mongodb[patch]: Remove embedding retrieval from mongodb payload (#19035)

## Description
Returning the embedding is not necessary in the vector search
functionality unless specified as a debugging step. This change defaults
the behavior such that the server _only_ returns the embedding key if
explicitly requested, such as in the case of
`max_marginal_relevance_search`.


- [x] **Add tests and docs**: If you're adding a new integration, please
include
* Added `test_from_documents_no_embedding_return`


- [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/

---------

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

libs/partners/mongodb/langchain_mongodb/vectorstores.py

@@ -183,6 +183,8 @@ class MongoDBAtlasVectorSearch(VectorStore):
         k: int = 4,
         pre_filter: Optional[Dict] = None,
         post_filter_pipeline: Optional[List[Dict]] = None,
+        include_embedding: bool = False,
+        **kwargs: Any,
     ) -> List[Tuple[Document, float]]:
         params = {
             "queryVector": embedding,
@@ -199,6 +201,11 @@ class MongoDBAtlasVectorSearch(VectorStore):
             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]
@@ -215,6 +222,7 @@ class MongoDBAtlasVectorSearch(VectorStore):
         k: int = 4,
         pre_filter: Optional[Dict] = None,
         post_filter_pipeline: Optional[List[Dict]] = None,
+        **kwargs: Any,
     ) -> List[Tuple[Document, float]]:
         """Return MongoDB documents most similar to the given query and their scores.
 
@@ -238,6 +246,7 @@ class MongoDBAtlasVectorSearch(VectorStore):
             k=k,
             pre_filter=pre_filter,
             post_filter_pipeline=post_filter_pipeline,
+            **kwargs,
         )
         return docs
 
@@ -271,6 +280,7 @@ class MongoDBAtlasVectorSearch(VectorStore):
             k=k,
             pre_filter=pre_filter,
             post_filter_pipeline=post_filter_pipeline,
+            **kwargs,
         )
 
         if additional and "similarity_score" in additional:
@@ -310,20 +320,15 @@ class MongoDBAtlasVectorSearch(VectorStore):
             List of documents selected by maximal marginal relevance.
         """
         query_embedding = self._embedding.embed_query(query)
-        docs = self._similarity_search_with_score(
+        return self.max_marginal_relevance_search_by_vector(
-            query_embedding,
+            embedding=query_embedding,
-            k=fetch_k,
+            k=k,
+            fetch_k=fetch_k,
+            lambda_mult=lambda_mult,
             pre_filter=pre_filter,
             post_filter_pipeline=post_filter_pipeline,
+            **kwargs,
         )
-        mmr_doc_indexes = maximal_marginal_relevance(
-            np.array(query_embedding),
-            [doc.metadata[self._embedding_key] for doc, _ in docs],
-            k=k,
-            lambda_mult=lambda_mult,
-        )
-        mmr_docs = [docs[i][0] for i in mmr_doc_indexes]
-        return mmr_docs
 
     @classmethod
     def from_texts(
@@ -433,6 +438,8 @@ class MongoDBAtlasVectorSearch(VectorStore):
             k=fetch_k,
             pre_filter=pre_filter,
             post_filter_pipeline=post_filter_pipeline,
+            include_embedding=kwargs.pop("include_embedding", True),
+            **kwargs,
         )
         mmr_doc_indexes = maximal_marginal_relevance(
             np.array(embedding),

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

@@ -91,6 +91,52 @@ class TestMongoDBAtlasVectorSearch:
         # Check for the presence of the metadata key
         assert any([key.page_content == output[0].page_content for key in documents])
 
+    def test_from_documents_no_embedding_return(
+        self, embedding_openai: Embeddings, collection: Any
+    ) -> 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,
+            embedding_openai,
+            collection=collection,
+            index_name=INDEX_NAME,
+        )
+        output = vectorstore.similarity_search("Sandwich", k=1)
+        assert len(output) == 1
+        # 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])
+
+    def test_from_documents_embedding_return(
+        self, embedding_openai: Embeddings, collection: Any
+    ) -> 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,
+            embedding_openai,
+            collection=collection,
+            index_name=INDEX_NAME,
+        )
+        output = vectorstore.similarity_search("Sandwich", k=1, include_embedding=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])
+
     def test_from_texts(self, embedding_openai: Embeddings, collection: Any) -> None:
         texts = [
             "Dogs are tough.",