LangChain-MongoDB: [Experimental] Driver-side index creation helper (#19359)

## Description
Created a helper method to make vector search indexes via client-side
pymongo.

**Recent Update** -- Removed error suppressing/overwriting layer in
favor of letting the original exception provide information.

## ToDo's
- [x] Make _wait_untils for integration test delete index
functionalities.
- [x] Add documentation for its use. Highlight it's experimental
- [x] Post Integration Test Results in a screenshot
- [x] Get review from MongoDB internal team (@shaneharvey, @blink1073 ,
@NoahStapp , @caseyclements)



- [x] **Add tests and docs**: If you're adding a new integration, please
include
1. Added new integration tests. Not eligible for unit testing since the
operation is Atlas Cloud specific.
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.

![image](https://github.com/langchain-ai/langchain/assets/2887713/a3fc8ee1-e04c-4976-accc-fea0eeae028a)


- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/

docs/docs/integrations/vectorstores/mongodb_atlas.ipynb

@@ -143,6 +143,28 @@
     "    }\n",
     "  ]\n",
     "}\n",
+    "```\n",
+    "\n",
+    "Additionally, if you are running a MongoDB M10 cluster with server version 6.0+, you can leverage the `MongoDBAtlasVectorSearch.create_index`. To add the above index its usage would look like this.\n",
+    "\n",
+    "```python\n",
+    "from langchain_community.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain_mongodb.vectorstores import MongoDBAtlasVectorSearch\n",
+    "from pymongo import MongoClient\n",
+    "\n",
+    "mongo_client = MongoClient(\"<YOUR-CONNECTION-STRING>\")\n",
+    "collection = mongo_client[\"<db_name>\"][\"<collection_name>\"]\n",
+    "embeddings = OpenAIEmbeddings()\n",
+    "\n",
+    "vectorstore = MongoDBAtlasVectorSearch(\n",
+    "  collection=collection,\n",
+    "  embedding=embeddings,\n",
+    "  index_name=\"<ATLAS_VECTOR_SEARCH_INDEX_NAME>\",\n",
+    "  relevance_score_fn=\"cosine\",\n",
+    ")\n",
+    "\n",
+    "# Creates an index using the index_name provided and relevance_score_fn type\n",
+    "vectorstore.create_index(dimensions=1536)\n",
     "```"
    ]
   },
@@ -296,6 +318,16 @@
     "    }\n",
     "  ]\n",
     "}\n",
+    "```\n",
+    "\n",
+    "You can also update the index programmatically using the `MongoDBAtlasVectorSearch.create_index` method.\n",
+    "\n",
+    "```python\n",
+    "vectorstore.create_index(\n",
+    "  dimensions=1536,\n",
+    "  filters=[{\"type\":\"filter\", \"path\":\"page\"}],\n",
+    "  update=True\n",
+    ")\n",
     "```"
    ]
   },

