CyborgDB v0.12.0
Deployment Model
- Embedded Library (Python & C++)
- REST API (Microservice via Python or Docker)
- Client SDK (Python, JavaScript, TypeScript & Go)
New Features
- Go Client SDK now available:
- Automatic index management enabled by default in CyborgDB Service:
- Simplifies setup and reduces manual configuration
IndexConfig
classes now optional - CyborgDB can auto-configure indexes on the fly based on data & usage characteristics- Training & retraining now handled automatically, optimizing index performance without manual intervention
- Queries can now automatically determine the optimal number of clusters to search
- LangChain integration now available for Python Client SDK
CyborgDB v0.11.0
Deployment Model
- Embedded Library (Python & C++)
- REST API (Microservice via Python or Docker)
- Client SDK (Python, JavaScript & TypeScript)
New Features
- CyborgDB is now available as a REST API microservice
- Available as a Python package (
cyborgdb-service
) - Available as a Docker image (
cyborginc/cyborgdb-service
)
- Available as a Python package (
- CyborgDB Client SDKs now available for REST API microservice
- Introduced API key management and a new Admin Dashboard for enhanced operational control
- Optimized concurrency handling using opportunistic mutexes to improve performance under load
- Improved Docker container reliability and stability across deployment environments
CyborgDB v0.10.0
Deployment Model
- Embedded Library (Python & C++)
- REST API (Microservice via Python or Docker - early access)
- Client SDK (Python, JavaScript & TypeScript - early access)
New Features
- CyborgDB REST API service in early access
- CyborgDB Client SDKs in early access
- Python
- JavaScript
- TypeScript
- LangChain integration for CyborgDB now available
- Install via
pip install cyborgdb-core[langchain]
- Use via
from cyborgdb_core.integrations.langchain import CyborgVectorStore
- Supports both
cyborgdb-core
andcyborgdb-lite
- Install via
- Added configurable logging utility for embedded library in C++ and Python
CyborgDB v0.9.1
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
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
CyborgDB v0.9.0
This release renamed Cyborg Vector Search to CyborgDB.
New Features
- Added support for metadata fields & query filtering
- Added support for item deletion
- Added support for automatic embedding generation
- An embedding model can be specified during index creation
upsert()
calls can generate embeddings directly from index contentsquery()
calls can use the model to generate embeddings for query vectors
Breaking Changes
- IDs are now
string
-type (instead ofint
)- This impacts
upsert()
,get()
andquery()
function signatures
- This impacts
get_item()
andget_items()
have been replaced byget()
Cyborg Vector Search v0.8.0
Deployment Model: Embedded Library (Python & C++)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 indexget_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
Breaking Changes
- Python & C++ APIs now split into two classes:
Client
class now handles DB backend connection, index creation and loadingEncryptedIndex
class now handles data operations (e.g.,upsert()
,query()
, etc.)