diff --git a/libs/partners/mongodb/README.md b/libs/partners/mongodb/README.md
index eda9f6841b1..a8b08a2a71b 100644
--- a/libs/partners/mongodb/README.md
+++ b/libs/partners/mongodb/README.md
@@ -18,7 +18,7 @@ MONGODB_ATLAS_CLUSTER_URI = os.environ.get("MONGODB_ATLAS_CLUSTER_URI")
 DB_NAME = "langchain_db"
 COLLECTION_NAME = "test"
 ATLAS_VECTOR_SEARCH_INDEX_NAME = "index_name"
-MONGODB_COLLECTION = client[DB_NAME][COLLECITON_NAME]
+MONGODB_COLLECTION = client[DB_NAME][COLLECTION_NAME]
 
 # Create the vector search via `from_connection_string`
 vector_search = MongoDBAtlasVectorSearch.from_connection_string(
diff --git a/libs/partners/mongodb/langchain_mongodb/__init__.py b/libs/partners/mongodb/langchain_mongodb/__init__.py
index ec256978518..d7aab3e785f 100644
--- a/libs/partners/mongodb/langchain_mongodb/__init__.py
+++ b/libs/partners/mongodb/langchain_mongodb/__init__.py
@@ -1,8 +1,16 @@
+"""
+Integrate your operational database and vector search in a single, unified,
+fully managed platform with full vector database capabilities on MongoDB Atlas.
+
+
+Store your operational data, metadata, and vector embeddings in oue VectorStore,
+MongoDBAtlasVectorSearch.
+Insert into a Chain via a Vector, FullText, or Hybrid Retriever.
+"""
+
 from langchain_mongodb.cache import MongoDBAtlasSemanticCache, MongoDBCache
 from langchain_mongodb.chat_message_histories import MongoDBChatMessageHistory
-from langchain_mongodb.vectorstores import (
-    MongoDBAtlasVectorSearch,
-)
+from langchain_mongodb.vectorstores import MongoDBAtlasVectorSearch
 
 __all__ = [
     "MongoDBAtlasVectorSearch",
diff --git a/libs/partners/mongodb/langchain_mongodb/cache.py b/libs/partners/mongodb/langchain_mongodb/cache.py
index 3dbde907b36..a6ce680c516 100644
--- a/libs/partners/mongodb/langchain_mongodb/cache.py
+++ b/libs/partners/mongodb/langchain_mongodb/cache.py
@@ -1,10 +1,4 @@
-"""
-LangChain MongoDB Caches
-
-Functions "_loads_generations" and "_dumps_generations"
-are duplicated in this utility from modules:
-    - "libs/community/langchain_community/cache.py"
-"""
+"""LangChain MongoDB Caches."""
 
 import json
 import logging
@@ -27,100 +21,6 @@ from langchain_mongodb.vectorstores import MongoDBAtlasVectorSearch
 logger = logging.getLogger(__file__)
 
 
-def _generate_mongo_client(connection_string: str) -> MongoClient:
-    return MongoClient(
-        connection_string,
-        driver=DriverInfo(name="Langchain", version=version("langchain-mongodb")),
-    )
-
-
-def _dumps_generations(generations: RETURN_VAL_TYPE) -> str:
-    """
-    Serialization for generic RETURN_VAL_TYPE, i.e. sequence of `Generation`
-
-    Args:
-        generations (RETURN_VAL_TYPE): A list of language model generations.
-
-    Returns:
-        str: a single string representing a list of generations.
-
-    This function (+ its counterpart `_loads_generations`) rely on
-    the dumps/loads pair with Reviver, so are able to deal
-    with all subclasses of Generation.
-
-    Each item in the list can be `dumps`ed to a string,
-    then we make the whole list of strings into a json-dumped.
-    """
-    return json.dumps([dumps(_item) for _item in generations])
-
-
-def _loads_generations(generations_str: str) -> Union[RETURN_VAL_TYPE, None]:
-    """
-    Deserialization of a string into a generic RETURN_VAL_TYPE
-    (i.e. a sequence of `Generation`).
-
-    See `_dumps_generations`, the inverse of this function.
-
-    Args:
-        generations_str (str): A string representing a list of generations.
-
-    Compatible with the legacy cache-blob format
-    Does not raise exceptions for malformed entries, just logs a warning
-    and returns none: the caller should be prepared for such a cache miss.
-
-    Returns:
-        RETURN_VAL_TYPE: A list of generations.
-    """
-    try:
-        generations = [loads(_item_str) for _item_str in json.loads(generations_str)]
-        return generations
-    except (json.JSONDecodeError, TypeError):
-        # deferring the (soft) handling to after the legacy-format attempt
-        pass
-
-    try:
-        gen_dicts = json.loads(generations_str)
-        # not relying on `_load_generations_from_json` (which could disappear):
-        generations = [Generation(**generation_dict) for generation_dict in gen_dicts]
-        logger.warning(
-            f"Legacy 'Generation' cached blob encountered: '{generations_str}'"
-        )
-        return generations
-    except (json.JSONDecodeError, TypeError):
-        logger.warning(
-            f"Malformed/unparsable cached blob encountered: '{generations_str}'"
-        )
-        return None
-
-
-def _wait_until(
-    predicate: Callable, success_description: Any, timeout: float = 10.0
-) -> None:
-    """Wait up to 10 seconds (by default) for predicate to be true.
-
-    E.g.:
-
-        wait_until(lambda: client.primary == ('a', 1),
-                'connect to the primary')
-
-    If the lambda-expression isn't true after 10 seconds, we raise
-    AssertionError("Didn't ever connect to the primary").
-
-    Returns the predicate's first true value.
-    """
-    start = time.time()
-    interval = min(float(timeout) / 100, 0.1)
-    while True:
-        retval = predicate()
-        if retval:
-            return retval
-
-        if time.time() - start > timeout:
-            raise TimeoutError("Didn't ever %s" % success_description)
-
-        time.sleep(interval)
-
-
 class MongoDBCache(BaseCache):
     """MongoDB Atlas cache
 
@@ -216,7 +116,7 @@ class MongoDBAtlasSemanticCache(BaseCache, MongoDBAtlasVectorSearch):
         collection_name: str = "default",
         database_name: str = "default",
         index_name: str = "default",
-        wait_until_ready: bool = False,
+        wait_until_ready: Optional[float] = None,
         score_threshold: Optional[float] = None,
         **kwargs: Dict[str, Any],
     ):
@@ -233,8 +133,8 @@ class MongoDBAtlasSemanticCache(BaseCache, MongoDBAtlasVectorSearch):
                 Defaults to "default".
             index_name: Name of the Atlas Search index.
                 defaults to 'default'
-            wait_until_ready (bool): Block until MongoDB Atlas finishes indexing
-                the stored text. Hard timeout of 10 seconds. Defaults to False.
+            wait_until_ready (float): Wait this time for Atlas to finish indexing
+                the stored text. Defaults to None.
         """
         client = _generate_mongo_client(connection_string)
         self.collection = client[database_name][collection_name]
@@ -272,7 +172,7 @@ class MongoDBAtlasSemanticCache(BaseCache, MongoDBAtlasVectorSearch):
         prompt: str,
         llm_string: str,
         return_val: RETURN_VAL_TYPE,
-        wait_until_ready: Optional[bool] = None,
+        wait_until_ready: Optional[float] = None,
     ) -> None:
         """Update cache based on prompt and llm_string."""
         self.add_texts(
@@ -290,7 +190,7 @@ class MongoDBAtlasSemanticCache(BaseCache, MongoDBAtlasVectorSearch):
             return self.lookup(prompt, llm_string) == return_val
 
         if wait:
-            _wait_until(is_indexed, return_val)
+            _wait_until(is_indexed, return_val, timeout=wait)
 
     def clear(self, **kwargs: Any) -> None:
         """Clear cache that can take additional keyword arguments.
@@ -302,3 +202,107 @@ class MongoDBAtlasSemanticCache(BaseCache, MongoDBAtlasVectorSearch):
             self.clear(llm_string="fake-model")
         """
         self.collection.delete_many({**kwargs})
+
+
+def _generate_mongo_client(connection_string: str) -> MongoClient:
+    return MongoClient(
+        connection_string,
+        driver=DriverInfo(name="Langchain", version=version("langchain-mongodb")),
+    )
+
+
+def _dumps_generations(generations: RETURN_VAL_TYPE) -> str:
+    """
+    Serialization for generic RETURN_VAL_TYPE, i.e. sequence of `Generation`
+
+    Args:
+        generations (RETURN_VAL_TYPE): A list of language model generations.
+
+    Returns:
+        str: a single string representing a list of generations.
+
+    This, and "_dumps_generations" are duplicated in this utility
+    from modules: "libs/community/langchain_community/cache.py"
+
+    This function and its counterpart rely on
+    the dumps/loads pair with Reviver, so are able to deal
+    with all subclasses of Generation.
+
+    Each item in the list can be `dumps`ed to a string,
+    then we make the whole list of strings into a json-dumped.
+    """
+    return json.dumps([dumps(_item) for _item in generations])
+
+
+def _loads_generations(generations_str: str) -> Union[RETURN_VAL_TYPE, None]:
+    """
+    Deserialization of a string into a generic RETURN_VAL_TYPE
+    (i.e. a sequence of `Generation`).
+
+    Args:
+        generations_str (str): A string representing a list of generations.
+
+    Returns:
+        RETURN_VAL_TYPE: A list of generations.
+
+
+    This function and its counterpart rely on
+    the dumps/loads pair with Reviver, so are able to deal
+    with all subclasses of Generation.
+
+    See `_dumps_generations`, the inverse of this function.
+
+    Compatible with the legacy cache-blob format
+    Does not raise exceptions for malformed entries, just logs a warning
+    and returns none: the caller should be prepared for such a cache miss.
+
+
+    """
+    try:
+        generations = [loads(_item_str) for _item_str in json.loads(generations_str)]
+        return generations
+    except (json.JSONDecodeError, TypeError):
+        # deferring the (soft) handling to after the legacy-format attempt
+        pass
+
+    try:
+        gen_dicts = json.loads(generations_str)
+        # not relying on `_load_generations_from_json` (which could disappear):
+        generations = [Generation(**generation_dict) for generation_dict in gen_dicts]
+        logger.warning(
+            f"Legacy 'Generation' cached blob encountered: '{generations_str}'"
+        )
+        return generations
+    except (json.JSONDecodeError, TypeError):
+        logger.warning(
+            f"Malformed/unparsable cached blob encountered: '{generations_str}'"
+        )
+        return None
+
+
+def _wait_until(
+    predicate: Callable, success_description: Any, timeout: float = 10.0
+) -> None:
+    """Wait up to 10 seconds (by default) for predicate to be true.
+
+    E.g.:
+
+        wait_until(lambda: client.primary == ('a', 1),
+                'connect to the primary')
+
+    If the lambda-expression isn't true after 10 seconds, we raise
+    AssertionError("Didn't ever connect to the primary").
+
+    Returns the predicate's first true value.
+    """
+    start = time.time()
+    interval = min(float(timeout) / 100, 0.1)
+    while True:
+        retval = predicate()
+        if retval:
+            return retval
+
+        if time.time() - start > timeout:
+            raise TimeoutError("Didn't ever %s" % success_description)
+
+        time.sleep(interval)
diff --git a/libs/partners/mongodb/langchain_mongodb/index.py b/libs/partners/mongodb/langchain_mongodb/index.py
index d0f99511fd7..59cb6d32eaa 100644
--- a/libs/partners/mongodb/langchain_mongodb/index.py
+++ b/libs/partners/mongodb/langchain_mongodb/index.py
@@ -1,3 +1,5 @@
+"""Search Index Commands"""
+
 import logging
 from time import monotonic, sleep
 from typing import Any, Callable, Dict, List, Optional
@@ -8,8 +10,6 @@ from pymongo.operations import SearchIndexModel
 
 logger = logging.getLogger(__file__)
 
-_DELAY = 0.5  # Interval between checks for index operations
-
 
 def _search_index_error_message() -> str:
     return (
@@ -25,19 +25,24 @@ def _vector_search_index_definition(
     dimensions: int,
     path: str,
     similarity: str,
-    filters: Optional[List[Dict[str, str]]],
+    filters: Optional[List[str]] = None,
+    **kwargs: Any,
 ) -> Dict[str, Any]:
-    return {
-        "fields": [
-            {
-                "numDimensions": dimensions,
-                "path": path,
-                "similarity": similarity,
-                "type": "vector",
-            },
-            *(filters or []),
-        ]
-    }
+    # https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-type/
+    fields = [
+        {
+            "numDimensions": dimensions,
+            "path": path,
+            "similarity": similarity,
+            "type": "vector",
+        },
+    ]
+    if filters:
+        for field in filters:
+            fields.append({"type": "filter", "path": field})
+    definition = {"fields": fields}
+    definition.update(kwargs)
+    return definition
 
 
 def create_vector_search_index(
@@ -46,9 +51,10 @@ def create_vector_search_index(
     dimensions: int,
     path: str,
     similarity: str,
-    filters: Optional[List[Dict[str, str]]] = None,
+    filters: Optional[List[str]] = None,
     *,
     wait_until_complete: Optional[float] = None,
+    **kwargs: Any,
 ) -> None:
     """Experimental Utility function to create a vector search index
 
@@ -58,9 +64,10 @@ def create_vector_search_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[Dict[str, str]]): additional filters for index definition.
+        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)
 
@@ -72,6 +79,7 @@ def create_vector_search_index(
                     path=path,
                     similarity=similarity,
                     filters=filters,
+                    **kwargs,
                 ),
                 name=index_name,
                 type="vectorSearch",
@@ -83,7 +91,7 @@ def create_vector_search_index(
     if wait_until_complete:
         _wait_for_predicate(
             predicate=lambda: _is_index_ready(collection, index_name),
-            err=f"Index {index_name} creation did not finish in {wait_until_complete}!",
+            err=f"{index_name=} did not complete in {wait_until_complete}!",
             timeout=wait_until_complete,
         )
     logger.info(result)
@@ -127,9 +135,10 @@ def update_vector_search_index(
     dimensions: int,
     path: str,
     similarity: str,
-    filters: List[Dict[str, str]],
+    filters: Optional[List[str]] = None,
     *,
     wait_until_complete: Optional[float] = None,
+    **kwargs: Any,
 ) -> None:
     """Update a search index.
 
@@ -138,12 +147,13 @@ def update_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.
+        dimensions (int): Number of dimensions in embedding
+        path (str): field with vector embedding
         similarity (str): The similarity score used for the index.
-        filters (List[Dict[str, str]]): additional filters for index definition.
+        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(
@@ -157,6 +167,7 @@ def update_vector_search_index(
                 path=path,
                 similarity=similarity,
                 filters=filters,
+                **kwargs,
             ),
         )
     except OperationFailure as e:
@@ -201,7 +212,7 @@ def _wait_for_predicate(
     Args:
         predicate (Callable[, bool]): A function that returns a boolean value
         err (str): Error message to raise if nothing occurs
-        timeout (float, optional):  wait time for predicate. Defaults to TIMEOUT.
+        timeout (float, optional): Wait time for predicate. Defaults to TIMEOUT.
         interval (float, optional): Interval to check predicate. Defaults to DELAY.
 
