---
title: "OllamaChatGenerator"
id: ollamachatgenerator
slug: "/ollamachatgenerator"
description: "This component enables chat completion using an LLM running on Ollama."
---

# OllamaChatGenerator

This component enables chat completion using an LLM running on Ollama.

<div className="key-value-table">

|  |  |
| --- | --- |
| **Most common position in a pipeline** | After a [ChatPromptBuilder](../builders/chatpromptbuilder.mdx)                                                 |
| **Mandatory run variables**            | `messages`: A list of [`ChatMessage`](../../concepts/data-classes/chatmessage.mdx)  objects representing the chat |
| **Output variables**                   | `replies`: A list of LLM’s alternative replies                                                       |
| **API reference**                      | [Ollama](/reference/integrations-ollama)                                                                    |
| **GitHub link**                        | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/ollama             |

</div>

## Overview

[Ollama](https://github.com/jmorganca/ollama) is a project focused on running LLMs locally. Internally, it uses the quantized GGUF format by default. This means it is possible to run LLMs on standard machines (even without GPUs) without having to handle complex installation procedures.

`OllamaChatGenerator` supports models running on Ollama, such as `llama2` and `mixtral`. Find the full list of supported models [here](https://ollama.ai/library).

`OllamaChatGenerator`  needs a `model`  name and a `url` to work. By default, it uses `"orca-mini"` model and `"http://localhost:11434"` url.

The way to operate with `OllamaChatGenerator` is by using  `ChatMessage` objects. [ChatMessage](../../concepts/data-classes/chatmessage.mdx)  is a data class that contains a message, a role (who generated the message, such as `user`, `assistant`, `system`, `function`), and optional metadata. See the [usage](#usage) section for an example.

### Tool Support

`OllamaChatGenerator` supports function calling through the `tools` parameter, which accepts flexible tool configurations:

- **A list of Tool objects**: Pass individual tools as a list
- **A single Toolset**: Pass an entire Toolset directly
- **Mixed Tools and Toolsets**: Combine multiple Toolsets with standalone tools in a single list

This allows you to organize related tools into logical groups while also including standalone tools as needed.

```python
from haystack.tools import Tool, Toolset
from haystack_integrations.components.generators.ollama import OllamaChatGenerator

# Create individual tools
weather_tool = Tool(name="weather", description="Get weather info", ...)
news_tool = Tool(name="news", description="Get latest news", ...)

# Group related tools into a toolset
math_toolset = Toolset([add_tool, subtract_tool, multiply_tool])

# Pass mixed tools and toolsets to the generator
generator = OllamaChatGenerator(
    model="llama2",
    tools=[math_toolset, weather_tool, news_tool]  # Mix of Toolset and Tool objects
)
```

For more details on working with tools, see the [Tool](../../tools/tool.mdx) and [Toolset](../../tools/toolset.mdx) documentation.

### Streaming

You can stream output as it’s generated. Pass a callback to `streaming_callback`. Use the built-in `print_streaming_chunk` to print text tokens and tool events (tool calls and tool results).

```python
from haystack.components.generators.utils import print_streaming_chunk

## Configure any `Generator` or `ChatGenerator` with a streaming callback
component = SomeGeneratorOrChatGenerator(streaming_callback=print_streaming_chunk)

## If this is a `ChatGenerator`, pass a list of messages:
## from haystack.dataclasses import ChatMessage
## component.run([ChatMessage.from_user("Your question here")])

## If this is a (non-chat) `Generator`, pass a prompt:
## component.run({"prompt": "Your prompt here"})
```

:::info
Streaming works only with a single response. If a provider supports multiple candidates, set `n=1`.
:::

See our [Streaming Support](guides-to-generators/choosing-the-right-generator.mdx#streaming-support) docs to learn more how `StreamingChunk` works and how to write a custom callback.

Give preference to `print_streaming_chunk` by default. Write a custom callback only if you need a specific transport (for example, SSE/WebSocket) or custom UI formatting.

