CyborgDB Release Notes

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CyborgDB v0.9.2

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New Features

• cyborgdb_lite is now available for evaluation and non-commercial use on PyPI

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CyborgDB v0.9.1

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Bug Fixes

• Fixed a bug where automatic embedding generation would not load the model correctly when using load_index()
• Fixed a bug which caused intermittent query() recall issues on Linux
• Fixed incorrect type returns for get() in C++ and Python APIs

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Improvements

• Enabled pre-filtering on metadata queries to improve performance
• Added annotations and IDE auto-completion for the Python API
• Expanded support to Python 3.9 - 3.13

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CyborgDB v0.9.0

Deployment Model: Core Library (Python & C++)

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New Features

• Added support for metadata fields & query filtering
  • Metadata fields can be added to items during upsert() calls
  • query() calls can filter results based on metadata fields
• Added support for item deletion
  • delete() calls can remove items from the encrypted index
  • upsert() conflicts are now resolved by overwriting existing items
• Added support for automatic embedding generation
  • An embedding model can be specified during index creation
  • upsert() calls can generate embeddings directly from index contents
  • query() calls can use the model to generate embeddings for query vectors

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Breaking Changes

• IDs are now string-type (instead of int)
  • This impacts upsert(), get() and query() function signatures
• get_item() and get_items() have been replaced by get()

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Cyborg Vector Search v0.8.0

Deployment Model: Core Library (Python & C++)

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New Features

• Added support for encrypted item storage & retrieval
  • Encrypted indexes can now handle item content storage
  • upsert() calls can take item content as bytes, encrypt and store them in the index
  • get_item() calls can retrieve encrypted item content and decrypt them
• Client-side caching now available for encrypted indexes (via max_cache_size parameter)
• Client can now handle more than one index via multiple EncryptedIndex objects
• Optimizations:
  • Full-pipeline GPU acceleration for encrypted indexes
  • Optimized quantization and ranking logic
  • Backing store batch operations for improved performance

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Breaking Changes

• Python & C++ APIs now split into two classes:
  • Client class now handles DB backend connection, index creation and loading
  • EncryptedIndex class now handles data operations (e.g., upsert(), query(), etc.)

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Cyborg Vector Search v0.7.0

Deployment Model: Core Library (Python & C++)

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New Features

• First public release of Cyborg Vector Search
• Python API available through PyBind11
• GPU acceleration with CUDA support