---
title: "NvidiaGenerator"
id: nvidiagenerator
slug: "/nvidiagenerator"
description: "This Generator enables text generation using NVIDIA-hosted models."
---

# NvidiaGenerator

This Generator enables text generation using NVIDIA-hosted models.

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

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

</div>

## Overview

`NvidiaGenerator` provides an interface for generating text using LLMs self-hosted with NVIDIA NIM or models hosted on the [NVIDIA API Catalog](https://build.nvidia.com/explore/discover).

## Usage

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

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

You can use `NvidiaGenerator` with all the LLMs available in the [NVIDIA API Catalog](https://docs.api.nvidia.com/nim/reference) or with a model deployed using NVIDIA NIM. For more information, refer to the [NVIDIA NIM for LLMs Playbook](https://developer.nvidia.com/docs/nemo-microservices/inference/playbooks/nmi_playbook.html).

### On its own

To use LLMs from the NVIDIA API Catalog, specify the `api_url` and your API key. You can get your API key from the [NVIDIA API Catalog](https://build.nvidia.com/explore/discover).

`NvidiaGenerator` uses the `NVIDIA_API_KEY` environment variable by default. Otherwise, you can pass an API key at initialization with the `api_key` parameter:

```python
from haystack.utils.auth import Secret
from haystack_integrations.components.generators.nvidia import NvidiaGenerator

generator = NvidiaGenerator(
    model="meta/llama-3.1-70b-instruct",
    api_url="https://integrate.api.nvidia.com/v1",
    api_key=Secret.from_token("<your-api-key>"),
    model_arguments={
        "temperature": 0.2,
        "top_p": 0.7,
        "max_tokens": 1024,
    },
)
result = generator.run(prompt="What is the answer?")
print(result["replies"])
print(result["meta"])
```

To use a locally deployed model, set the `api_url` to your localhost and set `api_key` to `None`:

```python
from haystack_integrations.components.generators.nvidia import NvidiaGenerator

generator = NvidiaGenerator(
    model="meta/llama-3.1-8b-instruct",
    api_url="http://localhost:9999/v1",
    api_key=None,
    model_arguments={
        "temperature": 0.2,
    },
)
result = generator.run(prompt="What is the answer?")
print(result["replies"])
print(result["meta"])
```

### In a pipeline

The following example shows a RAG pipeline:

```python
from haystack import Pipeline, Document
from haystack.utils.auth import Secret
from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
from haystack.components.builders.prompt_builder import PromptBuilder
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack_integrations.components.generators.nvidia import NvidiaGenerator

docstore = InMemoryDocumentStore()
docstore.write_documents(
    [
        Document(content="Rome is the capital of Italy"),
        Document(content="Paris is the capital of France"),
    ],
)

query = "What is the capital of France?"

template = """
Given the following information, answer the question.

Context:
{% for document in documents %}
    {{ document.content }}
{% endfor %}

Question: {{ query }}?
"""

pipe = Pipeline()

pipe.add_component("retriever", InMemoryBM25Retriever(document_store=docstore))
pipe.add_component("prompt_builder", PromptBuilder(template=template))
pipe.add_component(
    "llm",
    NvidiaGenerator(
        model="meta/llama-3.1-70b-instruct",
        api_url="https://integrate.api.nvidia.com/v1",
        api_key=Secret.from_token("<your-api-key>"),
        model_arguments={
            "temperature": 0.2,
            "top_p": 0.7,
            "max_tokens": 1024,
        },
    ),
)
pipe.connect("retriever", "prompt_builder.documents")
pipe.connect("prompt_builder", "llm")

res = pipe.run(
    {
        "prompt_builder": {"query": query},
        "retriever": {"query": query},
    },
)

print(res)
```

## Related

- Cookbook: [Haystack RAG Pipeline with Self-Deployed AI models using NVIDIA NIMs](https://haystack.deepset.ai/cookbook/rag-with-nims)
