Metadata-Version: 2.1
Name: fastembed-haystack
Version: 0.0.1
Summary: Haystack 2.x component to embed strings and Documents using fastembed embedding model
Project-URL: Source, https://github.com/deepset-ai/haystack-core-integrations
Project-URL: Documentation, https://github.com/deepset-ai/haystack-core-integrations/blob/main/integrations/fastembed/README.md
Project-URL: Issues, https://github.com/deepset-ai/haystack-core-integrations/issues
Author-email: deepset GmbH <info@deepset.ai>
License-Expression: Apache-2.0
License-File: LICENSE.txt
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Requires-Python: >=3.8
Requires-Dist: fastembed>=0.2
Requires-Dist: haystack-ai
Description-Content-Type: text/markdown

# fastembed-haystack

[![PyPI - Version](https://img.shields.io/pypi/v/fastembed-haystack.svg)](https://pypi.org/project/fastembed-haystack)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/fastembed-haystack.svg)](https://pypi.org/project/fastembed-haystack)

-----

**Table of Contents**

- [Installation](#installation)
- [License](#license)

## Installation

```console
pip install fastembed-haystack
```

## Usage

You can use `FastembedTextEmbedder` and `FastembedDocumentEmbedder` by importing as:

```python
from fastembed_haystack.fastembed_text_embedder import FastembedTextEmbedder

text = "fastembed is supported by and maintained by Qdrant."
text_embedder = FastembedTextEmbedder(
    model="BAAI/bge-small-en-v1.5"
)
embedding = text_embedder.run(text)
```

```python
from fastembed_haystack.fastembed__document_embedder import FastembedDocumentEmbedder
from haystack.dataclasses import Document

embedder = FastembedDocumentEmbedder(
    model="BAAI/bge-small-en-v1.5",
)
embedder.warm_up()
doc = Document(content="fastembed is supported by and maintained by Qdrant.", meta={"long_answer": "no",})
result = embedder.run(documents=[doc])
```

## License

`fastembed-haystack` is distributed under the terms of the [Apache-2.0](https://spdx.org/licenses/Apache-2.0.html) license.