libs/partners/mongodb/README.md

@@ -6,7 +6,7 @@ pip install -U langchain-mongodb
 ```
 
 # Usage
-- See [integrations doc](../../../docs/docs/integrations/vectorstores/mongodb.ipynb) for more in-depth usage instructions.
+- See [integrations doc](../../../docs/docs/integrations/providers/mongodb_atlas.ipynb) for more in-depth usage instructions.
 - See [Getting Started with the LangChain Integration](https://www.mongodb.com/docs/atlas/atlas-vector-search/ai-integrations/langchain/#get-started-with-the-langchain-integration) for a walkthrough on using your first LangChain implementation with MongoDB Atlas.
 
 ## Using MongoDBAtlasVectorSearch

libs/partners/mongodb/langchain_mongodb/index.py

@@ -0,0 +1,105 @@
+import logging
+from typing import Any, Dict, List, Optional
+
+from pymongo.collection import Collection
+from pymongo.operations import SearchIndexModel
+
+logger = logging.getLogger(__file__)
+
+
+def _vector_search_index_definition(
+    dimensions: int,
+    path: str,
+    similarity: str,
+    filters: Optional[List[Dict[str, str]]],
+) -> Dict[str, Any]:
+    return {
+        "fields": [
+            {
+                "numDimensions": dimensions,
+                "path": path,
+                "similarity": similarity,
+                "type": "vector",
+            },
+            *(filters or []),
+        ]
+    }
+
+
+def create_vector_search_index(
+    collection: Collection,
+    index_name: str,
+    dimensions: int,
+    path: str,
+    similarity: str,
+    filters: List[Dict[str, str]],
+) -> None:
+    """Experimental Utility function to create a vector search index
+
+    Args:
+        collection (Collection): MongoDB Collection
+        index_name (str): Name of Index
+        dimensions (int): Number of dimensions in embedding
+        path (str): field with vector embedding
+        similarity (str): The similarity score used for the index
+        filters (List[Dict[str, str]]): additional filters for index definition.
+    """
+    logger.info("Creating Search Index %s on %s", index_name, collection.name)
+    result = collection.create_search_index(
+        SearchIndexModel(
+            definition=_vector_search_index_definition(
+                dimensions=dimensions, path=path, similarity=similarity, filters=filters
+            ),
+            name=index_name,
+            type="vectorSearch",
+        )
+    )
+    logger.info(result)
+
+
+def drop_vector_search_index(collection: Collection, index_name: str) -> None:
+    """Drop a created vector search index
+
+    Args:
+        collection (Collection): MongoDB Collection with index to be dropped
+        index_name (str): Name of the MongoDB index
+    """
+    logger.info(
+        "Dropping Search Index %s from Collection: %s", index_name, collection.name
+    )
+    collection.drop_search_index(index_name)
+    logger.info("Vector Search index %s.%s dropped", collection.name, index_name)
+
+
+def update_vector_search_index(
+    collection: Collection,
+    index_name: str,
+    dimensions: int,
+    path: str,
+    similarity: str,
+    filters: List[Dict[str, str]],
+) -> None:
+    """Leverages the updateSearchIndex call
+
+    Args:
+        collection (Collection): MongoDB Collection
+        index_name (str): Name of Index
+        dimensions (int): Number of dimensions in embedding.
+        path (str): field with vector embedding.
+        similarity (str): The similarity score used for the index.
+        filters (List[Dict[str, str]]): additional filters for index definition.
+    """
+
+    logger.info(
+        "Updating Search Index %s from Collection: %s", index_name, collection.name
+    )
+    collection.update_search_index(
+        name=index_name,
+        definition=_vector_search_index_definition(
+            dimensions=dimensions,
+            path=path,
+            similarity=similarity,
+            filters=filters,
+        ),
+    )
+    logger.info("Update succeeded")

libs/partners/mongodb/langchain_mongodb/utils.py

@@ -18,6 +18,11 @@ 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:

libs/partners/mongodb/langchain_mongodb/vectorstores.py

@@ -24,7 +24,12 @@ from langchain_core.vectorstores import VectorStore
 from pymongo import MongoClient
 from pymongo.collection import Collection
 from pymongo.driver_info import DriverInfo
+from pymongo.errors import CollectionInvalid
 
+from langchain_mongodb.index import (
+    create_vector_search_index,
+    update_vector_search_index,
+)
 from langchain_mongodb.utils import maximal_marginal_relevance
 
 MongoDBDocumentType = TypeVar("MongoDBDocumentType", bound=Dict[str, Any])
@@ -489,3 +494,42 @@ class MongoDBAtlasVectorSearch(VectorStore):
             lambda_mult=lambda_mult,
             **kwargs,
         )
+
+    def create_vector_search_index(
+        self,
+        dimensions: int,
+        filters: Optional[List[Dict[str, 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
+
+        Args:
+            dimensions (int): Number of dimensions in embedding
+            filters (Optional[List[Dict[str, str]]], optional): additional filters
+            for index definition.
+                Defaults to None.
+            update (bool, optional): Updates existing vectorSearch index.
+                Defaults to False.
+        """
+        try:
+            self._collection.database.create_collection(self._collection.name)
+        except CollectionInvalid:
+            pass
+
+        index_operation = (
+            update_vector_search_index if update else create_vector_search_index
+        )
+
+        index_operation(
+            collection=self._collection,
+            index_name=self._index_name,
+            dimensions=dimensions,
+            path=self._embedding_key,
+            similarity=self._relevance_score_fn,
+            filters=filters or [],
+        )

