ComponentsVector storesNeo4j Vector IndexOn this pageNeo4j Vector IndexNeo4j is an open-source graph database with integrated support for vector similarity searchIt supports:approximate nearest neighbor searchEuclidean similarity and cosine similarityHybrid search combining vector and keyword searchesThis notebook shows how to use the Neo4j vector index (Neo4jVector).See the installation instruction.# Pip install necessary packagepip install neo4jpip install openaipip install tiktokenWe want to use OpenAIEmbeddings so we have to get the OpenAI API Key.import osimport getpassos.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")    OpenAI API Key: ········from langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import Neo4jVectorfrom langchain.document_loaders import TextLoaderfrom langchain.docstore.document import Documentloader = TextLoader("../../../state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)embeddings = OpenAIEmbeddings()# Neo4jVector requires the Neo4j database credentialsurl = "bolt://localhost:7687"username = "neo4j"password = "pleaseletmein"# You can also use environment variables instead of directly passing named parameters#os.environ["NEO4J_URI"] = "bolt://localhost:7687"#os.environ["NEO4J_USERNAME"] = "neo4j"#os.environ["NEO4J_PASSWORD"] = "pleaseletmein"Similarity Search with Cosine Distance (Default)​# The Neo4jVector Module will connect to Neo4j and create a vector index if needed.db = Neo4jVector.from_documents(    docs, OpenAIEmbeddings(), url=url, username=username, password=password)    /home/tomaz/neo4j/langchain/libs/langchain/langchain/vectorstores/neo4j_vector.py:165: ExperimentalWarning: The configuration may change in the future.      self._driver.verify_connectivity()query = "What did the president say about Ketanji Brown Jackson"docs_with_score = db.similarity_search_with_score(query, k=2)for doc, score in docs_with_score:    print("-" * 80)    print("Score: ", score)    print(doc.page_content)    print("-" * 80)    --------------------------------------------------------------------------------    Score:  0.9099836349487305    Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections.         Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.         One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.         And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.    --------------------------------------------------------------------------------    --------------------------------------------------------------------------------    Score:  0.9099686145782471    Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections.         Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.         One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.         And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.    --------------------------------------------------------------------------------Working with vectorstore​Above, we created a vectorstore from scratch. However, often times we want to work with an existing vectorstore.
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​We 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)​Neo4j 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 keyword indices if needed.hybrid_db = Neo4jVector.from_documents(    docs,     OpenAIEmbeddings(),     url=url,     username=username,     password=password,    search_type="hybrid")    /home/tomaz/neo4j/langchain/libs/langchain/langchain/vectorstores/neo4j_vector.py:165: ExperimentalWarning: The configuration may change in the future.      self._driver.verify_connectivity()To load the hybrid search from existing indexes, you have to provide both the vector and keyword indicesindex_name = "vector"  # default index namekeyword_index_name = "keyword" #default keyword index namestore = Neo4jVector.from_existing_index(    OpenAIEmbeddings(),    url=url,    username=username,    password=password,    index_name=index_name,    keyword_index_name=keyword_index_name,    search_type="hybrid")    /home/tomaz/neo4j/langchain/libs/langchain/langchain/vectorstores/neo4j_vector.py:165: ExperimentalWarning: The configuration may change in the future.      self._driver.verify_connectivity()Retriever options​This section shows how to use Neo4jVector as a retriever.retriever = store.as_retriever()retriever.get_relevant_documents(query)[0]    Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../modules/state_of_the_union.txt'})Question Answering with Sources​This section goes over how to do question-answering with sources over an Index. It does this by using the RetrievalQAWithSourcesChain, which does the lookup of the documents from an Index. from langchain.chains import RetrievalQAWithSourcesChainfrom langchain.chat_models import ChatOpenAIchain = RetrievalQAWithSourcesChain.from_chain_type(    ChatOpenAI(temperature=0), chain_type="stuff", retriever=retriever)chain(    {"question": "What did the president say about Justice Breyer"},    return_only_outputs=True,)    {'answer': "The president honored Justice Stephen Breyer, who is retiring from the United States Supreme Court. He thanked him for his service and mentioned that he nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to continue Justice Breyer's legacy of excellence. \n",     'sources': '../../modules/state_of_the_union.txt'}PreviousMyScaleNextNucliaDBSimilarity Search with Cosine Distance (Default)Working with vectorstoreAdd documentsHybrid search (vector + keyword)Retriever optionsQuestion Answering with Sources