     Raises:
@@ -212,3 +223,48 @@ def _wait_for_predicate(
         if monotonic() - start > timeout:
             raise TimeoutError(err)
         sleep(interval)
+
+
+def create_fulltext_search_index(
+    collection: Collection,
+    index_name: str,
+    field: 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)
+
+    definition = {
+        "mappings": {"dynamic": False, "fields": {field: [{"type": "string"}]}}
+    }
+
+    try:
+        result = collection.create_search_index(
+            SearchIndexModel(
+                definition=definition,
+                name=index_name,
+                type="search",
+                **kwargs,
+            )
+        )
+    except OperationFailure as e:
+        raise OperationFailure(_search_index_error_message()) from e
+
+    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/partners/mongodb/langchain_mongodb/pipelines.py b/libs/partners/mongodb/langchain_mongodb/pipelines.py
new file mode 100644
index 00000000000..5820ce388f3
--- /dev/null
+++ b/libs/partners/mongodb/langchain_mongodb/pipelines.py
@@ -0,0 +1,160 @@
+"""Aggregation pipeline components used in Atlas Full-Text, Vector, and Hybrid Search
+
+See the following for more:
+    - `Full-Text Search <https://www.mongodb.com/docs/atlas/atlas-search/aggregation-stages/search/#mongodb-pipeline-pipe.-search>`_
+    - `MongoDB Operators <https://www.mongodb.com/docs/atlas/atlas-search/operators-and-collectors/#std-label-operators-ref>`_
+    - `Vector Search <https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-stage/>`_
+    - `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
+
+
+def text_search_stage(
+    query: str,
+    search_field: str,
+    index_name: str,
+    limit: Optional[int] = None,
+    filter: Optional[Dict[str, Any]] = None,
+    include_scores: Optional[bool] = True,
+    **kwargs: Any,
+) -> List[Dict[str, Any]]:  # noqa: E501
+    """Full-Text search using Lucene's standard (BM25) analyzer
+
+    Args:
+        query: Input text to search for
+        search_field: Field in Collection that will be searched
+        index_name: Atlas Search Index name
+        limit: Maximum number of documents to return. Default of no limit
+        filter: Any MQL match expression comparing an indexed field
+        include_scores: Scores provide measure of relative relevance
+
+    Returns:
+        Dictionary defining the $search stage
+    """
+    pipeline = [
+        {
+            "$search": {
+                "index": index_name,
+                "text": {"query": query, "path": search_field},
+            }
+        }
+    ]
+    if filter:
+        pipeline.append({"$match": filter})  # type: ignore
+    if include_scores:
+        pipeline.append({"$set": {"score": {"$meta": "searchScore"}}})
+    if limit:
+        pipeline.append({"$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({"$unionWith": {"coll": collection_name, "pipeline": stage}})
+    else:
+        pipeline.extend(stage)
+
+
+def reciprocal_rank_stage(
+    score_field: str, penalty: float = 0, **kwargs: Any
+) -> List[Dict[str, Any]]:
+    """Stage adds Reciprocal Rank Fusion weighting.
+
+        First, it pushes documents retrieved from previous stage
+        into a temporary sub-document. It then unwinds to establish
+        the rank to each and applies the penalty.
+
+    Args:
+        score_field: A unique string to identify the search being ranked
+        penalty: A non-negative float.
+        extra_fields: Any fields other than text_field that one wishes to keep.
+
+    Returns:
+        RRF score := \frac{1}{rank + penalty} with rank in [1,2,..,n]
+    """
+
+    rrf_pipeline = [
+        {"$group": {"_id": None, "docs": {"$push": "$$ROOT"}}},
+        {"$unwind": {"path": "$docs", "includeArrayIndex": "rank"}},
+        {
+            "$addFields": {
+                f"docs.{score_field}": {
+                    "$divide": [1.0, {"$add": ["$rank", penalty, 1]}]
+                },
+                "docs.rank": "$rank",
+                "_id": "$docs._id",
+            }
+        },
+        {"$replaceRoot": {"newRoot": "$docs"}},
+    ]
+
+    return rrf_pipeline  # type: ignore
+
+
+def final_hybrid_stage(
+    scores_fields: List[str], limit: int, **kwargs: Any
+) -> List[Dict[str, Any]]:
+    """Sum weighted scores, sort, and apply limit.
+
+    Args:
+        scores_fields: List of fields given to scores of vector and text searches
+        limit: Number of documents to return
+
+    Returns:
+        Final aggregation stages
+    """
+
+    return [
+        {"$group": {"_id": "$_id", "docs": {"$mergeObjects": "$$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/partners/mongodb/langchain_mongodb/retrievers/__init__.py b/libs/partners/mongodb/langchain_mongodb/retrievers/__init__.py
new file mode 100644
index 00000000000..c78e3fba44a
--- /dev/null
+++ b/libs/partners/mongodb/langchain_mongodb/retrievers/__init__.py
@@ -0,0 +1,15 @@
+"""Search Retrievers of various types.
+
+Use ``MongoDBAtlasVectorSearch.as_retriever(**)``
+to create MongoDB's core Vector Search Retriever.
+"""
+
+from langchain_mongodb.retrievers.full_text_search import (
+    MongoDBAtlasFullTextSearchRetriever,
+)
+from langchain_mongodb.retrievers.hybrid_search import MongoDBAtlasHybridSearchRetriever
+
+__all__ = [
+    "MongoDBAtlasHybridSearchRetriever",
+    "MongoDBAtlasFullTextSearchRetriever",
+]
diff --git a/libs/partners/mongodb/langchain_mongodb/retrievers/full_text_search.py b/libs/partners/mongodb/langchain_mongodb/retrievers/full_text_search.py
new file mode 100644
index 00000000000..52f12c4780a
--- /dev/null
+++ b/libs/partners/mongodb/langchain_mongodb/retrievers/full_text_search.py
@@ -0,0 +1,59 @@
+from typing import Any, Dict, List, Optional
+
+from langchain_core.callbacks.manager import CallbackManagerForRetrieverRun
+from langchain_core.documents import Document
+from langchain_core.retrievers import BaseRetriever
+from pymongo.collection import Collection
+
+from langchain_mongodb.pipelines import text_search_stage
+from langchain_mongodb.utils import make_serializable
+
+
+class MongoDBAtlasFullTextSearchRetriever(BaseRetriever):
+    """Hybrid Search Retriever performs full-text searches
+    using Lucene's standard (BM25) analyzer.
+    """
+
+    collection: Collection
+    """MongoDB Collection on an Atlas cluster"""
+    search_index_name: str
+    """Atlas Search Index name"""
+    search_field: str
+    """Collection field that contains the text to be searched. It must be indexed"""
+    top_k: Optional[int] = None
+    """Number of documents to return. Default is no limit"""
+    filter: Optional[Dict[str, Any]] = None
+    """(Optional) List of MQL match expression comparing an indexed field"""
+    show_embeddings: float = False
+    """If true, returned Document metadata will include vectors"""
+
+    def _get_relevant_documents(
+        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
+    ) -> List[Document]:
+        """Retrieve documents that are highest scoring / most similar  to query.
+
+        Args:
+            query: String to find relevant documents for
+            run_manager: The callback handler to use
+        Returns:
+            List of relevant documents
+        """
+
+        pipeline = text_search_stage(  # type: ignore
+            query=query,
+            search_field=self.search_field,
+            index_name=self.search_index_name,
+            limit=self.top_k,
+            filter=self.filter,
+        )
+
+        # Execution
+        cursor = self.collection.aggregate(pipeline)  # type: ignore[arg-type]
+
+        # Formatting
+        docs = []
+        for res in cursor:
+            text = res.pop(self.search_field)
+            make_serializable(res)
+            docs.append(Document(page_content=text, metadata=res))
+        return docs
diff --git a/libs/partners/mongodb/langchain_mongodb/retrievers/hybrid_search.py b/libs/partners/mongodb/langchain_mongodb/retrievers/hybrid_search.py
new file mode 100644
index 00000000000..9e876e9ff81
--- /dev/null
+++ b/libs/partners/mongodb/langchain_mongodb/retrievers/hybrid_search.py
@@ -0,0 +1,126 @@
+from typing import Any, Dict, List, Optional
+
+from langchain_core.callbacks.manager import CallbackManagerForRetrieverRun
+from langchain_core.documents import Document
+from langchain_core.retrievers import BaseRetriever
+from pymongo.collection import Collection
+
+from langchain_mongodb import MongoDBAtlasVectorSearch
+from langchain_mongodb.pipelines import (
+    combine_pipelines,
+    final_hybrid_stage,
+    reciprocal_rank_stage,
+    text_search_stage,
+    vector_search_stage,
+)
+from langchain_mongodb.utils import make_serializable
+
+
+class MongoDBAtlasHybridSearchRetriever(BaseRetriever):
+    """Hybrid Search Retriever combines vector and full-text searches
+    weighting them the via Reciprocal Rank Fusion (RRF) algorithm.
+
+    Increasing the vector_penalty will reduce the importance on the vector search.
+    Increasing the fulltext_penalty will correspondingly reduce the fulltext score.
+    For more on the algorithm,see
+    https://learn.microsoft.com/en-us/azure/search/hybrid-search-ranking
+    """
+
+    vectorstore: MongoDBAtlasVectorSearch
+    """MongoDBAtlas VectorStore"""
+    search_index_name: str
+    """Atlas Search Index (full-text) name"""
+    top_k: int = 4
+    """Number of documents to return."""
+    oversampling_factor: int = 10
+    """This times top_k is the number of candidates chosen at each step"""
+    pre_filter: Optional[Dict[str, Any]] = None
+    """(Optional) Any MQL match expression comparing an indexed field"""
+    post_filter: Optional[List[Dict[str, Any]]] = None
+    """(Optional) Pipeline of MongoDB aggregation stages for postprocessing."""
+    vector_penalty: float = 60.0
+    """Penalty applied to vector search results in RRF: scores=1/(rank + penalty)"""
+    fulltext_penalty: float = 60.0
+    """Penalty applied to full-text search results in RRF: scores=1/(rank + penalty)"""
+    show_embeddings: float = False
+    """If true, returned Document metadata will include vectors."""
+
+    @property
+    def collection(self) -> Collection:
+        return self.vectorstore._collection
+
+    def _get_relevant_documents(
+        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
+    ) -> List[Document]:
+        """Retrieve documents that are highest scoring / most similar  to query.
+
+        Note that the same query is used in both searches,
+        embedded for vector search, and as-is for full-text search.
+
+        Args:
+            query: String to find relevant documents for
+            run_manager: The callback handler to use
+        Returns:
+            List of relevant documents
+        """
+
+        query_vector = self.vectorstore._embedding.embed_query(query)
+
+        scores_fields = ["vector_score", "fulltext_score"]
+        pipeline: List[Any] = []
+
+        # First we build up the aggregation pipeline,
+        # then it is passed to the server to execute
+        # Vector Search stage
+        vector_pipeline = [
+            vector_search_stage(
+                query_vector=query_vector,
+                search_field=self.vectorstore._embedding_key,
+                index_name=self.vectorstore._index_name,
+                top_k=self.top_k,
+                filter=self.pre_filter,
+                oversampling_factor=self.oversampling_factor,
+            )
+        ]
+        vector_pipeline += reciprocal_rank_stage("vector_score", self.vector_penalty)
+
+        combine_pipelines(pipeline, vector_pipeline, self.collection.name)
+
+        # Full-Text Search stage
+        text_pipeline = text_search_stage(
+            query=query,
+            search_field=self.vectorstore._text_key,
+            index_name=self.search_index_name,
+            limit=self.top_k,
+            filter=self.pre_filter,
+        )
+
+        text_pipeline.extend(
+            reciprocal_rank_stage("fulltext_score", self.fulltext_penalty)
+        )
+
+        combine_pipelines(pipeline, text_pipeline, self.collection.name)
+
+        # Sum and sort stage
+        pipeline.extend(
+            final_hybrid_stage(scores_fields=scores_fields, limit=self.top_k)
+        )
+
+        # Removal of embeddings unless requested.
+        if not self.show_embeddings:
+            pipeline.append({"$project": {self.vectorstore._embedding_key: 0}})
+        # Post filtering
+        if self.post_filter is not None:
+            pipeline.extend(self.post_filter)
+
+        # Execution
+        cursor = self.collection.aggregate(pipeline)  # type: ignore[arg-type]
+
+        # Formatting
+        docs = []
+        for res in cursor:
+            text = res.pop(self.vectorstore._text_key)
+            # score = res.pop("score")  # The score remains buried!
+            make_serializable(res)
+            docs.append(Document(page_content=text, metadata=res))
+        return docs
diff --git a/libs/partners/mongodb/langchain_mongodb/utils.py b/libs/partners/mongodb/langchain_mongodb/utils.py
index c7c27f52f57..cb4348346b3 100644
--- a/libs/partners/mongodb/langchain_mongodb/utils.py
+++ b/libs/partners/mongodb/langchain_mongodb/utils.py
@@ -1,6 +1,13 @@
-"""
-Tools for the Maximal Marginal Relevance (MMR) reranking.
-Duplicated from langchain_community to avoid cross-dependencies.
+"""Various Utility Functions
+
+- Tools for handling bson.ObjectId
+
+The help IDs live as ObjectId in MongoDB and str in Langchain and JSON.
+
+
+- Tools for the Maximal Marginal Relevance (MMR) reranking
+
+These are duplicated from langchain_community to avoid cross-dependencies.
 
 Functions "maximal_marginal_relevance" and "cosine_similarity"
 are duplicated in this utility respectively from modules:
@@ -21,11 +28,6 @@ logger = logging.getLogger(__name__)
 Matrix = Union[List[List[float]], List[np.ndarray], np.ndarray]
 
 
-class FailCode:
-    INDEX_NOT_FOUND = 27
-    INDEX_ALREADY_EXISTS = 68
-
-
 def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray:
     """Row-wise cosine similarity between two equal-width matrices."""
     if len(X) == 0 or len(Y) == 0:
@@ -65,7 +67,37 @@ def maximal_marginal_relevance(
     lambda_mult: float = 0.5,
     k: int = 4,
 ) -> List[int]:
-    """Calculate maximal marginal relevance."""
+    """Compute Maximal Marginal Relevance (MMR).
+
+    MMR is a technique used to select documents that are both relevant to the query
+    and diverse among themselves. This function returns the indices
+    of the top-k embeddings that maximize the marginal relevance.
+
+    Args:
+        query_embedding (np.ndarray): The embedding vector of the query.
+        embedding_list (list of np.ndarray): A list containing the embedding vectors
+            of the candidate documents.
+        lambda_mult (float, optional): The trade-off parameter between
+            relevance and diversity. Defaults to 0.5.
+        k (int, optional): The number of embeddings to select. Defaults to 4.
+
+    Returns:
+        list of int: The indices of the embeddings that maximize the marginal relevance.
+
+    Notes:
+        The Maximal Marginal Relevance (MMR) is computed using the following formula:
+
+    MMR = argmax_{D_i ∈ R \ S} [λ * Sim(D_i, Q) - (1 - λ) * max_{D_j ∈ S} Sim(D_i, D_j)]
+
+        where:
+        - R is the set of candidate documents,
+        - S is the set of selected documents,
+        - Q is the query embedding,
+        - Sim(D_i, Q) is the similarity between document D_i and the query,
+        - Sim(D_i, D_j) is the similarity between documents D_i and D_j,
+        - λ is the trade-off parameter.
+    """
+
     if min(k, len(embedding_list)) <= 0:
         return []
     if query_embedding.ndim == 1:
@@ -137,6 +169,7 @@ def make_serializable(
     obj: Dict[str, Any],
 ) -> None:
     """Recursively cast values in a dict to a form able to json.dump"""
+
     from bson import ObjectId
 
     for k, v in obj.items():
diff --git a/libs/partners/mongodb/langchain_mongodb/vectorstores.py b/libs/partners/mongodb/langchain_mongodb/vectorstores.py
index beaf8b6cc36..7860281eda0 100644
--- a/libs/partners/mongodb/langchain_mongodb/vectorstores.py
+++ b/libs/partners/mongodb/langchain_mongodb/vectorstores.py
@@ -29,6 +29,7 @@ from langchain_mongodb.index import (
     create_vector_search_index,
     update_vector_search_index,
 )
+from langchain_mongodb.pipelines import vector_search_stage
 from langchain_mongodb.utils import (
     make_serializable,
     maximal_marginal_relevance,
@@ -36,7 +37,6 @@ from langchain_mongodb.utils import (
     str_to_oid,
 )
 
-MongoDBDocumentType = TypeVar("MongoDBDocumentType", bound=Dict[str, Any])
 VST = TypeVar("VST", bound=VectorStore)
 
 logger = logging.getLogger(__name__)
@@ -45,19 +45,40 @@ DEFAULT_INSERT_BATCH_SIZE = 100_000
 
 
 class MongoDBAtlasVectorSearch(VectorStore):
-    """MongoDBAtlas vector store integration.
+    """MongoDB Atlas vector store integration.
+
+    MongoDBAtlasVectorSearch performs data operations on
+    text, embeddings and arbitrary data. In addition to CRUD operations,
+    the VectorStore provides Vector Search
+    based on similarity of embedding vectors following the
+    Hierarchical Navigable Small Worlds (HNSW) algorithm.
+
+    This supports a number of models to ascertain scores,
+    "similarity" (default), "MMR", and "similarity_score_threshold".
+    These are described in the search_type argument to as_retriever,
+    which provides the Runnable.invoke(query) API, allowing
+    MongoDBAtlasVectorSearch to be used within a chain.
 
     Setup:
-        Install ``langchain-mongodb`` and ``pymongo`` and setup a MongoDB Atlas cluster (read through [this guide](https://www.mongodb.com/docs/manual/reference/connection-string/) to do so).
+        * Set up a MongoDB Atlas cluster. The free tier M0 will allow you to start.
+        Search Indexes are only available on Atlas, the fully managed cloud service,
+        not the self-managed MongoDB.
+        Follow [this guide](https://www.mongodb.com/basics/mongodb-atlas-tutorial)
+
+        * Create a Collection and a Vector Search Index.The procedure is described
+        [here](https://www.mongodb.com/docs/atlas/atlas-vector-search/create-index/#procedure).
+
+        * Install ``langchain-mongodb``
+
 
         .. code-block:: bash
 
             pip install -qU langchain-mongodb pymongo
 
+
         .. code-block:: python
 
             import getpass
-
             MONGODB_ATLAS_CLUSTER_URI = getpass.getpass("MongoDB Atlas Cluster URI:")
 
     Key init args — indexing params:
@@ -127,7 +148,7 @@ class MongoDBAtlasVectorSearch(VectorStore):
     Search with filter:
         .. code-block:: python
 
-            results = vector_store.similarity_search(query="thud",k=1,filter={"bar": "baz"})
+            results = vector_store.similarity_search(query="thud",k=1,post_filter=[{"bar": "baz"]})
             for doc in results:
                 print(f"* {doc.page_content} [{doc.metadata}]")
 
@@ -184,29 +205,24 @@ class MongoDBAtlasVectorSearch(VectorStore):
 
     def __init__(
         self,
-        collection: Collection[MongoDBDocumentType],
+        collection: Collection[Dict[str, Any]],
         embedding: Embeddings,
-        *,
-        index_name: str = "default",
+        index_name: str = "vector_index",
         text_key: str = "text",
         embedding_key: str = "embedding",
         relevance_score_fn: str = "cosine",
+        **kwargs: Any,
     ):
         """
         Args:
-            collection: MongoDB collection to add the texts to.
-            embedding: Text embedding model to use.
-            text_key: MongoDB field that will contain the text for each
-                document.
-                defaults to 'text'
-            embedding_key: MongoDB field that will contain the embedding for
-                each document.
-                defaults to 'embedding'
-            index_name: Name of the Atlas Search index.
-                defaults to 'default'
-            relevance_score_fn: The similarity score used for the index.
-                defaults to 'cosine'
-            Currently supported: 'euclidean', 'cosine', and 'dotProduct'.
+            collection: MongoDB collection to add the texts to
+            embedding: Text embedding model to use
+            text_key: MongoDB field that will contain the text for each document
+            index_name: Existing Atlas Vector Search Index
+            embedding_key: Field that will contain the embedding for each document
+            vector_index_name: Name of the Atlas Vector Search index
+            relevance_score_fn: The similarity score used for the index
+                Currently supported: 'euclidean', 'cosine', and 'dotProduct'
         """
         self._collection = collection
         self._embedding = embedding
@@ -412,69 +428,32 @@ class MongoDBAtlasVectorSearch(VectorStore):
             start = end
         return result_ids
 
-    def _similarity_search_with_score(
-        self,
-        embedding: List[float],
-        k: int = 4,
-        pre_filter: Optional[Dict] = None,
-        post_filter_pipeline: Optional[List[Dict]] = None,
-        include_embedding: bool = False,
-        include_ids: bool = False,
-        **kwargs: Any,
-    ) -> List[Tuple[Document, float]]:
-        """Core implementation."""
-        params = {
-            "queryVector": embedding,
-            "path": self._embedding_key,
-            "numCandidates": k * 10,
-            "limit": k,
-            "index": self._index_name,
-        }
-        if pre_filter:
-            params["filter"] = pre_filter
-        query = {"$vectorSearch": params}
-
-        pipeline = [
-            query,
-            {"$set": {"score": {"$meta": "vectorSearchScore"}}},
-        ]
-
-        # Exclude the embedding key from the return payload
-        if not include_embedding:
-            pipeline.append({"$project": {self._embedding_key: 0}})
-
-        if post_filter_pipeline is not None:
-            pipeline.extend(post_filter_pipeline)
-        cursor = self._collection.aggregate(pipeline)  # type: ignore[arg-type]
-        docs = []
-
-        for res in cursor:
-            text = res.pop(self._text_key)
-            score = res.pop("score")
-            make_serializable(res)
-            docs.append((Document(page_content=text, metadata=res), score))
-        return docs
-
     def similarity_search_with_score(
         self,
         query: str,
         k: int = 4,
-        pre_filter: Optional[Dict] = None,
+        pre_filter: Optional[Dict[str, Any]] = None,
         post_filter_pipeline: Optional[List[Dict]] = None,
+        oversampling_factor: int = 10,
+        include_embeddings: bool = False,
         **kwargs: Any,
-    ) -> List[Tuple[Document, float]]:
+    ) -> List[Tuple[Document, float]]:  # noqa: E501
         """Return MongoDB documents most similar to the given query and their scores.
 
-        Uses the vectorSearch operator available in MongoDB Atlas Search.
-        For more: https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-stage/
+        Atlas Vector Search eliminates the need to run a separate
+        search system alongside your database.
 
-        Args:
-            query: Text to look up documents similar to.
-            k: (Optional) number of documents to return. Defaults to 4.
-            pre_filter: (Optional) dictionary of argument(s) to prefilter document
-                fields on.
-            post_filter_pipeline: (Optional) Pipeline of MongoDB aggregation stages
-                following the vectorSearch stage.
+         Args:
+            query: Input text of semantic query
+            k: Number of documents to return. Also known as top_k.
+            pre_filter: List of MQL match expressions comparing an indexed field
+            post_filter_pipeline: (Optional) Arbitrary pipeline of MongoDB
+                aggregation stages applied after the search is complete.
+            oversampling_factor: This times k is the number of candidates chosen
+                at each step in the in HNSW Vector Search
+            include_embeddings: If True, the embedding vector of each result
+                will be included in metadata.
+            kwargs: Additional arguments are specific to the search_type
 
         Returns:
             List of documents most similar to the query and their scores.
@@ -485,6 +464,8 @@ class MongoDBAtlasVectorSearch(VectorStore):
             k=k,
             pre_filter=pre_filter,
             post_filter_pipeline=post_filter_pipeline,
+            oversampling_factor=oversampling_factor,
+            include_embeddings=include_embeddings,
             **kwargs,
         )
         return docs
@@ -493,36 +474,46 @@ class MongoDBAtlasVectorSearch(VectorStore):
         self,
         query: str,
         k: int = 4,
-        pre_filter: Optional[Dict] = None,
+        pre_filter: Optional[Dict[str, Any]] = None,
         post_filter_pipeline: Optional[List[Dict]] = None,
+        oversampling_factor: int = 10,
+        include_scores: bool = False,
+        include_embeddings: bool = False,
         **kwargs: Any,
-    ) -> List[Document]:
+    ) -> List[Document]:  # noqa: E501
         """Return MongoDB documents most similar to the given query.
 
-        Uses the vectorSearch operator available in MongoDB Atlas Search.
-        For more: https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-stage/
+        Atlas Vector Search eliminates the need to run a separate
+        search system alongside your database.
 
