cyborgdb 0.13.0

Python Client SDK for CyborgDB: The Confidential Vector Database

CyborgDB Python SDK

The CyborgDB Python SDK provides a comprehensive client library for interacting with CyborgDB, the first Confidential Vector Database. This SDK enables you to perform encrypted vector operations including ingestion, search, and retrieval while maintaining end-to-end encryption of your vector embeddings. Built for Python applications, it offers seamless integration into modern Python applications and services.

This SDK provides an interface to cyborgdb-service which you will need to separately install and run in order to use the SDK. For more info, please see our docs.

Key Features

• End-to-End Encryption: All vector operations maintain encryption with client-side keys
• Zero-Trust Design: Novel architecture keeps confidential inference data secure
• High Performance: GPU-accelerated indexing and retrieval with CUDA support
• Familiar API: Easy integration with existing AI workflows
• Flexible Indexing: Support for multiple index types (IVFFlat, IVFPQ, etc.) with customizable parameters

Getting Started

To get started in minutes, check out our Quickstart Guide.

Installation

1. Install cyborgdb-service

# Install the CyborgDB Service
pip install cyborgdb-service

# Or via Docker
docker pull cyborginc/cyborgdb-service

2. Install cyborgdb SDK:

# Install the CyborgDB Python SDK
pip install cyborgdb

Usage

from cyborgdb import Client

# Initialize the client
client = Client('https://localhost:8000', 'your-api-key')

# Generate a 32-byte encryption key
index_key = client.generate_key()

# Create an encrypted index
index = await client.create_index(
    index_name='my-index', 
    index_key=index_key
)

# Add encrypted vector items
items = [
    {
        'id': 'doc1',
        'vector': [([0.1] * 128)],  # Replace with real embeddings
        'contents': 'Hello world!',
        'metadata': {'category': 'greeting', 'language': 'en'}
    },
    {
        'id': 'doc2',
        'vector': [([0.1] * 128)],  # Replace with real embeddings
        'contents': 'Bonjour le monde!',
        'metadata': {'category': 'greeting', 'language': 'fr'}
    }
]

await index.upsert(items)

# Query the encrypted index
query_vector = [0.1, 0.2, 0.3, *([0.0] * 128)]  # 128 dimensions
results = await index.query(query_vector, 10)

# Print the results
for result in results.results:
    print(f"ID: {result.id}, Distance: {result.distance}")

Advanced Usage

Batch Queries

# Search with multiple query vectors simultaneously
query_vectors = [
    [0.1, 0.2, 0.3, *([0.0] * 125)],
    [0.4, 0.5, 0.6, *([0.0] * 125)]
]

batch_results = await index.query(query_vectors, 5)

Metadata Filtering

# Search with metadata filters
results = await index.query(
    query_vector,
    10,      # top_k
    1,       # n_probes
    False,   # greedy
    {'category': 'greeting', 'language': 'en'},  # filters
    ['distance', 'metadata', 'contents']         # include
)

Documentation

For more information on CyborgDB, see the Cyborg Docs.

License

The CyborgDB Python SDK is licensed under the MIT License.