cyborgdb-core 0.17.0

Core Python Library for CyborgDB: The Confidential Vector Database

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 supports in-memory, local-disk, and S3 backing stores, 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 in-memory, local disk, and S3 storage
• Cloud Ready: Supports AWS S3 (and S3-compatible stores like MinIO) out of the box

API Key (Optional)

cyborgdb-core works out of the box with no API key — the client runs in free-tier mode with a per-index cap of 1,000,000 items. For unlimited usage, get an API key from db.cyborg.co: sign up for a free account, create an API key, and pass it to the client.

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

# Create a client with memory storage (no API key required for free-tier usage)
# You can replace this with cyborgdb.StorageConfig.disk(...) or .s3(...)
client = cyborgdb.Client(storage_config=cyborgdb.StorageConfig.memory())

# For unlimited usage, pass your CyborgDB API key:
# client = cyborgdb.Client("cyborgdb_000000", cyborgdb.StorageConfig.memory())

# 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

Disk (local RocksDB)

storage_config = cyborgdb.StorageConfig.disk("/tmp/cyborgdb_disk")
client = cyborgdb.Client(storage_config=storage_config)

S3

storage_config = cyborgdb.StorageConfig.s3(
    "my-bucket",
    region="us-east-1",
    # Omit credentials= to use the AWS default credential chain
    credentials=cyborgdb.S3Credentials(access_key="...", secret_key="..."),
)
client = cyborgdb.Client(storage_config=storage_config)

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