Create a New Encrypted Index

To create an encrypted index, you need to specify an index name (must be unique) and an index key:

import cyborgdb_core as cyborgdb
import secrets

# Create a client (use the same backing store you'll load the index with)
client = cyborgdb.Client(storage_config=cyborgdb.StorageConfig.disk("/tmp/cyborgdb"))

# 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)

#include "cyborgdb_core/client.hpp"
#include "cyborgdb_core/encrypted_index.hpp"
#include <openssl/rand.h>
#include <array>

// Create a client (use the same backing store you'll load the index with)
cyborg::Client client("", cyborg::StorageConfig::Disk("/tmp/cyborgdb"), 0, cyborg::kNone);

// Generate a 32-byte secure encryption key using OpenSSL
std::array<uint8_t, 32> index_key;
if (RAND_bytes(index_key.data(), index_key.size()) != 1) {
    throw std::runtime_error("Failed to generate secure random key");
}

// Create an encrypted index (returns std::unique_ptr<cyborg::EncryptedIndex>)
auto index = client.CreateIndex("my_index", index_key);

This creates a new DiskIVF encrypted index. The vector dimension is auto-detected from the first upsert (or derived from an embedding_model); you can also set it explicitly with the keyword-only dimension parameter. Other optional parameters include metric ("euclidean" default, "cosine", or "squared_euclidean") and storage_precision (float32 default, or float16 to halve the on-disk rerank-vector footprint).

Store index_key before you upsert any data. CyborgDB has no key-recovery path — data encrypted with a lost key is unrecoverable. For anything past evaluation, keep the key in AWS Secrets Manager, Vault, or your KMS. See Managing Keys.

To lift the free-tier 1M-items-per-index cap, pass an API key as the first argument (cyborgdb.Client(api_key, storage_config=...) in Python; cyborg::Client(api_key, ...) in C++). Get a key from the CyborgDB Admin Dashboard. See Get an API Key.

Automatic Embedding Generation

This feature is only available in Python. To use it, use pip install cyborgdb-core[embeddings]

In the Python version of CyborgDB, you can enable automatic embedding generation for the encrypted index by setting embedding_model in create_index():

Python

import cyborgdb_core as cyborgdb
import secrets

# Create a client (use the same backing store you'll load the index with)
client = cyborgdb.Client(storage_config=cyborgdb.StorageConfig.disk("/tmp/cyborgdb"))

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

# Create an encrypted index with managed embedding generation (model from HuggingFace)
index = client.create_index(
    "my_index",
    index_key,
    embedding_model="all-MiniLM-L6-v2"
)

API Reference

For more information on creating encrypted indexes, refer to the API reference:

Python API Reference

API reference for create_index() in Python

C++ API Reference

API reference for CreateIndex() in C++

Intro

Encrypted Indexes

Data Operations

Advanced

Create a New Encrypted Index

Automatic Embedding Generation

API Reference

Python API Reference

C++ API Reference

​Automatic Embedding Generation

​API Reference

Python API Reference

C++ API Reference

Automatic Embedding Generation

API Reference