-        Args:
-            query: Text to look up documents similar to.
+         Args:
+            query: Input text of semantic query
             k: (Optional) number of documents to return. Defaults to 4.
-            pre_filter: (Optional) dictionary of argument(s) to prefilter document
-                fields on.
+            pre_filter: List of MQL match expressions comparing an indexed field
             post_filter_pipeline: (Optional) Pipeline of MongoDB aggregation stages
-                following the vectorSearch stage.
+                to filter/process results after $vectorSearch.
+            oversampling_factor: Multiple of k used when generating number of candidates
+                at each step in the HNSW Vector Search,
+            include_scores: If True, the query score of each result
+                will be included in metadata.
+            include_embeddings: If True, the embedding vector of each result
+                will be included in metadata.
+            kwargs: Additional arguments are specific to the search_type
 
         Returns:
             List of documents most similar to the query and their scores.
         """
-        additional = kwargs.get("additional")
         docs_and_scores = self.similarity_search_with_score(
             query,
             k=k,
             pre_filter=pre_filter,
             post_filter_pipeline=post_filter_pipeline,
+            oversampling_factor=oversampling_factor,
+            include_embeddings=include_embeddings,
             **kwargs,
         )
 
-        if additional and "similarity_score" in additional:
+        if include_scores:
             for doc, score in docs_and_scores:
                 doc.metadata["score"] = score
         return [doc for doc, _ in docs_and_scores]
@@ -533,7 +524,7 @@ class MongoDBAtlasVectorSearch(VectorStore):
         k: int = 4,
         fetch_k: int = 20,
         lambda_mult: float = 0.5,
-        pre_filter: Optional[Dict] = None,
+        pre_filter: Optional[Dict[str, Any]] = None,
         post_filter_pipeline: Optional[List[Dict]] = None,
         **kwargs: Any,
     ) -> List[Document]:
@@ -548,19 +539,16 @@ class MongoDBAtlasVectorSearch(VectorStore):
             fetch_k: (Optional) number of documents to fetch before passing to MMR
                 algorithm. Defaults to 20.
             lambda_mult: Number between 0 and 1 that determines the degree
-                        of diversity among the results with 0 corresponding
-                        to maximum diversity and 1 to minimum diversity.
-                        Defaults to 0.5.
-            pre_filter: (Optional) dictionary of argument(s) to prefilter on document
-                fields.
+                of diversity among the results with 0 corresponding
+                to maximum diversity and 1 to minimum diversity. Defaults to 0.5.
+            pre_filter: List of MQL match expressions comparing an indexed field
             post_filter_pipeline: (Optional) pipeline of MongoDB aggregation stages
-                following the vectorSearch stage.
+                following the $vectorSearch stage.
         Returns:
             List of documents selected by maximal marginal relevance.
         """
-        query_embedding = self._embedding.embed_query(query)
         return self.max_marginal_relevance_search_by_vector(
-            embedding=query_embedding,
+            embedding=self._embedding.embed_query(query),
             k=k,
             fetch_k=fetch_k,
             lambda_mult=lambda_mult,
@@ -575,7 +563,7 @@ class MongoDBAtlasVectorSearch(VectorStore):
         texts: List[str],
         embedding: Embeddings,
         metadatas: Optional[List[Dict]] = None,
-        collection: Optional[Collection[MongoDBDocumentType]] = None,
+        collection: Optional[Collection] = None,
         ids: Optional[List[str]] = None,
         **kwargs: Any,
     ) -> MongoDBAtlasVectorSearch:
@@ -588,6 +576,9 @@ class MongoDBAtlasVectorSearch(VectorStore):
 
         This is intended to be a quick way to get started.
 
+        See `MongoDBAtlasVectorSearch` for kwargs and further description.
+
+
         Example:
             .. code-block:: python
                 from pymongo import MongoClient
@@ -649,8 +640,9 @@ class MongoDBAtlasVectorSearch(VectorStore):
         k: int = 4,
         fetch_k: int = 20,
         lambda_mult: float = 0.5,
-        pre_filter: Optional[Dict] = None,
+        pre_filter: Optional[Dict[str, Any]] = None,
         post_filter_pipeline: Optional[List[Dict]] = None,
+        oversampling_factor: int = 10,
         **kwargs: Any,
     ) -> List[Document]:  # type: ignore
         """Return docs selected using the maximal marginal relevance.
@@ -666,10 +658,13 @@ class MongoDBAtlasVectorSearch(VectorStore):
                         of diversity among the results with 0 corresponding
                         to maximum diversity and 1 to minimum diversity.
                         Defaults to 0.5.
-            pre_filter: (Optional) dictionary of argument(s) to prefilter on document
-                fields.
+            pre_filter: (Optional) dictionary of arguments to filter document fields on.
             post_filter_pipeline: (Optional) pipeline of MongoDB aggregation stages
                 following the vectorSearch stage.
+            oversampling_factor: Multiple of k used when generating number
+                of candidates in HNSW Vector Search,
+            kwargs: Additional arguments are specific to the search_type
+
         Returns:
             List of Documents selected by maximal marginal relevance.
         """
@@ -678,7 +673,8 @@ class MongoDBAtlasVectorSearch(VectorStore):
             k=fetch_k,
             pre_filter=pre_filter,
             post_filter_pipeline=post_filter_pipeline,
-            include_embedding=kwargs.pop("include_embedding", True),
+            include_embeddings=True,
+            oversampling_factor=oversampling_factor,
             **kwargs,
         )
         mmr_doc_indexes = maximal_marginal_relevance(
@@ -696,31 +692,82 @@ class MongoDBAtlasVectorSearch(VectorStore):
         k: int = 4,
         fetch_k: int = 20,
         lambda_mult: float = 0.5,
+        pre_filter: Optional[Dict[str, Any]] = None,
+        post_filter_pipeline: Optional[List[Dict]] = None,
+        oversampling_factor: int = 10,
         **kwargs: Any,
     ) -> List[Document]:
         """Return docs selected using the maximal marginal relevance."""
         return await run_in_executor(
             None,
-            self.max_marginal_relevance_search_by_vector,
+            self.max_marginal_relevance_search_by_vector,  # type: ignore[arg-type]
             embedding,
             k=k,
             fetch_k=fetch_k,
             lambda_mult=lambda_mult,
+            pre_filter=pre_filter,
+            post_filter_pipeline=post_filter_pipeline,
+            oversampling_factor=oversampling_factor,
             **kwargs,
         )
 
+    def _similarity_search_with_score(
+        self,
+        query_vector: List[float],
+        k: int = 4,
+        pre_filter: Optional[Dict[str, Any]] = None,
+        post_filter_pipeline: Optional[List[Dict]] = None,
+        oversampling_factor: int = 10,
+        include_embeddings: bool = False,
+        **kwargs: Any,
+    ) -> List[Tuple[Document, float]]:
+        """Core search routine. See external methods for details."""
+
+        # Atlas Vector Search, potentially with filter
+        pipeline = [
+            vector_search_stage(
+                query_vector,
+                self._embedding_key,
+                self._index_name,
+                k,
+                pre_filter,
+                oversampling_factor,
+                **kwargs,
+            ),
+            {"$set": {"score": {"$meta": "vectorSearchScore"}}},
+        ]
+
+        # Remove embeddings unless requested.
+        if not include_embeddings:
+            pipeline.append({"$project": {self._embedding_key: 0}})
+        # Post-processing
+        if post_filter_pipeline is not None:
+            pipeline.extend(post_filter_pipeline)
+
+        # Execution
+        cursor = self._collection.aggregate(pipeline)  # type: ignore[arg-type]
+        docs = []
+
+        # Format
+        for res in cursor:
+            text = res.pop(self._text_key)
+            score = res.pop("score")
+            make_serializable(res)
+            docs.append((Document(page_content=text, metadata=res), score))
+        return docs
+
     def create_vector_search_index(
         self,
         dimensions: int,
-        filters: Optional[List[Dict[str, str]]] = None,
+        filters: Optional[List[str]] = None,
         update: bool = False,
     ) -> None:
         """Creates a MongoDB Atlas vectorSearch index for the VectorStore
 
-        Note**: This method may fail as it requires a MongoDB Atlas with
-        these pre-requisites:
-            - M10 cluster or higher
-            - https://www.mongodb.com/docs/atlas/atlas-vector-search/create-index/#prerequisites
+        Note**: This method may fail as it requires a MongoDB Atlas with these
+        `pre-requisites <https://www.mongodb.com/docs/atlas/atlas-vector-search/create-index/#prerequisites>`.
+        Currently, vector and full-text search index operations need to be
+        performed manually on the Atlas UI for shared M0 clusters.
 
         Args:
             dimensions (int): Number of dimensions in embedding
diff --git a/libs/partners/mongodb/poetry.lock b/libs/partners/mongodb/poetry.lock
index f34febba591..c30e0b034b3 100644
--- a/libs/partners/mongodb/poetry.lock
+++ b/libs/partners/mongodb/poetry.lock
@@ -1,91 +1,103 @@
 # This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
 
