Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKQA over structured dataSQLRetrieval-augmented generation (RAG)Interacting with APIsChatbotsExtractionSummarizationTaggingWeb scrapingSynthetic data generationGraph queryingDiffbot Graph TransformerArangoDB QA chainNeo4j DB QA chainFalkorDBQAChainHugeGraph QA ChainKuzuQAChainMemgraph QA chainNebulaGraphQAChainNetworkX Graph QAGraphSparqlQAChainNeptune Open Cypher QA ChainGraph queryingFalkorDBQAChainOn this pageFalkorDBQAChainThis notebook shows how to use LLMs to provide a natural language interface to FalkorDB database.FalkorDB is a low latency property graph database management system. You can simply run its docker locally:docker run -p 6379:6379 -it --rm falkordb/falkordb:edgeOnce launched, you can simply start creating a database on the local machine and connect to it.from langchain.chat_models import ChatOpenAIfrom langchain.graphs import FalkorDBGraphfrom langchain.chains import FalkorDBQAChainCreate a graph connection and insert some demo data.‚Äãgraph = FalkorDBGraph(database="movies")graph.query("""    CREATE         (al:Person {name: 'Al Pacino', birthDate: '1940-04-25'}),        (robert:Person {name: 'Robert De Niro', birthDate: '1943-08-17'}),        (tom:Person {name: 'Tom Cruise', birthDate: '1962-07-3'}),        (val:Person {name: 'Val Kilmer', birthDate: '1959-12-31'}),        (anthony:Person {name: 'Anthony Edwards', birthDate: '1962-7-19'}),        (meg:Person {name: 'Meg Ryan', birthDate: '1961-11-19'}),        (god1:Movie {title: 'The Godfather'}),        (god2:Movie {title: 'The Godfather: Part II'}),        (god3:Movie {title: 'The Godfather Coda: The Death of Michael Corleone'}),        (top:Movie {title: 'Top Gun'}),        (al)-[:ACTED_IN]->(god1),        (al)-[:ACTED_IN]->(god2),        (al)-[:ACTED_IN]->(god3),        (robert)-[:ACTED_IN]->(god2),        (tom)-[:ACTED_IN]->(top),        (val)-[:ACTED_IN]->(top),
This notebook shows how to use LLMs to provide a natural language interface to FalkorDB database.
This notebook shows how to use LLMs to provide a natural language interface to FalkorDB database. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKQA over structured dataSQLRetrieval-augmented generation (RAG)Interacting with APIsChatbotsExtractionSummarizationTaggingWeb scrapingSynthetic data generationGraph queryingDiffbot Graph TransformerArangoDB QA chainNeo4j DB QA chainFalkorDBQAChainHugeGraph QA ChainKuzuQAChainMemgraph QA chainNebulaGraphQAChainNetworkX Graph QAGraphSparqlQAChainNeptune Open Cypher QA ChainGraph queryingFalkorDBQAChainOn this pageFalkorDBQAChainThis notebook shows how to use LLMs to provide a natural language interface to FalkorDB database.FalkorDB is a low latency property graph database management system. You can simply run its docker locally:docker run -p 6379:6379 -it --rm falkordb/falkordb:edgeOnce launched, you can simply start creating a database on the local machine and connect to it.from langchain.chat_models import ChatOpenAIfrom langchain.graphs import FalkorDBGraphfrom langchain.chains import FalkorDBQAChainCreate a graph connection and insert some demo data.‚Äãgraph = FalkorDBGraph(database="movies")graph.query("""    CREATE         (al:Person {name: 'Al Pacino', birthDate: '1940-04-25'}),        (robert:Person {name: 'Robert De Niro', birthDate: '1943-08-17'}),        (tom:Person {name: 'Tom Cruise', birthDate: '1962-07-3'}),        (val:Person {name: 'Val Kilmer', birthDate: '1959-12-31'}),        (anthony:Person {name: 'Anthony Edwards', birthDate: '1962-7-19'}),        (meg:Person {name: 'Meg Ryan', birthDate: '1961-11-19'}),        (god1:Movie {title: 'The Godfather'}),        (god2:Movie {title: 'The Godfather: Part II'}),        (god3:Movie {title: 'The Godfather Coda: The Death of Michael Corleone'}),        (top:Movie {title: 'Top Gun'}),        (al)-[:ACTED_IN]->(god1),        (al)-[:ACTED_IN]->(god2),        (al)-[:ACTED_IN]->(god3),        (robert)-[:ACTED_IN]->(god2),        (tom)-[:ACTED_IN]->(top),        (val)-[:ACTED_IN]->(top),