diff --git a/libs/langchain-mongodb/langchain_mongodb/index.py b/libs/langchain-mongodb/langchain_mongodb/index.py
index fd53956c..f2c99c94 100644
--- a/libs/langchain-mongodb/langchain_mongodb/index.py
+++ b/libs/langchain-mongodb/langchain_mongodb/index.py
@@ -2,10 +2,15 @@
 
 import logging
 from time import monotonic, sleep
-from typing import Any, Callable, Dict, List, Optional, Union
+from typing import Any, Callable, Dict, List, Optional
 
 from pymongo.collection import Collection
-from pymongo.operations import SearchIndexModel
+from pymongo_search_utils import (
+    create_fulltext_search_index,  # noqa: F401
+    create_vector_search_index,  # noqa: F401
+    drop_vector_search_index,  # noqa: F401
+    update_vector_search_index,  # noqa: F401
+)
 
 logger = logging.getLogger(__file__)
 
@@ -34,135 +39,6 @@ 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,
-    *,
-    wait_until_complete: Optional[float] = None,
-) -> None:
-    """Drop a created vector search index
-
-    Args:
-        collection (Collection): MongoDB Collection with index to be dropped
-        index_name (str): Name of the MongoDB index
-        wait_until_complete (Optional[float]): If provided, number of seconds to wait
-            until search index is ready.
-    """
-    logger.info(
-        "Dropping Search Index %s from Collection: %s", index_name, collection.name
-    )
-    collection.drop_search_index(index_name)
-    if wait_until_complete:
-        _wait_for_predicate(
-            predicate=lambda: len(list(collection.list_search_indexes())) == 0,
-            err=f"Index {index_name} did not drop in {wait_until_complete}!",
-            timeout=wait_until_complete,
-        )
-    logger.info("Vector Search index %s.%s dropped", collection.name, index_name)
-
-
-def update_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:
-    """Update a search index.
-
-    Replace the existing index definition with the provided definition.
-
-    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(
-        "Updating Search Index %s from Collection: %s", index_name, collection.name
-    )
-    collection.update_search_index(
-        name=index_name,
-        definition=_vector_search_index_definition(
-            dimensions=dimensions,
-            path=path,
-            similarity=similarity,
-            filters=filters,
-            **kwargs,
-        ),
-    )
-    if wait_until_complete:
-        _wait_for_predicate(
-            predicate=lambda: _is_index_ready(collection, index_name),
-            err=f"Index {index_name} update did not complete in {wait_until_complete}!",
-            timeout=wait_until_complete,
-        )
-    logger.info("Update succeeded")
-
-
 def _is_index_ready(collection: Collection, index_name: str) -> bool:
     """Check for the index name in the list of available search indexes to see if the
     specified index is of status READY
@@ -199,50 +75,3 @@ def _wait_for_predicate(
         if monotonic() - start > timeout:
             raise TimeoutError(err)
         sleep(interval)
-
-
-def create_fulltext_search_index(
-    collection: Collection,
-    index_name: str,
-    field: Union[str, List[str]],
-    *,
-    wait_until_complete: Optional[float] = None,
-    **kwargs: Any,
-) -> None:
-    """Experimental Utility function to create an Atlas Search index
-
-    Args:
-        collection (Collection): MongoDB Collection
-        index_name (str): Name of Index
-        field (str): Field to index
-        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)
-
-    if isinstance(field, str):
-        fields_definition = {field: [{"type": "string"}]}
-    else:
-        fields_definition = {f: [{"type": "string"}] for f in field}
-    definition = {"mappings": {"dynamic": False, "fields": fields_definition}}
-    result = collection.create_search_index(
-        SearchIndexModel(
-            definition=definition,
-            name=index_name,
-            type="search",
-            **kwargs,
-        )
-    )
-    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)
diff --git a/libs/langchain-mongodb/langchain_mongodb/pipelines.py b/libs/langchain-mongodb/langchain_mongodb/pipelines.py
index bc5fff18..d6b09ea9 100644
--- a/libs/langchain-mongodb/langchain_mongodb/pipelines.py
+++ b/libs/langchain-mongodb/langchain_mongodb/pipelines.py
@@ -9,6 +9,13 @@
 