## Usage

1. You need a running instance of Ollama. The installation instructions are [in the Ollama GitHub repository](https://github.com/jmorganca/ollama).
   A fast way to run Ollama is using Docker:

```bash
docker run -d -p 11434:11434 --name ollama ollama/ollama:latest
```

2. You need to download or pull the desired LLM. The model library is available on the [Ollama website](https://ollama.ai/library).
   If you are using Docker, you can, for example, pull the Zephyr model:

```bash
docker exec ollama ollama pull zephyr
```

If you already installed Ollama in your system, you can execute:

```bash
ollama pull zephyr
```

:::tip[Choose a specific version of a model]

You can also specify a tag to choose a specific (quantized) version of your model. The available tags are shown in the model card of the Ollama models library. This is an [example](https://ollama.ai/library/zephyr/tags) for Zephyr.
In this case, simply run

```shell
# ollama pull model:tag
ollama pull zephyr:7b-alpha-q3_K_S
```
:::

3. You also need to install the `ollama-haystack` package:

```bash
pip install ollama-haystack
```

### On its own

```python
from haystack_integrations.components.generators.ollama import OllamaChatGenerator
from haystack.dataclasses import ChatMessage

generator = OllamaChatGenerator(model="zephyr",
                            url = "http://localhost:11434",
                            generation_kwargs={
                              "num_predict": 100,
                              "temperature": 0.9,
                              })

messages = [ChatMessage.from_system("\nYou are a helpful, respectful and honest assistant"),
ChatMessage.from_user("What's Natural Language Processing?")]

print(generator.run(messages=messages))
>> {
    "replies": [
        ChatMessage(
            _role=<ChatRole.ASSISTANT: 'assistant'>,
            _content=[
                TextContent(
                    text=(
                        "Natural Language Processing (NLP) is a subfield of "
                        "Artificial Intelligence that deals with understanding, "
                        "interpreting, and generating human language in a meaningful "
                        "way. It enables tasks such as language translation, sentiment "
                        "analysis, and text summarization."
                    )
                )
            ],
            _name=None,
            _meta={
                "model": "zephyr",...
            }
        )
    ]
}
```

With multimodal inputs:

```python
from haystack.dataclasses import ChatMessage, ImageContent
from haystack_integrations.components.generators.ollama import OllamaChatGenerator

llm = OllamaChatGenerator(model="llava", url="http://localhost:11434")

image = ImageContent.from_file_path("apple.jpg")
user_message = ChatMessage.from_user(
    content_parts=["What does the image show? Max 5 words.", image],
)

response = llm.run([user_message])["replies"][0].text
print(response)

# Red apple on straw.
```

### In a Pipeline

```python
from haystack.components.builders import ChatPromptBuilder
from haystack_integrations.components.generators.ollama import OllamaChatGenerator
from haystack.dataclasses import ChatMessage
from haystack import Pipeline

## no parameter init, we don't use any runtime template variables
prompt_builder = ChatPromptBuilder()
generator = OllamaChatGenerator(model="zephyr",
                            url = "http://localhost:11434",
                            generation_kwargs={
                              "temperature": 0.9,
                              })

pipe = Pipeline()
pipe.add_component("prompt_builder", prompt_builder)
pipe.add_component("llm", generator)
pipe.connect("prompt_builder.prompt", "llm.messages")
location = "Berlin"
messages = [ChatMessage.from_system("Always respond in Spanish even if some input data is in other languages."),
            ChatMessage.from_user("Tell me about {{location}}")]
print(pipe.run(data={"prompt_builder": {"template_variables":{"location": location}, "template": messages}}))

>> {
    "llm": {
        "replies": [
            ChatMessage(
                _role=<ChatRole.ASSISTANT: 'assistant'>,
                _content=[
                    TextContent(
                        text=(
                            "Berlín es la capital y la mayor ciudad de Alemania. "
                            "Está ubicada en el estado federado de Berlín, y tiene más..."
                        )
                    )
                ],
                _name=None,
                _meta={
                    "model": "zephyr",...
                }
            )
        ]
    }
}
```
