Create Vector Index | The most advanced AI retrieval system. Agentic Retrieval-Augmented Generation (RAG) with a RESTful API.

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):
- Best for: High-dimensional vectors requiring fast approximate nearest neighbor search
- Pros: Very fast search, good recall, memory-resident for speed
- Cons: Slower index construction, more memory usage
- Key parameters:
  - m: Number of connections per layer (higher = better recall but more memory)
  - ef_construction: Build-time search width (higher = better recall but slower build)
  - ef: Query-time search width (higher = better recall but slower search)
IVF-Flat (Inverted File with Flat Storage):
- Best for: Balance between build speed, search speed, and recall
- Pros: Faster index construction, less memory usage
- Cons: Slightly slower search than HNSW
- Key parameters:
  - lists: Number of clusters (usually sqrt(n) where n is number of vectors)
  - probe: Number of nearest clusters to search

Supported similarity measures:

cosine_distance: Best for comparing semantic similarity
l2_distance: Best for comparing absolute distances
ip_distance: Best for comparing raw dot products

Notes:

Index creation can be resource-intensive for large datasets
Use run_with_orchestration=True for large indices to prevent timeouts
The ‘concurrently’ option allows other operations while building
Index names must be unique per table

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: 1. HNSW (Hierarchical Navigable Small World): - Best for: High-dimensional vectors requiring fast approximate nearest neighbor search - Pros: Very fast search, good recall, memory-resident for speed - Cons: Slower index construction, more memory usage - Key parameters: * m: Number of connections per layer (higher = better recall but more memory) * ef_construction: Build-time search width (higher = better recall but slower build) * ef: Query-time search width (higher = better recall but slower search) 2. IVF-Flat (Inverted File with Flat Storage): - Best for: Balance between build speed, search speed, and recall - Pros: Faster index construction, less memory usage - Cons: Slightly slower search than HNSW - Key parameters: * lists: Number of clusters (usually sqrt(n) where n is number of vectors) * probe: Number of nearest clusters to search Supported similarity measures: - cosine_distance: Best for comparing semantic similarity - l2_distance: Best for comparing absolute distances - ip_distance: Best for comparing raw dot products Notes: - Index creation can be resource-intensive for large datasets - Use run_with_orchestration=True for large indices to prevent timeouts - The 'concurrently' option allows other operations while building - Index names must be unique per table

Request

This endpoint expects an object.

configobjectRequired

run_with_orchestrationbooleanOptional

Whether to run index creation as an orchestrated task (recommended for large indices)

Response

Successful Response

resultsobject

Errors