 from typing import Any, Dict, List, Optional, Union
 
+from pymongo_search_utils import (
+    combine_pipelines,  # noqa: F401
+    final_hybrid_stage,  # noqa: F401
+    reciprocal_rank_stage,  # noqa: F401
+    vector_search_stage,  # noqa: F401
+)
+
 
 def text_search_stage(
     query: str,
@@ -48,115 +55,3 @@ def text_search_stage(
         pipeline.append({"$limit": limit})  # type: ignore
 
     return pipeline  # type: ignore
-
-
-def vector_search_stage(
-    query_vector: List[float],
-    search_field: str,
-    index_name: str,
-    top_k: int = 4,
-    filter: Optional[Dict[str, Any]] = None,
-    oversampling_factor: int = 10,
-    **kwargs: Any,
-) -> Dict[str, Any]:  # noqa: E501
-    """Vector Search Stage without Scores.
-
-    Scoring is applied later depending on strategy.
-    vector search includes a vectorSearchScore that is typically used.
-    hybrid uses Reciprocal Rank Fusion.
-
-    Args:
-        query_vector: List of embedding vector
-        search_field: Field in Collection containing embedding vectors
-        index_name: Name of Atlas Vector Search Index tied to Collection
-        top_k: Number of documents to return
-        oversampling_factor: this times limit is the number of candidates
-        filter: MQL match expression comparing an indexed field.
-            Some operators are not supported.
-            See `vectorSearch filter docs <https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-stage/#atlas-vector-search-pre-filter>`_
-
-
-    Returns:
-        Dictionary defining the $vectorSearch
-    """
-    stage = {
-        "index": index_name,
-        "path": search_field,
-        "queryVector": query_vector,
-        "numCandidates": top_k * oversampling_factor,
-        "limit": top_k,
-    }
-    if filter:
-        stage["filter"] = filter
-    return {"$vectorSearch": stage}
-
-
-def combine_pipelines(
-    pipeline: List[Any], stage: List[Dict[str, Any]], collection_name: str
-) -> None:
-    """Combines two aggregations into a single result set in-place."""
-    if pipeline:
-        pipeline.append({"$unionWith": {"coll": collection_name, "pipeline": stage}})
-    else:
-        pipeline.extend(stage)
-
-
-def reciprocal_rank_stage(
-    score_field: str, penalty: float = 0, weight: float = 1, **kwargs: Any
-) -> List[Dict[str, Any]]:
-    """
-    Stage adds Weighted Reciprocal Rank Fusion (WRRF) scoring.
-
-    First, it groups documents into an array, assigns rank by array index,
-    and then computes a weighted RRF score.
-
-    Args:
-        score_field: A unique string to identify the search being ranked.
-        penalty: A non-negative float (e.g., 60 for RRF-60). Controls the denominator.
-        weight: A float multiplier for this source's importance.
-        **kwargs: Ignored; allows future extensions or passthrough args.
-
-    Returns:
-        Aggregation pipeline stage for weighted RRF scoring.
-    """
-
-    return [
-        {"$group": {"_id": None, "docs": {"$push": "$$ROOT"}}},
-        {"$unwind": {"path": "$docs", "includeArrayIndex": "rank"}},
-        {
-            "$addFields": {
-                f"docs.{score_field}": {
-                    "$multiply": [
-                        weight,
-                        {"$divide": [1.0, {"$add": ["$rank", penalty, 1]}]},
-                    ]
-                },
-                "docs.rank": "$rank",
-                "_id": "$docs._id",
-            }
-        },
-        {"$replaceRoot": {"newRoot": "$docs"}},
-    ]
-
-
-def final_hybrid_stage(
-    scores_fields: List[str], limit: int, **kwargs: Any
-) -> List[Dict[str, Any]]:
-    """Sum weighted scores, sort, and apply limit.
-
-    Args:
-        scores_fields: List of fields given to scores of vector and text searches
-        limit: Number of documents to return
-
-    Returns:
-        Final aggregation stages
-    """
-
-    return [
-        {"$group": {"_id": "$_id", "docs": {"$mergeObjects": "$$ROOT"}}},
-        {"$replaceRoot": {"newRoot": "$docs"}},
-        {"$set": {score: {"$ifNull": [f"${score}", 0]} for score in scores_fields}},
-        {"$addFields": {"score": {"$add": [f"${score}" for score in scores_fields]}}},
-        {"$sort": {"score": -1}},
-        {"$limit": limit},
-    ]
diff --git a/libs/langchain-mongodb/langchain_mongodb/utils.py b/libs/langchain-mongodb/langchain_mongodb/utils.py
index 826ce56e..8e4f717e 100644
--- a/libs/langchain-mongodb/langchain_mongodb/utils.py
+++ b/libs/langchain-mongodb/langchain_mongodb/utils.py
@@ -26,6 +26,7 @@
 import numpy as np
 from pymongo import MongoClient
 from pymongo.driver_info import DriverInfo
+from pymongo_search_utils import append_client_metadata
 
