---
title: "NvidiaDocumentEmbedder"
id: nvidiadocumentembedder
slug: "/nvidiadocumentembedder"
description: "This component computes the embeddings of a list of documents and stores the obtained vectors in the embedding field of each document."
---

# NvidiaDocumentEmbedder

This component computes the embeddings of a list of documents and stores the obtained vectors in the embedding field of each document.

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

|  |  |
| --- | --- |
| **Most common position in a pipeline** | Before a [`DocumentWriter`](../writers/documentwriter.mdx) in an indexing pipeline |
| **Mandatory init variables** | `api_key`: API key for the NVIDIA NIM. Can be set with `NVIDIA_API_KEY` env var. |
| **Mandatory run variables** | `documents`: A list of documents |
| **Output variables** | `documents`: A list of documents (enriched with embeddings)  <br /> <br />`meta`: A dictionary of metadata |
| **API reference** | [NVIDIA](/reference/integrations-nvidia) |
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/nvidia |

</div>

## Overview

`NvidiaDocumentEmbedder` enriches documents with an embedding of their content.

You can use this component with self-hosted models using NVIDIA NIM or models hosted on the [NVIDIA API Catalog](https://build.nvidia.com/explore/discover).

To embed a string, use [`NvidiaTextEmbedder`](nvidiatextembedder.mdx).

## Usage

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

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

You can use `NvidiaDocumentEmbedder` with all the embedding models available on the [NVIDIA API Catalog](https://docs.api.nvidia.com/nim/reference) or with a model deployed using NVIDIA NIM. For more information, refer to [Deploying Text Embedding Models](https://developer.nvidia.com/docs/nemo-microservices/embedding/source/deploy.html).

### On its own

To use models from the NVIDIA API Catalog, you need to 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).

`NvidiaDocumentEmbedder` 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 import Document
from haystack.utils.auth import Secret
from haystack_integrations.components.embedders.nvidia import NvidiaDocumentEmbedder

documents = [
    Document(content="A transformer is a deep learning architecture"),
    Document(content="Large language models use transformer architectures"),
]

embedder = NvidiaDocumentEmbedder(
    model="nvidia/nv-embedqa-e5-v5",
    api_url="https://integrate.api.nvidia.com/v1",
    api_key=Secret.from_token("<your-api-key>"),
)

result = embedder.run(documents=documents)
print(result["documents"])
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 import Document
from haystack_integrations.components.embedders.nvidia import NvidiaDocumentEmbedder

documents = [
    Document(content="A transformer is a deep learning architecture"),
    Document(content="Large language models use transformer architectures"),
]

embedder = NvidiaDocumentEmbedder(
    model="nvidia/nv-embedqa-e5-v5",
    api_url="http://localhost:9999/v1",
    api_key=None,
)

result = embedder.run(documents=documents)
print(result["documents"])
print(result["meta"])
```

### In a pipeline

The following example shows how to use `NvidiaDocumentEmbedder` in a RAG pipeline:

```python
from haystack import Pipeline, Document
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.writers import DocumentWriter
from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
from haystack.utils.auth import Secret
from haystack_integrations.components.embedders.nvidia import (
    NvidiaTextEmbedder,
    NvidiaDocumentEmbedder,
)

document_store = InMemoryDocumentStore(embedding_similarity_function="cosine")

documents = [
    Document(content="My name is Wolfgang and I live in Berlin"),
    Document(content="I saw a black horse running"),
    Document(content="Germany has many big cities"),
]

indexing_pipeline = Pipeline()
indexing_pipeline.add_component(
    "embedder",
    NvidiaDocumentEmbedder(
        model="nvidia/nv-embedqa-e5-v5",
        api_url="https://integrate.api.nvidia.com/v1",
        api_key=Secret.from_token("<your-api-key>"),
    ),
)
indexing_pipeline.add_component("writer", DocumentWriter(document_store=document_store))
indexing_pipeline.connect("embedder", "writer")

indexing_pipeline.run({"embedder": {"documents": documents}})

query_pipeline = Pipeline()
query_pipeline.add_component(
    "text_embedder",
    NvidiaTextEmbedder(
        model="nvidia/nv-embedqa-e5-v5",
        api_url="https://integrate.api.nvidia.com/v1",
        api_key=Secret.from_token("<your-api-key>"),
    ),
)
query_pipeline.add_component(
    "retriever",
    InMemoryEmbeddingRetriever(document_store=document_store),
)
query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")

query = "Who lives in Berlin?"

result = query_pipeline.run({"text_embedder": {"text": query}})

print(result["retriever"]["documents"][0])
```

## Related

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