Metadata-Version: 2.3
Name: swarmauri_vectorstore_cloudweaviate
Version: 0.6.1
Summary: Swarmauri Weaviate Vector Store
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: swarmauri_base (>=0.6.1,<0.7.0)
Requires-Dist: swarmauri_core (>=0.6.1,<0.7.0)
Requires-Dist: swarmauri_embedding_doc2vec (>=0.6.1,<0.7.0)
Requires-Dist: swarmauri_standard (>=0.6.1,<0.7.0)
Requires-Dist: weaviate-client (>=4.9.2,<5.0.0)
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)

<div align="center">

![PyPI - Downloads](https://img.shields.io/pypi/dm/swarmauri_vectorstore_cloudweaviate)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/swarmauri_vectorstore_cloudweaviate)
![PyPI - License](https://img.shields.io/pypi/l/swarmauri_vectorstore_cloudweaviate)
![PyPI - Version](https://img.shields.io/pypi/v/swarmauri_vectorstore_cloudweaviate?label=swarmauri_vectorstore_cloudweaviate&color=green)

</div>

---

# Swarmauri Cloud Weaviate Vector Store

A Weaviate-based vector store implementation for Swarmauri, providing cloud-based document storage and retrieval with vector similarity search capabilities.

## Installation

```bash
pip install swarmauri_vectorstore_cloudweaviate
```

## Usage
Here's a basic example of how to use the CloudWeaviateVectorStore:

```python
from swarmauri.vector_stores.CloudWeaviateVectorStore import CloudWeaviateVectorStore
from swarmauri.documents.Document import Document

# Initialize the vector store
vector_store = CloudWeaviateVectorStore(
    url="your-weaviate-url",
    api_key="your-api-key",
    collection_name="example",
    vector_size=100
)

# Connect to Weaviate
vector_store.connect()

# Add documents
document = Document(
    id="doc-001",
    content="This is a sample document content.",
    metadata={"author": "Alice", "date": "2024-01-01"}
)
vector_store.add_document(document)

# Retrieve similar documents
results = vector_store.retrieve(query="sample content", top_k=5)

# Clean up
vector_store.disconnect()
```

## 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.