 logger = logging.getLogger(__name__)
 
@@ -35,9 +36,7 @@
 
 
 def _append_client_metadata(client: MongoClient) -> None:
-    # append_metadata was added in PyMongo 4.14.0, but is a valid database name on earlier versions
-    if callable(client.append_metadata):
-        client.append_metadata(DRIVER_METADATA)
+    append_client_metadata(client=client, driver_info=DRIVER_METADATA)
 
 
 def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray:
diff --git a/libs/langchain-mongodb/langchain_mongodb/vectorstores.py b/libs/langchain-mongodb/langchain_mongodb/vectorstores.py
index a1adf7e5..15b593b9 100644
--- a/libs/langchain-mongodb/langchain_mongodb/vectorstores.py
+++ b/libs/langchain-mongodb/langchain_mongodb/vectorstores.py
@@ -22,9 +22,10 @@
 from langchain_core.embeddings import Embeddings
 from langchain_core.runnables.config import run_in_executor
 from langchain_core.vectorstores import VectorStore
-from pymongo import MongoClient, ReplaceOne
+from pymongo import MongoClient
 from pymongo.collection import Collection
 from pymongo.errors import CollectionInvalid
+from pymongo_search_utils import bulk_embed_and_insert_texts
 
 from langchain_mongodb.index import (
     create_vector_search_index,
@@ -429,28 +430,15 @@ def bulk_embed_and_insert_texts(
 
         See add_texts for additional details.
         """
-        if not texts:
-            return []
-        # Compute embedding vectors
-        embeddings = self._embedding.embed_documents(list(texts))
-        if not ids:
-            ids = [str(ObjectId()) for _ in range(len(list(texts)))]
-        docs = [
-            {
-                "_id": str_to_oid(i),
-                self._text_key: t,
-                self._embedding_key: embedding,
-                **m,
-            }
-            for i, t, m, embedding in zip(
-                ids, texts, metadatas, embeddings, strict=True
-            )
-        ]
-        operations = [ReplaceOne({"_id": doc["_id"]}, doc, upsert=True) for doc in docs]
-        # insert the documents in MongoDB Atlas
-        result = self._collection.bulk_write(operations)
-        assert result.upserted_ids is not None
-        return [oid_to_str(_id) for _id in result.upserted_ids.values()]
+        return bulk_embed_and_insert_texts(
+            texts=texts,
+            metadatas=metadatas,
+            embedding_func=self._embedding.embed_documents,
+            collection=self._collection,
+            text_key=self._text_key,
+            embedding_key=self._embedding_key,
+            ids=ids,
+        )
 
     def add_documents(
         self,
diff --git a/libs/langchain-mongodb/pyproject.toml b/libs/langchain-mongodb/pyproject.toml
index fe6d7426..90f607ee 100644
--- a/libs/langchain-mongodb/pyproject.toml
+++ b/libs/langchain-mongodb/pyproject.toml
@@ -17,6 +17,7 @@ dependencies = [
     "langchain-text-splitters>=1.0",
     "numpy>=1.26",
     "lark<2.0.0,>=1.1.9",
+    "pymongo-search-utils>=0.1.0",
 ]
 
 [dependency-groups]
@@ -65,6 +66,7 @@ filterwarnings = [
 
 [tool.mypy]
 disallow_untyped_defs = true
+disable_error_code = ["import-untyped"]
 
