Metadata-Version: 2.3
Name: swarmauri_embedding_doc2vec
Version: 0.6.1.dev6
Summary: A Doc2Vec based Embedding Model.
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: gensim (==4.3.3)
Requires-Dist: swarmauri_base (>=0.6.1.dev6,<0.7.0)
Requires-Dist: swarmauri_core (>=0.6.1.dev6,<0.7.0)
Requires-Dist: swarmauri_standard (>=0.6.1.dev6,<0.7.0)
Project-URL: Repository, http://github.com/swarmauri/swarmauri-sdk
Description-Content-Type: text/markdown

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---

# Doc2Vec Embedding

A Gensim-based Doc2Vec implementation for document embedding in the Swarmauri ecosystem. This package provides document vectorization capabilities using the Doc2Vec algorithm.

## Installation

```bash
pip install swarmauri_embedding_doc2vec
```

## Usage

```python
from swarmauri.embeddings.Doc2VecEmbedding import Doc2VecEmbedding

# Initialize the embedder
embedder = Doc2VecEmbedding(vector_size=3000)

# Prepare your documents
documents = ["This is the first document.", "Here is another document.", "And a third one"]

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

# Transform new documents
new_doc = "This is a new document"
vector = embedder.transform([new_doc])

# Save and load the model
embedder.save_model("doc2vec.model")
embedder.load_model("doc2vec.model")
```

## 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.

