Fix Typing

Author: DRXD1000

libs/langchain-mongodb/langchain_mongodb/index.py

@@ -2,7 +2,7 @@
 
 import logging
 from time import monotonic, sleep
-from typing import Any, Callable, Dict, List, Optional
+from typing import Any, Callable, Dict, List, Optional, Union
 
 from pymongo.collection import Collection
 from pymongo.operations import SearchIndexModel
@@ -202,7 +202,7 @@ def _wait_for_predicate(
 def create_fulltext_search_index(
     collection: Collection,
     index_name: str,
-    field: str | List[str],
+    field: Union[str, List[str]],
     *,
     wait_until_complete: Optional[float] = None,
     **kwargs: Any,

libs/langchain-mongodb/langchain_mongodb/pipelines.py

@@ -7,12 +7,12 @@
     - `Filter Example <https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-stage/#atlas-vector-search-pre-filter>`_
 """
 
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Optional, Union
 
 
 def text_search_stage(
     query: str,
-    search_field: str | list[str],
+    search_field: Union[str, List[str]],
     index_name: str,
     limit: Optional[int] = None,
     filter: Optional[Dict[str, Any]] = None,

libs/langchain-mongodb/langchain_mongodb/retrievers/full_text_search.py

@@ -1,4 +1,4 @@
-from typing import Annotated, Any, Dict, List, Optional
+from typing import Annotated, Any, Dict, List, Optional, Union
 
 from langchain_core.callbacks.manager import CallbackManagerForRetrieverRun
 from langchain_core.documents import Document
@@ -17,7 +17,7 @@ class MongoDBAtlasFullTextSearchRetriever(BaseRetriever):
     """MongoDB Collection on an Atlas cluster"""
     search_index_name: str
     """Atlas Search Index name"""
-    search_field: str | list[str]
+    search_field: Union[str, List[str]]
     """Collection field that contains the text to be searched. It must be indexed"""
     k: Optional[int] = None
     """Number of documents to return. Default is no limit"""

libs/langchain-mongodb/langchain_mongodb/vectorstores.py

@@ -204,7 +204,7 @@ def __init__(
         collection: Collection[Dict[str, Any]],
         embedding: Embeddings,
         index_name: str = "vector_index",
-        text_key: str | list[str] = "text",
+        text_key: Union[str, List[str]] = "text",
         embedding_key: str = "embedding",
         relevance_score_fn: str = "cosine",
         dimensions: int = -1,