ollama_embed()

Generate vector embeddings using local Ollama models

Generate vector embeddings from text using locally hosted Ollama models. Embeddings are numerical representations of text that capture semantic meaning, ideal for semantic search, recommendations, and clustering without sending data to external APIs.

Samples

Generate an embedding

Create a vector embedding using a local model:

SELECT ai.ollama_embed(
    'llama2',
    'PostgreSQL is a powerful database'
);

Specify Ollama host

Connect to a specific Ollama server:

SELECT ai.ollama_embed(
    'llama2',
    'PostgreSQL is a powerful database',
    host => 'http://ollama-server:11434'
);

Configure model options

Customize the embedding generation:

SELECT ai.ollama_embed(
    'llama2',
    'PostgreSQL is a powerful database',
    embedding_options => '{"temperature": 0.5}'::jsonb
);

Store embeddings in a table

Generate and store embeddings for your data:

UPDATE documents
SET embedding = ai.ollama_embed(
    'llama2',
    content,
    host => 'http://localhost:11434'
)
WHERE embedding IS NULL;

Arguments

Name	Type	Default	Required	Description
model	TEXT	-	✔	The Ollama model to use (e.g., llama2, mistral, nomic-embed-text)
input_text	TEXT	-	✔	Text input to embed
host	TEXT	NULL	✖	Ollama server URL (defaults to http://localhost:11434)
keep_alive	TEXT	NULL	✖	How long to keep the model loaded (e.g., 5m, 1h)
embedding_options	JSONB	NULL	✖	Model-specific options as JSON
verbose	BOOLEAN	FALSE	✖	Enable verbose logging for debugging

Returns

vector: A pgvector compatible vector containing the embedding.

Related functions

• ollama_generate(): generate text completions
• ollama_chat_complete(): chat with local models
• ollama_list_models(): see available models