+[[package]]
+name = "aiohappyeyeballs"
+version = "2.3.4"
+description = "Happy Eyeballs for asyncio"
+optional = false
+python-versions = "<4.0,>=3.8"
+files = [
+    {file = "aiohappyeyeballs-2.3.4-py3-none-any.whl", hash = "sha256:40a16ceffcf1fc9e142fd488123b2e218abc4188cf12ac20c67200e1579baa42"},
+    {file = "aiohappyeyeballs-2.3.4.tar.gz", hash = "sha256:7e1ae8399c320a8adec76f6c919ed5ceae6edd4c3672f4d9eae2b27e37c80ff6"},
+]
+
 [[package]]
 name = "aiohttp"
-version = "3.9.5"
+version = "3.10.1"
 description = "Async http client/server framework (asyncio)"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "aiohttp-3.9.5-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:fcde4c397f673fdec23e6b05ebf8d4751314fa7c24f93334bf1f1364c1c69ac7"},
-    {file = "aiohttp-3.9.5-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:5d6b3f1fabe465e819aed2c421a6743d8debbde79b6a8600739300630a01bf2c"},
-    {file = "aiohttp-3.9.5-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:6ae79c1bc12c34082d92bf9422764f799aee4746fd7a392db46b7fd357d4a17a"},
-    {file = "aiohttp-3.9.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4d3ebb9e1316ec74277d19c5f482f98cc65a73ccd5430540d6d11682cd857430"},
-    {file = "aiohttp-3.9.5-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:84dabd95154f43a2ea80deffec9cb44d2e301e38a0c9d331cc4aa0166fe28ae3"},
-    {file = "aiohttp-3.9.5-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c8a02fbeca6f63cb1f0475c799679057fc9268b77075ab7cf3f1c600e81dd46b"},
-    {file = "aiohttp-3.9.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c26959ca7b75ff768e2776d8055bf9582a6267e24556bb7f7bd29e677932be72"},
-    {file = "aiohttp-3.9.5-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:714d4e5231fed4ba2762ed489b4aec07b2b9953cf4ee31e9871caac895a839c0"},
-    {file = "aiohttp-3.9.5-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:e7a6a8354f1b62e15d48e04350f13e726fa08b62c3d7b8401c0a1314f02e3558"},
-    {file = "aiohttp-3.9.5-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:c413016880e03e69d166efb5a1a95d40f83d5a3a648d16486592c49ffb76d0db"},
-    {file = "aiohttp-3.9.5-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:ff84aeb864e0fac81f676be9f4685f0527b660f1efdc40dcede3c251ef1e867f"},
-    {file = "aiohttp-3.9.5-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:ad7f2919d7dac062f24d6f5fe95d401597fbb015a25771f85e692d043c9d7832"},
-    {file = "aiohttp-3.9.5-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:702e2c7c187c1a498a4e2b03155d52658fdd6fda882d3d7fbb891a5cf108bb10"},
-    {file = "aiohttp-3.9.5-cp310-cp310-win32.whl", hash = "sha256:67c3119f5ddc7261d47163ed86d760ddf0e625cd6246b4ed852e82159617b5fb"},
-    {file = "aiohttp-3.9.5-cp310-cp310-win_amd64.whl", hash = "sha256:471f0ef53ccedec9995287f02caf0c068732f026455f07db3f01a46e49d76bbb"},
-    {file = "aiohttp-3.9.5-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:e0ae53e33ee7476dd3d1132f932eeb39bf6125083820049d06edcdca4381f342"},
-    {file = "aiohttp-3.9.5-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:c088c4d70d21f8ca5c0b8b5403fe84a7bc8e024161febdd4ef04575ef35d474d"},
-    {file = "aiohttp-3.9.5-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:639d0042b7670222f33b0028de6b4e2fad6451462ce7df2af8aee37dcac55424"},
-    {file = "aiohttp-3.9.5-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f26383adb94da5e7fb388d441bf09c61e5e35f455a3217bfd790c6b6bc64b2ee"},
-    {file = "aiohttp-3.9.5-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:66331d00fb28dc90aa606d9a54304af76b335ae204d1836f65797d6fe27f1ca2"},
-    {file = "aiohttp-3.9.5-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:4ff550491f5492ab5ed3533e76b8567f4b37bd2995e780a1f46bca2024223233"},
-    {file = "aiohttp-3.9.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f22eb3a6c1080d862befa0a89c380b4dafce29dc6cd56083f630073d102eb595"},
-    {file = "aiohttp-3.9.5-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a81b1143d42b66ffc40a441379387076243ef7b51019204fd3ec36b9f69e77d6"},
-    {file = "aiohttp-3.9.5-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:f64fd07515dad67f24b6ea4a66ae2876c01031de91c93075b8093f07c0a2d93d"},
-    {file = "aiohttp-3.9.5-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:93e22add827447d2e26d67c9ac0161756007f152fdc5210277d00a85f6c92323"},
-    {file = "aiohttp-3.9.5-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:55b39c8684a46e56ef8c8d24faf02de4a2b2ac60d26cee93bc595651ff545de9"},
-    {file = "aiohttp-3.9.5-cp311-cp311-musllinux_1_1_s390x.whl", hash = "sha256:4715a9b778f4293b9f8ae7a0a7cef9829f02ff8d6277a39d7f40565c737d3771"},
-    {file = "aiohttp-3.9.5-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:afc52b8d969eff14e069a710057d15ab9ac17cd4b6753042c407dcea0e40bf75"},
-    {file = "aiohttp-3.9.5-cp311-cp311-win32.whl", hash = "sha256:b3df71da99c98534be076196791adca8819761f0bf6e08e07fd7da25127150d6"},
-    {file = "aiohttp-3.9.5-cp311-cp311-win_amd64.whl", hash = "sha256:88e311d98cc0bf45b62fc46c66753a83445f5ab20038bcc1b8a1cc05666f428a"},
-    {file = "aiohttp-3.9.5-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:c7a4b7a6cf5b6eb11e109a9755fd4fda7d57395f8c575e166d363b9fc3ec4678"},
-    {file = "aiohttp-3.9.5-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:0a158704edf0abcac8ac371fbb54044f3270bdbc93e254a82b6c82be1ef08f3c"},
-    {file = "aiohttp-3.9.5-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:d153f652a687a8e95ad367a86a61e8d53d528b0530ef382ec5aaf533140ed00f"},
-    {file = "aiohttp-3.9.5-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:82a6a97d9771cb48ae16979c3a3a9a18b600a8505b1115cfe354dfb2054468b4"},
-    {file = "aiohttp-3.9.5-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:60cdbd56f4cad9f69c35eaac0fbbdf1f77b0ff9456cebd4902f3dd1cf096464c"},
-    {file = "aiohttp-3.9.5-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:8676e8fd73141ded15ea586de0b7cda1542960a7b9ad89b2b06428e97125d4fa"},
-    {file = "aiohttp-3.9.5-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:da00da442a0e31f1c69d26d224e1efd3a1ca5bcbf210978a2ca7426dfcae9f58"},
-    {file = "aiohttp-3.9.5-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:18f634d540dd099c262e9f887c8bbacc959847cfe5da7a0e2e1cf3f14dbf2daf"},
-    {file = "aiohttp-3.9.5-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:320e8618eda64e19d11bdb3bd04ccc0a816c17eaecb7e4945d01deee2a22f95f"},
-    {file = "aiohttp-3.9.5-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:2faa61a904b83142747fc6a6d7ad8fccff898c849123030f8e75d5d967fd4a81"},
-    {file = "aiohttp-3.9.5-cp312-cp312-musllinux_1_1_ppc64le.whl", hash = "sha256:8c64a6dc3fe5db7b1b4d2b5cb84c4f677768bdc340611eca673afb7cf416ef5a"},
-    {file = "aiohttp-3.9.5-cp312-cp312-musllinux_1_1_s390x.whl", hash = "sha256:393c7aba2b55559ef7ab791c94b44f7482a07bf7640d17b341b79081f5e5cd1a"},
-    {file = "aiohttp-3.9.5-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:c671dc117c2c21a1ca10c116cfcd6e3e44da7fcde37bf83b2be485ab377b25da"},
-    {file = "aiohttp-3.9.5-cp312-cp312-win32.whl", hash = "sha256:5a7ee16aab26e76add4afc45e8f8206c95d1d75540f1039b84a03c3b3800dd59"},
-    {file = "aiohttp-3.9.5-cp312-cp312-win_amd64.whl", hash = "sha256:5ca51eadbd67045396bc92a4345d1790b7301c14d1848feaac1d6a6c9289e888"},
-    {file = "aiohttp-3.9.5-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:694d828b5c41255e54bc2dddb51a9f5150b4eefa9886e38b52605a05d96566e8"},
-    {file = "aiohttp-3.9.5-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:0605cc2c0088fcaae79f01c913a38611ad09ba68ff482402d3410bf59039bfb8"},
-    {file = "aiohttp-3.9.5-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:4558e5012ee03d2638c681e156461d37b7a113fe13970d438d95d10173d25f78"},
-    {file = "aiohttp-3.9.5-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9dbc053ac75ccc63dc3a3cc547b98c7258ec35a215a92bd9f983e0aac95d3d5b"},
-    {file = "aiohttp-3.9.5-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:4109adee842b90671f1b689901b948f347325045c15f46b39797ae1bf17019de"},
-    {file = "aiohttp-3.9.5-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:a6ea1a5b409a85477fd8e5ee6ad8f0e40bf2844c270955e09360418cfd09abac"},
-    {file = "aiohttp-3.9.5-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f3c2890ca8c59ee683fd09adf32321a40fe1cf164e3387799efb2acebf090c11"},
-    {file = "aiohttp-3.9.5-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:3916c8692dbd9d55c523374a3b8213e628424d19116ac4308e434dbf6d95bbdd"},
-    {file = "aiohttp-3.9.5-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:8d1964eb7617907c792ca00b341b5ec3e01ae8c280825deadbbd678447b127e1"},
-    {file = "aiohttp-3.9.5-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:d5ab8e1f6bee051a4bf6195e38a5c13e5e161cb7bad83d8854524798bd9fcd6e"},
-    {file = "aiohttp-3.9.5-cp38-cp38-musllinux_1_1_ppc64le.whl", hash = "sha256:52c27110f3862a1afbcb2af4281fc9fdc40327fa286c4625dfee247c3ba90156"},
-    {file = "aiohttp-3.9.5-cp38-cp38-musllinux_1_1_s390x.whl", hash = "sha256:7f64cbd44443e80094309875d4f9c71d0401e966d191c3d469cde4642bc2e031"},
-    {file = "aiohttp-3.9.5-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:8b4f72fbb66279624bfe83fd5eb6aea0022dad8eec62b71e7bf63ee1caadeafe"},
-    {file = "aiohttp-3.9.5-cp38-cp38-win32.whl", hash = "sha256:6380c039ec52866c06d69b5c7aad5478b24ed11696f0e72f6b807cfb261453da"},
-    {file = "aiohttp-3.9.5-cp38-cp38-win_amd64.whl", hash = "sha256:da22dab31d7180f8c3ac7c7635f3bcd53808f374f6aa333fe0b0b9e14b01f91a"},
-    {file = "aiohttp-3.9.5-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:1732102949ff6087589408d76cd6dea656b93c896b011ecafff418c9661dc4ed"},
-    {file = "aiohttp-3.9.5-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:c6021d296318cb6f9414b48e6a439a7f5d1f665464da507e8ff640848ee2a58a"},
-    {file = "aiohttp-3.9.5-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:239f975589a944eeb1bad26b8b140a59a3a320067fb3cd10b75c3092405a1372"},
-    {file = "aiohttp-3.9.5-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3b7b30258348082826d274504fbc7c849959f1989d86c29bc355107accec6cfb"},
-    {file = "aiohttp-3.9.5-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:cd2adf5c87ff6d8b277814a28a535b59e20bfea40a101db6b3bdca7e9926bc24"},
-    {file = "aiohttp-3.9.5-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e9a3d838441bebcf5cf442700e3963f58b5c33f015341f9ea86dcd7d503c07e2"},
-    {file = "aiohttp-3.9.5-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9e3a1ae66e3d0c17cf65c08968a5ee3180c5a95920ec2731f53343fac9bad106"},
-    {file = "aiohttp-3.9.5-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:9c69e77370cce2d6df5d12b4e12bdcca60c47ba13d1cbbc8645dd005a20b738b"},
-    {file = "aiohttp-3.9.5-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:0cbf56238f4bbf49dab8c2dc2e6b1b68502b1e88d335bea59b3f5b9f4c001475"},
-    {file = "aiohttp-3.9.5-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:d1469f228cd9ffddd396d9948b8c9cd8022b6d1bf1e40c6f25b0fb90b4f893ed"},
-    {file = "aiohttp-3.9.5-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:45731330e754f5811c314901cebdf19dd776a44b31927fa4b4dbecab9e457b0c"},
-    {file = "aiohttp-3.9.5-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:3fcb4046d2904378e3aeea1df51f697b0467f2aac55d232c87ba162709478c46"},
-    {file = "aiohttp-3.9.5-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:8cf142aa6c1a751fcb364158fd710b8a9be874b81889c2bd13aa8893197455e2"},
-    {file = "aiohttp-3.9.5-cp39-cp39-win32.whl", hash = "sha256:7b179eea70833c8dee51ec42f3b4097bd6370892fa93f510f76762105568cf09"},
-    {file = "aiohttp-3.9.5-cp39-cp39-win_amd64.whl", hash = "sha256:38d80498e2e169bc61418ff36170e0aad0cd268da8b38a17c4cf29d254a8b3f1"},
-    {file = "aiohttp-3.9.5.tar.gz", hash = "sha256:edea7d15772ceeb29db4aff55e482d4bcfb6ae160ce144f2682de02f6d693551"},
+    {file = "aiohttp-3.10.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:47b4c2412960e64d97258f40616efddaebcb34ff664c8a972119ed38fac2a62c"},
+    {file = "aiohttp-3.10.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e7dbf637f87dd315fa1f36aaed8afa929ee2c607454fb7791e74c88a0d94da59"},
+    {file = "aiohttp-3.10.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:c8fb76214b5b739ce59e2236a6489d9dc3483649cfd6f563dbf5d8e40dbdd57d"},
+    {file = "aiohttp-3.10.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1c577cdcf8f92862363b3d598d971c6a84ed8f0bf824d4cc1ce70c2fb02acb4a"},
+    {file = "aiohttp-3.10.1-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:777e23609899cb230ad2642b4bdf1008890f84968be78de29099a8a86f10b261"},
+    {file = "aiohttp-3.10.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b07286a1090483799599a2f72f76ac396993da31f6e08efedb59f40876c144fa"},
+    {file = "aiohttp-3.10.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b9db600a86414a9a653e3c1c7f6a2f6a1894ab8f83d11505247bd1b90ad57157"},
+    {file = "aiohttp-3.10.1-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:01c3f1eb280008e51965a8d160a108c333136f4a39d46f516c64d2aa2e6a53f2"},
+    {file = "aiohttp-3.10.1-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:f5dd109a925fee4c9ac3f6a094900461a2712df41745f5d04782ebcbe6479ccb"},
+    {file = "aiohttp-3.10.1-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:8c81ff4afffef9b1186639506d70ea90888218f5ddfff03870e74ec80bb59970"},
+    {file = "aiohttp-3.10.1-cp310-cp310-musllinux_1_2_ppc64le.whl", hash = "sha256:2a384dfbe8bfebd203b778a30a712886d147c61943675f4719b56725a8bbe803"},
+    {file = "aiohttp-3.10.1-cp310-cp310-musllinux_1_2_s390x.whl", hash = "sha256:b9fb6508893dc31cfcbb8191ef35abd79751db1d6871b3e2caee83959b4d91eb"},
+    {file = "aiohttp-3.10.1-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:88596384c3bec644a96ae46287bb646d6a23fa6014afe3799156aef42669c6bd"},
+    {file = "aiohttp-3.10.1-cp310-cp310-win32.whl", hash = "sha256:68164d43c580c2e8bf8e0eb4960142919d304052ccab92be10250a3a33b53268"},
+    {file = "aiohttp-3.10.1-cp310-cp310-win_amd64.whl", hash = "sha256:d6bbe2c90c10382ca96df33b56e2060404a4f0f88673e1e84b44c8952517e5f3"},
+    {file = "aiohttp-3.10.1-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:f6979b4f20d3e557a867da9d9227de4c156fcdcb348a5848e3e6190fd7feb972"},
+    {file = "aiohttp-3.10.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:03c0c380c83f8a8d4416224aafb88d378376d6f4cadebb56b060688251055cd4"},
+    {file = "aiohttp-3.10.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:1c2b104e81b3c3deba7e6f5bc1a9a0e9161c380530479970766a6655b8b77c7c"},
+    {file = "aiohttp-3.10.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b023b68c61ab0cd48bd38416b421464a62c381e32b9dc7b4bdfa2905807452a4"},
+    {file = "aiohttp-3.10.1-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1a07c76a82390506ca0eabf57c0540cf5a60c993c442928fe4928472c4c6e5e6"},
+    {file = "aiohttp-3.10.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:41d8dab8c64ded1edf117d2a64f353efa096c52b853ef461aebd49abae979f16"},
+    {file = "aiohttp-3.10.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:615348fab1a9ef7d0960a905e83ad39051ae9cb0d2837da739b5d3a7671e497a"},
+    {file = "aiohttp-3.10.1-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:256ee6044214ee9d66d531bb374f065ee94e60667d6bbeaa25ca111fc3997158"},
+    {file = "aiohttp-3.10.1-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:b7d5bb926805022508b7ddeaad957f1fce7a8d77532068d7bdb431056dc630cd"},
+    {file = "aiohttp-3.10.1-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:028faf71b338f069077af6315ad54281612705d68889f5d914318cbc2aab0d50"},
+    {file = "aiohttp-3.10.1-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:5c12310d153b27aa630750be44e79313acc4e864c421eb7d2bc6fa3429c41bf8"},
+    {file = "aiohttp-3.10.1-cp311-cp311-musllinux_1_2_s390x.whl", hash = "sha256:de1a91d5faded9054957ed0a9e01b9d632109341942fc123947ced358c5d9009"},
+    {file = "aiohttp-3.10.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:9c186b270979fb1dee3ababe2d12fb243ed7da08b30abc83ebac3a928a4ddb15"},
+    {file = "aiohttp-3.10.1-cp311-cp311-win32.whl", hash = "sha256:4a9ce70f5e00380377aac0e568abd075266ff992be2e271765f7b35d228a990c"},
+    {file = "aiohttp-3.10.1-cp311-cp311-win_amd64.whl", hash = "sha256:a77c79bac8d908d839d32c212aef2354d2246eb9deb3e2cb01ffa83fb7a6ea5d"},
+    {file = "aiohttp-3.10.1-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:2212296cdb63b092e295c3e4b4b442e7b7eb41e8a30d0f53c16d5962efed395d"},
+    {file = "aiohttp-3.10.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:4dcb127ca3eb0a61205818a606393cbb60d93b7afb9accd2fd1e9081cc533144"},
+    {file = "aiohttp-3.10.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:cb8b79a65332e1a426ccb6290ce0409e1dc16b4daac1cc5761e059127fa3d134"},
+    {file = "aiohttp-3.10.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:68cc24f707ed9cb961f6ee04020ca01de2c89b2811f3cf3361dc7c96a14bfbcc"},
+    {file = "aiohttp-3.10.1-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9cb54f5725b4b37af12edf6c9e834df59258c82c15a244daa521a065fbb11717"},
+    {file = "aiohttp-3.10.1-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:51d03e948e53b3639ce4d438f3d1d8202898ec6655cadcc09ec99229d4adc2a9"},
+    {file = "aiohttp-3.10.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:786299d719eb5d868f161aeec56d589396b053925b7e0ce36e983d30d0a3e55c"},
+    {file = "aiohttp-3.10.1-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:abda4009a30d51d3f06f36bc7411a62b3e647fa6cc935ef667e3e3d3a7dd09b1"},
+    {file = "aiohttp-3.10.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:67f7639424c313125213954e93a6229d3a1d386855d70c292a12628f600c7150"},
+    {file = "aiohttp-3.10.1-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:8e5a26d7aac4c0d8414a347da162696eea0629fdce939ada6aedf951abb1d745"},
+    {file = "aiohttp-3.10.1-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:120548d89f14b76a041088b582454d89389370632ee12bf39d919cc5c561d1ca"},
+    {file = "aiohttp-3.10.1-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:f5293726943bdcea24715b121d8c4ae12581441d22623b0e6ab12d07ce85f9c4"},
+    {file = "aiohttp-3.10.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:1f8605e573ed6c44ec689d94544b2c4bb1390aaa723a8b5a2cc0a5a485987a68"},
+    {file = "aiohttp-3.10.1-cp312-cp312-win32.whl", hash = "sha256:e7168782621be4448d90169a60c8b37e9b0926b3b79b6097bc180c0a8a119e73"},
+    {file = "aiohttp-3.10.1-cp312-cp312-win_amd64.whl", hash = "sha256:8fbf8c0ded367c5c8eaf585f85ca8dd85ff4d5b73fb8fe1e6ac9e1b5e62e11f7"},
+    {file = "aiohttp-3.10.1-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:54b7f4a20d7cc6bfa4438abbde069d417bb7a119f870975f78a2b99890226d55"},
+    {file = "aiohttp-3.10.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:2fa643ca990323db68911b92f3f7a0ca9ae300ae340d0235de87c523601e58d9"},
+    {file = "aiohttp-3.10.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:d8311d0d690487359fe2247ec5d2cac9946e70d50dced8c01ce9e72341c21151"},
+    {file = "aiohttp-3.10.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:222821c60b8f6a64c5908cb43d69c0ee978a1188f6a8433d4757d39231b42cdb"},
+    {file = "aiohttp-3.10.1-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:e7b55d9ede66af7feb6de87ff277e0ccf6d51c7db74cc39337fe3a0e31b5872d"},
+    {file = "aiohttp-3.10.1-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:5a95151a5567b3b00368e99e9c5334a919514f60888a6b6d2054fea5e66e527e"},
+    {file = "aiohttp-3.10.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4e9e9171d2fe6bfd9d3838a6fe63b1e91b55e0bf726c16edf265536e4eafed19"},
+    {file = "aiohttp-3.10.1-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a57e73f9523e980f6101dc9a83adcd7ac0006ea8bf7937ca3870391c7bb4f8ff"},
+    {file = "aiohttp-3.10.1-cp38-cp38-musllinux_1_2_aarch64.whl", hash = "sha256:0df51a3d70a2bfbb9c921619f68d6d02591f24f10e9c76de6f3388c89ed01de6"},
+    {file = "aiohttp-3.10.1-cp38-cp38-musllinux_1_2_i686.whl", hash = "sha256:b0de63ff0307eac3961b4af74382d30220d4813f36b7aaaf57f063a1243b4214"},
+    {file = "aiohttp-3.10.1-cp38-cp38-musllinux_1_2_ppc64le.whl", hash = "sha256:8db9b749f589b5af8e4993623dbda6716b2b7a5fcb0fa2277bf3ce4b278c7059"},
+    {file = "aiohttp-3.10.1-cp38-cp38-musllinux_1_2_s390x.whl", hash = "sha256:6b14c19172eb53b63931d3e62a9749d6519f7c121149493e6eefca055fcdb352"},
+    {file = "aiohttp-3.10.1-cp38-cp38-musllinux_1_2_x86_64.whl", hash = "sha256:5cd57ad998e3038aa87c38fe85c99ed728001bf5dde8eca121cadee06ee3f637"},
+    {file = "aiohttp-3.10.1-cp38-cp38-win32.whl", hash = "sha256:df31641e3f02b77eb3c5fb63c0508bee0fc067cf153da0e002ebbb0db0b6d91a"},
+    {file = "aiohttp-3.10.1-cp38-cp38-win_amd64.whl", hash = "sha256:93094eba50bc2ad4c40ff4997ead1fdcd41536116f2e7d6cfec9596a8ecb3615"},
+    {file = "aiohttp-3.10.1-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:440954ddc6b77257e67170d57b1026aa9545275c33312357472504eef7b4cc0b"},
+    {file = "aiohttp-3.10.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:f9f8beed277488a52ee2b459b23c4135e54d6a819eaba2e120e57311015b58e9"},
+    {file = "aiohttp-3.10.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:d8a8221a63602008550022aa3a4152ca357e1dde7ab3dd1da7e1925050b56863"},
+    {file = "aiohttp-3.10.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a702bd3663b5cbf3916e84bf332400d24cdb18399f0877ca6b313ce6c08bfb43"},
+    {file = "aiohttp-3.10.1-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1988b370536eb14f0ce7f3a4a5b422ab64c4e255b3f5d7752c5f583dc8c967fc"},
+    {file = "aiohttp-3.10.1-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:7ccf1f0a304352c891d124ac1a9dea59b14b2abed1704aaa7689fc90ef9c5be1"},
+    {file = "aiohttp-3.10.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bc3ea6ef2a83edad84bbdb5d96e22f587b67c68922cd7b6f9d8f24865e655bcf"},
+    {file = "aiohttp-3.10.1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:89b47c125ab07f0831803b88aeb12b04c564d5f07a1c1a225d4eb4d2f26e8b5e"},
+    {file = "aiohttp-3.10.1-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:21778552ef3d44aac3278cc6f6d13a6423504fa5f09f2df34bfe489ed9ded7f5"},
+    {file = "aiohttp-3.10.1-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:bde0693073fd5e542e46ea100aa6c1a5d36282dbdbad85b1c3365d5421490a92"},
+    {file = "aiohttp-3.10.1-cp39-cp39-musllinux_1_2_ppc64le.whl", hash = "sha256:bf66149bb348d8e713f3a8e0b4f5b952094c2948c408e1cfef03b49e86745d60"},
+    {file = "aiohttp-3.10.1-cp39-cp39-musllinux_1_2_s390x.whl", hash = "sha256:587237571a85716d6f71f60d103416c9df7d5acb55d96d3d3ced65f39bff9c0c"},
+    {file = "aiohttp-3.10.1-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:bfe33cba6e127d0b5b417623c9aa621f0a69f304742acdca929a9fdab4593693"},
+    {file = "aiohttp-3.10.1-cp39-cp39-win32.whl", hash = "sha256:9fbff00646cf8211b330690eb2fd64b23e1ce5b63a342436c1d1d6951d53d8dd"},
+    {file = "aiohttp-3.10.1-cp39-cp39-win_amd64.whl", hash = "sha256:5951c328f9ac42d7bce7a6ded535879bc9ae13032818d036749631fa27777905"},
+    {file = "aiohttp-3.10.1.tar.gz", hash = "sha256:8b0d058e4e425d3b45e8ec70d49b402f4d6b21041e674798b1f91ba027c73f28"},
 ]
 
 [package.dependencies]
+aiohappyeyeballs = ">=2.3.0"
 aiosignal = ">=1.1.2"
 async-timeout = {version = ">=4.0,<5.0", markers = "python_version < \"3.11\""}
 attrs = ">=17.3.0"
@@ -94,7 +106,7 @@ multidict = ">=4.5,<7.0"
 yarl = ">=1.0,<2.0"
 
 [package.extras]
-speedups = ["Brotli", "aiodns", "brotlicffi"]
+speedups = ["Brotli", "aiodns (>=3.2.0)", "brotlicffi"]
 
 [[package]]
 name = "aiosignal"
@@ -124,6 +136,28 @@ files = [
 [package.dependencies]
 typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""}
 
+[[package]]
+name = "anyio"
+version = "4.4.0"
+description = "High level compatibility layer for multiple asynchronous event loop implementations"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "anyio-4.4.0-py3-none-any.whl", hash = "sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7"},
+    {file = "anyio-4.4.0.tar.gz", hash = "sha256:5aadc6a1bbb7cdb0bede386cac5e2940f5e2ff3aa20277e991cf028e0585ce94"},
+]
+
+[package.dependencies]
+exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
+idna = ">=2.8"
+sniffio = ">=1.1"
+typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
+
+[package.extras]
+doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
+trio = ["trio (>=0.23)"]
+
 [[package]]
 name = "async-timeout"
 version = "4.0.3"
@@ -137,22 +171,22 @@ files = [
 
 [[package]]
 name = "attrs"
-version = "23.2.0"
+version = "24.1.0"
 description = "Classes Without Boilerplate"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "attrs-23.2.0-py3-none-any.whl", hash = "sha256:99b87a485a5820b23b879f04c2305b44b951b502fd64be915879d77a7e8fc6f1"},
-    {file = "attrs-23.2.0.tar.gz", hash = "sha256:935dc3b529c262f6cf76e50877d35a4bd3c1de194fd41f47a2b7ae8f19971f30"},
+    {file = "attrs-24.1.0-py3-none-any.whl", hash = "sha256:377b47448cb61fea38533f671fba0d0f8a96fd58facd4dc518e3dac9dbea0905"},
+    {file = "attrs-24.1.0.tar.gz", hash = "sha256:adbdec84af72d38be7628e353a09b6a6790d15cd71819f6e9d7b0faa8a125745"},
 ]
 
 [package.extras]
-cov = ["attrs[tests]", "coverage[toml] (>=5.3)"]
-dev = ["attrs[tests]", "pre-commit"]
-docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"]
-tests = ["attrs[tests-no-zope]", "zope-interface"]
-tests-mypy = ["mypy (>=1.6)", "pytest-mypy-plugins"]
-tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"]
+benchmark = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-codspeed", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+cov = ["cloudpickle", "coverage[toml] (>=5.3)", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+dev = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pre-commit", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
+tests = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
 
 [[package]]
 name = "certifi"
@@ -292,6 +326,17 @@ files = [
     {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
 ]
 
+[[package]]
+name = "distro"
+version = "1.9.0"
+description = "Distro - an OS platform information API"
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "distro-1.9.0-py3-none-any.whl", hash = "sha256:7bffd925d65168f85027d8da9af6bddab658135b840670a223589bc0c8ef02b2"},
+    {file = "distro-1.9.0.tar.gz", hash = "sha256:2fa77c6fd8940f116ee1d6b94a2f90b13b5ea8d019b98bc8bafdcabcdd9bdbed"},
+]
+
 [[package]]
 name = "dnspython"
 version = "2.6.1"
@@ -497,6 +542,62 @@ files = [
 docs = ["Sphinx", "furo"]
 test = ["objgraph", "psutil"]
 
+[[package]]
+name = "h11"
+version = "0.14.0"
+description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "h11-0.14.0-py3-none-any.whl", hash = "sha256:e3fe4ac4b851c468cc8363d500db52c2ead036020723024a109d37346efaa761"},
+    {file = "h11-0.14.0.tar.gz", hash = "sha256:8f19fbbe99e72420ff35c00b27a34cb9937e902a8b810e2c88300c6f0a3b699d"},
+]
+
+[[package]]
+name = "httpcore"
+version = "1.0.5"
+description = "A minimal low-level HTTP client."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "httpcore-1.0.5-py3-none-any.whl", hash = "sha256:421f18bac248b25d310f3cacd198d55b8e6125c107797b609ff9b7a6ba7991b5"},
+    {file = "httpcore-1.0.5.tar.gz", hash = "sha256:34a38e2f9291467ee3b44e89dd52615370e152954ba21721378a87b2960f7a61"},
+]
+
+[package.dependencies]
+certifi = "*"
+h11 = ">=0.13,<0.15"
+
+[package.extras]
+asyncio = ["anyio (>=4.0,<5.0)"]
+http2 = ["h2 (>=3,<5)"]
+socks = ["socksio (==1.*)"]
+trio = ["trio (>=0.22.0,<0.26.0)"]
+
+[[package]]
+name = "httpx"
+version = "0.27.0"
+description = "The next generation HTTP client."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "httpx-0.27.0-py3-none-any.whl", hash = "sha256:71d5465162c13681bff01ad59b2cc68dd838ea1f10e51574bac27103f00c91a5"},
+    {file = "httpx-0.27.0.tar.gz", hash = "sha256:a0cb88a46f32dc874e04ee956e4c2764aba2aa228f650b06788ba6bda2962ab5"},
+]
+
+[package.dependencies]
+anyio = "*"
+certifi = "*"
+httpcore = "==1.*"
+idna = "*"
+sniffio = "*"
+
+[package.extras]
+brotli = ["brotli", "brotlicffi"]
+cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<14)"]
+http2 = ["h2 (>=3,<5)"]
+socks = ["socksio (==1.*)"]
+
 [[package]]
 name = "idna"
 version = "3.7"
@@ -546,7 +647,7 @@ files = [
 
 [[package]]
 name = "langchain"
-version = "0.2.9"
+version = "0.2.12"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.8.1,<4.0"
@@ -556,7 +657,7 @@ develop = true
 [package.dependencies]
 aiohttp = "^3.8.3"
 async-timeout = {version = "^4.0.0", markers = "python_version < \"3.11\""}
-langchain-core = "^0.2.20"
+langchain-core = "^0.2.27"
 langchain-text-splitters = "^0.2.0"
 langsmith = "^0.1.17"
 numpy = [
@@ -575,7 +676,7 @@ url = "../../langchain"
 
