---
title: "NvidiaChatGenerator"
id: nvidiachatgenerator
slug: "/nvidiachatgenerator"
description: "This Generator enables chat completion using Nvidia-hosted models."
---

# NvidiaChatGenerator

This Generator enables chat completion using Nvidia-hosted models.

|                                        |                                                                                          |
| -------------------------------------- | ---------------------------------------------------------------------------------------- |
| **Most common position in a pipeline** | After a [ChatPromptBuilder](../builders/chatpromptbuilder.mdx)                                     |
| **Mandatory init variables**           | "api_key": API key for the NVIDIA NIM. Can be set with `NVIDIA_API_KEY` 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**                      | [NVIDIA API](https://build.nvidia.com/models)                                            |
| **GitHub link**                        | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/nvidia |

## Overview

`NvidiaChatGenerator` enables chat completions using NVIDIA's generative models via the NVIDIA API. It is compatible with the [ChatMessage](../../concepts/data-classes/chatmessage.mdx) format for both input and output, ensuring seamless integration in chat-based pipelines.

You can use LLMs self-hosted with NVIDIA NIM or models hosted on the [NVIDIA API catalog](https://build.nvidia.com/explore/discover). The default model for this component is `meta/llama-3.1-8b-instruct`.

To use this integration, you must have a NVIDIA API key. You can provide it with the `NVIDIA_API_KEY` environment variable or by using a [Secret](../../concepts/secret-management.mdx).

This generator supports [streaming](guides-to-generators/choosing-the-right-generator.mdx#streaming-support) responses from the LLM. To enable streaming, pass a callable to the `streaming_callback` parameter during initialization.

## Usage

To start using `NvidiaChatGenerator`, first, install the `nvidia-haystack` package:

```shell
pip install nvidia-haystack
```

You can use the `NvidiaChatGenerator` with all the LLMs available in the [NVIDIA API catalog](https://docs.api.nvidia.com/nim/reference) or a model deployed with NVIDIA NIM. Follow the [NVIDIA NIM for LLMs Playbook](https://developer.nvidia.com/docs/nemo-microservices/inference/playbooks/nmi_playbook.html) to learn how to deploy your desired model on your infrastructure.

### On its own

 To use LLMs from the NVIDIA API catalog, you need to specify the correct `api_url` if needed (the default one is `https://integrate.api.nvidia.com/v1`), and your API key. You can get your API key directly from the [catalog website](https://build.nvidia.com/explore/discover).

```python
from haystack_integrations.components.generators.nvidia import NvidiaChatGenerator
from haystack.dataclasses import ChatMessage

generator = NvidiaChatGenerator(
    model="meta/llama-3.1-8b-instruct",  # or any supported NVIDIA model
    api_key=Secret.from_env_var("NVIDIA_API_KEY"),
)

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

### In a Pipeline

```python
from haystack import Pipeline
from haystack.components.builders import ChatPromptBuilder
from haystack.dataclasses import ChatMessage
from haystack_integrations.components.generators.nvidia import NvidiaChatGenerator
from haystack.utils import Secret

pipe = Pipeline()
pipe.add_component("prompt_builder", ChatPromptBuilder())
pipe.add_component(
    "llm",
    NvidiaChatGenerator(
        model="meta/llama-3.1-8b-instruct",
        api_key=Secret.from_env_var("NVIDIA_API_KEY"),
    ),
)
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)
```
