Vector Hello World - Supabase

#Vector "Hello, World" Quickstart

vecs is a python client for managing and querying vector stores in PostgreSQL with the pgvector extension. This guide will help you get started with using vecs.

If you don't have a Postgres database with the pgvector extension installed, see hosting for easy options.

##Installation

Requires:

Python 3.7+

You can install vecs using pip:

python

pip install vecs

Usage

Connecting

Before you can interact with vecs, create the client to communicate with Postgres.

In Supabase, you can find the Postgres connection string in the project connect screen of your Supabase project.

Note: SQLAlchemy requires the connection string to start with postgresql:// (instead of postgres://). Don't forget to rename this after copying the string from the dashboard.

Note: You must use the "connection pooling" string (domain ending in *.pooler.supabase.com) with Google Colab since Colab does not support IPv6.

This will also work with any other Postgres provider that supports pgvector.

python

import vecs

DB_CONNECTION = "postgresql://<user>:<password>@<host>:<port>/<db_name>"

# create vector store client
vx = vecs.create_client(DB_CONNECTION)

###Get or Create Collection

You can get or create a collection to store vectors specifying the collections name and the number of dimensions in the vectors you intend to store.

python

docs = vx.get_or_create_collection(name="docs", dimension=3)

###Upserting vectors

vecs combines the concepts of "insert" and "update" into "upsert". Upserting records adds them to the collection if the id is not present, or updates the existing record if the id does exist.

python

# add records to the collection
docs.upsert(
    records=[
        (
         "vec0",           # the vector's identifier
         [0.1, 0.2, 0.3],  # the vector. list or np.array
         {"year": 1973}    # associated  metadata
        ),
        (
         "vec1",
         [0.7, 0.8, 0.9],
         {"year": 2012}
        )
    ]
)

###Create an index

Collections can be queried immediately after being created. However, for good performance, the collection should be indexed after records have been upserted.

Indexes should be created after the collection has been populated with records. Building an index on an empty collection will result in significantly reduced recall. Once the index has been created you can still upsert new documents into the collection but you should rebuild the index if the size of the collection more than doubles.

Only one index may exist per-collection. By default, creating an index will replace any existing index.

To create an index:

python

##
# INSERT RECORDS HERE
##

# index the collection to be queried by cosine distance
docs.create_index(measure=vecs.IndexMeasure.cosine_distance)

Available options for query measure are:

vecs.IndexMeasure.cosine_distance
vecs.IndexMeasure.l2_distance
vecs.IndexMeasure.max_inner_product

which correspond to different methods for comparing query vectors to the vectors in the database.

If you aren't sure which to use, stick with the default (cosine_distance) by omitting the parameter i.e.

python

docs.create_index()

Note: The time required to create an index grows with the number of records and size of vectors. For a few thousand records expect sub-minute a response in under a minute. It may take a few minutes for larger collections.

###Query

Given a collection docs with several records:

####Basic

The simplest form of search is to provide a query vector.

Note: Indexes are essential for good performance. See creating an index for more info.

If you do not create an index, every query will return a warning

query does not have a covering index for cosine_similarity. See Collection.create_index

that includes the IndexMeasure you should index.

python

docs.query(
    data=[0.4,0.5,0.6],  # required
    limit=5,                     # number of records to return
    filters={},                  # metadata filters
    measure="cosine_distance",   # distance measure to use
    include_value=False,         # should distance measure values be returned?
    include_metadata=False,      # should record metadata be returned?
)

Which returns a list of vector record ids.

Metadata Filtering

The metadata that is associated with each record can also be filtered during a query.

As an example, {"year": {"$eq": 2005}} filters a year metadata key to be equal to 2005

In context:

python

docs.query(
    query_vector=[0.4,0.5,0.6],
    filters={"year": {"$eq": 2012}}, # metadata filters
)

For a complete reference, see the metadata guide.