 [[package]]
 name = "langchain-core"
-version = "0.2.21"
+version = "0.2.28"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.8.1,<4.0"
@@ -592,14 +693,33 @@ pydantic = [
 ]
 PyYAML = ">=5.3"
 tenacity = "^8.1.0,!=8.4.0"
+typing-extensions = ">=4.7"
 
 [package.source]
 type = "directory"
 url = "../../core"
 
+[[package]]
+name = "langchain-openai"
+version = "0.1.20"
+description = "An integration package connecting OpenAI and LangChain"
+optional = false
+python-versions = ">=3.8.1,<4.0"
+files = []
+develop = true
+
+[package.dependencies]
+langchain-core = "^0.2.26"
+openai = "^1.32.0"
+tiktoken = ">=0.7,<1"
+
+[package.source]
+type = "directory"
+url = "../openai"
+
 [[package]]
 name = "langchain-text-splitters"
-version = "0.2.2"
+version = "0.2.3"
 description = "LangChain text splitting utilities"
 optional = false
 python-versions = ">=3.8.1,<4.0"
@@ -615,13 +735,13 @@ url = "../../text-splitters"
 
 [[package]]
 name = "langsmith"
-version = "0.1.90"
+version = "0.1.96"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langsmith-0.1.90-py3-none-any.whl", hash = "sha256:40b43d908ae0ff061a7ef6f4e7eabf2624db54359210b6bc784ddfa32484e524"},
-    {file = "langsmith-0.1.90.tar.gz", hash = "sha256:f7032acf3736a3db52698126b60da0d909aba20b20ad8a70b678e64bf878c17d"},
+    {file = "langsmith-0.1.96-py3-none-any.whl", hash = "sha256:1e8285c3f84cffebc761ff5624647de20686dbbf659f5d1135918261f85bad13"},
+    {file = "langsmith-0.1.96.tar.gz", hash = "sha256:01b7fa7d538b6409ee74bff458cc3dcdc1799fc70d329f79eb26ba54c32991ae"},
 ]
 
 [package.dependencies]
@@ -733,44 +853,44 @@ files = [
 
 [[package]]
 name = "mypy"
-version = "1.10.1"
+version = "1.11.1"
 description = "Optional static typing for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mypy-1.10.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e36f229acfe250dc660790840916eb49726c928e8ce10fbdf90715090fe4ae02"},
-    {file = "mypy-1.10.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:51a46974340baaa4145363b9e051812a2446cf583dfaeba124af966fa44593f7"},
-    {file = "mypy-1.10.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:901c89c2d67bba57aaaca91ccdb659aa3a312de67f23b9dfb059727cce2e2e0a"},
-    {file = "mypy-1.10.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:0cd62192a4a32b77ceb31272d9e74d23cd88c8060c34d1d3622db3267679a5d9"},
-    {file = "mypy-1.10.1-cp310-cp310-win_amd64.whl", hash = "sha256:a2cbc68cb9e943ac0814c13e2452d2046c2f2b23ff0278e26599224cf164e78d"},
-    {file = "mypy-1.10.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:bd6f629b67bb43dc0d9211ee98b96d8dabc97b1ad38b9b25f5e4c4d7569a0c6a"},
-    {file = "mypy-1.10.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:a1bbb3a6f5ff319d2b9d40b4080d46cd639abe3516d5a62c070cf0114a457d84"},
-    {file = "mypy-1.10.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b8edd4e9bbbc9d7b79502eb9592cab808585516ae1bcc1446eb9122656c6066f"},
-    {file = "mypy-1.10.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:6166a88b15f1759f94a46fa474c7b1b05d134b1b61fca627dd7335454cc9aa6b"},
-    {file = "mypy-1.10.1-cp311-cp311-win_amd64.whl", hash = "sha256:5bb9cd11c01c8606a9d0b83ffa91d0b236a0e91bc4126d9ba9ce62906ada868e"},
-    {file = "mypy-1.10.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:d8681909f7b44d0b7b86e653ca152d6dff0eb5eb41694e163c6092124f8246d7"},
-    {file = "mypy-1.10.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:378c03f53f10bbdd55ca94e46ec3ba255279706a6aacaecac52ad248f98205d3"},
-    {file = "mypy-1.10.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6bacf8f3a3d7d849f40ca6caea5c055122efe70e81480c8328ad29c55c69e93e"},
-    {file = "mypy-1.10.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:701b5f71413f1e9855566a34d6e9d12624e9e0a8818a5704d74d6b0402e66c04"},
-    {file = "mypy-1.10.1-cp312-cp312-win_amd64.whl", hash = "sha256:3c4c2992f6ea46ff7fce0072642cfb62af7a2484efe69017ed8b095f7b39ef31"},
-    {file = "mypy-1.10.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:604282c886497645ffb87b8f35a57ec773a4a2721161e709a4422c1636ddde5c"},
-    {file = "mypy-1.10.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:37fd87cab83f09842653f08de066ee68f1182b9b5282e4634cdb4b407266bade"},
-    {file = "mypy-1.10.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8addf6313777dbb92e9564c5d32ec122bf2c6c39d683ea64de6a1fd98b90fe37"},
-    {file = "mypy-1.10.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:5cc3ca0a244eb9a5249c7c583ad9a7e881aa5d7b73c35652296ddcdb33b2b9c7"},
-    {file = "mypy-1.10.1-cp38-cp38-win_amd64.whl", hash = "sha256:1b3a2ffce52cc4dbaeee4df762f20a2905aa171ef157b82192f2e2f368eec05d"},
-    {file = "mypy-1.10.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:fe85ed6836165d52ae8b88f99527d3d1b2362e0cb90b005409b8bed90e9059b3"},
-    {file = "mypy-1.10.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:c2ae450d60d7d020d67ab440c6e3fae375809988119817214440033f26ddf7bf"},
-    {file = "mypy-1.10.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6be84c06e6abd72f960ba9a71561c14137a583093ffcf9bbfaf5e613d63fa531"},
-    {file = "mypy-1.10.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:2189ff1e39db399f08205e22a797383613ce1cb0cb3b13d8bcf0170e45b96cc3"},
-    {file = "mypy-1.10.1-cp39-cp39-win_amd64.whl", hash = "sha256:97a131ee36ac37ce9581f4220311247ab6cba896b4395b9c87af0675a13a755f"},
-    {file = "mypy-1.10.1-py3-none-any.whl", hash = "sha256:71d8ac0b906354ebda8ef1673e5fde785936ac1f29ff6987c7483cfbd5a4235a"},
-    {file = "mypy-1.10.1.tar.gz", hash = "sha256:1f8f492d7db9e3593ef42d4f115f04e556130f2819ad33ab84551403e97dd4c0"},
+    {file = "mypy-1.11.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:a32fc80b63de4b5b3e65f4be82b4cfa362a46702672aa6a0f443b4689af7008c"},
+    {file = "mypy-1.11.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:c1952f5ea8a5a959b05ed5f16452fddadbaae48b5d39235ab4c3fc444d5fd411"},
+    {file = "mypy-1.11.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e1e30dc3bfa4e157e53c1d17a0dad20f89dc433393e7702b813c10e200843b03"},
+    {file = "mypy-1.11.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:2c63350af88f43a66d3dfeeeb8d77af34a4f07d760b9eb3a8697f0386c7590b4"},
+    {file = "mypy-1.11.1-cp310-cp310-win_amd64.whl", hash = "sha256:a831671bad47186603872a3abc19634f3011d7f83b083762c942442d51c58d58"},
+    {file = "mypy-1.11.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:7b6343d338390bb946d449677726edf60102a1c96079b4f002dedff375953fc5"},
+    {file = "mypy-1.11.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:e4fe9f4e5e521b458d8feb52547f4bade7ef8c93238dfb5bbc790d9ff2d770ca"},
+    {file = "mypy-1.11.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:886c9dbecc87b9516eff294541bf7f3655722bf22bb898ee06985cd7269898de"},
+    {file = "mypy-1.11.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:fca4a60e1dd9fd0193ae0067eaeeb962f2d79e0d9f0f66223a0682f26ffcc809"},
+    {file = "mypy-1.11.1-cp311-cp311-win_amd64.whl", hash = "sha256:0bd53faf56de9643336aeea1c925012837432b5faf1701ccca7fde70166ccf72"},
+    {file = "mypy-1.11.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:f39918a50f74dc5969807dcfaecafa804fa7f90c9d60506835036cc1bc891dc8"},
+    {file = "mypy-1.11.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:0bc71d1fb27a428139dd78621953effe0d208aed9857cb08d002280b0422003a"},
+    {file = "mypy-1.11.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b868d3bcff720dd7217c383474008ddabaf048fad8d78ed948bb4b624870a417"},
+    {file = "mypy-1.11.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:a707ec1527ffcdd1c784d0924bf5cb15cd7f22683b919668a04d2b9c34549d2e"},
+    {file = "mypy-1.11.1-cp312-cp312-win_amd64.whl", hash = "sha256:64f4a90e3ea07f590c5bcf9029035cf0efeae5ba8be511a8caada1a4893f5525"},
+    {file = "mypy-1.11.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:749fd3213916f1751fff995fccf20c6195cae941dc968f3aaadf9bb4e430e5a2"},
+    {file = "mypy-1.11.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:b639dce63a0b19085213ec5fdd8cffd1d81988f47a2dec7100e93564f3e8fb3b"},
+    {file = "mypy-1.11.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4c956b49c5d865394d62941b109728c5c596a415e9c5b2be663dd26a1ff07bc0"},
+    {file = "mypy-1.11.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:45df906e8b6804ef4b666af29a87ad9f5921aad091c79cc38e12198e220beabd"},
+    {file = "mypy-1.11.1-cp38-cp38-win_amd64.whl", hash = "sha256:d44be7551689d9d47b7abc27c71257adfdb53f03880841a5db15ddb22dc63edb"},
+    {file = "mypy-1.11.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:2684d3f693073ab89d76da8e3921883019ea8a3ec20fa5d8ecca6a2db4c54bbe"},
+    {file = "mypy-1.11.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:79c07eb282cb457473add5052b63925e5cc97dfab9812ee65a7c7ab5e3cb551c"},
+    {file = "mypy-1.11.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:11965c2f571ded6239977b14deebd3f4c3abd9a92398712d6da3a772974fad69"},
+    {file = "mypy-1.11.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:a2b43895a0f8154df6519706d9bca8280cda52d3d9d1514b2d9c3e26792a0b74"},
+    {file = "mypy-1.11.1-cp39-cp39-win_amd64.whl", hash = "sha256:1a81cf05975fd61aec5ae16501a091cfb9f605dc3e3c878c0da32f250b74760b"},
+    {file = "mypy-1.11.1-py3-none-any.whl", hash = "sha256:0624bdb940255d2dd24e829d99a13cfeb72e4e9031f9492148f410ed30bcab54"},
+    {file = "mypy-1.11.1.tar.gz", hash = "sha256:f404a0b069709f18bbdb702eb3dcfe51910602995de00bd39cea3050b5772d08"},
 ]
 
 [package.dependencies]
 mypy-extensions = ">=1.0.0"
 tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
-typing-extensions = ">=4.1.0"
+typing-extensions = ">=4.6.0"
 
 [package.extras]
 dmypy = ["psutil (>=4.0)"]
@@ -871,6 +991,29 @@ files = [
     {file = "numpy-1.26.4.tar.gz", hash = "sha256:2a02aba9ed12e4ac4eb3ea9421c420301a0c6460d9830d74a9df87efa4912010"},
 ]
 
+[[package]]
+name = "openai"
+version = "1.38.0"
+description = "The official Python library for the openai API"
+optional = false
+python-versions = ">=3.7.1"
+files = [
+    {file = "openai-1.38.0-py3-none-any.whl", hash = "sha256:a19ef052f1676320f52183ae6f9775da6d888fbe3aec57886117163c095d9f7c"},
+    {file = "openai-1.38.0.tar.gz", hash = "sha256:30fb324bf452ecb1194ca7dbc64566a4d7aa054c6a5da857937ede7d517a220b"},
+]
+
+[package.dependencies]
+anyio = ">=3.5.0,<5"
+distro = ">=1.7.0,<2"
+httpx = ">=0.23.0,<1"
+pydantic = ">=1.9.0,<3"
+sniffio = "*"
+tqdm = ">4"
+typing-extensions = ">=4.7,<5"
+
+[package.extras]
+datalib = ["numpy (>=1)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)"]
+
 [[package]]
 name = "orjson"
 version = "3.10.6"
@@ -1298,6 +1441,94 @@ files = [
     {file = "PyYAML-6.0.1.tar.gz", hash = "sha256:bfdf460b1736c775f2ba9f6a92bca30bc2095067b8a9d77876d1fad6cc3b4a43"},
 ]
 
