In order to do that, we can initialize it directly.index_name = "vector"  # default index namestore = Neo4jVector.from_existing_index(    OpenAIEmbeddings(),    url=url,    username=username,    password=password,    index_name=index_name,)    /home/tomaz/neo4j/langchain/libs/langchain/langchain/vectorstores/neo4j_vector.py:165: ExperimentalWarning: The configuration may change in the future.      self._driver.verify_connectivity()We can also initialize a vectorstore from existing graph using the from_existing_graph method. This method pulls relevant text information from the database, and calculates and stores the text embeddings back to the database.# First we create sample data in graphstore.query(    "CREATE (p:Person {name: 'Tomaz', location:'Slovenia', hobby:'Bicycle'})")    []# Now we initialize from existing graphexisting_graph = Neo4jVector.from_existing_graph(        embedding=OpenAIEmbeddings(),        url=url,        username=username,        password=password,        index_name="person_index",        node_label="Person",        text_node_properties=["name", "location"],        embedding_node_property="embedding",    )result = existing_graph.similarity_search("Slovenia", k = 1)    /home/tomaz/neo4j/langchain/libs/langchain/langchain/vectorstores/neo4j_vector.py:165: ExperimentalWarning: The configuration may change in the future.      self._driver.verify_connectivity()result[0]    Document(page_content='\nname: Tomaz\nlocation: Slovenia', metadata={'hobby': 'Bicycle'})Add documents‚ÄãWe can add documents to the existing vectorstore.store.add_documents([Document(page_content="foo")])    ['187fc53a-5dde-11ee-ad78-1f6b05bf8513']docs_with_score = store.similarity_search_with_score("foo")docs_with_score[0]    (Document(page_content='foo', metadata={}), 1.0)Hybrid search (vector + keyword)‚ÄãNeo4j integrates both vector and keyword indexes, which allows you to use a hybrid search approach# The Neo4jVector Module will connect to Neo4j and create a vector and
Neo4j is an open-source graph database with integrated support for vector similarity search
Neo4j is an open-source graph database with integrated support for vector similarity search ->: In order to do that, we can initialize it directly.index_name = "vector"  # default index namestore = Neo4jVector.from_existing_index(    OpenAIEmbeddings(),    url=url,    username=username,    password=password,    index_name=index_name,)    /home/tomaz/neo4j/langchain/libs/langchain/langchain/vectorstores/neo4j_vector.py:165: ExperimentalWarning: The configuration may change in the future.      self._driver.verify_connectivity()We can also initialize a vectorstore from existing graph using the from_existing_graph method. This method pulls relevant text information from the database, and calculates and stores the text embeddings back to the database.# First we create sample data in graphstore.query(    "CREATE (p:Person {name: 'Tomaz', location:'Slovenia', hobby:'Bicycle'})")    []# Now we initialize from existing graphexisting_graph = Neo4jVector.from_existing_graph(        embedding=OpenAIEmbeddings(),        url=url,        username=username,        password=password,        index_name="person_index",        node_label="Person",        text_node_properties=["name", "location"],        embedding_node_property="embedding",    )result = existing_graph.similarity_search("Slovenia", k = 1)    /home/tomaz/neo4j/langchain/libs/langchain/langchain/vectorstores/neo4j_vector.py:165: ExperimentalWarning: The configuration may change in the future.      self._driver.verify_connectivity()result[0]    Document(page_content='\nname: Tomaz\nlocation: Slovenia', metadata={'hobby': 'Bicycle'})Add documents‚ÄãWe can add documents to the existing vectorstore.store.add_documents([Document(page_content="foo")])    ['187fc53a-5dde-11ee-ad78-1f6b05bf8513']docs_with_score = store.similarity_search_with_score("foo")docs_with_score[0]    (Document(page_content='foo', metadata={}), 1.0)Hybrid search (vector + keyword)‚ÄãNeo4j integrates both vector and keyword indexes, which allows you to use a hybrid search approach# The Neo4jVector Module will connect to Neo4j and create a vector and