create_vectorizer()

Create and configure an automated embedding system for a table

Set up and configure an automated system for generating and managing embeddings for a specific table in your database. This function creates the necessary infrastructure (tables, views, triggers, columns) and configures the embedding generation process.

You use this function to:

• Automate the process of creating embeddings for table data
• Set up necessary infrastructure (tables, views, triggers, columns)
• Configure the embedding generation process
• Integrate with AI providers for embedding creation
• Set up scheduling for background processing

Samples

Table destination (default)

Create a separate table to store embeddings with a view that joins with the source table:

SELECT ai.create_vectorizer(
    'website.blog'::regclass,
    name => 'website_blog_vectorizer',
    loading => ai.loading_column('contents'),
    embedding => ai.embedding_ollama('nomic-embed-text', 768),
    chunking => ai.chunking_character_text_splitter(128, 10),
    formatting => ai.formatting_python_template('title: $title published: $published $chunk'),
    grant_to => ai.grant_to('bob', 'alice'),
    destination => ai.destination_table(
        target_schema => 'website',
        target_table => 'blog_embeddings_store',
        view_name => 'blog_embeddings'
    )
);

This creates:

1. A vectorizer named ‘website_blog_vectorizer’ for the website.blog table
2. A separate table website.blog_embeddings_store to store embeddings
3. A view website.blog_embeddings joining source and embeddings
4. Loads the contents column
5. Uses Ollama nomic-embed-text model to create 768 dimensional embeddings
6. Chunks content into 128-character pieces with 10-character overlap
7. Formats each chunk with title and published date
8. Grants necessary permissions to roles bob and alice

Column destination

Store embeddings directly in the source table (requires no chunking):

SELECT ai.create_vectorizer(
    'website.product_descriptions'::regclass,
    name => 'product_descriptions_vectorizer',
    loading => ai.loading_column('description'),
    embedding => ai.embedding_openai('text-embedding-3-small', 768),
    chunking => ai.chunking_none(),  -- Required for column destination
    grant_to => ai.grant_to('marketing_team'),
    destination => ai.destination_column('description_embedding')
);

This creates:

1. A vectorizer named ‘product_descriptions_vectorizer’
2. A column description_embedding directly in the source table
3. Loads the description column
4. No chunking (required for column destination)
5. Uses OpenAI’s embedding model to create 768 dimensional embeddings
6. Grants necessary permissions to role marketing_team

Arguments

Name	Type	Default	Required	Description
source	regclass	-	✔	The source table that embeddings are generated for
name	text	Auto-generated	✖	Unique name for the vectorizer. Auto-generated based on destination type if not provided. Must follow snake_case pattern ^[a-z][a-z_0-9]*$
destination	Destination config	ai.destination_table()	✖	How embeddings will be stored: ai.destination_table() (default) or ai.destination_column()
embedding	Embedding config	-	✔	How to embed the data using ai.embedding_*() functions
loading	Loading config	-	✔	How to load data from source table using ai.loading_*() functions
parsing	Parsing config	ai.parsing_auto()	✖	How to parse the data using ai.parsing_*() functions
chunking	Chunking config	ai.chunking_recursive_character_text_splitter()	✖	How to split text data using ai.chunking_*() functions
indexing	Indexing config	ai.indexing_default()	✖	How to index embeddings using ai.indexing_*() functions
formatting	Formatting config	ai.formatting_python_template()	✖	How to format data before embedding
scheduling	Scheduling config	ai.scheduling_default()	✖	How often to run the vectorizer using ai.scheduling_*() functions
processing	Processing config	ai.processing_default()	✖	How to process embeddings
queue_schema	name	-	✖	Schema where the work queue table is created
queue_table	name	-	✖	Name of the work queue table
grant_to	Grant config	ai.grant_to_default()	✖	Which users can use objects created by the vectorizer
enqueue_existing	bool	true	✖	Whether existing rows should be immediately queued for embedding
if_not_exists	bool	false	✖	Avoid error if the vectorizer already exists

Returns

INT: The ID of the vectorizer created. You can also reference the vectorizer by its name in management functions.

Related functions

• drop_vectorizer(): remove a vectorizer
• destination_table(): store embeddings in separate table
• destination_column(): store embeddings in source table
• enable_vectorizer_schedule(): resume automatic processing
• disable_vectorizer_schedule(): pause automatic processing