+[[package]]
+name = "regex"
+version = "2024.7.24"
+description = "Alternative regular expression module, to replace re."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "regex-2024.7.24-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:228b0d3f567fafa0633aee87f08b9276c7062da9616931382993c03808bb68ce"},
+    {file = "regex-2024.7.24-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:3426de3b91d1bc73249042742f45c2148803c111d1175b283270177fdf669024"},
+    {file = "regex-2024.7.24-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:f273674b445bcb6e4409bf8d1be67bc4b58e8b46fd0d560055d515b8830063cd"},
+    {file = "regex-2024.7.24-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:23acc72f0f4e1a9e6e9843d6328177ae3074b4182167e34119ec7233dfeccf53"},
+    {file = "regex-2024.7.24-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:65fd3d2e228cae024c411c5ccdffae4c315271eee4a8b839291f84f796b34eca"},
+    {file = "regex-2024.7.24-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c414cbda77dbf13c3bc88b073a1a9f375c7b0cb5e115e15d4b73ec3a2fbc6f59"},
+    {file = "regex-2024.7.24-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bf7a89eef64b5455835f5ed30254ec19bf41f7541cd94f266ab7cbd463f00c41"},
+    {file = "regex-2024.7.24-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:19c65b00d42804e3fbea9708f0937d157e53429a39b7c61253ff15670ff62cb5"},
+    {file = "regex-2024.7.24-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:7a5486ca56c8869070a966321d5ab416ff0f83f30e0e2da1ab48815c8d165d46"},
+    {file = "regex-2024.7.24-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:6f51f9556785e5a203713f5efd9c085b4a45aecd2a42573e2b5041881b588d1f"},
+    {file = "regex-2024.7.24-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:a4997716674d36a82eab3e86f8fa77080a5d8d96a389a61ea1d0e3a94a582cf7"},
+    {file = "regex-2024.7.24-cp310-cp310-musllinux_1_2_ppc64le.whl", hash = "sha256:c0abb5e4e8ce71a61d9446040c1e86d4e6d23f9097275c5bd49ed978755ff0fe"},
+    {file = "regex-2024.7.24-cp310-cp310-musllinux_1_2_s390x.whl", hash = "sha256:18300a1d78cf1290fa583cd8b7cde26ecb73e9f5916690cf9d42de569c89b1ce"},
+    {file = "regex-2024.7.24-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:416c0e4f56308f34cdb18c3f59849479dde5b19febdcd6e6fa4d04b6c31c9faa"},
+    {file = "regex-2024.7.24-cp310-cp310-win32.whl", hash = "sha256:fb168b5924bef397b5ba13aabd8cf5df7d3d93f10218d7b925e360d436863f66"},
+    {file = "regex-2024.7.24-cp310-cp310-win_amd64.whl", hash = "sha256:6b9fc7e9cc983e75e2518496ba1afc524227c163e43d706688a6bb9eca41617e"},
+    {file = "regex-2024.7.24-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:382281306e3adaaa7b8b9ebbb3ffb43358a7bbf585fa93821300a418bb975281"},
+    {file = "regex-2024.7.24-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:4fdd1384619f406ad9037fe6b6eaa3de2749e2e12084abc80169e8e075377d3b"},
+    {file = "regex-2024.7.24-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:3d974d24edb231446f708c455fd08f94c41c1ff4f04bcf06e5f36df5ef50b95a"},
+    {file = "regex-2024.7.24-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a2ec4419a3fe6cf8a4795752596dfe0adb4aea40d3683a132bae9c30b81e8d73"},
+    {file = "regex-2024.7.24-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:eb563dd3aea54c797adf513eeec819c4213d7dbfc311874eb4fd28d10f2ff0f2"},
+    {file = "regex-2024.7.24-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:45104baae8b9f67569f0f1dca5e1f1ed77a54ae1cd8b0b07aba89272710db61e"},
+    {file = "regex-2024.7.24-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:994448ee01864501912abf2bad9203bffc34158e80fe8bfb5b031f4f8e16da51"},
+    {file = "regex-2024.7.24-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:3fac296f99283ac232d8125be932c5cd7644084a30748fda013028c815ba3364"},
+    {file = "regex-2024.7.24-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:7e37e809b9303ec3a179085415cb5f418ecf65ec98cdfe34f6a078b46ef823ee"},
+    {file = "regex-2024.7.24-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:01b689e887f612610c869421241e075c02f2e3d1ae93a037cb14f88ab6a8934c"},
+    {file = "regex-2024.7.24-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:f6442f0f0ff81775eaa5b05af8a0ffa1dda36e9cf6ec1e0d3d245e8564b684ce"},
+    {file = "regex-2024.7.24-cp311-cp311-musllinux_1_2_s390x.whl", hash = "sha256:871e3ab2838fbcb4e0865a6e01233975df3a15e6fce93b6f99d75cacbd9862d1"},
+    {file = "regex-2024.7.24-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:c918b7a1e26b4ab40409820ddccc5d49871a82329640f5005f73572d5eaa9b5e"},
+    {file = "regex-2024.7.24-cp311-cp311-win32.whl", hash = "sha256:2dfbb8baf8ba2c2b9aa2807f44ed272f0913eeeba002478c4577b8d29cde215c"},
+    {file = "regex-2024.7.24-cp311-cp311-win_amd64.whl", hash = "sha256:538d30cd96ed7d1416d3956f94d54e426a8daf7c14527f6e0d6d425fcb4cca52"},
+    {file = "regex-2024.7.24-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:fe4ebef608553aff8deb845c7f4f1d0740ff76fa672c011cc0bacb2a00fbde86"},
+    {file = "regex-2024.7.24-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:74007a5b25b7a678459f06559504f1eec2f0f17bca218c9d56f6a0a12bfffdad"},
+    {file = "regex-2024.7.24-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:7df9ea48641da022c2a3c9c641650cd09f0cd15e8908bf931ad538f5ca7919c9"},
+    {file = "regex-2024.7.24-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6a1141a1dcc32904c47f6846b040275c6e5de0bf73f17d7a409035d55b76f289"},
+    {file = "regex-2024.7.24-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:80c811cfcb5c331237d9bad3bea2c391114588cf4131707e84d9493064d267f9"},
+    {file = "regex-2024.7.24-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:7214477bf9bd195894cf24005b1e7b496f46833337b5dedb7b2a6e33f66d962c"},
+    {file = "regex-2024.7.24-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d55588cba7553f0b6ec33130bc3e114b355570b45785cebdc9daed8c637dd440"},
+    {file = "regex-2024.7.24-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:558a57cfc32adcf19d3f791f62b5ff564922942e389e3cfdb538a23d65a6b610"},
+    {file = "regex-2024.7.24-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:a512eed9dfd4117110b1881ba9a59b31433caed0c4101b361f768e7bcbaf93c5"},
+    {file = "regex-2024.7.24-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:86b17ba823ea76256b1885652e3a141a99a5c4422f4a869189db328321b73799"},
+    {file = "regex-2024.7.24-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:5eefee9bfe23f6df09ffb6dfb23809f4d74a78acef004aa904dc7c88b9944b05"},
+    {file = "regex-2024.7.24-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:731fcd76bbdbf225e2eb85b7c38da9633ad3073822f5ab32379381e8c3c12e94"},
+    {file = "regex-2024.7.24-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:eaef80eac3b4cfbdd6de53c6e108b4c534c21ae055d1dbea2de6b3b8ff3def38"},
+    {file = "regex-2024.7.24-cp312-cp312-win32.whl", hash = "sha256:185e029368d6f89f36e526764cf12bf8d6f0e3a2a7737da625a76f594bdfcbfc"},
+    {file = "regex-2024.7.24-cp312-cp312-win_amd64.whl", hash = "sha256:2f1baff13cc2521bea83ab2528e7a80cbe0ebb2c6f0bfad15be7da3aed443908"},
+    {file = "regex-2024.7.24-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:66b4c0731a5c81921e938dcf1a88e978264e26e6ac4ec96a4d21ae0354581ae0"},
+    {file = "regex-2024.7.24-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:88ecc3afd7e776967fa16c80f974cb79399ee8dc6c96423321d6f7d4b881c92b"},
+    {file = "regex-2024.7.24-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:64bd50cf16bcc54b274e20235bf8edbb64184a30e1e53873ff8d444e7ac656b2"},
+    {file = "regex-2024.7.24-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:eb462f0e346fcf41a901a126b50f8781e9a474d3927930f3490f38a6e73b6950"},
+    {file = "regex-2024.7.24-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:a82465ebbc9b1c5c50738536fdfa7cab639a261a99b469c9d4c7dcbb2b3f1e57"},
+    {file = "regex-2024.7.24-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:68a8f8c046c6466ac61a36b65bb2395c74451df2ffb8458492ef49900efed293"},
+    {file = "regex-2024.7.24-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:dac8e84fff5d27420f3c1e879ce9929108e873667ec87e0c8eeb413a5311adfe"},
+    {file = "regex-2024.7.24-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ba2537ef2163db9e6ccdbeb6f6424282ae4dea43177402152c67ef869cf3978b"},
+    {file = "regex-2024.7.24-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:43affe33137fcd679bdae93fb25924979517e011f9dea99163f80b82eadc7e53"},
+    {file = "regex-2024.7.24-cp38-cp38-musllinux_1_2_aarch64.whl", hash = "sha256:c9bb87fdf2ab2370f21e4d5636e5317775e5d51ff32ebff2cf389f71b9b13750"},
+    {file = "regex-2024.7.24-cp38-cp38-musllinux_1_2_i686.whl", hash = "sha256:945352286a541406f99b2655c973852da7911b3f4264e010218bbc1cc73168f2"},
+    {file = "regex-2024.7.24-cp38-cp38-musllinux_1_2_ppc64le.whl", hash = "sha256:8bc593dcce679206b60a538c302d03c29b18e3d862609317cb560e18b66d10cf"},
+    {file = "regex-2024.7.24-cp38-cp38-musllinux_1_2_s390x.whl", hash = "sha256:3f3b6ca8eae6d6c75a6cff525c8530c60e909a71a15e1b731723233331de4169"},
+    {file = "regex-2024.7.24-cp38-cp38-musllinux_1_2_x86_64.whl", hash = "sha256:c51edc3541e11fbe83f0c4d9412ef6c79f664a3745fab261457e84465ec9d5a8"},
+    {file = "regex-2024.7.24-cp38-cp38-win32.whl", hash = "sha256:d0a07763776188b4db4c9c7fb1b8c494049f84659bb387b71c73bbc07f189e96"},
+    {file = "regex-2024.7.24-cp38-cp38-win_amd64.whl", hash = "sha256:8fd5afd101dcf86a270d254364e0e8dddedebe6bd1ab9d5f732f274fa00499a5"},
+    {file = "regex-2024.7.24-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:0ffe3f9d430cd37d8fa5632ff6fb36d5b24818c5c986893063b4e5bdb84cdf24"},
+    {file = "regex-2024.7.24-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:25419b70ba00a16abc90ee5fce061228206173231f004437730b67ac77323f0d"},
+    {file = "regex-2024.7.24-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:33e2614a7ce627f0cdf2ad104797d1f68342d967de3695678c0cb84f530709f8"},
+    {file = "regex-2024.7.24-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d33a0021893ede5969876052796165bab6006559ab845fd7b515a30abdd990dc"},
+    {file = "regex-2024.7.24-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:04ce29e2c5fedf296b1a1b0acc1724ba93a36fb14031f3abfb7abda2806c1535"},
+    {file = "regex-2024.7.24-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b16582783f44fbca6fcf46f61347340c787d7530d88b4d590a397a47583f31dd"},
+    {file = "regex-2024.7.24-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:836d3cc225b3e8a943d0b02633fb2f28a66e281290302a79df0e1eaa984ff7c1"},
+    {file = "regex-2024.7.24-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:438d9f0f4bc64e8dea78274caa5af971ceff0f8771e1a2333620969936ba10be"},
+    {file = "regex-2024.7.24-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:973335b1624859cb0e52f96062a28aa18f3a5fc77a96e4a3d6d76e29811a0e6e"},
+    {file = "regex-2024.7.24-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:c5e69fd3eb0b409432b537fe3c6f44ac089c458ab6b78dcec14478422879ec5f"},
+    {file = "regex-2024.7.24-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:fbf8c2f00904eaf63ff37718eb13acf8e178cb940520e47b2f05027f5bb34ce3"},
+    {file = "regex-2024.7.24-cp39-cp39-musllinux_1_2_ppc64le.whl", hash = "sha256:ae2757ace61bc4061b69af19e4689fa4416e1a04840f33b441034202b5cd02d4"},
+    {file = "regex-2024.7.24-cp39-cp39-musllinux_1_2_s390x.whl", hash = "sha256:44fc61b99035fd9b3b9453f1713234e5a7c92a04f3577252b45feefe1b327759"},
+    {file = "regex-2024.7.24-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:84c312cdf839e8b579f504afcd7b65f35d60b6285d892b19adea16355e8343c9"},
+    {file = "regex-2024.7.24-cp39-cp39-win32.whl", hash = "sha256:ca5b2028c2f7af4e13fb9fc29b28d0ce767c38c7facdf64f6c2cd040413055f1"},
+    {file = "regex-2024.7.24-cp39-cp39-win_amd64.whl", hash = "sha256:7c479f5ae937ec9985ecaf42e2e10631551d909f203e31308c12d703922742f9"},
+    {file = "regex-2024.7.24.tar.gz", hash = "sha256:9cfd009eed1a46b27c14039ad5bbc5e71b6367c5b2e6d5f5da0ea91600817506"},
+]
+
 [[package]]
 name = "requests"
 version = "2.32.3"
@@ -1321,29 +1552,29 @@ use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
 [[package]]
 name = "ruff"
-version = "0.5.3"
+version = "0.5.6"
 description = "An extremely fast Python linter and code formatter, written in Rust."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "ruff-0.5.3-py3-none-linux_armv6l.whl", hash = "sha256:b12424d9db7347fa63c5ed9af010003338c63c629fb9c9c6adb2aa4f5699729b"},
-    {file = "ruff-0.5.3-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:b8d72c5684bbd4ed304a9a955ee2e67f57b35f6193222ade910cca8a805490e3"},
-    {file = "ruff-0.5.3-py3-none-macosx_11_0_arm64.whl", hash = "sha256:d2fc2cdb85ccac1e816cc9d5d8cedefd93661bd957756d902543af32a6b04a71"},
-    {file = "ruff-0.5.3-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:cf4bc751240b2fab5d19254571bcacb315c7b0b00bf3c912d52226a82bbec073"},
-    {file = "ruff-0.5.3-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:bc697ec874fdd7c7ba0a85ec76ab38f8595224868d67f097c5ffc21136e72fcd"},
-    {file = "ruff-0.5.3-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e791d34d3557a3819b3704bc1f087293c821083fa206812842fa363f6018a192"},
-    {file = "ruff-0.5.3-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:76bb5a87fd397520b91a83eae8a2f7985236d42dd9459f09eef58e7f5c1d8316"},
-    {file = "ruff-0.5.3-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:a8cfc7a26422c78e94f1ec78ec02501bbad2df5834907e75afe474cc6b83a8c1"},
-    {file = "ruff-0.5.3-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:96066c4328a49fce2dd40e80f7117987369feec30ab771516cf95f1cc2db923c"},
-    {file = "ruff-0.5.3-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:03bfe9ab5bdc0b08470c3b261643ad54ea86edc32b64d1e080892d7953add3ad"},
-    {file = "ruff-0.5.3-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:7704582a026fa02cca83efd76671a98ee6eb412c4230209efe5e2a006c06db62"},
-    {file = "ruff-0.5.3-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:08058d077e21b856d32ebf483443390e29dc44d927608dc8f092ff6776519da9"},
-    {file = "ruff-0.5.3-py3-none-musllinux_1_2_i686.whl", hash = "sha256:77d49484429ed7c7e6e2e75a753f153b7b58f875bdb4158ad85af166a1ec1822"},
-    {file = "ruff-0.5.3-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:642cbff6cbfa38d2566d8db086508d6f472edb136cbfcc4ea65997745368c29e"},
-    {file = "ruff-0.5.3-py3-none-win32.whl", hash = "sha256:eafc45dd8bdc37a00b28e68cc038daf3ca8c233d73fea276dcd09defb1352841"},
-    {file = "ruff-0.5.3-py3-none-win_amd64.whl", hash = "sha256:cbaec2ddf4f78e5e9ecf5456ea0f496991358a1d883862ed0b9e947e2b6aea93"},
-    {file = "ruff-0.5.3-py3-none-win_arm64.whl", hash = "sha256:05fbd2cb404775d6cd7f2ff49504e2d20e13ef95fa203bd1ab22413af70d420b"},
-    {file = "ruff-0.5.3.tar.gz", hash = "sha256:2a3eb4f1841771fa5b67a56be9c2d16fd3cc88e378bd86aaeaec2f7e6bcdd0a2"},
+    {file = "ruff-0.5.6-py3-none-linux_armv6l.whl", hash = "sha256:a0ef5930799a05522985b9cec8290b185952f3fcd86c1772c3bdbd732667fdcd"},
+    {file = "ruff-0.5.6-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:b652dc14f6ef5d1552821e006f747802cc32d98d5509349e168f6bf0ee9f8f42"},
+    {file = "ruff-0.5.6-py3-none-macosx_11_0_arm64.whl", hash = "sha256:80521b88d26a45e871f31e4b88938fd87db7011bb961d8afd2664982dfc3641a"},
+    {file = "ruff-0.5.6-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d9bc8f328a9f1309ae80e4d392836e7dbc77303b38ed4a7112699e63d3b066ab"},
+    {file = "ruff-0.5.6-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:4d394940f61f7720ad371ddedf14722ee1d6250fd8d020f5ea5a86e7be217daf"},
+    {file = "ruff-0.5.6-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:111a99cdb02f69ddb2571e2756e017a1496c2c3a2aeefe7b988ddab38b416d36"},
+    {file = "ruff-0.5.6-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:e395daba77a79f6dc0d07311f94cc0560375ca20c06f354c7c99af3bf4560c5d"},
+    {file = "ruff-0.5.6-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:c476acb43c3c51e3c614a2e878ee1589655fa02dab19fe2db0423a06d6a5b1b6"},
+    {file = "ruff-0.5.6-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e2ff8003f5252fd68425fd53d27c1f08b201d7ed714bb31a55c9ac1d4c13e2eb"},
+    {file = "ruff-0.5.6-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c94e084ba3eaa80c2172918c2ca2eb2230c3f15925f4ed8b6297260c6ef179ad"},
+    {file = "ruff-0.5.6-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:1f77c1c3aa0669fb230b06fb24ffa3e879391a3ba3f15e3d633a752da5a3e670"},
+    {file = "ruff-0.5.6-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:f908148c93c02873210a52cad75a6eda856b2cbb72250370ce3afef6fb99b1ed"},
+    {file = "ruff-0.5.6-py3-none-musllinux_1_2_i686.whl", hash = "sha256:563a7ae61ad284187d3071d9041c08019975693ff655438d8d4be26e492760bd"},
+    {file = "ruff-0.5.6-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:94fe60869bfbf0521e04fd62b74cbca21cbc5beb67cbb75ab33fe8c174f54414"},
+    {file = "ruff-0.5.6-py3-none-win32.whl", hash = "sha256:e6a584c1de6f8591c2570e171cc7ce482bb983d49c70ddf014393cd39e9dfaed"},
+    {file = "ruff-0.5.6-py3-none-win_amd64.whl", hash = "sha256:d7fe7dccb1a89dc66785d7aa0ac283b2269712d8ed19c63af908fdccca5ccc1a"},
+    {file = "ruff-0.5.6-py3-none-win_arm64.whl", hash = "sha256:57c6c0dd997b31b536bff49b9eee5ed3194d60605a4427f735eeb1f9c1b8d264"},
+    {file = "ruff-0.5.6.tar.gz", hash = "sha256:07c9e3c2a8e1fe377dd460371c3462671a728c981c3205a5217291422209f642"},
 ]
 
 [[package]]
@@ -1357,6 +1588,17 @@ files = [
     {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
 ]
 
+[[package]]
+name = "sniffio"
+version = "1.3.1"
+description = "Sniff out which async library your code is running under"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "sniffio-1.3.1-py3-none-any.whl", hash = "sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2"},
+    {file = "sniffio-1.3.1.tar.gz", hash = "sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc"},
+]
+
 [[package]]
 name = "sqlalchemy"
 version = "2.0.31"
@@ -1473,6 +1715,58 @@ files = [
 doc = ["reno", "sphinx"]
 test = ["pytest", "tornado (>=4.5)", "typeguard"]
 
+[[package]]
+name = "tiktoken"
+version = "0.7.0"
+description = "tiktoken is a fast BPE tokeniser for use with OpenAI's models"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "tiktoken-0.7.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:485f3cc6aba7c6b6ce388ba634fbba656d9ee27f766216f45146beb4ac18b25f"},
+    {file = "tiktoken-0.7.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:e54be9a2cd2f6d6ffa3517b064983fb695c9a9d8aa7d574d1ef3c3f931a99225"},
+    {file = "tiktoken-0.7.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:79383a6e2c654c6040e5f8506f3750db9ddd71b550c724e673203b4f6b4b4590"},
+    {file = "tiktoken-0.7.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5d4511c52caacf3c4981d1ae2df85908bd31853f33d30b345c8b6830763f769c"},
+    {file = "tiktoken-0.7.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:13c94efacdd3de9aff824a788353aa5749c0faee1fbe3816df365ea450b82311"},
+    {file = "tiktoken-0.7.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:8e58c7eb29d2ab35a7a8929cbeea60216a4ccdf42efa8974d8e176d50c9a3df5"},
+    {file = "tiktoken-0.7.0-cp310-cp310-win_amd64.whl", hash = "sha256:21a20c3bd1dd3e55b91c1331bf25f4af522c525e771691adbc9a69336fa7f702"},
+    {file = "tiktoken-0.7.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:10c7674f81e6e350fcbed7c09a65bca9356eaab27fb2dac65a1e440f2bcfe30f"},
+    {file = "tiktoken-0.7.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:084cec29713bc9d4189a937f8a35dbdfa785bd1235a34c1124fe2323821ee93f"},
+    {file = "tiktoken-0.7.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:811229fde1652fedcca7c6dfe76724d0908775b353556d8a71ed74d866f73f7b"},
+    {file = "tiktoken-0.7.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:86b6e7dc2e7ad1b3757e8a24597415bafcfb454cebf9a33a01f2e6ba2e663992"},
+    {file = "tiktoken-0.7.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:1063c5748be36344c7e18c7913c53e2cca116764c2080177e57d62c7ad4576d1"},
+    {file = "tiktoken-0.7.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:20295d21419bfcca092644f7e2f2138ff947a6eb8cfc732c09cc7d76988d4a89"},
+    {file = "tiktoken-0.7.0-cp311-cp311-win_amd64.whl", hash = "sha256:959d993749b083acc57a317cbc643fb85c014d055b2119b739487288f4e5d1cb"},
+    {file = "tiktoken-0.7.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:71c55d066388c55a9c00f61d2c456a6086673ab7dec22dd739c23f77195b1908"},
+    {file = "tiktoken-0.7.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:09ed925bccaa8043e34c519fbb2f99110bd07c6fd67714793c21ac298e449410"},
+    {file = "tiktoken-0.7.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:03c6c40ff1db0f48a7b4d2dafeae73a5607aacb472fa11f125e7baf9dce73704"},
+    {file = "tiktoken-0.7.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d20b5c6af30e621b4aca094ee61777a44118f52d886dbe4f02b70dfe05c15350"},
+    {file = "tiktoken-0.7.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:d427614c3e074004efa2f2411e16c826f9df427d3c70a54725cae860f09e4bf4"},
+    {file = "tiktoken-0.7.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:8c46d7af7b8c6987fac9b9f61041b452afe92eb087d29c9ce54951280f899a97"},
+    {file = "tiktoken-0.7.0-cp312-cp312-win_amd64.whl", hash = "sha256:0bc603c30b9e371e7c4c7935aba02af5994a909fc3c0fe66e7004070858d3f8f"},
+    {file = "tiktoken-0.7.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:2398fecd38c921bcd68418675a6d155fad5f5e14c2e92fcf5fe566fa5485a858"},
+    {file = "tiktoken-0.7.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:8f5f6afb52fb8a7ea1c811e435e4188f2bef81b5e0f7a8635cc79b0eef0193d6"},
+    {file = "tiktoken-0.7.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:861f9ee616766d736be4147abac500732b505bf7013cfaf019b85892637f235e"},
+    {file = "tiktoken-0.7.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:54031f95c6939f6b78122c0aa03a93273a96365103793a22e1793ee86da31685"},
+    {file = "tiktoken-0.7.0-cp38-cp38-musllinux_1_2_aarch64.whl", hash = "sha256:fffdcb319b614cf14f04d02a52e26b1d1ae14a570f90e9b55461a72672f7b13d"},
+    {file = "tiktoken-0.7.0-cp38-cp38-musllinux_1_2_x86_64.whl", hash = "sha256:c72baaeaefa03ff9ba9688624143c858d1f6b755bb85d456d59e529e17234769"},
+    {file = "tiktoken-0.7.0-cp38-cp38-win_amd64.whl", hash = "sha256:131b8aeb043a8f112aad9f46011dced25d62629091e51d9dc1adbf4a1cc6aa98"},
+    {file = "tiktoken-0.7.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:cabc6dc77460df44ec5b879e68692c63551ae4fae7460dd4ff17181df75f1db7"},
+    {file = "tiktoken-0.7.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:8d57f29171255f74c0aeacd0651e29aa47dff6f070cb9f35ebc14c82278f3b25"},
+    {file = "tiktoken-0.7.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2ee92776fdbb3efa02a83f968c19d4997a55c8e9ce7be821ceee04a1d1ee149c"},
+    {file = "tiktoken-0.7.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e215292e99cb41fbc96988ef62ea63bb0ce1e15f2c147a61acc319f8b4cbe5bf"},
+    {file = "tiktoken-0.7.0-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:8a81bac94769cab437dd3ab0b8a4bc4e0f9cf6835bcaa88de71f39af1791727a"},
+    {file = "tiktoken-0.7.0-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:d6d73ea93e91d5ca771256dfc9d1d29f5a554b83821a1dc0891987636e0ae226"},
+    {file = "tiktoken-0.7.0-cp39-cp39-win_amd64.whl", hash = "sha256:2bcb28ddf79ffa424f171dfeef9a4daff61a94c631ca6813f43967cb263b83b9"},
+    {file = "tiktoken-0.7.0.tar.gz", hash = "sha256:1077266e949c24e0291f6c350433c6f0971365ece2b173a23bc3b9f9defef6b6"},
+]
+
+[package.dependencies]
+regex = ">=2022.1.18"
+requests = ">=2.26.0"
+
+[package.extras]
+blobfile = ["blobfile (>=2)"]
+
 [[package]]
 name = "tomli"
 version = "2.0.1"
@@ -1484,6 +1778,26 @@ files = [
     {file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"},
 ]
 