libs/partners/mongodb/tests/integration_tests/test_vectorstores.py

@@ -3,22 +3,27 @@
 from __future__ import annotations
 
 import os
-from time import sleep
-from typing import Any, Dict, List
+from time import monotonic, sleep
+from typing import Any, Dict, List, Optional
 
 import pytest
 from langchain_core.documents import Document
 from langchain_core.embeddings import Embeddings
 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 tests.utils import ConsistentFakeEmbeddings
 
 INDEX_NAME = "langchain-test-index-vectorstores"
+INDEX_CREATION_NAME = "langchain-test-index-vectorstores-create-test"
 NAMESPACE = "langchain_test_db.langchain_test_vectorstores"
 CONNECTION_STRING = os.environ.get("MONGODB_ATLAS_URI")
 DB_NAME, COLLECTION_NAME = NAMESPACE.split(".")
+INDEX_COLLECTION_NAME = "langchain_test_vectorstores_index"
+INDEX_DB_NAME = "langchain_test_index_db"
 DIMENSIONS = 1536
 TIMEOUT = 10.0
 INTERVAL = 0.5
@@ -28,16 +33,53 @@ class PatchedMongoDBAtlasVectorSearch(MongoDBAtlasVectorSearch):
     def _insert_texts(self, texts: List[str], metadatas: List[Dict[str, Any]]) -> List:
         """Patched insert_texts that waits for data to be indexed before returning"""
         ids = super()._insert_texts(texts, metadatas)
-        timeout = TIMEOUT
-        while len(ids) != self.similarity_search("sandwich") and timeout >= 0:
+        start = monotonic()
+        while len(ids) != self.similarity_search("sandwich") and (
+            monotonic() - start <= TIMEOUT
+        ):
             sleep(INTERVAL)
-            timeout -= INTERVAL
         return ids
 
+    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)
 
-def get_collection() -> Collection:
+        raise TimeoutError(f"{self._index_name} never reached 'status: READY'")
+
+
+def _await_index_deletion(coll: Collection, index_name: str) -> None:
+    start = monotonic()
+    try:
+        drop_vector_search_index(coll, index_name)
+    except OperationFailure:
+        # This most likely means an ongoing drop request was made so skip
+        pass
+
+    while list(coll.list_search_indexes(name=index_name)):
+        if monotonic() - start > TIMEOUT:
+            raise TimeoutError(f"Index Name: {index_name} never dropped")
+        sleep(INTERVAL)
+
+
+def get_collection(
+    database_name: str = DB_NAME, collection_name: str = COLLECTION_NAME
+) -> Collection:
     test_client: MongoClient = MongoClient(CONNECTION_STRING)
-    return test_client[DB_NAME][COLLECTION_NAME]
+    return test_client[database_name][collection_name]
 
 
 @pytest.fixture()
@@ -45,6 +87,11 @@ def collection() -> Collection:
     return get_collection()
 
 
+@pytest.fixture()
+def index_collection() -> Collection:
+    return get_collection(INDEX_DB_NAME, INDEX_COLLECTION_NAME)
+
+
 class TestMongoDBAtlasVectorSearch:
     @classmethod
     def setup_class(cls) -> None:
@@ -65,6 +112,11 @@ class TestMongoDBAtlasVectorSearch:
         # delete all the documents in the collection
         collection.delete_many({})  # type: ignore[index]
 
+        # delete all indexes on index collection name
+        _await_index_deletion(
+            get_collection(INDEX_DB_NAME, INDEX_COLLECTION_NAME), INDEX_CREATION_NAME
+        )
+
     @pytest.fixture
     def embedding_openai(self) -> Embeddings:
         return ConsistentFakeEmbeddings(DIMENSIONS)
@@ -85,7 +137,6 @@ class TestMongoDBAtlasVectorSearch:
             collection=collection,
             index_name=INDEX_NAME,
         )
-        # sleep(5)  # waits for mongot to update Lucene's index
         output = vectorstore.similarity_search("Sandwich", k=1)
         assert len(output) == 1
         # Check for the presence of the metadata key
@@ -150,7 +201,6 @@ class TestMongoDBAtlasVectorSearch:
             collection=collection,
             index_name=INDEX_NAME,
         )
-        # sleep(5)  # waits for mongot to update Lucene's index
         output = vectorstore.similarity_search("Sandwich", k=1)
         assert len(output) == 1
 
@@ -172,7 +222,6 @@ class TestMongoDBAtlasVectorSearch:
             collection=collection,
             index_name=INDEX_NAME,
         )
-        # sleep(5)  # waits for mongot to update Lucene's index
         output = vectorstore.similarity_search("Sandwich", k=1)
         assert len(output) == 1
         # Check for the presence of the metadata key
@@ -195,7 +244,6 @@ class TestMongoDBAtlasVectorSearch:
             collection=collection,
             index_name=INDEX_NAME,
         )
-        # sleep(5)  # waits for mongot to update Lucene's index
         output = vectorstore.similarity_search(
             "Sandwich", k=1, pre_filter={"c": {"$lte": 0}}
         )
@@ -209,9 +257,25 @@ class TestMongoDBAtlasVectorSearch:
             collection=collection,
             index_name=INDEX_NAME,
         )
-        # sleep(5)  # waits for mongot to update Lucene's index
         query = "foo"
         output = vectorstore.max_marginal_relevance_search(query, k=10, lambda_mult=0.1)
         assert len(output) == len(texts)
         assert output[0].page_content == "foo"
         assert output[1].page_content != "foo"
+
+    def test_index_creation(
+        self, embedding_openai: Embeddings, index_collection: Any
+    ) -> None:
+        vectorstore = PatchedMongoDBAtlasVectorSearch(
+            index_collection, embedding_openai, index_name=INDEX_CREATION_NAME
+        )
+        vectorstore.create_vector_search_index(dimensions=1536)
+
+    def test_index_update(
+        self, embedding_openai: Embeddings, index_collection: Any
+    ) -> None:
+        vectorstore = PatchedMongoDBAtlasVectorSearch(
+            index_collection, embedding_openai, index_name=INDEX_CREATION_NAME
+        )
+        vectorstore.create_vector_search_index(dimensions=1536)
+        vectorstore.create_vector_search_index(dimensions=1536, update=True)