
Most common position in a pipeline	Before an embedding Retriever in a query/RAG pipeline
Mandatory init variables	"token": A HF API token. Can be set with `HF_API_TOKEN` env var.
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

The component uses a HF_API_TOKEN environment variable by default. Otherwise, you can pass a Hugging Face API token at initialization with token – see code examples below.

The token is needed:

If you use the Serverless Inference API, or
If you use the Inference Endpoints.

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"])