 [[tool.mypy.overrides]]
 module = ["tests.*"]
diff --git a/libs/langchain-mongodb/uv.lock b/libs/langchain-mongodb/uv.lock
index ba0b4acb..6835769d 100644
--- a/libs/langchain-mongodb/uv.lock
+++ b/libs/langchain-mongodb/uv.lock
@@ -799,6 +799,7 @@ dependencies = [
     { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
     { name = "numpy", version = "2.3.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "pymongo" },
+    { name = "pymongo-search-utils" },
 ]
 
 [package.dev-dependencies]
@@ -836,6 +837,7 @@ requires-dist = [
     { name = "lark", specifier = ">=1.1.9,<2.0.0" },
     { name = "numpy", specifier = ">=1.26" },
     { name = "pymongo", specifier = ">=4.6.1" },
+    { name = "pymongo-search-utils", specifier = ">=0.1.0" },
 ]
 
 [package.metadata.requires-dev]
@@ -1872,6 +1874,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/2d/fa/68b1555e62ed3ee87f8a2de99d5fb840cf045748da4488870b4dced44a95/pymongo-4.14.0-cp313-cp313t-win_amd64.whl", hash = "sha256:e506af9b25aac77cc5c5ea4a72f81764e4f5ea90ca799aac43d665ab269f291d", size = 1011181, upload-time = "2025-08-06T13:40:48.641Z" },
 ]
 
+[[package]]
+name = "pymongo-search-utils"
+version = "0.1.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pymongo" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/d9/17/161b9a61a4a66d267a2eecab7157352b02a2b8fff7349311183be84f4c96/pymongo_search_utils-0.1.0.tar.gz", hash = "sha256:e2e2adc7292a8ae6031ab5c935fccb8c40542a45d3d217afea56995659da79e5", size = 11976, upload-time = "2025-11-24T15:12:12.642Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/94/05/7944a1cfb4a844d75a5c28f19ad94c3facf520e494e3e8fcd31b17f085c3/pymongo_search_utils-0.1.0-py3-none-any.whl", hash = "sha256:44f7601a99e8d979bb7ef7be611863c1a98943c92fb192bfa549ac8b1c281580", size = 17186, upload-time = "2025-11-24T15:12:11.2Z" },
+]
+
 [[package]]
 name = "pypdf"
 version = "6.0.0"
diff --git a/uv.lock b/uv.lock
index fd8fa82f..8bffb3c8 100644
--- a/uv.lock
+++ b/uv.lock
@@ -793,6 +793,7 @@ dependencies = [
     { name = "lark" },
     { name = "numpy" },
     { name = "pymongo" },
+    { name = "pymongo-search-utils" },
 ]
 
 [package.metadata]
@@ -804,6 +805,7 @@ requires-dist = [
     { name = "lark", specifier = ">=1.1.9,<2.0.0" },
     { name = "numpy", specifier = ">=1.26" },
     { name = "pymongo", specifier = ">=4.6.1" },
+    { name = "pymongo-search-utils", specifier = ">=0.1.0" },
 ]
 
 [package.metadata.requires-dev]
@@ -1805,6 +1807,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/b5/9c/00301a6df26f0f8d5c5955192892241e803742e7c3da8c2c222efabc0df6/pymongo-4.13.2-cp313-cp313t-win_amd64.whl", hash = "sha256:c38168263ed94a250fc5cf9c6d33adea8ab11c9178994da1c3481c2a49d235f8", size = 1011057, upload-time = "2025-06-16T18:16:07.917Z" },
 ]
 
+[[package]]
+name = "pymongo-search-utils"
+version = "0.1.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pymongo" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/d9/17/161b9a61a4a66d267a2eecab7157352b02a2b8fff7349311183be84f4c96/pymongo_search_utils-0.1.0.tar.gz", hash = "sha256:e2e2adc7292a8ae6031ab5c935fccb8c40542a45d3d217afea56995659da79e5", size = 11976, upload-time = "2025-11-24T15:12:12.642Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/94/05/7944a1cfb4a844d75a5c28f19ad94c3facf520e494e3e8fcd31b17f085c3/pymongo_search_utils-0.1.0-py3-none-any.whl", hash = "sha256:44f7601a99e8d979bb7ef7be611863c1a98943c92fb192bfa549ac8b1c281580", size = 17186, upload-time = "2025-11-24T15:12:11.2Z" },
+]
+
 [[package]]
 name = "python-dotenv"
 version = "1.1.0"