> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify-poc.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# 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:

```sql theme={"dark"}
SELECT ai.voyageai_embed(
    'voyage-3',
    'PostgreSQL is a powerful database'
);
```

### Specify input type for queries

Optimize embeddings for search queries:

```sql theme={"dark"}
SELECT ai.voyageai_embed(
    'voyage-3',
    'best time-series database',
    input_type => 'query'
);
```

### Specify input type for documents

Optimize embeddings for documents:

```sql theme={"dark"}
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:

```sql theme={"dark"}
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:

```sql theme={"dark"}
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:

```sql theme={"dark"}
-- 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()`][cohere_embed]: alternative with Cohere models
* [`openai_embed()`][openai_embed]: alternative with OpenAI models

[cohere_embed]: /api-reference/pgai/model-calling/cohere/cohere_embed

[openai_embed]: /api-reference/pgai/model-calling/openai/openai_embed