vecx 0.33.0b5

Encrypted Vector Database for Secure and Fast ANN Searches

VectorX - Encrypted Vector Database

VectorX is an encrypted vector database designed for maximum security and speed. Utilizing client-side encryption with private keys, VectorX ensures data confidentiality while enabling rapid Approximate Nearest Neighbor (ANN) searches within encrypted datasets. Leveraging a proprietary algorithm, VectorX provides unparalleled performance and security for applications requiring robust vector search capabilities in an encrypted environment.

Key Features

• Client-side Encryption: Vectors are encrypted using private keys before being sent to the server
• Fast ANN Searches: Efficient similarity searches on encrypted vector data
• Multiple Distance Metrics: Support for cosine, L2, and inner product distance metrics
• Metadata Support: Attach and search with metadata and filters
• High Performance: Optimized for speed and efficiency with encrypted data

Installation

pip install vecx

Quick Start

from vecx.vectorx import VectorX

# Initialize client with your API token
vx = VectorX(token="your_user_id:your_api_token:region")

# Generate a secure encryption key
encryption_key = vx.generate_key()

# Create a new index
vx.create_index(
    name="my_vectors",
    dimension=1536,  # Your vector dimension
    key=encryption_key,  # Encryption key
    space_type="cosine"  # Distance metric (cosine, l2, ip)
)

# Get index reference
index = vx.get_index(name="my_vectors", key=encryption_key)

# Insert vectors
index.upsert([
    {
        "id": "doc1",
        "vector": [0.1, 0.2, 0.3, ...],  # Your vector data
        "meta": {"text": "Example document", "category": "reference"}
    }
])

# Query similar vectors
results = index.query(
    vector=[0.2, 0.3, 0.4, ...],  # Query vector
    top_k=10,
    filter={"category": "reference"}  # Optional filter
)

# Process results
for item in results:
    print(f"ID: {item['id']}, Similarity: {item['similarity']}")
    print(f"Metadata: {item['meta']}")

Basic Usage

Initializing the Client

from vecx.vectorx import VectorX

# Production with specific region
vx = VectorX(token="user_id:api_token:region")

Managing Indexes

# List all indexes
indexes = vx.list_indexes()

# Create an index with custom parameters
vx.create_index(
    name="my_custom_index",
    dimension=384,
    key=encryption_key,
    space_type="l2",
    M=32,             # Graph connectivity parameter
    ef_con=200,       # Construction-time parameter
    use_fp16=True     # Use half-precision for storage optimization
)

# Delete an index
vx.delete_index("my_index")

Working with Vectors

# Get index reference
index = vx.get_index(name="my_index", key=encryption_key)

# Insert multiple vectors in a batch
index.upsert([
    {
        "id": "vec1",
        "vector": [...],  # Your vector
        "meta": {"title": "First document", "tags": ["important"]}
    },
    {
        "id": "vec2",
        "vector": [...],  # Another vector
        "filter": {"visibility": "public"}  # Optional filter values
    }
])

# Query with custom parameters
results = index.query(
    vector=[...],      # Query vector
    top_k=5,           # Number of results to return
    filter={"tags": "important"},  # Filter for matching
    ef=128,            # Runtime parameter for search quality
    include_vectors=True  # Include vector data in results
)

# Delete vectors
index.delete_vector("vec1")
index.delete_with_filter({"visibility": "public"})

# Get a specific vector
vector = index.get_vector("vec1")

API Reference

VectorX Class

• __init__(token=None): Initialize with optional API token
• set_token(token): Set API token
• set_base_url(base_url): Set custom API endpoint
• generate_key(): Generate a secure encryption key
• create_index(name, dimension, key, space_type, ...): Create a new index
• list_indexes(): List all indexes
• delete_index(name): Delete an index
• get_index(name, key): Get reference to an index

Index Class

• upsert(input_array): Insert or update vectors
• query(vector, top_k, filter, ef, include_vectors): Search for similar vectors
• delete_vector(id): Delete a vector by ID
• delete_with_filter(filter): Delete vectors matching a filter
• get_vector(id): Get a specific vector
• describe(): Get index statistics and info

Security Considerations

• Key Management: Store your encryption key securely. Loss of the key will result in permanent data loss.
• Client-Side Encryption: All sensitive data is encrypted before transmission.