create_vector_search_index#

langchain_mongodb.index.create_vector_search_index(collection: Collection, index_name: str, dimensions: int, path: str, similarity: str, filters: List[str] | None = None, *, wait_until_complete: float | None = None, **kwargs: Any) → None[source]#

Experimental Utility function to create a vector search index

Parameters:

  • collection (Collection) – MongoDB Collection

  • index_name (str) – Name of Index

  • dimensions (int) – Number of dimensions in embedding

  • path (str) – field with vector embedding

  • similarity (str) – The similarity score used for the index

  • filters (List[str]) – Fields/paths to index to allow filtering in $vectorSearch

  • wait_until_complete (Optional[float]) – If provided, number of seconds to wait until search index is ready.

  • kwargs (Any) – Keyword arguments supplying any additional options to SearchIndexModel.

Return type:

None