+[[package]]
+name = "tqdm"
+version = "4.66.5"
+description = "Fast, Extensible Progress Meter"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "tqdm-4.66.5-py3-none-any.whl", hash = "sha256:90279a3770753eafc9194a0364852159802111925aa30eb3f9d85b0e805ac7cd"},
+    {file = "tqdm-4.66.5.tar.gz", hash = "sha256:e1020aef2e5096702d8a025ac7d16b1577279c9d63f8375b63083e9a5f0fcbad"},
+]
+
+[package.dependencies]
+colorama = {version = "*", markers = "platform_system == \"Windows\""}
+
+[package.extras]
+dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"]
+notebook = ["ipywidgets (>=6)"]
+slack = ["slack-sdk"]
+telegram = ["requests"]
+
 [[package]]
 name = "typing-extensions"
 version = "4.12.2"
@@ -1662,4 +1976,4 @@ multidict = ">=4.0"
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<4.0"
-content-hash = "e456e34ed87e1dbec96547f5a124cb408b4c557e5e14c61e9cb5ebf3a7e07ef5"
+content-hash = "0118e4cc42983fecdb592157b956a4036d666ff4fc06de95bc964195f97f39bf"
diff --git a/libs/partners/mongodb/pyproject.toml b/libs/partners/mongodb/pyproject.toml
index e055cdb53e6..603ad38068c 100644
--- a/libs/partners/mongodb/pyproject.toml
+++ b/libs/partners/mongodb/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "langchain-mongodb"
-version = "0.1.7"
+version = "0.1.8"
 description = "An integration package connecting MongoDB and LangChain"
 authors = []
 readme = "README.md"
@@ -67,7 +67,9 @@ pytest-asyncio = "^0.21.1"
 [tool.poetry.group.codespell.dependencies]
 codespell = "^2.2.0"
 
-[tool.poetry.group.test_integration.dependencies]
+[tool.poetry.group.test_integration.dependencies.langchain-openai]
+path = "../openai"
+develop = true
 
 [tool.poetry.group.lint.dependencies]
 ruff = "^0.5"
diff --git a/libs/partners/mongodb/tests/integration_tests/test_cache.py b/libs/partners/mongodb/tests/integration_tests/test_cache.py
index 7547f840fcd..c0fe7367004 100644
--- a/libs/partners/mongodb/tests/integration_tests/test_cache.py
+++ b/libs/partners/mongodb/tests/integration_tests/test_cache.py
@@ -10,7 +10,8 @@ from langchain_core.messages import AIMessage, BaseMessage, HumanMessage
 from langchain_core.outputs import ChatGeneration, Generation, LLMResult
 
 from langchain_mongodb.cache import MongoDBAtlasSemanticCache, MongoDBCache
-from tests.utils import ConsistentFakeEmbeddings, FakeChatModel, FakeLLM
+
+from ..utils import ConsistentFakeEmbeddings, FakeChatModel, FakeLLM
 
 CONN_STRING = os.environ.get("MONGODB_ATLAS_URI")
 INDEX_NAME = "langchain-test-index-semantic-cache"
@@ -31,7 +32,7 @@ def llm_cache(cls: Any) -> BaseCache:
             database_name=DATABASE,
             index_name=INDEX_NAME,
             score_threshold=0.5,
-            wait_until_ready=True,
+            wait_until_ready=15.0,
         )
     )
     assert get_llm_cache()
diff --git a/libs/partners/mongodb/tests/integration_tests/test_chain_example.py b/libs/partners/mongodb/tests/integration_tests/test_chain_example.py
new file mode 100644
index 00000000000..c5c33c537d6
--- /dev/null
+++ b/libs/partners/mongodb/tests/integration_tests/test_chain_example.py
@@ -0,0 +1,144 @@
+"Demonstrates MongoDBAtlasVectorSearch.as_retriever() invoked in a chain" ""
+
+from __future__ import annotations
+
+import os
+from time import sleep
+
+import pytest  # type: ignore[import-not-found]
+from langchain_core.documents import Document
+from langchain_core.output_parsers.string import StrOutputParser
+from langchain_core.prompts.chat import ChatPromptTemplate
+from langchain_core.runnables import RunnablePassthrough
+from pymongo import MongoClient
+from pymongo.collection import Collection
+
+from langchain_mongodb import index
+
+from ..utils import PatchedMongoDBAtlasVectorSearch
+
+CONNECTION_STRING = os.environ.get("MONGODB_ATLAS_URI")
+DB_NAME = "langchain_test_db"
+COLLECTION_NAME = "langchain_test_chain_example"
+INDEX_NAME = "vector_index_with_filter"
+DIMENSIONS = 1536
+TIMEOUT = 60.0
+INTERVAL = 0.5
+
+
+@pytest.fixture
+def collection() -> Collection:
+    """A Collection with both a Vector and a Full-text Search Index"""
+    client: MongoClient = MongoClient(CONNECTION_STRING)
+    if COLLECTION_NAME not in client[DB_NAME].list_collection_names():
+        clxn = client[DB_NAME].create_collection(COLLECTION_NAME)
+    else:
+        clxn = client[DB_NAME][COLLECTION_NAME]
+
+    clxn.delete_many({})
+
+    if all([INDEX_NAME != ix["name"] for ix in clxn.list_search_indexes()]):
+        index.create_vector_search_index(
+            collection=clxn,
+            index_name=INDEX_NAME,
+            dimensions=DIMENSIONS,
+            path="embedding",
+            similarity="cosine",
+            filters=None,
+            wait_until_complete=TIMEOUT,
+        )
+
+    return clxn
+
+
+@pytest.mark.skipif(
+    "OPENAI_API_KEY" not in os.environ, reason="Requires OpenAI for chat responses."
+)
+def test_chain(
+    collection: Collection,
+) -> None:
+    """Demonstrate usage of MongoDBAtlasVectorSearch in a realistic chain
+
+    Follows example in the docs: https://python.langchain.com/v0.2/docs/how_to/hybrid/
+
+    Requires OpenAI_API_KEY for embedding and chat model.
+    Requires INDEX_NAME to have been set up on MONGODB_ATLAS_URI
+    """
+
+    from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+
+    embedding_openai = OpenAIEmbeddings(
+        openai_api_key=os.environ["OPENAI_API_KEY"],  # type: ignore # noqa
+        model="text-embedding-3-small",
+    )
+
+    vectorstore = PatchedMongoDBAtlasVectorSearch(
+        collection=collection,
+        embedding=embedding_openai,
+        index_name=INDEX_NAME,
+        text_key="page_content",
+    )
+
+    texts = [
+        "In 2023, I visited Paris",
+        "In 2022, I visited New York",
+        "In 2021, I visited New Orleans",
+        "In 2019, I visited San Francisco",
+        "In 2020, I visited Vancouver",
+    ]
+    vectorstore.add_texts(texts)
+
+    # Give the index time to build (For CI)
+    sleep(TIMEOUT)
+
+    query = "In the United States, what city did I visit last?"
+    # One can do vector search on the vector store, using its various search types.
+    k = len(texts)
+
+    store_output = list(vectorstore.similarity_search(query=query, k=k))
+    assert len(store_output) == k
+    assert isinstance(store_output[0], Document)
+
+    # Unfortunately, the VectorStore output cannot be given to a Chat Model
+    # If we wish Chat Model to answer based on our own data,
+    # we have to give it the right things to work with.
+    # The way that Langchain does this is by piping results along in
+    # a Chain: https://python.langchain.com/v0.1/docs/modules/chains/
+
+    # Now, we can turn our VectorStore into something Runnable in a Chain
+    # by turning it into a Retriever.
+    # For the simple VectorSearch Retriever, we can do this like so.
+
+    retriever = vectorstore.as_retriever(search_kwargs=dict(k=k))
+
+    # This does not do much other than expose our search function
+    # as an invoke() method with a a certain API, a Runnable.
+    retriever_output = retriever.invoke(query)
+    assert len(retriever_output) == len(texts)
+    assert retriever_output[0].page_content == store_output[0].page_content
+
+    # To get a natural language response to our question,
+    # we need ChatOpenAI, a template to better frame the question as a prompt,
+    # and a parser to send the output to a string.
+    # Together, these become our Chain!
+    # Here goes:
+
+    template = """Answer the question based only on the following context.
+     Answer in as few words as possible.
+     {context}
+     Question: {question}
+     """
+    prompt = ChatPromptTemplate.from_template(template)
+
+    model = ChatOpenAI()
+
+    chain = (
+        {"context": retriever, "question": RunnablePassthrough()}  # type: ignore
+        | prompt
+        | model
+        | StrOutputParser()
+    )
+
+    answer = chain.invoke("What city did I visit last?")
+
+    assert "Paris" in answer
diff --git a/libs/partners/mongodb/tests/integration_tests/test_index.py b/libs/partners/mongodb/tests/integration_tests/test_index.py
index dd1697bfa22..bc06fd4e3e1 100644
--- a/libs/partners/mongodb/tests/integration_tests/test_index.py
+++ b/libs/partners/mongodb/tests/integration_tests/test_index.py
@@ -1,6 +1,7 @@
 """Search index commands are only supported on Atlas Clusters >=M10"""
 
 import os
+from typing import Generator, List, Optional
 
 import pytest
 from pymongo import MongoClient
@@ -8,23 +9,28 @@ from pymongo.collection import Collection
 
 from langchain_mongodb import index
 
+TIMEOUT = 120
+DIMENSIONS = 10
+
 
 @pytest.fixture
-def collection() -> Collection:
+def collection() -> Generator:
     """Depending on uri, this could point to any type of cluster."""
     uri = os.environ.get("MONGODB_ATLAS_URI")
     client: MongoClient = MongoClient(uri)
-    clxn = client["db"].create_collection("collection")
-    return clxn
+    clxn = client["db"]["collection"]
+    clxn.insert_one({"foo": "bar"})
+    yield clxn
+    clxn.drop()
 
 
 def test_search_index_commands(collection: Collection) -> None:
     index_name = "vector_index"
-    dimensions = 1536
+    dimensions = DIMENSIONS
     path = "embedding"
     similarity = "cosine"
-    filters: list = []
-    wait_until_complete = 120
+    filters: Optional[List[str]] = None
+    wait_until_complete = TIMEOUT
 
     for index_info in collection.list_search_indexes():
         index.drop_vector_search_index(
@@ -52,10 +58,10 @@ def test_search_index_commands(collection: Collection) -> None:
     index.update_vector_search_index(
         collection,
         index_name,
-        1536,
+        DIMENSIONS,
         "embedding",
         new_similarity,
-        [],
+        filters=[],
         wait_until_complete=wait_until_complete,
     )
 
diff --git a/libs/partners/mongodb/tests/integration_tests/test_retrievers.py b/libs/partners/mongodb/tests/integration_tests/test_retrievers.py
new file mode 100644
index 00000000000..8723396f974
--- /dev/null
+++ b/libs/partners/mongodb/tests/integration_tests/test_retrievers.py
@@ -0,0 +1,176 @@
+import os
+from time import sleep
+from typing import List
+
+import pytest
+from langchain_core.documents import Document
+from langchain_core.embeddings import Embeddings
+from pymongo import MongoClient
+from pymongo.collection import Collection
+
+from langchain_mongodb import index
+from langchain_mongodb.retrievers import (
+    MongoDBAtlasFullTextSearchRetriever,
+    MongoDBAtlasHybridSearchRetriever,
+)
+
+from ..utils import ConsistentFakeEmbeddings, PatchedMongoDBAtlasVectorSearch
+
+CONNECTION_STRING = os.environ.get("MONGODB_ATLAS_URI")
+DB_NAME = "langchain_test_db"
+COLLECTION_NAME = "test_retrievers"
+VECTOR_INDEX_NAME = "vector_index"
+EMBEDDING_FIELD = "embedding"
+PAGE_CONTENT_FIELD = "text"
+SEARCH_INDEX_NAME = "text_index"
+
+DIMENSIONS = 1536
+TIMEOUT = 60.0
+INTERVAL = 0.5
+
+
+@pytest.fixture
+def example_documents() -> List[Document]:
+    return [
+        Document(page_content="In 2023, I visited Paris"),
+        Document(page_content="In 2022, I visited New York"),
+        Document(page_content="In 2021, I visited New Orleans"),
+        Document(page_content="Sandwiches are beautiful. Sandwiches are fine."),
+    ]
+
+
+@pytest.fixture
+def embedding_openai() -> Embeddings:
+    from langchain_openai import OpenAIEmbeddings
+
+    try:
+        return OpenAIEmbeddings(
+            openai_api_key=os.environ["OPENAI_API_KEY"],  # type: ignore # noqa
+            model="text-embedding-3-small",
+        )
+    except Exception:
+        return ConsistentFakeEmbeddings(DIMENSIONS)
+
+
+@pytest.fixture
+def collection() -> Collection:
+    """A Collection with both a Vector and a Full-text Search Index"""
+    client: MongoClient = MongoClient(CONNECTION_STRING)
+    if COLLECTION_NAME not in client[DB_NAME].list_collection_names():
+        clxn = client[DB_NAME].create_collection(COLLECTION_NAME)
+    else:
+        clxn = client[DB_NAME][COLLECTION_NAME]
+
+    clxn.delete_many({})
+
+    if not any([VECTOR_INDEX_NAME == ix["name"] for ix in clxn.list_search_indexes()]):
+        index.create_vector_search_index(
+            collection=clxn,
+            index_name=VECTOR_INDEX_NAME,
+            dimensions=DIMENSIONS,
+            path="embedding",
+            similarity="cosine",
+            wait_until_complete=TIMEOUT,
+        )
+
+    if not any([SEARCH_INDEX_NAME == ix["name"] for ix in clxn.list_search_indexes()]):
+        index.create_fulltext_search_index(
+            collection=clxn,
+            index_name=SEARCH_INDEX_NAME,
+            field=PAGE_CONTENT_FIELD,
+            wait_until_complete=TIMEOUT,
+        )
+
+    return clxn
+
+
+def test_hybrid_retriever(
+    embedding_openai: Embeddings,
+    collection: Collection,
+    example_documents: List[Document],
+) -> None:
+    """Test basic usage of MongoDBAtlasHybridSearchRetriever"""
+
+    vectorstore = PatchedMongoDBAtlasVectorSearch(
+        collection=collection,
+        embedding=embedding_openai,
+        index_name=VECTOR_INDEX_NAME,
+        text_key=PAGE_CONTENT_FIELD,
+    )
+
+    vectorstore.add_documents(example_documents)
+
+    sleep(TIMEOUT)  # Wait for documents to be sync'd
+
+    retriever = MongoDBAtlasHybridSearchRetriever(
+        vectorstore=vectorstore,
+        search_index_name=SEARCH_INDEX_NAME,
+        top_k=3,
+    )
+
+    query1 = "What was the latest city that I visited?"
+    results = retriever.invoke(query1)
+    assert len(results) == 3
+    assert "Paris" in results[0].page_content
+
+    query2 = "When was the last time I visited new orleans?"
+    results = retriever.invoke(query2)
+    assert "New Orleans" in results[0].page_content
+
+
+def test_fulltext_retriever(
+    collection: Collection,
+    example_documents: List[Document],
+) -> None:
+    """Test result of performing fulltext search
+
+    Independent of the VectorStore, one adds documents
+    via MongoDB's Collection API
+    """
+    #
+
+    collection.insert_many(
+        [{PAGE_CONTENT_FIELD: doc.page_content} for doc in example_documents]
+    )
+    sleep(TIMEOUT)  # Wait for documents to be sync'd
+
+    retriever = MongoDBAtlasFullTextSearchRetriever(
+        collection=collection,
+        search_index_name=SEARCH_INDEX_NAME,
+        search_field=PAGE_CONTENT_FIELD,
+    )
+
+    query = "When was the last time I visited new orleans?"
+    results = retriever.invoke(query)
+    assert "New Orleans" in results[0].page_content
+    assert "score" in results[0].metadata
+
+
+def test_vector_retriever(
+    embedding_openai: Embeddings,
+    collection: Collection,
+    example_documents: List[Document],
+) -> None:
+    """Test VectorStoreRetriever"""
+
+    vectorstore = PatchedMongoDBAtlasVectorSearch(
+        collection=collection,
+        embedding=embedding_openai,
+        index_name=VECTOR_INDEX_NAME,
+        text_key=PAGE_CONTENT_FIELD,
+    )
+
+    vectorstore.add_documents(example_documents)
+
+    sleep(TIMEOUT)  # Wait for documents to be sync'd
+
+    retriever = vectorstore.as_retriever()
+
+    query1 = "What was the latest city that I visited?"
+    results = retriever.invoke(query1)
+    assert len(results) == 4
+    assert "Paris" in results[0].page_content
+
+    query2 = "When was the last time I visited new orleans?"
+    results = retriever.invoke(query2)
+    assert "New Orleans" in results[0].page_content
diff --git a/libs/partners/mongodb/tests/integration_tests/test_vectorstores.py b/libs/partners/mongodb/tests/integration_tests/test_vectorstores.py
index c5fb6d26086..3033b20f490 100644
--- a/libs/partners/mongodb/tests/integration_tests/test_vectorstores.py
+++ b/libs/partners/mongodb/tests/integration_tests/test_vectorstores.py
@@ -4,7 +4,7 @@ from __future__ import annotations
 
 import os
 from time import monotonic, sleep
-from typing import Any, Dict, Generator, Iterable, List, Optional, Union
+from typing import Any, Dict, List
 
 import pytest  # type: ignore[import-not-found]
 from bson import ObjectId
@@ -14,11 +14,10 @@ from pymongo import MongoClient
 from pymongo.collection import Collection
 from pymongo.errors import OperationFailure
 
-from langchain_mongodb import MongoDBAtlasVectorSearch
 from langchain_mongodb.index import drop_vector_search_index
 from langchain_mongodb.utils import oid_to_str
 
-from ..utils import ConsistentFakeEmbeddings
+from ..utils import ConsistentFakeEmbeddings, PatchedMongoDBAtlasVectorSearch
 
 INDEX_NAME = "langchain-test-index-vectorstores"
 INDEX_CREATION_NAME = "langchain-test-index-vectorstores-create-test"
@@ -32,41 +31,14 @@ TIMEOUT = 120.0
 INTERVAL = 0.5
 
 
-class PatchedMongoDBAtlasVectorSearch(MongoDBAtlasVectorSearch):
-    def bulk_embed_and_insert_texts(
-        self,
-        texts: Union[List[str], Iterable[str]],
-        metadatas: Union[List[dict], Generator[dict, Any, Any]],
-        ids: Optional[List[str]] = None,
-    ) -> List:
-        """Patched insert_texts that waits for data to be indexed before returning"""
-        ids_inserted = super().bulk_embed_and_insert_texts(texts, metadatas, ids)
-        start = monotonic()
-        while len(ids_inserted) != len(self.similarity_search("sandwich")) and (
-            monotonic() - start <= TIMEOUT
-        ):
-            sleep(INTERVAL)
-        return ids_inserted
-
-    def create_vector_search_index(
-        self,
-        dimensions: int,
-        filters: Optional[List[Dict[str, str]]] = None,
-        update: bool = False,
-    ) -> None:
-        result = super().create_vector_search_index(
-            dimensions=dimensions, filters=filters, update=update
-        )
-        start = monotonic()
-        while monotonic() - start <= TIMEOUT:
-            if indexes := list(
-                self._collection.list_search_indexes(name=self._index_name)
-            ):
-                if indexes[0].get("status") == "READY":
-                    return result
-            sleep(INTERVAL)
-
-        raise TimeoutError(f"{self._index_name} never reached 'status: READY'")
+@pytest.fixture
+def example_documents() -> List[Document]:
+    return [
+        Document(page_content="Dogs are tough.", metadata={"a": 1}),
+        Document(page_content="Cats have fluff.", metadata={"b": 1}),
+        Document(page_content="What is a sandwich?", metadata={"c": 1}),
+        Document(page_content="That fence is purple.", metadata={"d": 1, "e": 2}),
+    ]
 
 
 def _await_index_deletion(coll: Collection, index_name: str) -> None:
@@ -137,40 +109,46 @@ class TestMongoDBAtlasVectorSearch:
 
     @pytest.fixture
     def embeddings(self) -> Embeddings:
-        return ConsistentFakeEmbeddings(DIMENSIONS)
+        try:
+            from langchain_openai import OpenAIEmbeddings
 
