Skip to main content

Swarmauri Weaviate Vector Store

Project description

Swamauri Logo

PyPI - Downloads GitHub Hits PyPI - Python Version PyPI - License PyPI - swarmauri_vectorstore_cloudweaviate


Swarmauri Vectorstore CloudWeaviate

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

Installation

pip install swarmauri_vectorstore_cloudweaviate

Usage

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

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 that will help you get started.

Project details


Release history Release notifications | RSS feed

This version

0.7.2

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

swarmauri_vectorstore_cloudweaviate-0.7.2.tar.gz (8.1 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

File details

Details for the file swarmauri_vectorstore_cloudweaviate-0.7.2.tar.gz.

File metadata

File hashes

Hashes for swarmauri_vectorstore_cloudweaviate-0.7.2.tar.gz
Algorithm Hash digest
SHA256 ffd63553cc27677f8f5b0b7376d6a06d7fb67f382b3fa77b294bfef429baa53f
MD5 9d562194da5712c0b62170d43c140e88
BLAKE2b-256 f56350475cdccfd4e8329dcc08d4a94fc2c4a05bbbfb435d55b98ac5ca2439f8

See more details on using hashes here.

File details

Details for the file swarmauri_vectorstore_cloudweaviate-0.7.2-py3-none-any.whl.

File metadata

File hashes

Hashes for swarmauri_vectorstore_cloudweaviate-0.7.2-py3-none-any.whl
Algorithm Hash digest
SHA256 7bdee0fa664f087186d277a91005a13d4db78b7de148a90ce8e5895eaef4b60f
MD5 fd3f7663a53267f6aa03622861ca711f
BLAKE2b-256 aab688fdcaf5fe21c876fcb209555519d44fdd5aeee4152c5dbd42b0a93caa80

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page