> ## 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.

# embedding_voyageai()

> Use Voyage AI models for retrieval-optimized embeddings

Use a Voyage AI model to generate retrieval-optimized embeddings for your vectorizer.

You use this function to:

* Define which Voyage AI model to use
* Specify the dimensionality of the embeddings
* Configure the model's truncation behavior and API key name
* Configure the input type (query vs document)

## Samples

### Basic Voyage AI embedding

```sql theme={"dark"}
SELECT ai.create_vectorizer(
    'blog_posts'::regclass,
    loading => ai.loading_column('content'),
    embedding => ai.embedding_voyageai('voyage-3-lite', 512),
    chunking => ai.chunking_character_text_splitter(512)
);
```

### With custom API key name

```sql theme={"dark"}
SELECT ai.create_vectorizer(
    'documents'::regclass,
    loading => ai.loading_column('content'),
    embedding => ai.embedding_voyageai(
        'voyage-3',
        1024,
        api_key_name => 'MY_VOYAGE_API_KEY'
    ),
    chunking => ai.chunking_character_text_splitter(512)
);
```

### With input type

```sql theme={"dark"}
SELECT ai.create_vectorizer(
    'search_content'::regclass,
    loading => ai.loading_column('text'),
    embedding => ai.embedding_voyageai(
        'voyage-3',
        1024,
        input_type => 'document'
    ),
    chunking => ai.chunking_character_text_splitter(512)
);
```

## Arguments

| Name           | Type   | Default          | Required | Description                                                            |
| -------------- | ------ | ---------------- | -------- | ---------------------------------------------------------------------- |
| `model`        | `text` | -                | ✔        | Name of the Voyage AI model to use (e.g., `voyage-3-lite`, `voyage-3`) |
| `dimensions`   | `int`  | -                | ✔        | Number of dimensions for the embedding vectors                         |
| `input_type`   | `text` | `'document'`     | ✖        | Type of input text: `null`, `'query'`, or `'document'`                 |
| `api_key_name` | `text` | `VOYAGE_API_KEY` | ✖        | Name of the environment variable containing the Voyage AI API key      |

## Returns

A JSON configuration object for use in [`create_vectorizer()`][create_vectorizer].

## Related functions

* [`embedding_openai()`][embedding_openai]: use OpenAI models
* [`embedding_ollama()`][embedding_ollama]: use local Ollama models
* [`embedding_litellm()`][embedding_litellm]: use any provider through LiteLLM

[create_vectorizer]: /api-reference/pgai/vectorizer/create_vectorizer

[embedding_litellm]: /api-reference/pgai/vectorizer/embedding_litellm

[embedding_ollama]: /api-reference/pgai/vectorizer/embedding_ollama

[embedding_openai]: /api-reference/pgai/vectorizer/embedding_openai