---
title: "WatsonxGenerator"
id: watsonxgenerator
slug: "/watsonxgenerator"
description: "Use this component with IBM watsonx models like `granite-3-2b-instruct` for simple text generation tasks."
---

# WatsonxGenerator

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

|  |  |
| --- | --- |
| **Most common position in a pipeline** | After a [PromptBuilder](../builders/promptbuilder.mdx)  |
| **Mandatory init variables** | "api_key": An IBM Cloud API key. Can be set with `WATSONX_API_KEY` env var.  <br /> <br />"project_id": An IBM Cloud project ID. Can be set with `WATSONX_PROJECT_ID` env var. |
| **Mandatory run variables** | "prompt": A string containing the prompt for the LLM |
| **Output variables** | "replies": A list of strings with all the replies generated by the LLM  <br /> <br />"meta": A list of dictionaries with the metadata associated with each reply, such as token count, finish reason, and so on |
| **API reference** | [Watsonx](/reference/integrations-watsonx) |
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/watsonx |

## Overview

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 text generation 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).

### Parameters

`WatsonxGenerator` needs IBM Cloud credentials to work. You can provide these in:

- The `WATSONX_API_KEY` environment variable (recommended)
- The `WATSONX_PROJECT_ID` environment variable (recommended)
- The `api_key` and `project_id` init parameters using Haystack [Secret](../../concepts/secret-management.mdx) API: `Secret.from_token("your-api-key-here")`

Set your preferred IBM watsonx.ai model in the `model` parameter when initializing the component. The default model is `ibm/granite-3-2b-instruct`.

`WatsonxGenerator` requires a prompt to generate text, but you can pass any text generation parameters available in 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, see [IBM watsonx.ai documentation](https://cloud.ibm.com/apidocs/watsonx-ai).

The component also supports system prompts that can be set at initialization or passed during runtime to provide context or instructions for the generation.

Finally, the component run method requires a single string prompt to generate text.

### 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

Install the `watsonx-haystack` package to use the `WatsonxGenerator`:

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

### On its own

```python
from haystack_integrations.components.generators.watsonx.generator import (
    WatsonxGenerator,
)
from haystack.utils import Secret

generator = WatsonxGenerator(
    api_key=Secret.from_env_var("WATSONX_API_KEY"),
    project_id=Secret.from_env_var("WATSONX_PROJECT_ID"),
)

print(generator.run("What's Natural Language Processing? Be brief."))
```

### In a pipeline

You can also use `WatsonxGenerator` with the IBM watsonx.ai models in your pipeline.

```python
from haystack import Pipeline
from haystack.components.builders import PromptBuilder
from haystack_integrations.components.generators.watsonx.generator import (
    WatsonxGenerator,
)
from haystack.utils import Secret

template = """
You are an assistant giving out valuable information to language learners.
Answer this question, be brief.

Question: {{ query }}?
"""

pipe = Pipeline()
pipe.add_component("prompt_builder", PromptBuilder(template))
pipe.add_component(
    "llm",
    WatsonxGenerator(
        api_key=Secret.from_env_var("WATSONX_API_KEY"),
        project_id=Secret.from_env_var("WATSONX_PROJECT_ID"),
    ),
)
pipe.connect("prompt_builder", "llm")

query = "What language is spoken in Germany?"
res = pipe.run(data={"prompt_builder": {"query": query}})

print(res)
```
