> ## 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_openai()

> Use OpenAI models to generate embeddings

Use an OpenAI model to generate embeddings for your vectorizer.

You use this function to:

* Define which OpenAI embedding model to use
* Specify the dimensionality of the embeddings
* Configure optional parameters like user identifier for API calls
* Set the name of the environment variable that holds your OpenAI API key

## Samples

### Basic OpenAI embedding

```sql theme={"dark"}
SELECT ai.create_vectorizer(
    'blog_posts'::regclass,
    loading => ai.loading_column('content'),
    embedding => ai.embedding_openai('text-embedding-3-small', 768),
    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_openai(
        'text-embedding-3-small',
        768,
        api_key_name => 'MY_OPENAI_API_KEY'
    ),
    chunking => ai.chunking_character_text_splitter(512)
);
```

### With user tracking

```sql theme={"dark"}
SELECT ai.create_vectorizer(
    'user_content'::regclass,
    loading => ai.loading_column('text'),
    embedding => ai.embedding_openai(
        'text-embedding-3-small',
        768,
        chat_user => 'analytics_team'
    ),
    chunking => ai.chunking_character_text_splitter(512)
);
```

### With custom base URL

```sql theme={"dark"}
SELECT ai.create_vectorizer(
    'data_table'::regclass,
    loading => ai.loading_column('content'),
    embedding => ai.embedding_openai(
        'text-embedding-3-small',
        768,
        base_url => 'https://custom-openai-endpoint.com/v1'
    ),
    chunking => ai.chunking_character_text_splitter(512)
);
```

## Arguments

| Name           | Type   | Default          | Required | Description                                                           |
| -------------- | ------ | ---------------- | -------- | --------------------------------------------------------------------- |
| `model`        | `text` | -                | ✔        | Name of the OpenAI embedding model (e.g., `text-embedding-3-small`)   |
| `dimensions`   | `int`  | -                | ✔        | Number of dimensions for the embedding vectors                        |
| `chat_user`    | `text` | -                | ✖        | Identifier for the user making the API call (for tracking/monitoring) |
| `api_key_name` | `text` | `OPENAI_API_KEY` | ✖        | Name of the environment variable containing the OpenAI API key        |
| `base_url`     | `text` | -                | ✖        | Custom base URL for the OpenAI API                                    |

## Returns

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

## Related functions

* [`embedding_ollama()`][embedding_ollama]: use local Ollama models
* [`embedding_litellm()`][embedding_litellm]: use any provider through LiteLLM
* [`embedding_voyageai()`][embedding_voyageai]: use Voyage AI models

[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_voyageai]: /api-reference/pgai/vectorizer/embedding_voyageai