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)

This creates a new 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. storage_precision picks the on-disk format for the rerank vectors: "float32" (default) or "float16" to halve the footprint at a slight precision cost, or one of the TurboQuant tiers "tq12" / "tq8" / "tq6" / "tq4" (12/8/6/4 bits per dimension) to trade storage for a small recall/latency cost — "tq4" is the most aggressive (~8x smaller than float32, ~94% recall@100 on a 768-dimensional benchmark dataset). All tiers work with every metric; the choice is fixed at create time.

Vector dimension is capped at 65535 (0xFFFF). Passing dimension=0 still means auto-detect. Explicit values outside [0, 65535], including negatives, raise ValueError at create_index() time.

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=...)). Get a key from the CyborgDB Admin Dashboard. See Get an API Key.

Automatic Embedding Generation

Text embedding is built into cyborgdb-core — no [embeddings] extra or sentence-transformers/torch install required. Call cyborgdb.supported_embedding_models() for the accepted model names.

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
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

Intro

Encrypted Indexes

Data Operations

Advanced

Create a New Encrypted Index

Automatic Embedding Generation

API Reference

Python API Reference

​Automatic Embedding Generation

​API Reference

Python API Reference

Automatic Embedding Generation

API Reference