INTPYTHON-452 Add hybrid retriever test with nested field (#54)

Author: blink1073

libs/langchain-mongodb/langchain_mongodb/vectorstores.py

@@ -758,6 +758,8 @@ def _similarity_search_with_score(
 
         # Format
         for res in cursor:
+            if self._text_key not in res:
+                continue
             text = res.pop(self._text_key)
             score = res.pop("score")
             make_serializable(res)

libs/langchain-mongodb/tests/integration_tests/test_retrievers.py

@@ -21,10 +21,13 @@
 
 DB_NAME = "langchain_test_db"
 COLLECTION_NAME = "langchain_test_retrievers"
+COLLECTION_NAME_NESTED = "langchain_test_retrievers_nested"
 VECTOR_INDEX_NAME = "vector_index"
 EMBEDDING_FIELD = "embedding"
 PAGE_CONTENT_FIELD = "text"
+PAGE_CONTENT_FIELD_NESTED = "title.text"
 SEARCH_INDEX_NAME = "text_index"
+SEARCH_INDEX_NAME_NESTED = "text_index_nested"
 
 TIMEOUT = 60.0
 INTERVAL = 0.5
@@ -71,6 +74,39 @@ def collection(client: MongoClient, dimensions: int) -> Collection:
     return clxn
 
 
+@pytest.fixture(scope="module")
+def collection_nested(client: MongoClient, dimensions: int) -> Collection:
+    """A Collection with both a Vector and a Full-text Search Index"""
+    if COLLECTION_NAME_NESTED not in client[DB_NAME].list_collection_names():
+        clxn = client[DB_NAME].create_collection(COLLECTION_NAME_NESTED)
+    else:
+        clxn = client[DB_NAME][COLLECTION_NAME_NESTED]
+
+    clxn.delete_many({})
+
+    if not any([VECTOR_INDEX_NAME == ix["name"] for ix in clxn.list_search_indexes()]):
+        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_NESTED == ix["name"] for ix in clxn.list_search_indexes()]
+    ):
+        create_fulltext_search_index(
+            collection=clxn,
+            index_name=SEARCH_INDEX_NAME_NESTED,
+            field=PAGE_CONTENT_FIELD_NESTED,
+            wait_until_complete=TIMEOUT,
+        )
+
+    return clxn
+
+
 @pytest.fixture(scope="module")
 def indexed_vectorstore(
     collection: Collection,
@@ -93,6 +129,28 @@ def indexed_vectorstore(
     vectorstore.collection.delete_many({})
 
 
+@pytest.fixture(scope="module")
+def indexed_nested_vectorstore(
+    collection_nested: Collection,
+    example_documents: List[Document],
+    embedding: Embeddings,
+) -> Generator[MongoDBAtlasVectorSearch, None, None]:
+    """Return a VectorStore with example document embeddings indexed."""
+
+    vectorstore = PatchedMongoDBAtlasVectorSearch(
+        collection=collection_nested,
+        embedding=embedding,
+        index_name=VECTOR_INDEX_NAME,
+        text_key=PAGE_CONTENT_FIELD_NESTED,
+    )
+
+    vectorstore.add_documents(example_documents)
+
+    yield vectorstore
+
+    vectorstore.collection.delete_many({})
+
+
 def test_vector_retriever(indexed_vectorstore: PatchedMongoDBAtlasVectorSearch) -> None:
     """Test VectorStoreRetriever"""
     retriever = indexed_vectorstore.as_retriever()
@@ -125,6 +183,26 @@ def test_hybrid_retriever(indexed_vectorstore: PatchedMongoDBAtlasVectorSearch)
     assert "New Orleans" in results[0].page_content
 
 
+def test_hybrid_retriever_nested(
+    indexed_nested_vectorstore: PatchedMongoDBAtlasVectorSearch,
+) -> None:
+    """Test basic usage of MongoDBAtlasHybridSearchRetriever"""
+    retriever = MongoDBAtlasHybridSearchRetriever(
+        vectorstore=indexed_nested_vectorstore,
+        search_index_name=SEARCH_INDEX_NAME_NESTED,
+        top_k=3,
+    )
+
+    query1 = "What did I visit France?"
+    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(
     indexed_vectorstore: PatchedMongoDBAtlasVectorSearch,
 ) -> None: