice abstraction.

I used FAISS as it also allowed me to trivially store them together.

Idk how well it scales though, it's just doing it's job on my hobby project scale

For my few 100'000s embeddings I must say the performance was satisfactory.

Re: Vector databases are the wrong abstraction

#13

Post by cevian »

mattxxx wrote: post #8

This reads solely as a sales pitch, which quickly cuts to the "we're selling this product so you don't have to think about it." ...when you actually do want to think about it (in 2024). Right now, we're collectively still figuring out: 1. Best chunking strategies for documents 2. Best ways to add context around chunks of documents 3. How to mix and match similarity search with hybrid search 4. Best way to version and…

(post co-author here)

We agree a lot of stuff still needs to be figured out. Which is why we made vectorizer very configurable. You can configure chunking strategies, formatting (which is a way to add context back into chunks). You can mix semantic and lexical search on the results. That handles your 1,2,3. Versioning can mean a different version of the data (in which case the versioning info lives with the source data) OR a different embedding config, which we also support[1].

Admittedly, right now we have predefined chunking strategies. But we plan to add custom-code options very soon.

Our broader point is that the things you highlight above are the right things to worry about, not the data workflow ops and babysitting your lambda jobs. That's what we want to handle for you.

[1]: https://www.timescale.com/blog/which-rag-chunking-and-format...

Re: Vector databases are the wrong abstraction

#14

Post by avthar »

jdthedisciple wrote: post #9

Whats wrong with using FAISS as your single db? Its like sqlite for vector embeddings, and you can store metadata (the primary data, foreign keys, etc) along with the vectors, preserving the relationship. Not sure if the metadata is indexxed but at least iirc it's more or less trivial to update the embeddings when your data changes (tho i haven't used it in a while so not sure).

Good q. For most standalone vector search use cases, FAISS or a library like it is good.

However, FAISS is not a database. It can store metadata alongside vectors, but it doesn't have things you'd want in your app db like ACID compliance, non-vector indexing, and proper backup/recovery mechanisms. You're basically giving up all the DBMS capabilities.

For new RAG and search apps, many teams prefer just using a single app db with vector search capabilities included (Postgres, Mongo, MySQL etc) vs managing an app db and a separate vector db.

Re: Vector databases are the wrong abstraction

#15

Post by pjot »

morgango wrote: post #6

Great point! (Disclaimer: I work for Elastic) Elasticsearch has recently added a data type called semantic_text, which automatically chunks text, calculates embeddings, and stores the chunks with sensible defaults. Queries are similarly simplified, where vectors are calculated and compared internally, which makes a lot less I/O and a lot simpler client code. https://www.elastic.co/search-labs/blog/semantic-search-sim…

I made something similar, but used duckDB as the vector store (and query engine)! It’s impressively fast

https://github.com/patricktrainer/duckdb-embedding-search

Re: Vector databases are the wrong abstraction

#16

Post by binarymax »

jdthedisciple wrote: post #10
morgango wrote: post #6

Great point! (Disclaimer: I work for Elastic) Elasticsearch has recently added a data type called semantic_text, which automatically chunks text, calculates embeddings, and stores the chunks with sensible defaults. Queries are similarly simplified, where vectors are calculated and compared internally, which makes a lot less I/O and a lot simpler client code. https://www.elastic.co/search-labs/blog/semantic-search-sim…

How does their embedding model compare in terms of retrieval accuracy to, say `text-embedding-3-small` and `text-embedding-3-large`?

It’s impossible to answer that question without knowing what content/query domain you are embedding. Checkout MTEB leaderboard, dig into the retrieval benchmark, and look for analogous datasets.

Re: Vector databases are the wrong abstraction

#17

Post by splike »

jdthedisciple wrote: post #10
morgango wrote: post #6

Great point! (Disclaimer: I work for Elastic) Elasticsearch has recently added a data type called semantic_text, which automatically chunks text, calculates embeddings, and stores the chunks with sensible defaults. Queries are similarly simplified, where vectors are calculated and compared internally, which makes a lot less I/O and a lot simpler client code. https://www.elastic.co/search-labs/blog/semantic-search-sim…

How does their embedding model compare in terms of retrieval accuracy to, say `text-embedding-3-small` and `text-embedding-3-large`?

You can use openai embeddings in elastic if you don't want to use their elser sparse embeddings

Re: Vector databases are the wrong abstraction

#18

Post by cevian »

hhdhdbdb wrote: post #11
avthar wrote: post #2

Hey HN! Post co-author here, excited to share our new open-source PostgreSQL tool that re-imagines vector embeddings as database indexes. It's not literally an index but it functions like one to update embeddings as source data gets added, deleted or changed. Right now the system only supports OpenAI as an embedding provider, but we plan to extend with local and OSS model support soon. Eager to hear your feedback and…

Pretty smart. Why is the DB api the abstraction layer though? Why not two columns and a microservice. I assume you are making async calls to get the embeddings? I say that because it seems n unsual. Index would suit sync better. But async things like embeddings, geo for an address, is this email considered a spammer etc. feel like app level stuff.

(post co-author here)

The DB is the right layer from a interface point of view -- because that's where the data properties should be defined. We also use the DB for bookkeeping what needs to be done because we can leverage transactions and triggers to make sure we never miss any data. From an implementation point of view, the actual embedding does happen outside the database in a python worker or cloud functions.

Merging the embeddings and the original data into a single view allows the full feature set of SQL rather than being constrained by a REST API.

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