> ## 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 functions

> Run local LLMs with embeddings, chat completion, and model management

export const PG = 'Postgres';

Call Ollama's local LLM API directly from SQL to generate embeddings, completions, and chat responses using open-source
models running on your infrastructure.

## What is Ollama?

Ollama is a tool for running large language models locally on your own hardware. Unlike cloud-based APIs, Ollama
provides complete control over your models, data privacy, and costs. It supports popular open-source models like Llama,
Mistral, and CodeLlama.

## Key features

* **Privacy-first**: All data stays on your infrastructure
* **Cost-effective**: No per-token API costs
* **Offline operation**: Works without internet connectivity
* **Open-source models**: Access to Llama 2, Mistral, CodeLlama, and more
* **Full control**: Manage model versions and configurations

## Prerequisites

Before using Ollama functions, you need to:

1. Install and run Ollama on your infrastructure
2. Pull the models you want to use
3. Ensure your {PG} database can access the Ollama host

For installation instructions, visit [ollama.com][ollamacom].

## Quick start

### Generate embeddings

Create vector embeddings using a local model:

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

### Generate completions

Get text completions from a local model:

```sql theme={"dark"}
SELECT ai.ollama_generate(
    'llama2',
    'Explain what PostgreSQL is in one sentence'
)->'response';
```

### Chat completion

Have a conversation with a local model:

```sql theme={"dark"}
SELECT ai.ollama_chat_complete(
    'llama2',
    jsonb_build_array(
        jsonb_build_object('role', 'user', 'content', 'What is PostgreSQL?')
    )
)->'message'->>'content';
```

## Available functions

### Embeddings

* [`ollama_embed()`][ollama_embed]: generate vector embeddings from text

### Completions and chat

* [`ollama_generate()`][ollama_generate]: generate text completions with optional images
* [`ollama_chat_complete()`][ollama_chat_complete]: multi-turn conversations with tool support

### Model management

* [`ollama_list_models()`][ollama_list_models]: list all locally installed models
* [`ollama_ps()`][ollama_ps]: show currently running models and their resource usage

## Configuration

All Ollama functions accept a `host` parameter to specify the Ollama server location:

```sql theme={"dark"}
-- Use default host (http://localhost:11434)
SELECT ai.ollama_embed('llama2', 'sample text');

-- Specify custom host
SELECT ai.ollama_embed('llama2', 'sample text', host => 'http://ollama-server:11434');
```

## Resources

* [Ollama documentation][ollama-documentation]
* [Ollama models library][ollama-models-library]
* [Ollama API reference][ollama-api-reference]

[ollama-api-reference]: https://github.com/ollama/ollama/blob/main/docs/api.md

[ollama-documentation]: https://github.com/ollama/ollama/tree/main/docs

[ollama-models-library]: https://ollama.com/library

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

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

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

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

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

[ollamacom]: https://ollama.com