> ## Documentation Index
> Fetch the complete documentation index at: https://docs.memmachine.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Using Ollama Models

> Guide to configure MemMachine to use Ollama models.

## Prerequisites

Before you begin the installation and configuration of MemMachine, you must ensure that your local environment is ready by having Ollama installed and the necessary models downloaded.

### 1. Ollama Service

MemMachine connects directly to **Ollama** using its local API, which must be running in the background.

* **Installation:** If you do not yet have Ollama installed, please follow the official setup guide.

  * **Resource:** [Download Ollama](https://ollama.com/download)

* **Start the Service:** Once installed, start the Ollama service to make the local API available. Open your terminal or command prompt and run:

  ```bash theme={null}
  ollama serve
  ```

* **Verification:** You can confirm the service is running successfully by opening your web browser and navigating to the following address: `http://localhost:11434`
  You should see the Ollama web interface if the service is active.

### 2. Required Ollama Models

MemMachine requires **two** types of models to function: a Large Language Model (LLM) for generative tasks and an embedding model for converting text into vectors (e.g., for retrieval-augmented generation).

You must download these models to your local Ollama repository **before** starting MemMachine.

* **Download Models:** Use the `ollama pull` command to download the models you want.

| **Model Type**                 | **Example Model ID** | **Command to Run**             |
| ------------------------------ | -------------------- | ------------------------------ |
| **Large Language Model (LLM)** | Llama 3              | `ollama pull llama3`           |
| **Embedding Model**            | Nomic Embed Text     | `ollama pull nomic-embed-text` |

<Note> You can choose any compatible LLM (like `mixtral`, `gemma`, etc.) and embedding model available on Ollama, but the examples above are recommended starting points.</Note>

* **View Downloaded Models:** To see a list of all models currently available in your local Ollama repository, run:

  ```bash theme={null}
  ollama list
  ```

## Installation: QuickStart Configuration

The installation script will automatically guide you through setting up your **Large Language Model (LLM) provider**. When prompted, you **must** select **Ollama** to integrate with **Ollama**.

Your prompt input should match the following example:

```bash theme={null}
[PROMPT] Which provider would you like to use? (OpenAI/Bedrock/Ollama) [OpenAI]: Ollama
[INFO] Selected provider: OLLAMA
```

Ollama Configuration and Model Choices
You’ll then be prompted to select:

•	Ollama base URL (default: [http://host.docker.internal:11434/v1](http://host.docker.internal:11434/v1))

•	Choice of LLM (Large Language Model)

example: llama3

•	Choice of Embedding Model

example: nomic-embed-text

<Note> If you are unsure about model selection, simply press **Enter** at the respective prompts to use the recommended default options.</Note>

Congratulations!  You have now successfully deployed MemMachine using Ollama!

## Manually Configuring MemMachine to use Ollama

To manually configure MemMachine to use your local Ollama instance, you need to define your resources in the `resources` section of your `cfg.yml` file, pointing them to your local Ollama API endpoint (usually `http://host.docker.internal:11434/v1` if running MemMachine in Docker).

### 1. Define Ollama Resources

Add or update the `resources` block in your `cfg.yml`. We will use the `openai-chat-completions` provider for the LLM and the `openai` provider for embeddings, as Ollama is API-compatible.

```yaml theme={null}
resources:
  # LLM Configuration
  language_models:
    my-ollama-llm:
      provider: openai-chat-completions
      config:
        model: llama3
        api_key: "EMPTY" # Ollama doesn't require a key
        base_url: "http://host.docker.internal:11434/v1"

  # Embedder Configuration
  embedders:
    my-ollama-embedder:
      provider: openai
      config:
        model: nomic-embed-text
        api_key: "EMPTY"
        base_url: "http://host.docker.internal:11434/v1"
        dimensions: 768 # Ensure this matches your model's output
  
  # Reranker Configuration (Optional - using BM25 as fallback)
  rerankers:
    my-bm25-reranker:
      provider: bm25
```

### 2. Update Memory Configuration

Now, reference these resource IDs in your `episodic_memory` and `semantic_memory` sections.

```yaml theme={null}
episodic_memory:
  enabled: true
  long_term_memory:
    vector_graph_store: my-neo4j-db # Assumes you defined this in resources.databases
    embedder: my-ollama-embedder
    reranker: my-bm25-reranker
  short_term_memory:
    llm_model: my-ollama-llm

semantic_memory:
  database: my-postgres-db # Assumes you defined this in resources.databases
  llm_model: my-ollama-llm
  embedding_model: my-ollama-embedder
```

<Note>
  Make sure to restart the MemMachine server for these changes to take effect.
</Note>