---
title: "WatsonxChatGenerator"
id: watsonxchatgenerator
slug: "/watsonxchatgenerator"
description: "Use this component with IBM watsonx models like `granite-3-2b-instruct` for chat generation."
---

# WatsonxChatGenerator

Use this component with IBM watsonx models like `granite-3-2b-instruct` for chat generation.

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

|  |  |
| --- | --- |
| **Most common position in a pipeline** | After a [ChatPromptBuilder](../builders/chatpromptbuilder.mdx) |
| **Mandatory init variables** | `api_key`: The IBM Cloud API key. Can be set with `WATSONX_API_KEY` env var.  <br /> <br />`project_id`: The IBM Cloud project ID. Can be set with `WATSONX_PROJECT_ID` env var. |
| **Mandatory run variables** | `messages` A list of [`ChatMessage`](../../concepts/data-classes/chatmessage.mdx)  objects |
| **Output variables** | `replies`: A list of [`ChatMessage`](../../concepts/data-classes/chatmessage.mdx)  objects |
| **API reference** | [Watsonx](/reference/integrations-watsonx) |
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/watsonx |

</div>

This integration supports IBM watsonx.ai foundation models such as `ibm/granite-13b-chat-v2`, `ibm/llama-2-70b-chat`, `ibm/llama-3-70b-instruct`, and similar. These models provide high-quality chat completion capabilities through IBM's cloud platform. Check out the most recent full list in the [IBM watsonx.ai documentation](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-models-ibm.html?context=wx).

## Overview

`WatsonxChatGenerator` needs IBM Cloud credentials to work. You can set these in:

- The `api_key` and `project_id` init parameters using [Secret API](../../concepts/secret-management.mdx)
- The `WATSONX_API_KEY` and `WATSONX_PROJECT_ID` environment variables (recommended)

Then, the component needs a prompt to operate, but you can pass any text generation parameters valid for the IBM watsonx.ai API directly to this component using the `generation_kwargs` parameter, both at initialization and to `run()` method. For more details on the parameters supported by the IBM watsonx.ai API, refer to the [IBM watsonx.ai documentation](https://cloud.ibm.com/apidocs/watsonx-ai).

Finally, the component needs a list of `ChatMessage` objects to operate. `ChatMessage` is a data class that contains a message, a role (who generated the message, such as `user`, `assistant`, `system`, `function`), and optional metadata.

### Streaming

This Generator supports [streaming](guides-to-generators/choosing-the-right-generator.mdx#streaming-support) the tokens from the LLM directly in output. To do so, pass a function to the `streaming_callback` init parameter.

## Usage

You need to install `watsonx-haystack` package to use the `WatsonxChatGenerator`:

```shell
pip install watsonx-haystack
```

#### On its own

```python
from haystack_integrations.components.generators.watsonx.chat.chat_generator import (
    WatsonxChatGenerator,
)
from haystack.dataclasses import ChatMessage
from haystack.utils import Secret

generator = WatsonxChatGenerator(
    api_key=Secret.from_env_var("WATSONX_API_KEY"),
    project_id=Secret.from_env_var("WATSONX_PROJECT_ID"),
    model="ibm/granite-13b-instruct-v2",
)

message = ChatMessage.from_user("What's Natural Language Processing? Be brief.")
print(generator.run([message]))
```

With multimodal inputs:

```python
from haystack.dataclasses import ChatMessage, ImageContent
from haystack_integrations.components.generators.watsonx.chat.chat_generator import (
    WatsonxChatGenerator,
)

# Use a multimodal model
llm = WatsonxChatGenerator(model="meta-llama/llama-3-2-11b-vision-instruct")

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

You can also use `WatsonxChatGenerator` to use IBM watsonx.ai chat models in your pipeline.

```python
from haystack import Pipeline
from haystack.components.builders import ChatPromptBuilder
from haystack.dataclasses import ChatMessage
from haystack_integrations.components.generators.watsonx.chat.chat_generator import (
    WatsonxChatGenerator,
)
from haystack.utils import Secret

pipe = Pipeline()
pipe.add_component("prompt_builder", ChatPromptBuilder())
pipe.add_component(
    "llm",
    WatsonxChatGenerator(
        api_key=Secret.from_env_var("WATSONX_API_KEY"),
        project_id=Secret.from_env_var("WATSONX_PROJECT_ID"),
        model="ibm/granite-13b-instruct-v2",
    ),
)
pipe.connect("prompt_builder", "llm")

country = "Germany"
system_message = ChatMessage.from_system(
    "You are an assistant giving out valuable information to language learners.",
)
messages = [
    system_message,
    ChatMessage.from_user("What's the official language of {{ country }}?"),
]

res = pipe.run(
    data={
        "prompt_builder": {
            "template_variables": {"country": country},
            "template": messages,
        },
    },
)
print(res)
```