Create a new vector similarity search index in over the target table. Allowed tables include ‘vectors’, ‘entity’, ‘document_collections’. Vectors correspond to the chunks of text that are indexed for similarity search, whereas entity and document_collections are created during knowledge graph construction.
This endpoint creates a database index optimized for efficient similarity search over vector embeddings. It supports two main indexing methods:
HNSW (Hierarchical Navigable Small World):
IVF-Flat (Inverted File with Flat Storage):
Supported similarity measures:
Notes:
Whether to run index creation as an orchestrated task (recommended for large indices)
Successful Response