cyborgdb-core-cu12 0.16.1

Core Python Library for CyborgDB: The Confidential Vector Database (CUDA 12)

CyborgDB Core

CyborgDB is the first Confidential Vector Database that enables you to ingest & search vectors embeddings in a privacy-preserving manner, without revealing the contents of the vectors themselves. It works with existing DBs (e.g., Postgres, Redis) and enables you to add, query and retrieve vector embeddings with transparent end-to-end encryption.

Why Confidential?

Vector Search is at the heart of popular AI applications like RAG, recommendation systems, and semantic search. CyborgDB leverages cryptographic hashing and symmetric encryption to enable Approximate Nearest-Neighbor (ANN) search over encrypted space, keeping your vector embeddings secure throughout their lifecycle.

Key Features

• End-to-End Encryption: Vector embeddings remain encrypted throughout their lifecycle, including at search time
• 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
• Multiple Backing Stores: Works with PostgreSQL, Redis, S3, and in-memory storage
• Cloud Ready: Supports AWS RDS, AWS ElastiCache, Azure Database for PostgreSQL, Azure Cache for Redis, Google Cloud SQL, and Google Cloud Memorystore

Getting an API Key

To start using cyborgdb-core, you will need to get an API key from db.cyborg.co. You can sign up for a free account, create an API key and use it immediately.

Installation

# Install CyborgDB Core (CPU):
pip install cyborgdb-core

# Install CyborgDB Core (CUDA):
pip install cyborgdb-core-cu12

Quickstart

import cyborgdb_core as cyborgdb
import secrets

# Set your CyborgDB API key
api_key = 'cyborgdb_000000' # Replace with your actual API key

# Create a client with memory storage
# You can replace this with Postgres
index_location = cyborgdb.DBConfig("memory", "index")  # For index contents
config_location = cyborgdb.DBConfig("memory", "config") # For config/loading
items_location = cyborgdb.DBConfig("memory", "items")  # For item contents

# Create a client
client = cyborgdb.Client(api_key, index_location, config_location, items_location)

# Generate an encryption key for the index
index_key = secrets.token_bytes(32)

# Create an encrypted index
index = client.create_index("my_index", index_key)

# Add items to the encrypted index
items = [
    {"id": "item_1", "vector": [0.1, 0.2, 0.3, 0.4], "contents": "Hello!"},
    {"id": "item_2", "vector": [0.5, 0.6, 0.7, 0.8], "contents": "Bonjour!"},
    {"id": "item_3", "vector": [0.9, 0.10, 0.11, 0.12], "contents": "Hola!"}
]

index.upsert(items)

# Query the encrypted index
query_vector = [0.1, 0.2, 0.3, 0.4]
results = index.query(query_vector)

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

Connecting to Different Backing Stores

PostgreSQL

index_location = cyborgdb.DBConfig(
    location="postgres",
    table_name="index_table", 
    connection_string="host=localhost dbname=cyborgdb user=postgres password=postgres"
)

Redis

index_location = cyborgdb.DBConfig(
    location="redis",
    connection_string="redis://localhost"
)

Documentation

For more detailed documentation, visit:

• CyborgDB Documentation
• API Reference

System Requirements

• Python 3.10 - 3.14
• Operating Systems: Linux, macOS, or WSL

License

CyborgDB is provided under a commercial license with a perpetual free tier. Contact Cyborg or visit the pricing page for more information.

About Cyborg

Cyborg is dedicated to making AI safe and secure. We develop solutions that enable organizations to leverage AI while maintaining the confidentiality and privacy of their data.

Visit our website | Contact Us