voyageai_embed()

Generate retrieval-optimized embeddings using Voyage AI models

Generate vector embeddings from text using Voyage AI’s retrieval-optimized models. Voyage embeddings excel at semantic search, retrieval augmented generation (RAG), and clustering tasks.

Samples

Generate a single embedding

Create a vector embedding:

SELECT ai.voyageai_embed(
    'voyage-3',
    'PostgreSQL is a powerful database'
);

Specify input type for queries

Optimize embeddings for search queries:

SELECT ai.voyageai_embed(
    'voyage-3',
    'best time-series database',
    input_type => 'query'
);

Specify input type for documents

Optimize embeddings for documents:

SELECT ai.voyageai_embed(
    'voyage-3',
    'TimescaleDB is an extension for PostgreSQL that adds time-series capabilities.',
    input_type => 'document'
);

Generate embeddings for multiple texts

Process multiple texts in one API call:

SELECT index, embedding
FROM ai.voyageai_embed(
    'voyage-3',
    ARRAY[
        'PostgreSQL is a powerful database',
        'TimescaleDB extends PostgreSQL',
        'pgai brings AI to PostgreSQL'
    ],
    input_type => 'document'
);

Store embeddings in a table

Generate and store embeddings for your data:

UPDATE documents
SET embedding = ai.voyageai_embed(
    'voyage-3',
    content,
    input_type => 'document'
)
WHERE embedding IS NULL;

Use domain-specific models

Use specialized models for your domain:

-- For code search
SELECT ai.voyageai_embed(
    'voyage-code-3',
    'def calculate_sum(a, b): return a + b',
    input_type => 'document'
);

-- For financial documents
SELECT ai.voyageai_embed(
    'voyage-finance-2',
    'Q3 revenue increased by 15% year-over-year',
    input_type => 'document'
);

Arguments

Name	Type	Default	Required	Description
model	TEXT	-	✔	Voyage AI model (e.g., voyage-3, voyage-code-3)
input_text	TEXT	-	✔	Single text input to embed (use this OR input_texts)
input_texts	TEXT[]	-	✔	Array of text inputs to embed in a batch
input_type	TEXT	NULL	✖	Type of input: query for search queries, document for documents to search
api_key	TEXT	NULL	✖	Voyage AI API key. If not provided, uses configured secret
api_key_name	TEXT	NULL	✖	Name of the secret containing the API key
verbose	BOOLEAN	FALSE	✖	Enable verbose logging for debugging

Returns

For single text input:

• vector: A pgvector compatible vector containing the embedding

For array input:

• TABLE(index INT, embedding vector): A table with an index and embedding for each input text

Related functions

• cohere_embed(): alternative with Cohere models
• openai_embed(): alternative with OpenAI models