pyenvector 1.4.3

Python SDK for enVector: encrypted vector search powered by homomorphic encryption

pyenvector — enVector Python SDK

Python SDK for enVector — encrypted vector search powered by fully homomorphic encryption (FHE).

Your vectors and similarity scores stay encrypted during the entire computation. The server never sees plaintext data.

Install

pip install pyenvector

Quick Start

import numpy as np
import pyenvector as ev

# Connect and load keys
ev.init(host="localhost", port=50050, key_path="./keys", key_id="my_key")

# Create an index
index = ev.create_index("my_index", dim=512)

# Insert vectors
vectors = np.random.randn(100, 512).astype(np.float32)
vectors /= np.linalg.norm(vectors, axis=1, keepdims=True)
metadata = [f"item_{i}" for i in range(100)]

index.insert(vectors, metadata=metadata)

# Search (encrypted end-to-end)
result = index.search(vectors[0], top_k=5, output_fields=["metadata"])
print(result)

# Clean up
ev.drop_index("my_index")
# ev.delete_key("my_key")  # optional: remove keys from server

Key Features

• End-to-end encryption — vectors are encrypted on the client. Search runs on ciphertext via FHE. Scores are decrypted only on the client.
• Familiar API — create_index, insert, search, drop_index. Works like Milvus or Pinecone.
• Key management CLI — generate, seal, and upload HE keys to AWS S3 or GCP Cloud Storage.
• Cloud-ready — deploy the enVector server on GKE, EKS, or on-prem.

Documentation

• enVector Docs — deployment, architecture, API reference
• GitHub — source, examples, issues

License

Proprietary. See LICENSE for details.