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

# Create a client
client = Client(
    base_url='http://localhost:8000', 
    api_key='your-api-key'
)

# Generate an encryption key for the index
index_key = client.generate_key(save=False)

# Create an encrypted index
index = client.create_index(
    index_name="my_index", 
    index_key=index_key
)
This creates a new encrypted DiskIVF index — the single index type in v0.17. For details on configuration knobs (dimension, metric, storage_precision, embedding_model), see Configure an Encrypted Index.
v0.17 removed the polymorphic index_config argument and the IndexIVFFlat / IndexIVFPQ / IndexIVFSQ constructors from previous versions. Optional parameters — metric ("euclidean", "squared_euclidean", "cosine"), dimension, embedding_model, storage_precision, and kms_name — are now flat keyword arguments on create_index. See the API Reference for details.
The example above creates a random 32 byte (256-bit) index key. This is fine for evaluation purposes, but for production use, we recommend that you use an HSM or KMS solution. For more details, see Managing Encryption Keys.

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Index Caching

The service-based SDKs handle caching automatically on the server side. Unlike the embedded SDKs, you don’t need to specify cache sizes when creating indexes. The CyborgDB service optimizes query performance through:
  • Automatic index caching based on usage patterns
  • Server-side query optimization
  • Efficient index loading and memory management

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Automatic Embedding Generation

To use automatic embedding generation, use the Docker Service or install with pip install cyborgdb-service[embeddings]
All Client SDKs support automatic embedding generation. You can specify an embedding model when creating the index: 
from cyborgdb import Client

# Create a client
client = Client(
    base_url='http://localhost:8000', 
    api_key='your-api-key'
)

# Generate an encryption key for the index.
# `save=True` is available for local evaluation (caches the key under ~/.cyborgdb/);
# leave it as `save=False` (default) and persist the key in your own secret store.
index_key = client.generate_key(save=False)

# Set embedding model (from HuggingFace)
embedding_model = "all-MiniLM-L6-v2"

# Create an encrypted index with managed embedding generation
index = client.create_index(
    index_name="my_index", 
    index_key=index_key, 
    embedding_model=embedding_model
)

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API Reference

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

REST API Reference

REST API reference for /v1/indexes/create

Python SDK Reference

API reference for create_index() in Python

JS/TS SDK Reference

API reference for createIndex() in JavaScript/TypeScript

Go SDK Reference

API reference for CreateIndex() in Go