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recent vector database fundraises:
- Chroma - $18M seed https://www.trychroma.com/blog/seed
- Weaviate - $50m A https://www.theinformation.com/articles/index-ventures-leads...
- Pinecone - $100M B
Vector databases, so hot right now. I haven't really looked into them, I'm just wondering when or if postgres will do it better?
Only a sucker being forced to by their investors would use pinecone.
I was using pinecone before installing pgvector in Postgres. Pinecone works and all but having the vectors in Postgres resulted in an explosion of use for us. Full relational queries with where clauses and order by etc AND vector embeddings is wicked.
I honestly hope they use it to improve their documentation. I consider myself and pretty adept developer but without much background in AI and was looking for a solution for building out a recommendation engine and ended up at Pinecone. Maybe I'm not the target audience but after spending some time poking around I couldn't honestly couldn't even figure out how to use it. Even a simple Goole Search for "What is a Vect…
When they say “vector-search” they mean semantic search. I.e. “which document is the most semantically similar to the query text”.
So how do we establish semantic similarity?
In a database like Elasticsearch, you store text and the DB indexes the text so you can search.
In a vector DB you don’t just store the raw text, you store a vectorized version of the text.
A vector can be thought of as an array of numbers. To get a vector representation we need some way to take a string and map it to an array while also capturing the notion of semantics.
This is hard, but machine learning models save the day! The first popular model used for this was called “word2vec” while a more modern model is BERT.
These take an input like “fish” and output a vector like [ 3.12 … 4.092 ] (with over a thousand more elements).
So let’s say we have a sentence that we vectorized and we want to compare some user input to see how similar it is to that sentence. How?
If we call our sentence A and the input vector B, we can compute a number between zero and one that tells us how similar they are.
This is called cosine similarity and is computed by taking the dot product of the two vectors and dividing by both of their magnitudes.
When you load a bunch of vectors in a vector DB, the principal operation you will perform is “give me the top K documents that are similar to the input”. The databases indexing process computes k nearest neighbors algorithm on all vectors in the DB and stores this for use at query time.
Without the indexing process there is no real difference between a vector db and key value store.
I was using pinecone before installing pgvector in Postgres. Pinecone works and all but having the vectors in Postgres resulted in an explosion of use for us. Full relational queries with where clauses and order by etc AND vector embeddings is wicked.
How is disk usage in pgvector?
Supabase was also asking for sparse vectors https://github.com/pgvector/pgvector/issues/81
Speaking of the repo, they have a number of features they want to add if anyone is interested in contributing, there's lots of room for advancement. Many of these features already have active branches https://github.com/pgvector/pgvector/issues/27
Perfect example of AI gold rush nonsense. Pinecone has zero moat and quite a few free alternatives (Faiss, Weviate, pg-vector). Their biggest selling point is that AI hype train people don’t Google “alternatives to pinecone” when cloning the newest trending repo (or I guess, ask ChatGPT).
Maybe I am fundamentally missing something, but a "cloud database company" seems like the most boring tech? No one is calling Planetscale or Yugabyte nonsense because there are free alternatives like Postgres.
there's at least $168m being poured into vector db's this year. recent vector database fundraises: - Chroma - $18M seed https://www.trychroma.com/blog/seed - Weaviate - $50m A https://www.theinformation.com/articles/index-ventures-leads... - Pinecone - $100M B
Harder to set up than wrappers like Chroma, but very powerful.
I honestly hope they use it to improve their documentation. I consider myself and pretty adept developer but without much background in AI and was looking for a solution for building out a recommendation engine and ended up at Pinecone. Maybe I'm not the target audience but after spending some time poking around I couldn't honestly couldn't even figure out how to use it. Even a simple Goole Search for "What is a Vect…
Respectfully if you don’t know what a vector is, you probably don’t need a vector DB. When they say “vector-search” they mean semantic search. I.e. “which document is the most semantically similar to the query text”. So how do we establish semantic similarity? In a database like Elasticsearch, you store text and the DB indexes the text so you can search. In a vector DB you don’t just store the raw text, you store a v…
I wasn't looking for one ;-) I was looking for a recommendation engine, similarly most often I'm looking for various ways to use ML and AI to improve various features and workflows.
Which I guess is my point, I don't know who Pinecone's target market is but from following this thread it seems like all the folks who know how to do what they do have alternatives that suit them better. If they are targeting folks like me they're not doing it well.
Pinecone's examples[1] (hat tip to Jurrasic in this thread - I've seen these) all show potential use cases that I might want to leverage, but when you dive into them (for example the Movie Recommender[2] - my use case) I end up with this:
The user_model and movie_model are trained using Tensorflow Keras. The user_model transforms a given user_id into a 32-dimensional embedding in the same vector space as the movies, representing the user’s movie preference. The movie recommendations are then fetched based on proximity to the user’s location in the multi-dimensional space.
It took me another 5 minutes of googling stuff to parse that sentence. And while I could easily get the examples to run I was still running back and forth to Google to figure out what it was doing in the examples - again the documentation is poor here. I'm not a Python dev but I could follow it but I still had to google tqdm to figure out it was a progress bar library?
Also, and this is not unique to Pinecone, I've found generally that while some things are fairly well documented on "Here's how to build a Movie Recommender based on these datasets) frequently in this space there's very little data on how to build a model using your own datasets ie how to take this example and do it with your own data.