Database / Vector

Capabilities

Version:

ID	Title	Description
CCC.Vector.CP01	Embedding Storage	Supports storage of high-dimensional vector embeddings derived from raw input data such as text, images, or audio.
CCC.Vector.CP02	Vector Indexing	Provides creation and management of indexes optimized for similarity search, such as HNSW, IVF, or PQ.
CCC.Vector.CP03	Similarity Search	Enables nearest-neighbor queries using a query embedding to return the most similar vectors from the store.
CCC.Vector.CP04	Metadata Filtering	Supports structured filtering on metadata fields alongside vector similarity search queries.
CCC.Vector.CP05	Batch Ingestion	Allows for high-throughput batch upload and deletion of vectors and associated metadata.
CCC.Vector.CP06	Real-Time Querying	Provides low-latency response to vector similarity queries suitable for interactive applications.
CCC.Vector.CP07	Index Lifecycle Management	Enables automated or manual creation, optimization, and removal of vector indexes.
CCC.Vector.CP08	Embedding Format Compatibility	Supports standard vector formats and integrates with common embedding generators (e.g., OpenAI, HuggingFace, TensorFlow).
CCC.Vector.CP09	Vector Dimension Management	Supports storing and managing vectors of specific or dynamic dimensionality, depending on model needs.
CCC.Vector.CP10	Multi-modal Vector Support	Supports storing and searching across vectors derived from multiple modalities (e.g., text, image, audio).
CCC.Vector.CP11	Query Access Control	Provides the ability to restrict who can run vector similarity or metadata filter queries, separate from data modification rights.
CCC.Vector.CP12	Approximate or Exact Search Modes	Supports both approximate nearest neighbor (ANN) algorithms for speed and exact search modes for precision-critical applications.