Metadata-Version: 2.3
Name: swarmauri_vectorstore_doc2vec
Version: 0.7.0.dev6
Summary: A Doc2Vec based Vector Store and 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 :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Dist: swarmauri_base
Requires-Dist: swarmauri_core
Requires-Dist: swarmauri_embedding_doc2vec
Requires-Dist: swarmauri_standard
Description-Content-Type: text/markdown

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---

# Doc2Vec Vector Store

A vector store implementation using Doc2Vec for document embedding and similarity search.

## Installation

```bash
pip install swarmauri_vectorstore_doc2vec
```

## Usage

```python
from swarmauri.vectorstores.Doc2VecVectorStore import Doc2VecVectorStore
from swarmauri.documents.Document import Document


# Initialize vector store
vector_store = Doc2VecVectorStore()

# Add documents
documents = [
    Document(content="This is the first document"),
    Document(content="Here is another document"),
    Document(content="And a third document")
]
vector_store.add_documents(documents)

# Retrieve similar documents
results = vector_store.retrieve(query="document", top_k=2)
```

## 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.


