OllamaChatGenerator

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

Most common position in a pipeline	After a ChatPromptBuilder
Mandatory run variables	“messages”: A list of ChatMessage objects representing the chat
Output variables	“replies”: A list of LLM’s alternative replies
API reference	Ollama
GitHub link	https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/ollama

Overview

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.

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 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 section for an example.

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"})

note

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

See our 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. 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. 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 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",...
            }
        )
    ]
}

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",...
                }
            )
        ]
    }
}