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Utilities

Text to Embeddings

Transforms a block of text to embeddings using the specified transformer.

Requires the vector-serve container to be set via vectorize.embedding_service_url, or an OpenAI key to be set if using OpenAI embedding models.

vectorize."encode"(
    "input" TEXT,
    "model_name" TEXT DEFAULT 'sentence-transformers/all-MiniLM-L6-v2',
    "api_key" TEXT DEFAULT NULL
) RETURNS double precision[]

Parameters:

Parameter Type Description
input text Raw text to be transformed to an embedding
model_name text Name of the sentence-transformer or OpenAI model to use.
api_key text API key for the transformer. Defaults to NULL.

Example

select vectorize.encode(
    input       => 'the quick brown fox jumped over the lazy dogs',
    model_name  => 'sentence-transformers/multi-qa-MiniLM-L6-dot-v1'
);

{-0.2556323707103729,-0.3213586211204529 ..., -0.0951206386089325}

Updating the Database

Configure vectorize to run on a database other than the default postgres.

Note that when making this change, it's also required to update pg_cron such that its corresponding background workers also connect to the appropriate database.

Example

CREATE DATABASE my_new_db;

ALTER SYSTEM SET cron.database_name TO 'my_new_db';
ALTER SYSTEM SET vectorize.database_name TO 'my_new_db';

Then, restart postgres to apply the changes and, if you haven't already, enable vectorize in your new database.

\c my_new_db

CREATE EXTENSION vectorize CASCADE;

SHOW cron.database_name;
SHOW vectorize.database_name;

 cron.database_name 
--------------------
 my_new_db
(1 row)

 vectorize.database_name 
-------------------------
 my_new_db
(1 row)