---
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.

|  |  |
| --- | --- |
| **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 |

## Overview

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

It can be used with self-hosted models with NVIDIA NIM or models hosted on the [NVIDIA API catalog](https://build.nvidia.com/explore/discover).

To embed a string, use the [`NvidiaTextEmbedder`](/docs/nvidiatextembedder).

## Usage

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

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

You can use the `NvidiaDocumentEmbedder` with all the embedder models available on the [NVIDIA API catalog](https://docs.api.nvidia.com/nim/reference) or using a model deployed with NVIDIA NIM. Follow the [Deploying Text Embedding Models](https://developer.nvidia.com/docs/nemo-microservices/embedding/source/deploy.html) guide to learn how to deploy the model you want on your infrastructure.

### On its own

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

The `NvidiaDocumentEmbedder` needs an Nvidia API key to work. It uses the `NVIDIA_API_KEY` environment variable by default. Otherwise, you can pass an API key at initialization with `api_key`, as in the following example.

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

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

result = embedder.run("A transformer is a deep learning architecture")
print(result["embedding"])
print(result["meta"])
```

To use a locally deployed model, you need to set the `api_url` to your localhost and unset your `api_key`.

```python
from haystack_integrations.components.embedders.nvidia import NvidiaDocumentEmbedder

embedder = NvidiaDocumentEmbedder(
    model="nvidia/nv-embedqa-e5-v5",
    api_url="http://0.0.0.0:9999/v1",
    api_key=None,
)
embedder.warm_up()

result = embedder.run("A transformer is a deep learning architecture")
print(result["embedding"])
print(result["meta"])
```

### In a pipeline

Here's an example of a RAG pipeline:

```python
from haystack import Pipeline, Document
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack_integrations.components.embedders.nvidia import NvidiaTextEmbedder, NvidiaDocumentEmbedder
from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever

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])
```

## Additional References

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