---
title: "NvidiaTextEmbedder"
id: nvidiatextembedder
slug: "/nvidiatextembedder"
description: "This component transforms a string into a vector that captures its semantics using Nvidia-hosted models."
---

# NvidiaTextEmbedder

This component transforms a string into a vector that captures its semantics using Nvidia-hosted models.

|  |  |
| --- | --- |
| **Most common position in a pipeline** | Before an embedding [Retriever](../retrievers.mdx)  in a query/RAG pipeline |
| **Mandatory init variables** | "api_key": API key for the NVIDIA NIM. Can be set with `NVIDIA_API_KEY` env var. |
| **Mandatory run variables** | “text”: A string |
| **Output variables** | “embedding”: A list of float numbers (vectors)  <br /> <br />“meta”: A dictionary of metadata strings |
| **API reference** | [Nvidia](/reference/integrations-nvidia) |
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/nvidia |

## Overview

`NvidiaTextEmbedder` embeds a simple string (such as a query) into a vector.

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 list of documents, use the [`NvidiaDocumentEmbedder`](nvidiadocumentembedder.mdx), which enriches the document with the computed embedding, also known as vector.

## Usage

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

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

You can use the `NvidiaTextEmbedder` 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 `NvidiaTextEmbedder` 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 NvidiaTextEmbedder

embedder = NvidiaTextEmbedder(
    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 NvidiaTextEmbedder

embedder = NvidiaTextEmbedder(
    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="NV-Embed-QA",
        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

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