Metadata-Version: 2.3
Name: swarmauri_embedding_tfidf
Version: 0.6.1.dev16
Summary: Tfidf Embedding for Swarmauri
License: Apache-2.0
Author: Jacob Stewart
Author-email: jacob@swarmauri.com
Requires-Python: >=3.10,<3.13
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Dist: scikit-learn (>=1.4.2,<2.0.0)
Requires-Dist: swarmauri_base (>=0.6.1.dev16,<0.7.0)
Requires-Dist: swarmauri_core (>=0.6.1.dev16,<0.7.0)
Requires-Dist: swarmauri_standard (>=0.6.1.dev3,<0.7.0)
Project-URL: Repository, http://github.com/swarmauri/swarmauri-sdk
Description-Content-Type: text/markdown

![Swarmauri Logo](https://res.cloudinary.com/dbjmpekvl/image/upload/v1730099724/Swarmauri-logo-lockup-2048x757_hww01w.png)

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---

# TF-IDF Embedding

A TF-IDF (Term Frequency-Inverse Document Frequency) embedding implementation for the Swarmauri SDK, providing document vectorization capabilities.

## Installation

```bash
pip install swarmauri_embedding_tfidf
```

## Usage

```python
from swarmauri_embedding_tfidf.TfidfEmbedding import TfidfEmbedding

# Initialize the embedder
embedder = TfidfEmbedding()

# Prepare documents
documents = ["this is a sample", "another example text", "third document here"]

# Fit and transform documents
vectors = embedder.fit_transform(documents)

# Infer vector for new text
query_vector = embedder.infer_vector("new document", documents)

# Extract features
features = embedder.extract_features()
```

## Want to help?

If you want to contribute to swarmauri-sdk, read up on our [guidelines for contributing](https://github.com/swarmauri/swarmauri-sdk/blob/master/contributing.md) that will help you get started.

