---
title: "OptimumTextEmbedder"
id: optimumtextembedder
slug: "/optimumtextembedder"
description: "A component to embed text using models loaded with the Hugging Face Optimum library."
---

# OptimumTextEmbedder

A component to embed text using models loaded with the Hugging Face Optimum library.

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

|                                        |                                                                                           |
| :------------------------------------- | :---------------------------------------------------------------------------------------- |
| **Most common position in a pipeline** | Before an embedding [Retriever](../retrievers.mdx)  in a query/RAG pipeline                |
| **Mandatory run variables**            | `text`: A string                                                                          |
| **Output variables**                   | `embedding`: A list of float numbers (vectors)                                            |
| **API reference**                      | [Optimum](/reference/integrations-optimum)                                                       |
| **GitHub link**                        | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/optimum |

</div>

## Overview

`OptimumTextEmbedder` embeds text strings using models loaded with the [HuggingFace Optimum](https://huggingface.co/docs/optimum/index) library. It uses the [ONNX runtime](https://onnxruntime.ai/) for high-speed inference.

The default model is `sentence-transformers/all-mpnet-base-v2`.

Similarly to other Embedders, this component allows adding prefixes (and suffixes) to include instructions. For more details, refer to the component’s API reference.

There are three useful parameters specific to the Optimum Embedder that you can control with various modes:

- [Pooling](/reference/integrations-optimum#optimumembedderpooling): generate a fixed-sized sentence embedding from a variable-sized sentence embedding
- [Optimization](https://huggingface.co/docs/optimum/onnxruntime/usage_guides/optimization): apply graph optimization to the model and improve inference speed
- [Quantization](https://huggingface.co/docs/optimum/onnxruntime/usage_guides/quantization): reduce the computational and memory costs

Find all the available mode details in our Optimum [API Reference](/reference/integrations-optimum).

### Authentication

Authentication with a Hugging Face API Token is only required to access private or gated models through Serverless Inference API or the Inference Endpoints.

The component uses an `HF_API_TOKEN` or `HF_TOKEN` environment variable, or you can pass a Hugging Face API token at initialization. See our [Secret Management](../../concepts/secret-management.mdx) page for more information.

## Usage

To start using this integration with Haystack, install it with:

```shell
pip install optimum-haystack
```

### On its own

```python
from haystack_integrations.components.embedders.optimum import OptimumTextEmbedder

text_to_embed = "I love pizza!"

text_embedder = OptimumTextEmbedder(model="sentence-transformers/all-mpnet-base-v2")
text_embedder.warm_up()

print(text_embedder.run(text_to_embed))

## {'embedding': [-0.07804739475250244, 0.1498992145061493,, ...]}
```

### In a pipeline

Note that this example requires GPU support to execute.

```python
from haystack import Pipeline

from haystack_integrations.components.embedders.optimum import (
    OptimumTextEmbedder,
    OptimumEmbedderPooling,
    OptimumEmbedderOptimizationConfig,
    OptimumEmbedderOptimizationMode,
)

pipeline = Pipeline()
embedder = OptimumTextEmbedder(
    model="intfloat/e5-base-v2",
    normalize_embeddings=True,
    onnx_execution_provider="CUDAExecutionProvider",
    optimizer_settings=OptimumEmbedderOptimizationConfig(
        mode=OptimumEmbedderOptimizationMode.O4,
        for_gpu=True,
    ),
    working_dir="/tmp/optimum",
    pooling_mode=OptimumEmbedderPooling.MEAN,
)
pipeline.add_component("embedder", embedder)

results = pipeline.run(
    {
        "embedder": {
            "text": "Ex profunditate antique doctrinae, Ad caelos supra semper, Hoc incantamentum evoco, draco apparet, Incantamentum iam transactum est"
        },
    }
)

print(results["embedder"]["embedding"])
```
