os.environ["NEO4J_URI"] = os.getenv("NEO4J_URI")os.environ["NEO4J_USERNAME"] = "neo4j"os.environ["NEO4J_PASSWORD"] = os.getenv("NEO4J_PASSWORD")api_key = os.getenv("AZURE_OPENAI_API_KEY")azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")api_version = "2023-07-01-preview"

llm = AzureChatOpenAI(model="gpt-4",azure_deployment="gpt-4",api_key=api_key,azure_endpoint=azure_endpoint,openai_api_version=api_version,)

# 初始化 LLMTransformer 模型llm_transformer = LLMGraphTransformer(llm=llm)
# 转换文件from langchain_core.documents import Document
text = """Mr. and Mrs. Dursley, of number four, Privet Drive, were proud to saythat they were perfectly normal, thank you very much. They were the lastpeople you'd expect to be involved in anything strange or mysterious,because they just didn't hold with such nonsense.Mr. Dursley was the director of a firm called Grunnings, which madedrills. He was a big, beefy man with hardly any neck, although he didhave a very large mustache. Mrs. Dursley was thin and blonde and hadnearly twice the usual amount of neck, which came in very useful as shespent so much of her time craning over garden fences, spying on theneighbors. The Dursleys had a small son called Dudley and in theiropinion there was no finer boy anywhere.The Dursleys had everything they wanted, but they also had a secret, andtheir greatest fear was that somebody would discover it. They didn'tthink they could bear it if anyone found out about the Potters. Mrs.Potter was Mrs. Dursley's sister, but they hadn't met for several years;in fact, Mrs. Dursley pretended she didn't have a sister, because hersister and her good-for-nothing husband were as unDursleyish as it waspossible to be. The Dursleys shuddered to think what the neighbors wouldsay if the Potters arrived in the street. The Dursleys knew that thePotters had a small son, too, but they had never even seen him. This boywas another good reason for keeping the Potters away; they didn't wantDudley mixing with a child like that."""documents = [Document(page_content=text)]graph_documents = llm_transformer.convert_to_graph_documents(documents)print(f"Nodes:{graph_documents[0].nodes}")print(f"Relationships:{graph_documents[0].relationships}")

Nodes:[Node(id='Mr. Dursley', type='Person'), Node(id='Mrs. Dursley', type='Person'), Node(id='Dudley', type='Person'), Node(id='Privet Drive', type='Location'), Node(id='Grunnings', type='Organization'), Node(id='Mrs. Potter', type='Person'), Node(id='The Potters', type='Family')]Relationships:[Relationship(source=Node(id='Mr. Dursley', type='Person'), target=Node(id='Mrs. Dursley', type='Person'), type='MARRIED_TO'), Relationship(source=Node(id='Mr. Dursley', type='Person'), target=Node(id='Dudley', type='Person'), type='PARENT_OF'), Relationship(source=Node(id='Mrs. Dursley', type='Person'), target=Node(id='Dudley', type='Person'), type='PARENT_OF'), Relationship(source=Node(id='Mr. Dursley', type='Person'), target=Node(id='Grunnings', type='Organization'), type='WORKS_AT'), Relationship(source=Node(id='Mr. Dursley', type='Person'), target=Node(id='Privet Drive', type='Location'), type='LIVES_AT'), Relationship(source=Node(id='Mrs. Dursley', type='Person'), target=Node(id='Privet Drive', type='Location'), type='LIVES_AT'), Relationship(source=Node(id='Mrs. Dursley', type='Person'), target=Node(id='Mrs. Potter', type='Person'), type='SISTER_OF'), Relationship(source=Node(id='The Dursleys', type='Family'), target=Node(id='The Potters', type='Family'), type='WANTS_TO_AVOID')]

graph.add_graph_documents(graph_documents,baseEntityLabel=True,include_source=True)

# 直接显示给定Cypher查询的结果图default_cypher = "MATCH (s)-[r:!MENTIONS]->(t) RETURN s,r,t LIMIT 50"
def showGraph(cypher: str = default_cypher):# 创建一个neo4j会话来运行查询driver = GraphDatabase.driver(uri = os.environ["NEO4J_URI"],auth = (os.environ["NEO4J_USERNAME"],os.environ["NEO4J_PASSWORD"]))session = driver.session()widget = GraphWidget(graph = session.run(cypher).graph())widget.node_label_mapping = 'id'#display(widget)return widget
showGraph()

from langchain_openai import AzureOpenAIEmbeddings
embeddings = AzureOpenAIEmbeddings(model="text-embedding-ada-002",api_key=api_key,azure_endpoint=azure_endpoint,openai_api_version=api_version,)

vector_index = Neo4jVector.from_existing_graph(embeddings,search_type="hybrid",node_label="Document",text_node_properties=["text"],embedding_node_property="embedding")

query = "Who is Dudley?"
results = vector_index.similarity_search(query, k=1)print(results[0].page_content)

Mr. and Mrs. Dursley, of number four, Privet Drive, were proud to saythat they were perfectly normal, thank you very much. They were the lastpeople you'd expect to be involved in anything strange or mysterious,because they just didn't hold with such nonsense.Mr. Dursley was the director of a firm called Grunnings, which madedrills. He was a big, beefy man with hardly any neck, although he didhave a very large mustache. Mrs. Dursley was thin and blonde and hadnearly twice the usual amount of neck, which came in very useful as shespent so much of her time craning over garden fences, spying on theneighbors. The Dursleys had a small son called Dudley and in theiropinion there was no finer boy anywhere.The Dursleys had everything they wanted, but they also had a secret, andtheir greatest fear was that somebody would discover it. They didn'tthink they could bear it if anyone found out about the Potters. Mrs.Potter was Mrs. Dursley's sister, but they hadn't met for several years;in fact, Mrs. Dursley pretended she didn't have a sister, because hersister and her good-for-nothing husband were as unDursleyish as it waspossible to be. The Dursleys shuddered to think what the neighbors wouldsay if the Potters arrived in the street. The Dursleys knew that thePotters had a small son, too, but they had never even seen him. This boywas another good reason for keeping the Potters away; they didn't wantDudley mixing with a child like that.

from langchain.chains import GraphCypherQAChain
chain = GraphCypherQAChain.from_llm(graph=graph, llm=llm, verbose=True)response = chain.invoke({"query": "What is Mr. Dursley's job?"})response

> Entering new GraphCypherQAChain chain...Generated Cypher:MATCH (p:Person {id: "Mr. Dursley"})-[:WORKS_AT]->(o:Organization) RETURN o.idFull Context:[{'o.id': 'Grunnings'}]
> Finished chain.{'query': "What is Mr. Dursley's job?", 'result': 'Mr. Dursley works at Grunnings.'}

from langchain.chains import RetrievalQA
qa_chain = RetrievalQA.from_chain_type(llm, retriever=vector_index.as_retriever())
result = qa_chain({"query": "What is Mr. Dursley's job?"})result["result"]

'Mr. Dursley is the director of a firm called Grunnings, which makes drills.'