---
title: "FastembedTextEmbedder"
id: fastembedtextembedder
slug: "/fastembedtextembedder"
description: "This component computes the embeddings of a string using embedding models supported by FastEmbed."
---

# FastembedTextEmbedder

This component computes the embeddings of a string using embedding models supported by FastEmbed.

<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 vector (list of float numbers)                                               |
| **API reference**                      | [FastEmbed](/reference/fastembed-embedders)                                                        |
| **GitHub link**                        | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/fastembed |

</div>

This component should be used to embed a simple string (such as a query) into a vector. For embedding lists of documents, use the [`FastembedDocumentEmbedder`](fastembeddocumentembedder.mdx), which enriches the document with the computed embedding, known as vector.

## Overview

`FastembedTextEmbedder` transforms a string into a vector that captures its semantics using embedding [models supported by FastEmbed](https://qdrant.github.io/fastembed/examples/Supported_Models/).

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

### Compatible models

You can find the original models in the [FastEmbed documentation](https://qdrant.github.io/fastembed/).

Currently, most of the models in the [Massive Text Embedding Benchmark (MTEB) Leaderboard](https://huggingface.co/spaces/mteb/leaderboard) are compatible with FastEmbed. You can look for compatibility in the [supported model list](https://qdrant.github.io/fastembed/examples/Supported_Models/).

### Installation

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

```bash
pip install fastembed-haystack
```

### Instructions

Some recent models that you can find in MTEB require prepending the text with an instruction to work better for retrieval.
For example, if you use `[BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5#model-list)` model, you should prefix your query with the `instruction: “passage:”`.

This is how it works with `FastembedTextEmbedder`:

```python
instruction = "passage:"
embedder = FastembedTextEmbedder(
	*model="*BAAI/bge-large-en-v1.5",
	prefix=instruction)
```

### 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 = FastembedTextEmbedder(
	*model="*BAAI/bge-large-en-v1.5",
	cache_dir=cache_dir,
	threads=2
)
```

If you want to use the data parallel encoding, you can set the parameters `parallel` and `batch_size`.

- 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 default `onnxruntime` threading instead.

:::tip
If you create a Text Embedder and a Document Embedder based on the same model, Haystack uses the same resource behind the scenes to save resources.
:::

## Usage

### On its own

```python
from haystack_integrations.components.embedders.fastembed import FastembedTextEmbedder

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 = FastembedTextEmbedder(model="BAAI/bge-small-en-v1.5")
embedding = text_embedder.run(text)["embedding"]
```

### In a pipeline

```python
from haystack import Document, Pipeline
from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack_integrations.components.embedders.fastembed import (
    FastembedDocumentEmbedder,
    FastembedTextEmbedder,
)

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

document_embedder = FastembedDocumentEmbedder()
documents_with_embeddings = document_embedder.run(documents)["documents"]
document_store.write_documents(documents_with_embeddings)

query_pipeline = Pipeline()
query_pipeline.add_component("text_embedder", FastembedTextEmbedder())
query_pipeline.add_component(
    "retriever",
    InMemoryEmbeddingRetriever(document_store=document_store),
)
query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")

query = "Who supports FastEmbed?"

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

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

## Document(id=...,
## content: 'FastEmbed is supported by and maintained by Qdrant.',
## score: 0.758..)
```

## Additional References

🧑‍🍳 Cookbook: [RAG Pipeline Using FastEmbed for Embeddings Generation](https://haystack.deepset.ai/cookbook/rag_fastembed)
