Replace create_vector_search_index

Remove create_vector_search_index from langchain-mongodb and import from
pymongo-search-utils.

Author: aclark4life

libs/langchain-mongodb/langchain_mongodb/index.py

@@ -6,6 +6,9 @@
 
 from pymongo.collection import Collection
 from pymongo.operations import SearchIndexModel
+from pymongo_search_utils import (
+    create_vector_search_index,  # noqa: F401  # type: ignore
+)
 
 logger = logging.getLogger(__file__)
 
@@ -34,60 +37,6 @@ def _vector_search_index_definition(
     return definition
 
 
-def create_vector_search_index(
-    collection: Collection,
-    index_name: str,
-    dimensions: int,
-    path: str,
-    similarity: str,
-    filters: Optional[List[str]] = None,
-    *,
-    wait_until_complete: Optional[float] = None,
-    **kwargs: Any,
-) -> None:
-    """Experimental Utility function to create a vector search index
-
-    Args:
-        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: Keyword arguments supplying any additional options to SearchIndexModel.
-    """
-    logger.info("Creating Search Index %s on %s", index_name, collection.name)
-
-    if collection.name not in collection.database.list_collection_names(
-        authorizedCollections=True
-    ):
-        collection.database.create_collection(collection.name)
-
-    result = collection.create_search_index(
-        SearchIndexModel(
-            definition=_vector_search_index_definition(
-                dimensions=dimensions,
-                path=path,
-                similarity=similarity,
-                filters=filters,
-                **kwargs,
-            ),
-            name=index_name,
-            type="vectorSearch",
-        )
-    )
-
-    if wait_until_complete:
-        _wait_for_predicate(
-            predicate=lambda: _is_index_ready(collection, index_name),
-            err=f"{index_name=} did not complete in {wait_until_complete}!",
-            timeout=wait_until_complete,
-        )
-    logger.info(result)
-
-
 def drop_vector_search_index(
     collection: Collection,
     index_name: str,