-    def test_from_documents(self, embeddings: Embeddings, collection: Any) -> None:
+            return OpenAIEmbeddings(
+                openai_api_key=os.environ["OPENAI_API_KEY"],  # type: ignore # noqa
+                model="text-embedding-3-small",
+            )
+        except Exception:
+            return ConsistentFakeEmbeddings(DIMENSIONS)
+
+    def test_from_documents(
+        self,
+        embeddings: Embeddings,
+        collection: Any,
+        example_documents: List[Document],
+    ) -> None:
         """Test end to end construction and search."""
-        documents = [
-            Document(page_content="Dogs are tough.", metadata={"a": 1}),
-            Document(page_content="Cats have fluff.", metadata={"b": 1}),
-            Document(page_content="What is a sandwich?", metadata={"c": 1}),
-            Document(page_content="That fence is purple.", metadata={"d": 1, "e": 2}),
-        ]
         vectorstore = PatchedMongoDBAtlasVectorSearch.from_documents(
-            documents,
-            embeddings,
+            example_documents,
+            embedding=embeddings,
             collection=collection,
             index_name=INDEX_NAME,
         )
         output = vectorstore.similarity_search("Sandwich", k=1)
         assert len(output) == 1
         # Check for the presence of the metadata key
-        assert any([key.page_content == output[0].page_content for key in documents])
+        assert any(
+            [key.page_content == output[0].page_content for key in example_documents]
+        )
 
     def test_from_documents_no_embedding_return(
-        self, embeddings: Embeddings, collection: Any
+        self,
+        embeddings: Embeddings,
+        collection: Any,
+        example_documents: List[Document],
     ) -> None:
         """Test end to end construction and search."""
-        documents = [
-            Document(page_content="Dogs are tough.", metadata={"a": 1}),
-            Document(page_content="Cats have fluff.", metadata={"b": 1}),
-            Document(page_content="What is a sandwich?", metadata={"c": 1}),
-            Document(page_content="That fence is purple.", metadata={"d": 1, "e": 2}),
-        ]
         vectorstore = PatchedMongoDBAtlasVectorSearch.from_documents(
-            documents,
-            embeddings,
+            example_documents,
+            embedding=embeddings,
             collection=collection,
             index_name=INDEX_NAME,
         )
@@ -179,37 +157,38 @@ class TestMongoDBAtlasVectorSearch:
         # Check for presence of embedding in each document
         assert all(["embedding" not in key.metadata for key in output])
         # Check for the presence of the metadata key
-        assert any([key.page_content == output[0].page_content for key in documents])
+        assert any(
+            [key.page_content == output[0].page_content for key in example_documents]
+        )
 
     def test_from_documents_embedding_return(
-        self, embeddings: Embeddings, collection: Any
+        self,
+        embeddings: Embeddings,
+        collection: Any,
+        example_documents: List[Document],
     ) -> None:
         """Test end to end construction and search."""
-        documents = [
-            Document(page_content="Dogs are tough.", metadata={"a": 1}),
-            Document(page_content="Cats have fluff.", metadata={"b": 1}),
-            Document(page_content="What is a sandwich?", metadata={"c": 1}),
-            Document(page_content="That fence is purple.", metadata={"d": 1, "e": 2}),
-        ]
         vectorstore = PatchedMongoDBAtlasVectorSearch.from_documents(
-            documents,
-            embeddings,
+            example_documents,
+            embedding=embeddings,
             collection=collection,
             index_name=INDEX_NAME,
         )
-        output = vectorstore.similarity_search("Sandwich", k=1, include_embedding=True)
+        output = vectorstore.similarity_search("Sandwich", k=1, include_embeddings=True)
         assert len(output) == 1
         # Check for presence of embedding in each document
         assert all([key.metadata.get("embedding") for key in output])
         # Check for the presence of the metadata key
-        assert any([key.page_content == output[0].page_content for key in documents])
+        assert any(
+            [key.page_content == output[0].page_content for key in example_documents]
+        )
 
     def test_from_texts(
         self, embeddings: Embeddings, collection: Collection, texts: List[str]
     ) -> None:
         vectorstore = PatchedMongoDBAtlasVectorSearch.from_texts(
             texts,
-            embeddings,
+            embedding=embeddings,
             collection=collection,
             index_name=INDEX_NAME,
         )
@@ -226,7 +205,7 @@ class TestMongoDBAtlasVectorSearch:
         metakeys = ["a", "b", "c", "d", "e"]
         vectorstore = PatchedMongoDBAtlasVectorSearch.from_texts(
             texts,
-            embeddings,
+            embedding=embeddings,
             metadatas=metadatas,
             collection=collection,
             index_name=INDEX_NAME,
@@ -242,21 +221,26 @@ class TestMongoDBAtlasVectorSearch:
         metadatas = [{"a": 1}, {"b": 1}, {"c": 1}, {"d": 1, "e": 2}]
         vectorstore = PatchedMongoDBAtlasVectorSearch.from_texts(
             texts,
-            embeddings,
+            embedding=embeddings,
             metadatas=metadatas,
             collection=collection,
             index_name=INDEX_NAME,
         )
-        output = vectorstore.similarity_search(
+        does_not_match_filter = vectorstore.similarity_search(
             "Sandwich", k=1, pre_filter={"c": {"$lte": 0}}
         )
-        assert output == []
+        assert does_not_match_filter == []
+
+        matches_filter = vectorstore.similarity_search(
+            "Sandwich", k=3, pre_filter={"c": {"$gt": 0}}
+        )
+        assert len(matches_filter) == 1
 
     def test_mmr(self, embeddings: Embeddings, collection: Any) -> None:
         texts = ["foo", "foo", "fou", "foy"]
         vectorstore = PatchedMongoDBAtlasVectorSearch.from_texts(
             texts,
-            embeddings,
+            embedding=embeddings,
             collection=collection,
             index_name=INDEX_NAME,
         )
@@ -266,10 +250,57 @@ class TestMongoDBAtlasVectorSearch:
         assert output[0].page_content == "foo"
         assert output[1].page_content != "foo"
 
+    def test_retriever(
+        self,
+        embeddings: Embeddings,
+        collection: Any,
+        example_documents: List[Document],
+    ) -> None:
+        """Demonstrate usage and parity of VectorStore similarity_search
+        with Retriever.invoke."""
+        vectorstore = PatchedMongoDBAtlasVectorSearch.from_documents(
+            example_documents,
+            embedding=embeddings,
+            collection=collection,
+            index_name=INDEX_NAME,
+        )
+        query = "sandwich"
+
+        retriever_default_kwargs = vectorstore.as_retriever()
+        result_retriever = retriever_default_kwargs.invoke(query)
+        result_vectorstore = vectorstore.similarity_search(query)
+        assert all(
+            [
+                result_retriever[i].page_content == result_vectorstore[i].page_content
+                for i in range(len(result_retriever))
+            ]
+        )
+
+    def test_include_embeddings(
+        self,
+        embeddings: Embeddings,
+        collection: Any,
+        example_documents: List[Document],
+    ) -> None:
+        """Test explicitly passing vector kwarg matches default."""
+        vectorstore = PatchedMongoDBAtlasVectorSearch.from_documents(
+            documents=example_documents,
+            embedding=embeddings,
+            collection=collection,
+            index_name=INDEX_NAME,
+        )
+
+        output_with = vectorstore.similarity_search(
+            "Sandwich", include_embeddings=True, k=1
+        )
+        assert vectorstore._embedding_key in output_with[0].metadata
+        output_without = vectorstore.similarity_search("Sandwich", k=1)
+        assert vectorstore._embedding_key not in output_without[0].metadata
+
     def test_delete(
         self, embeddings: Embeddings, collection: Any, texts: List[str]
     ) -> None:
-        vectorstore = MongoDBAtlasVectorSearch(  # PatchedMongoDBAtlasVectorSearch(
+        vectorstore = PatchedMongoDBAtlasVectorSearch(
             collection=collection,
             embedding=embeddings,
             index_name=INDEX_NAME,
@@ -296,7 +327,10 @@ class TestMongoDBAtlasVectorSearch:
         collection: Collection,
         texts: List[str],
     ) -> None:
-        """Tests API of add_texts, focussing on id treatment"""
+        """Tests API of add_texts, focussing on id treatment
+
+        Warning: This is slow because of the number of cases
+        """
         metadatas: List[Dict[str, Any]] = [
             {"a": 1},
             {"b": 1},
@@ -380,7 +414,6 @@ class TestMongoDBAtlasVectorSearch:
         self,
         embeddings: Embeddings,
         collection: Collection,
-        index_name: str = INDEX_NAME,
     ) -> None:
         """Tests add_documents."""
         vectorstore = PatchedMongoDBAtlasVectorSearch(
@@ -428,13 +461,13 @@ class TestMongoDBAtlasVectorSearch:
         self, embeddings: Embeddings, index_collection: Any
     ) -> None:
         vectorstore = PatchedMongoDBAtlasVectorSearch(
-            index_collection, embeddings, index_name=INDEX_CREATION_NAME
+            index_collection, embedding=embeddings, index_name=INDEX_CREATION_NAME
         )
         vectorstore.create_vector_search_index(dimensions=1536)
 
     def test_index_update(self, embeddings: Embeddings, index_collection: Any) -> None:
         vectorstore = PatchedMongoDBAtlasVectorSearch(
-            index_collection, embeddings, index_name=INDEX_CREATION_NAME
+            index_collection, embedding=embeddings, index_name=INDEX_CREATION_NAME
         )
         vectorstore.create_vector_search_index(dimensions=1536)
         vectorstore.create_vector_search_index(dimensions=1536, update=True)
diff --git a/libs/partners/mongodb/tests/unit_tests/test_cache.py b/libs/partners/mongodb/tests/unit_tests/test_cache.py
index ae070d7dde3..56f5b857140 100644
--- a/libs/partners/mongodb/tests/unit_tests/test_cache.py
+++ b/libs/partners/mongodb/tests/unit_tests/test_cache.py
@@ -12,7 +12,8 @@ from pymongo.collection import Collection
 
 from langchain_mongodb.cache import MongoDBAtlasSemanticCache, MongoDBCache
 from langchain_mongodb.vectorstores import MongoDBAtlasVectorSearch
-from tests.utils import ConsistentFakeEmbeddings, FakeChatModel, FakeLLM, MockCollection
+
+from ..utils import ConsistentFakeEmbeddings, FakeChatModel, FakeLLM, MockCollection
 
 CONN_STRING = "MockString"
 COLLECTION = "default"
@@ -74,7 +75,7 @@ def llm_cache(cls: Any) -> BaseCache:
             connection_string=CONN_STRING,
             collection_name=COLLECTION,
             database_name=DATABASE,
-            wait_until_ready=True,
+            wait_until_ready=15.0,
         )
     )
     assert get_llm_cache()
@@ -207,7 +208,7 @@ def test_mongodb_atlas_cache_matrix(
     for prompt_i, llm_generations_i in zip(prompts, llm_generations):
         _execute_test(prompt_i, llm_string, llm_generations_i)
 
-    get_llm_cache()._collection._simluate_cache_aggregation_query = True  # type: ignore
+    get_llm_cache()._collection._simulate_cache_aggregation_query = True  # type: ignore
     assert llm.generate(prompts) == LLMResult(
         generations=llm_generations, llm_output={}
     )
diff --git a/libs/partners/mongodb/tests/unit_tests/test_chat_message_histories.py b/libs/partners/mongodb/tests/unit_tests/test_chat_message_histories.py
index 28fb399cb39..4f3fa4af2b3 100644
--- a/libs/partners/mongodb/tests/unit_tests/test_chat_message_histories.py
+++ b/libs/partners/mongodb/tests/unit_tests/test_chat_message_histories.py
@@ -4,7 +4,8 @@ from langchain.memory import ConversationBufferMemory  # type: ignore[import-not
 from langchain_core.messages import message_to_dict
 
 from langchain_mongodb.chat_message_histories import MongoDBChatMessageHistory
-from tests.utils import MockCollection
+
+from ..utils import MockCollection
 
 
 class PatchedMongoDBChatMessageHistory(MongoDBChatMessageHistory):
diff --git a/libs/partners/mongodb/tests/unit_tests/test_index.py b/libs/partners/mongodb/tests/unit_tests/test_index.py
index 61776cf55d2..acb6cb5b189 100644
--- a/libs/partners/mongodb/tests/unit_tests/test_index.py
+++ b/libs/partners/mongodb/tests/unit_tests/test_index.py
@@ -10,6 +10,9 @@ from pymongo.errors import OperationFailure, ServerSelectionTimeoutError
 
 from langchain_mongodb import index
 
+DIMENSION = 10
+TIMEOUT = 10
+
 
 @pytest.fixture
 def collection() -> Collection:
@@ -25,19 +28,33 @@ def collection() -> Collection:
 def test_create_vector_search_index(collection: Collection) -> None:
     with pytest.raises((OperationFailure, ServerSelectionTimeoutError)):
         index.create_vector_search_index(
-            collection, "index_name", 1536, "embedding", "cosine", []
+            collection,
+            "index_name",
+            DIMENSION,
+            "embedding",
+            "cosine",
+            [],
+            wait_until_complete=TIMEOUT,
         )
 
 
 def test_drop_vector_search_index(collection: Collection) -> None:
     with pytest.raises((OperationFailure, ServerSelectionTimeoutError)):
-        index.drop_vector_search_index(collection, "index_name")
+        index.drop_vector_search_index(
+            collection, "index_name", wait_until_complete=TIMEOUT
+        )
 
 
 def test_update_vector_search_index(collection: Collection) -> None:
     with pytest.raises((OperationFailure, ServerSelectionTimeoutError)):
         index.update_vector_search_index(
-            collection, "index_name", 1536, "embedding", "cosine", []
+            collection,
+            "index_name",
+            DIMENSION,
+            "embedding",
+            "cosine",
+            [],
+            wait_until_complete=TIMEOUT,
         )
 
 
diff --git a/libs/partners/mongodb/tests/unit_tests/test_vectorstores.py b/libs/partners/mongodb/tests/unit_tests/test_vectorstores.py
index 0ac6898f5ba..5ed936ab7e4 100644
--- a/libs/partners/mongodb/tests/unit_tests/test_vectorstores.py
+++ b/libs/partners/mongodb/tests/unit_tests/test_vectorstores.py
@@ -2,14 +2,13 @@ from json import dumps, loads
 from typing import Any, Optional
 
 import pytest  # type: ignore[import-not-found]
-from bson import ObjectId, json_util
 from langchain_core.documents import Document
 from langchain_core.embeddings import Embeddings
 from pymongo.collection import Collection
 
 from langchain_mongodb import MongoDBAtlasVectorSearch
-from langchain_mongodb.utils import str_to_oid
-from tests.utils import ConsistentFakeEmbeddings, MockCollection
+
+from ..utils import ConsistentFakeEmbeddings, MockCollection
 
 INDEX_NAME = "langchain-test-index"
 NAMESPACE = "langchain_test_db.langchain_test_collection"
@@ -81,8 +80,7 @@ class TestMongoDBAtlasVectorSearch:
         # Validate the ObjectId provided is json serializable
         assert loads(dumps(output[0].page_content)) == output[0].page_content
         assert loads(dumps(output[0].metadata)) == output[0].metadata
-        json_metadata = dumps(output[0].metadata)  # normal json.dumps
-        isinstance(str_to_oid(json_util.loads(json_metadata)["_id"]), ObjectId)
+        assert isinstance(output[0].metadata["_id"], str)
 
     def test_from_documents(
         self, embedding_openai: Embeddings, collection: MockCollection
@@ -98,7 +96,7 @@ class TestMongoDBAtlasVectorSearch:
             documents,
             embedding_openai,
             collection=collection,
-            index_name=INDEX_NAME,
+            vector_index_name=INDEX_NAME,
         )
         self._validate_search(
             vectorstore, collection, metadata=documents[2].metadata["c"]
@@ -117,7 +115,7 @@ class TestMongoDBAtlasVectorSearch:
             texts,
             embedding_openai,
             collection=collection,
-            index_name=INDEX_NAME,
+            vector_index_name=INDEX_NAME,
         )
         self._validate_search(vectorstore, collection, metadata=None)
 
@@ -136,7 +134,7 @@ class TestMongoDBAtlasVectorSearch:
             embedding_openai,
             metadatas=metadatas,
             collection=collection,
-            index_name=INDEX_NAME,
+            vector_index_name=INDEX_NAME,
         )
         self._validate_search(vectorstore, collection, metadata=metadatas[2]["c"])
 
@@ -155,7 +153,7 @@ class TestMongoDBAtlasVectorSearch:
             embedding_openai,
             metadatas=metadatas,
             collection=collection,
-            index_name=INDEX_NAME,
+            vector_index_name=INDEX_NAME,
         )
         collection._aggregate_result = list(
             filter(
@@ -175,9 +173,9 @@ class TestMongoDBAtlasVectorSearch:
         texts = ["foo", "foo", "fou", "foy"]
         vectorstore = MongoDBAtlasVectorSearch.from_texts(
             texts,
-            embedding_openai,
+            embedding=embedding_openai,
             collection=collection,
-            index_name=INDEX_NAME,
+            vector_index_name=INDEX_NAME,
         )
         query = "foo"
         self._validate_search(
diff --git a/libs/partners/mongodb/tests/utils.py b/libs/partners/mongodb/tests/utils.py
index 7b06991da82..f9126f5211d 100644
--- a/libs/partners/mongodb/tests/utils.py
+++ b/libs/partners/mongodb/tests/utils.py
@@ -1,7 +1,8 @@
 from __future__ import annotations
 
 from copy import deepcopy
-from typing import Any, Dict, List, Mapping, Optional, cast
+from time import monotonic, sleep
+from typing import Any, Dict, Generator, Iterable, List, Mapping, Optional, Union, cast
 
 from bson import ObjectId
 from langchain_core.callbacks.manager import (
@@ -20,8 +21,47 @@ from langchain_core.pydantic_v1 import validator
 from pymongo.collection import Collection
 from pymongo.results import DeleteResult, InsertManyResult
 
+from langchain_mongodb import MongoDBAtlasVectorSearch
 from langchain_mongodb.cache import MongoDBAtlasSemanticCache
 
+TIMEOUT = 120
+INTERVAL = 0.5
+
+
+class PatchedMongoDBAtlasVectorSearch(MongoDBAtlasVectorSearch):
+    def bulk_embed_and_insert_texts(
+        self,
+        texts: Union[List[str], Iterable[str]],
+        metadatas: Union[List[dict], Generator[dict, Any, Any]],
+        ids: Optional[List[str]] = None,
+    ) -> List:
+        """Patched insert_texts that waits for data to be indexed before returning"""
+        ids_inserted = super().bulk_embed_and_insert_texts(texts, metadatas, ids)
+        start = monotonic()
+        while len(ids_inserted) != len(self.similarity_search("sandwich")) and (
+            monotonic() - start <= TIMEOUT
+        ):
+            sleep(INTERVAL)
+        return ids_inserted
+
+    def create_vector_search_index(
+        self,
+        dimensions: int,
+        filters: Optional[List[str]] = None,
+        update: bool = False,
+    ) -> None:
+        result = super().create_vector_search_index(
+            dimensions=dimensions, filters=filters, update=update
+        )
+        start = monotonic()
+        while monotonic() - start <= TIMEOUT:
+            if indexes := list(
+                self._collection.list_search_indexes(name=self._index_name)
+            ):
+                if indexes[0].get("status") == "READY":
+                    return result
+            sleep(INTERVAL)
+
 
 class ConsistentFakeEmbeddings(Embeddings):
     """Fake embeddings functionality for testing."""
@@ -147,13 +187,13 @@ class MockCollection(Collection):
     _aggregate_result: List[Any]
     _insert_result: Optional[InsertManyResult]
     _data: List[Any]
-    _simluate_cache_aggregation_query: bool
+    _simulate_cache_aggregation_query: bool
 
     def __init__(self) -> None:
         self._data = []
         self._aggregate_result = []
         self._insert_result = None
-        self._simluate_cache_aggregation_query = False
+        self._simulate_cache_aggregation_query = False
 
     def delete_many(self, *args, **kwargs) -> DeleteResult:  # type: ignore
         old_len = len(self._data)
@@ -201,7 +241,7 @@ class MockCollection(Collection):
         elif upsert:
             self._data.append({**find_query, **set_options})
 
-    def _execute_cache_aggreation_query(self, *args, **kwargs) -> List[Dict[str, Any]]:  # type: ignore
+    def _execute_cache_aggregation_query(self, *args, **kwargs) -> List[Dict[str, Any]]:  # type: ignore
         """Helper function only to be used for MongoDBAtlasSemanticCache Testing
 
         Returns:
@@ -223,12 +263,12 @@ class MockCollection(Collection):
         return acc
 
     def aggregate(self, *args, **kwargs) -> List[Any]:  # type: ignore
-        if self._simluate_cache_aggregation_query:
-            return deepcopy(self._execute_cache_aggreation_query(*args, **kwargs))
+        if self._simulate_cache_aggregation_query:
+            return deepcopy(self._execute_cache_aggregation_query(*args, **kwargs))
         return deepcopy(self._aggregate_result)
 
     def count_documents(self, *args, **kwargs) -> int:  # type: ignore
         return len(self._data)
 
     def __repr__(self) -> str:
-        return "FakeCollection"
+        return "MockCollection"