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Show HN: Supabase Vecs – vector client for Postgres

by oliverrice on 6/6/23, 1:15 PM with 2 comments

• by kiwicopple on 6/6/23, 3:15 PM

  for those who jump straight to the comments (like me), this is a python for managing unstructured vectors/embeddings in Postgres using pgvector. It works with any Postgres/pgvector database

  The core of the API is this:

      import vecs
  
      DB_CONNECTION = "postgresql://<user>:<password>@<host>:<port>/<db_name>"
  
      # create vector store client
      vx = vecs.create_client(DB_CONNECTION)
  
      # create a collection of vectors with 3 dimensions
      docs = vx.create_collection(name="docs", dimension=3)
      
      # Add embeddings
      docs.upsert(vectors=[("vec0", [0.1, 0.2, 0.3], {"year": 1973})])
  
      # Query embeddings
      docs.query(query_vector=[0.10,0.21,0.29], limit=1)
  

  This stores all the data in a new `vecs` schema in your database. It took me a while to grasp the nature of structured (eg, manage tables/data with migrations) with unstructured/nosql, but the use cases for vectors are very often geared towards data scientists/engineers. There is some more info about the interop between these approaches here: https://supabase.com/docs/guides/ai/structured-unstructured

• by oliverrice on 6/6/23, 1:32 PM

  hi, author here & happy to answer any questions

  Really looking forward to the next steps with vecs and would love to hear any thoughts about the `future ideas`.

  Mainly, what kind of interface would you like to see for adapters/transforms so collections can feel like you're inserting text/images/video/whatever rather than passing vectors around?