[docs]: vector store integration pages (#24858)

Co-authored-by: Erick Friis <erick@langchain.dev>

libs/partners/mongodb/langchain_mongodb/vectorstores.py

@@ -45,25 +45,142 @@ DEFAULT_INSERT_BATCH_SIZE = 100_000
 
 
 class MongoDBAtlasVectorSearch(VectorStore):
-    """`MongoDB Atlas Vector Search` vector store.
+    """MongoDBAtlas vector store integration.
 
-    To use, you should have both:
-    - the ``pymongo`` python package installed
-    - a connection string associated with a MongoDB Atlas Cluster having deployed an
-        Atlas Search index
+    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).
+
+        .. code-block:: bash
+
+            pip install -qU langchain-mongodb pymongo
 
-    Example:
         .. code-block:: python
 
-            from langchain_mongodb import MongoDBAtlasVectorSearch
-            from langchain_openai import OpenAIEmbeddings
-            from pymongo import MongoClient
+            import getpass
 
-            mongo_client = MongoClient("<YOUR-CONNECTION-STRING>")
-            collection = mongo_client["<db_name>"]["<collection_name>"]
-            embeddings = OpenAIEmbeddings()
-            vectorstore = MongoDBAtlasVectorSearch(collection, embeddings)
-    """
+            MONGODB_ATLAS_CLUSTER_URI = getpass.getpass("MongoDB Atlas Cluster URI:")
+
+    Key init args — indexing params:
+        embedding: Embeddings
+            Embedding function to use.
+
+    Key init args — client params:
+        collection: Collection
+            MongoDB collection to use.
+        index_name: str
+            Name of the Atlas Search index.
+
+    Instantiate:
+        .. code-block:: python
+
+            from pymongo import MongoClient
+            from langchain_mongodb.vectorstores import MongoDBAtlasVectorSearch
+            from pymongo import MongoClient
+            from langchain_openai import OpenAIEmbeddings
+
+            # initialize MongoDB python client
+            client = MongoClient(MONGODB_ATLAS_CLUSTER_URI)
+
+            DB_NAME = "langchain_test_db"
+            COLLECTION_NAME = "langchain_test_vectorstores"
+            ATLAS_VECTOR_SEARCH_INDEX_NAME = "langchain-test-index-vectorstores"
+
+            MONGODB_COLLECTION = client[DB_NAME][COLLECTION_NAME]
+
+            vector_store = MongoDBAtlasVectorSearch(
+                collection=MONGODB_COLLECTION,
+                embedding=OpenAIEmbeddings(),
+                index_name=ATLAS_VECTOR_SEARCH_INDEX_NAME,
+                relevance_score_fn="cosine",
+            )
+
+    Add Documents:
+        .. code-block:: python
+
+            from langchain_core.documents import Document
+
+            document_1 = Document(page_content="foo", metadata={"baz": "bar"})
+            document_2 = Document(page_content="thud", metadata={"bar": "baz"})
+            document_3 = Document(page_content="i will be deleted :(")
+
+            documents = [document_1, document_2, document_3]
+            ids = ["1", "2", "3"]
+            vector_store.add_documents(documents=documents, ids=ids)
+
+    Delete Documents:
+        .. code-block:: python
+
+            vector_store.delete(ids=["3"])
+
+    Search:
+        .. code-block:: python
+
+            results = vector_store.similarity_search(query="thud",k=1)
+            for doc in results:
+                print(f"* {doc.page_content} [{doc.metadata}]")
+
+        .. code-block:: python
+
+            * thud [{'_id': '2', 'baz': 'baz'}]
+
+
+    Search with filter:
+        .. code-block:: python
+
+            results = vector_store.similarity_search(query="thud",k=1,filter={"bar": "baz"})
+            for doc in results:
+                print(f"* {doc.page_content} [{doc.metadata}]")
+
+        .. code-block:: python
+
+            * thud [{'_id': '2', 'baz': 'baz'}]
+
+    Search with score:
+        .. code-block:: python
+
+            results = vector_store.similarity_search_with_score(query="qux",k=1)
+            for doc, score in results:
+                print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")
+
+        .. code-block:: python
+
+            * [SIM=0.916096] foo [{'_id': '1', 'baz': 'bar'}]
+
+    Async:
+        .. code-block:: python
+
+            # add documents
+            # await vector_store.aadd_documents(documents=documents, ids=ids)
+
+            # delete documents
+            # await vector_store.adelete(ids=["3"])
+
+            # search
+            # results = vector_store.asimilarity_search(query="thud",k=1)
+
+            # search with score
+            results = await vector_store.asimilarity_search_with_score(query="qux",k=1)
+            for doc,score in results:
+                print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")
+
+        .. code-block:: python
+
+            * [SIM=0.916096] foo [{'_id': '1', 'baz': 'bar'}]
+
+    Use as Retriever:
+        .. code-block:: python
+
+            retriever = vector_store.as_retriever(
+                search_type="mmr",
+                search_kwargs={"k": 1, "fetch_k": 2, "lambda_mult": 0.5},
+            )
+            retriever.invoke("thud")
+
+        .. code-block:: python
+
+            [Document(metadata={'_id': '2', 'embedding': [-0.01850726455450058, -0.0014740974875167012, -0.009762819856405258, ...], 'baz': 'baz'}, page_content='thud')]
+
+    """  # noqa: E501
 
     def __init__(
         self,