MoreAnalyzing graph dataNeo4j DB QA chainOn this pageNeo4j DB QA chainThis notebook shows how to use LLMs to provide a natural language interface to a graph database you can query with the Cypher query language.You will need to have a running Neo4j instance. One option is to create a free Neo4j database instance in their Aura cloud service. You can also run the database locally using the Neo4j Desktop application, or running a docker container.
You can run a local docker container by running the executing the following script:docker run \    --name neo4j \    -p 7474:7474 -p 7687:7687 \    -d \    -e NEO4J_AUTH=neo4j/pleaseletmein \    -e NEO4J_PLUGINS=\[\"apoc\"\]  \    neo4j:latestIf you are using the docker container, you need to wait a couple of second for the database to start.from langchain.chat_models import ChatOpenAIfrom langchain.chains import GraphCypherQAChainfrom langchain.graphs import Neo4jGraphgraph = Neo4jGraph(    url="bolt://localhost:7687", username="neo4j", password="pleaseletmein")    /home/tomaz/neo4j/langchain/libs/langchain/langchain/graphs/neo4j_graph.py:52: ExperimentalWarning: The configuration may change in the future.      self._driver.verify_connectivity()Seeding the database​Assuming your database is empty, you can populate it using Cypher query language. The following Cypher statement is idempotent, which means the database information will be the same if you run it one or multiple times.graph.query(    """MERGE (m:Movie {name:"Top Gun"})WITH mUNWIND ["Tom Cruise", "Val Kilmer", "Anthony Edwards", "Meg Ryan"] AS actorMERGE (a:Actor {name:actor})MERGE (a)-[:ACTED_IN]->(m)""")    []Refresh graph schema information​If the schema of database changes, you can refresh the schema information needed to generate Cypher statements.graph.refresh_schema()print(graph.schema)                Node properties are the following:            [{'properties': [{'property': 'name', 'type': 'STRING'}], 'labels': 'Movie'}, {'properties': [{'property': 'name', 'type': 'STRING'}], 'labels': 'Actor'}]            Relationship properties are the following:            []            The relationships are the following:            ['(:Actor)-[:ACTED_IN]->(:Movie)']            Querying the graph​We can now use the graph cypher QA chain to ask question of the graphchain = GraphCypherQAChain.from_llm(    ChatOpenAI(temperature=0), graph=graph, verbose=True)chain.run("Who played in Top Gun?")            > Entering new GraphCypherQAChain chain...    Generated Cypher:    MATCH (a:Actor)-[:ACTED_IN]->(m:Movie {name: 'Top Gun'})    RETURN a.name    Full Context:    [{'a.name': 'Tom Cruise'}, {'a.name': 'Val Kilmer'}, {'a.name': 'Anthony Edwards'}, {'a.name': 'Meg Ryan'}]        > Finished chain.    'Tom Cruise, Val Kilmer, Anthony Edwards, and Meg Ryan played in Top Gun.'Limit the number of results​You can limit the number of results from the Cypher QA Chain using the top_k parameter.
The default is 10.chain = GraphCypherQAChain.from_llm(    ChatOpenAI(temperature=0), graph=graph, verbose=True, top_k=2)chain.run("Who played in Top Gun?")            > Entering new GraphCypherQAChain chain...    Generated Cypher:    MATCH (a:Actor)-[:ACTED_IN]->(m:Movie {name: 'Top Gun'})    RETURN a.name    Full Context:    [{'a.name': 'Tom Cruise'}, {'a.name': 'Val Kilmer'}]        > Finished chain.    'Tom Cruise and Val Kilmer played in Top Gun.'Return intermediate results​You can return intermediate steps from the Cypher QA Chain using the return_intermediate_steps parameterchain = GraphCypherQAChain.from_llm(    ChatOpenAI(temperature=0), graph=graph, verbose=True, return_intermediate_steps=True)result = chain("Who played in Top Gun?")print(f"Intermediate steps: {result['intermediate_steps']}")print(f"Final answer: {result['result']}")            > Entering new GraphCypherQAChain chain...    Generated Cypher:    MATCH (a:Actor)-[:ACTED_IN]->(m:Movie {name: 'Top Gun'})    RETURN a.name    Full Context:    [{'a.name': 'Tom Cruise'}, {'a.name': 'Val Kilmer'}, {'a.name': 'Anthony Edwards'}, {'a.name': 'Meg Ryan'}]        > Finished chain.    Intermediate steps: [{'query': "MATCH (a:Actor)-[:ACTED_IN]->(m:Movie {name: 'Top Gun'})\nRETURN a.name"}, {'context': [{'a.name': 'Tom Cruise'}, {'a.name': 'Val Kilmer'}, {'a.name': 'Anthony Edwards'}, {'a.name': 'Meg Ryan'}]}]    Final answer: Tom Cruise, Val Kilmer, Anthony Edwards, and Meg Ryan played in Top Gun.Return direct results​You can return direct results from the Cypher QA Chain using the return_direct parameterchain = GraphCypherQAChain.from_llm(    ChatOpenAI(temperature=0), graph=graph, verbose=True, return_direct=True)chain.run("Who played in Top Gun?")            > Entering new GraphCypherQAChain chain...    Generated Cypher:    MATCH (a:Actor)-[:ACTED_IN]->(m:Movie {name: 'Top Gun'})    RETURN a.name        > Finished chain.    [{'a.name': 'Tom Cruise'},     {'a.name': 'Val Kilmer'},     {'a.name': 'Anthony Edwards'},     {'a.name': 'Meg Ryan'}]Add examples in the Cypher generation prompt​You can define the Cypher statement you want the LLM to generate for particular questionsfrom langchain.prompts.prompt import PromptTemplateCYPHER_GENERATION_TEMPLATE = """Task:Generate Cypher statement to query a graph database.Instructions:Use only the provided relationship types and properties in the schema.Do not use any other relationship types or properties that are not provided.Schema:{schema}Note: Do not include any explanations or apologies in your responses.Do not respond to any questions that might ask anything else than for you to construct a Cypher statement.Do not include any text except the generated Cypher statement.Examples: Here are a few examples of generated Cypher statements for particular questions:# How many people played in Top Gun?MATCH (m:Movie {{title:"Top Gun"}})<-[:ACTED_IN]-()RETURN count(*) AS numberOfActorsThe question is:{question}"""CYPHER_GENERATION_PROMPT = PromptTemplate(    input_variables=["schema", "question"], template=CYPHER_GENERATION_TEMPLATE)chain = GraphCypherQAChain.from_llm(    ChatOpenAI(temperature=0), graph=graph, verbose=True, cypher_prompt=CYPHER_GENERATION_PROMPT)chain.run("How many people played in Top Gun?")            > Entering new GraphCypherQAChain chain...    Generated Cypher:    MATCH (m:Movie {name:"Top Gun"})<-[:ACTED_IN]-(:Actor)    RETURN count(*) AS numberOfActors    Full Context:    [{'numberOfActors': 4}]        > Finished chain.    'Four people played in Top Gun.'Use separate LLMs for Cypher and answer generation​You can use the cypher_llm and qa_llm parameters to define different llmschain = GraphCypherQAChain.from_llm(     graph=graph,     cypher_llm=ChatOpenAI(temperature=0, model="gpt-3.5-turbo"),     qa_llm=ChatOpenAI(temperature=0, model="gpt-3.5-turbo-16k"),     verbose=True,)chain.run("Who played in Top Gun?")            > Entering new GraphCypherQAChain chain...    Generated Cypher:    MATCH (a:Actor)-[:ACTED_IN]->(m:Movie {name: 'Top Gun'})    RETURN a.name    Full Context:    [{'a.name': 'Tom Cruise'}, {'a.name': 'Val Kilmer'}, {'a.name': 'Anthony Edwards'}, {'a.name': 'Meg Ryan'}]        > Finished chain.    'Tom Cruise, Val Kilmer, Anthony Edwards, and Meg Ryan played in Top Gun.'Ignore specified node and relationship typesYou can use include_types or exclude_types to ignore parts of the graph schema when generating Cypher statements.chain = GraphCypherQAChain.from_llm(     graph=graph,     cypher_llm=ChatOpenAI(temperature=0, model="gpt-3.5-turbo"),     qa_llm=ChatOpenAI(temperature=0, model="gpt-3.5-turbo-16k"),     verbose=True,     exclude_types=['Movie'])# Inspect graph schemaprint(chain.graph_schema)    Node properties are the following:      {'Actor': [{'property': 'name', 'type': 'STRING'}]}    Relationships properties are the following:      {}    Relationships are:     []Validate generated Cypher statementsYou can use the validate_cypher parameter to validate and correct relationship directions in generated Cypher statementschain = GraphCypherQAChain.from_llm(     llm=ChatOpenAI(temperature=0, model="gpt-3.5-turbo"),     graph=graph,     verbose=True,     validate_cypher=True)chain.run("Who played in Top Gun?")            > Entering new GraphCypherQAChain chain...    Generated Cypher:    MATCH (a:Actor)-[:ACTED_IN]->(m:Movie {name: 'Top Gun'})    RETURN a.name    Full Context:    [{'a.name': 'Tom Cruise'}, {'a.name': 'Val Kilmer'}, {'a.name': 'Anthony Edwards'}, {'a.name': 'Meg Ryan'}]        > Finished chain.    'Tom Cruise, Val Kilmer, Anthony Edwards, and Meg Ryan played in Top Gun.'PreviousArangoDB QA chainNextFalkorDBQAChainSeeding the databaseRefresh graph schema informationQuerying the graphLimit the number of resultsReturn intermediate resultsReturn direct resultsAdd examples in the Cypher generation promptUse separate LLMs for Cypher and answer generation