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To create an encrypted index, you need to specify an index name (must be unique), an index key, and an index configuration. Here’s an example with an IVF index type: 
from cyborgdb import Client, IndexIVF, generate_key

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

# Create an IVF index config (can also be IVFFlat/IVFPQ)
# Using an example vector dimension of 768, and number of lists of 1024
index_config = IndexIVF(
    type='ivf',
    dimension=768,
    n_lists=1024,
    metric='cosine'
)

# Generate an encryption key for the index
index_key = generate_key()  # Returns 32-byte key as bytes

# Create an encrypted index
index = client.create_index("my_index", index_key, index_config)
This creates a new encrypted index with the IVFFlat type. For more details on IVFFlat and other index options, see Configure an Encrypted Index.
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

Both the Python and JavaScript/TypeScript service SDKs support automatic embedding generation. You can specify an embedding model when creating the index: 
from cyborgdb import Client, IndexIVF, generate_key

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

# Example index config with dimensions matching the embedding model
index_config = IndexIVF(
    type='ivf',
    dimension=384,  # dimension for all-MiniLM-L6-v2
    n_lists=1024,
    metric='cosine'
)

# Generate an encryption key for the index
index_key = generate_key()

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

# Create an encrypted index with managed embedding generation
index = client.create_index("my_index", index_key, index_config, 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 create_index() in JavaScript/TypeScript