---
title: "FastembedSparseTextEmbedder"
id: fastembedsparsetextembedder
slug: "/fastembedsparsetextembedder"
description: "Use this component to embed a simple string (such as a query) into a sparse vector."
---

# FastembedSparseTextEmbedder

Use this component to embed a simple string (such as a query) into a sparse vector.

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

|  |  |
| --- | --- |
| **Most common position in a pipeline** | Before a sparse embedding [Retriever](../retrievers.mdx)  in a query/RAG pipeline            |
| **Mandatory run variables**            | `text`: A string                                                                            |
| **Output variables**                   | `sparse_embedding`: A [`SparseEmbedding`](../../concepts/data-classes.mdx#sparseembedding)  object       |
| **API reference**                      | [FastEmbed](/reference/fastembed-embedders)                                                        |
| **GitHub link**                        | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/fastembed |

</div>

For embedding lists of documents, use the [`FastembedSparseDocumentEmbedder`](fastembedsparsedocumentembedder.mdx), which enriches the document with the computed sparse embedding.

## Overview

`FastembedSparseTextEmbedder` transforms a string into a sparse vector using sparse embedding [models](https://qdrant.github.io/fastembed/examples/Supported_Models/#supported-sparse-text-embedding-models) supported by FastEmbed.

When you perform sparse embedding retrieval, use this component first to transform your query into a sparse vector. Then, the sparse embedding Retriever will use the vector to search for similar or relevant documents.

### Compatible Models

You can find the supported models in the [FastEmbed documentation](https://qdrant.github.io/fastembed/examples/Supported_Models/#supported-sparse-text-embedding-models).

Currently, supported models are based on SPLADE, a technique for producing sparse representations for text, where each non-zero value in the embedding is the importance weight of a term in the BERT WordPiece vocabulary. For more information, see [our docs](../retrievers.mdx#sparse-embedding-based-retrievers) that explain sparse embedding-based Retrievers further.

### Installation

To start using this integration with Haystack, install the package with:

```shell
pip install fastembed-haystack
```

### Parameters

You can set the path where the model will be stored in a cache directory. Also, you can set the number of threads a single `onnxruntime` session can use:

```python
cache_dir = "/your_cacheDirectory"
embedder = FastembedSparseTextEmbedder(
    model="prithivida/Splade_PP_en_v1",
    cache_dir=cache_dir,
    threads=2,
)
```

If you want to use the data parallel encoding, you can set the `parallel` parameter.

- If `parallel` > 1, data-parallel encoding will be used. This is recommended for offline encoding of large datasets.
- If `parallel` is 0, use all available cores.
- If None, don't use data-parallel processing; use the default `onnxruntime` threading instead.

:::tip
If you create both a Sparse Text Embedder and a Sparse Document Embedder based on the same model, Haystack utilizes a shared resource behind the scenes to conserve resources.
:::

## Usage

### On its own

```python
from haystack_integrations.components.embedders.fastembed import (
    FastembedSparseTextEmbedder,
)

text = """It clearly says online this will work on a Mac OS system.
The disk comes and it does not, only Windows.
Do Not order this if you have a Mac!!"""

text_embedder = FastembedSparseTextEmbedder(model="prithivida/Splade_PP_en_v1")
text_embedder.warm_up()

sparse_embedding = text_embedder.run(text)["sparse_embedding"]
```

### In a pipeline

Currently, sparse embedding retrieval is only supported by `QdrantDocumentStore`.
First, install the package with:

```shell
pip install qdrant-haystack
```

Then, try out this pipeline:

```python
from haystack import Document, Pipeline
from haystack_integrations.document_stores.qdrant import QdrantDocumentStore
from haystack_integrations.components.retrievers.qdrant import (
    QdrantSparseEmbeddingRetriever,
)
from haystack_integrations.components.embedders.fastembed import (
    FastembedSparseTextEmbedder,
    FastembedSparseDocumentEmbedder,
    FastembedTextEmbedder,
)

document_store = QdrantDocumentStore(
    ":memory:",
    recreate_index=True,
    use_sparse_embeddings=True,
)

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"),
    Document(content="fastembed is supported by and maintained by Qdrant."),
]

sparse_document_embedder = FastembedSparseDocumentEmbedder(
    model="prithivida/Splade_PP_en_v1",
)

sparse_document_embedder.warm_up()
documents_with_sparse_embeddings = sparse_document_embedder.run(documents)["documents"]
document_store.write_documents(documents_with_sparse_embeddings)

query_pipeline = Pipeline()
query_pipeline.add_component("sparse_text_embedder", FastembedSparseTextEmbedder())
query_pipeline.add_component(
    "sparse_retriever",
    QdrantSparseEmbeddingRetriever(document_store=document_store),
)
query_pipeline.connect(
    "sparse_text_embedder.sparse_embedding",
    "sparse_retriever.query_sparse_embedding",
)

query = "Who supports fastembed?"

result = query_pipeline.run({"sparse_text_embedder": {"text": query}})

print(result["sparse_retriever"]["documents"][0])  # noqa: T201

## Document(id=...,
## content: 'fastembed is supported by and maintained by Qdrant.',
## score: 0.561..)
```

## Additional References

🧑‍🍳 Cookbook: [Sparse Embedding Retrieval with Qdrant and FastEmbed](https://haystack.deepset.ai/cookbook/sparse_embedding_retrieval)