> ## 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_ollama()

> Use local Ollama models to generate embeddings

Use a local Ollama model to generate embeddings for your vectorizer. Ollama allows you to run open-source models
locally for complete data privacy and control.

You use this function to:

* Define which Ollama model to use
* Specify the dimensionality of the embeddings
* Configure how the Ollama API is accessed
* Configure the model's truncation behavior and keep alive settings
* Configure optional, model-specific parameters like temperature

## Samples

### Basic Ollama embedding

```sql theme={"dark"}
SELECT ai.create_vectorizer(
    'blog_posts'::regclass,
    loading => ai.loading_column('content'),
    embedding => ai.embedding_ollama('nomic-embed-text', 768),
    chunking => ai.chunking_character_text_splitter(512)
);
```

### With custom Ollama server

```sql theme={"dark"}
SELECT ai.create_vectorizer(
    'documents'::regclass,
    loading => ai.loading_column('content'),
    embedding => ai.embedding_ollama(
        'nomic-embed-text',
        768,
        base_url => 'http://my.ollama.server:11434'
    ),
    chunking => ai.chunking_character_text_splitter(512)
);
```

### With model options and keep alive

```sql theme={"dark"}
SELECT ai.create_vectorizer(
    'text_data'::regclass,
    loading => ai.loading_column('text'),
    embedding => ai.embedding_ollama(
        'nomic-embed-text',
        768,
        options => '{"num_ctx": 1024, "temperature": 0.5}'::jsonb,
        keep_alive => '10m'
    ),
    chunking => ai.chunking_character_text_splitter(512)
);
```

## Arguments

| Name         | Type    | Default | Required | Description                                                                                                        |
| ------------ | ------- | ------- | -------- | ------------------------------------------------------------------------------------------------------------------ |
| `model`      | `text`  | -       | ✔        | Name of the Ollama model to use (e.g., `nomic-embed-text`). The model must already be pulled on your Ollama server |
| `dimensions` | `int`   | -       | ✔        | Number of dimensions for the embedding vectors                                                                     |
| `base_url`   | `text`  | -       | ✖        | Base URL of the Ollama API. If not provided, uses `OLLAMA_HOST` environment variable                               |
| `options`    | `jsonb` | -       | ✖        | Additional model parameters such as `temperature` or `num_ctx`                                                     |
| `keep_alive` | `text`  | -       | ✖        | How long the model stays loaded in memory after the request (e.g., `5m`, `1h`)                                     |

## Returns

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

## Related functions

* [`embedding_openai()`][embedding_openai]: use OpenAI 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_openai]: /api-reference/pgai/vectorizer/embedding_openai

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