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

Overview

OptimumTextEmbedder embeds text strings using models loaded with the HuggingFace Optimum library. It uses the ONNX runtime 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: generate a fixed-sized sentence embedding from a variable-sized sentence embedding
Optimization: apply graph optimization to the model and improve inference speed
Quantization: reduce the computational and memory costs

Find all the available mode details in our Optimum API Reference.

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 page for more information.

Usage

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

pip install optimum-haystack

On its own

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.

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 antiquae doctrinae, Ad caelos supra semper, Hoc incantamentum evoco, draco apparet, Incantamentum iam transactum est"
        },
    }
)

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