> ## 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.

# ollama_embed()

> Generate vector embeddings using local Ollama models

Generate vector embeddings from text using locally hosted Ollama models. Embeddings are numerical representations of
text that capture semantic meaning, ideal for semantic search, recommendations, and clustering without sending data to
external APIs.

## Samples

### Generate an embedding

Create a vector embedding using a local model:

```sql theme={"dark"}
SELECT ai.ollama_embed(
    'llama2',
    'PostgreSQL is a powerful database'
);
```

### Specify Ollama host

Connect to a specific Ollama server:

```sql theme={"dark"}
SELECT ai.ollama_embed(
    'llama2',
    'PostgreSQL is a powerful database',
    host => 'http://ollama-server:11434'
);
```

### Configure model options

Customize the embedding generation:

```sql theme={"dark"}
SELECT ai.ollama_embed(
    'llama2',
    'PostgreSQL is a powerful database',
    embedding_options => '{"temperature": 0.5}'::jsonb
);
```

### Store embeddings in a table

Generate and store embeddings for your data:

```sql theme={"dark"}
UPDATE documents
SET embedding = ai.ollama_embed(
    'llama2',
    content,
    host => 'http://localhost:11434'
)
WHERE embedding IS NULL;
```

## Arguments

| Name                | Type      | Default | Required | Description                                                             |
| ------------------- | --------- | ------- | -------- | ----------------------------------------------------------------------- |
| `model`             | `TEXT`    | -       | ✔        | The Ollama model to use (e.g., `llama2`, `mistral`, `nomic-embed-text`) |
| `input_text`        | `TEXT`    | -       | ✔        | Text input to embed                                                     |
| `host`              | `TEXT`    | `NULL`  | ✖        | Ollama server URL (defaults to `http://localhost:11434`)                |
| `keep_alive`        | `TEXT`    | `NULL`  | ✖        | How long to keep the model loaded (e.g., `5m`, `1h`)                    |
| `embedding_options` | `JSONB`   | `NULL`  | ✖        | Model-specific options as JSON                                          |
| `verbose`           | `BOOLEAN` | `FALSE` | ✖        | Enable verbose logging for debugging                                    |

## Returns

`vector`: A pgvector compatible vector containing the embedding.

## Related functions

* [`ollama_generate()`][ollama_generate]: generate text completions
* [`ollama_chat_complete()`][ollama_chat_complete]: chat with local models
* [`ollama_list_models()`][ollama_list_models]: see available models

[ollama_chat_complete]: /api-reference/pgai/model-calling/ollama/ollama_chat_complete

[ollama_generate]: /api-reference/pgai/model-calling/ollama/ollama_generate

[ollama_list_models]: /api-reference/pgai/model-calling/ollama/ollama_list_models