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1,800	NetworkX Graph QA \| ü¶úÔ∏èüîó Langchain	This notebook goes over how to do question answering over a graph data structure.	This notebook goes over how to do question answering over a graph data structure. ->: NetworkX Graph QA \| ü¶úÔ∏èüîó Langchain
1,801	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 queryingNetworkX Graph QAOn this pageNetworkX Graph QAThis notebook goes over how to do question answering over a graph data structure.Create the graph‚ÄãIn this section, we construct an example graph. At the moment, this works best for small pieces of text.from langchain.indexes import GraphIndexCreatorfrom langchain.llms import OpenAIfrom langchain.document_loaders import TextLoaderindex_creator = GraphIndexCreator(llm=OpenAI(temperature=0))with open("../../../modules/state_of_the_union.txt") as f: all_text = f.read()We will use just a small snippet, because extracting the knowledge triplets is a bit intensive at the moment.text = "\n".join(all_text.split("\n\n")[105:108])text 'It won‚Äôt look like much, but if you stop and look closely, you‚Äôll see a ‚ÄúField of dreams,‚Äù the ground on which America‚Äôs future will be built. \nThis is where Intel, the American company that helped build Silicon Valley, is going to build its $20 billion semiconductor ‚Äúmega site‚Äù. \nUp to eight state-of-the-art factories in one place. 10,000 new good-paying jobs. 'graph = index_creator.from_text(text)We can inspect the created graph.graph.get_triples() [('Intel', '$20 billion semiconductor "mega site"', 'is going to build'), ('Intel', 'state-of-the-art factories', 'is building'), ('Intel', '10,000 new good-paying jobs', 'is creating'), ('Intel', 'Silicon Valley', 'is helping build'), ('Field of dreams', "America's future will be built", 'is the ground	This notebook goes over how to do question answering over a graph data structure.	This notebook goes over how to do question answering over a graph data structure. ->: 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 queryingNetworkX Graph QAOn this pageNetworkX Graph QAThis notebook goes over how to do question answering over a graph data structure.Create the graph‚ÄãIn this section, we construct an example graph. At the moment, this works best for small pieces of text.from langchain.indexes import GraphIndexCreatorfrom langchain.llms import OpenAIfrom langchain.document_loaders import TextLoaderindex_creator = GraphIndexCreator(llm=OpenAI(temperature=0))with open("../../../modules/state_of_the_union.txt") as f: all_text = f.read()We will use just a small snippet, because extracting the knowledge triplets is a bit intensive at the moment.text = "\n".join(all_text.split("\n\n")[105:108])text 'It won‚Äôt look like much, but if you stop and look closely, you‚Äôll see a ‚ÄúField of dreams,‚Äù the ground on which America‚Äôs future will be built. \nThis is where Intel, the American company that helped build Silicon Valley, is going to build its $20 billion semiconductor ‚Äúmega site‚Äù. \nUp to eight state-of-the-art factories in one place. 10,000 new good-paying jobs. 'graph = index_creator.from_text(text)We can inspect the created graph.graph.get_triples() [('Intel', '$20 billion semiconductor "mega site"', 'is going to build'), ('Intel', 'state-of-the-art factories', 'is building'), ('Intel', '10,000 new good-paying jobs', 'is creating'), ('Intel', 'Silicon Valley', 'is helping build'), ('Field of dreams', "America's future will be built", 'is the ground
1,802	future will be built", 'is the ground on which')]Querying the graph‚ÄãWe can now use the graph QA chain to ask question of the graphfrom langchain.chains import GraphQAChainchain = GraphQAChain.from_llm(OpenAI(temperature=0), graph=graph, verbose=True)chain.run("what is Intel going to build?") > Entering new GraphQAChain chain... Entities Extracted: Intel Full Context: Intel is going to build $20 billion semiconductor "mega site" Intel is building state-of-the-art factories Intel is creating 10,000 new good-paying jobs Intel is helping build Silicon Valley > Finished chain. ' Intel is going to build a $20 billion semiconductor "mega site" with state-of-the-art factories, creating 10,000 new good-paying jobs and helping to build Silicon Valley.'Save the graph‚ÄãWe can also save and load the graph.graph.write_to_gml("graph.gml")from langchain.indexes.graph import NetworkxEntityGraphloaded_graph = NetworkxEntityGraph.from_gml("graph.gml")loaded_graph.get_triples() [('Intel', '$20 billion semiconductor "mega site"', 'is going to build'), ('Intel', 'state-of-the-art factories', 'is building'), ('Intel', '10,000 new good-paying jobs', 'is creating'), ('Intel', 'Silicon Valley', 'is helping build'), ('Field of dreams', "America's future will be built", 'is the ground on which')]PreviousNebulaGraphQAChainNextGraphSparqlQAChainCreate the graphQuerying the graphSave the graphCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.	This notebook goes over how to do question answering over a graph data structure.	This notebook goes over how to do question answering over a graph data structure. ->: future will be built", 'is the ground on which')]Querying the graph‚ÄãWe can now use the graph QA chain to ask question of the graphfrom langchain.chains import GraphQAChainchain = GraphQAChain.from_llm(OpenAI(temperature=0), graph=graph, verbose=True)chain.run("what is Intel going to build?") > Entering new GraphQAChain chain... Entities Extracted: Intel Full Context: Intel is going to build $20 billion semiconductor "mega site" Intel is building state-of-the-art factories Intel is creating 10,000 new good-paying jobs Intel is helping build Silicon Valley > Finished chain. ' Intel is going to build a $20 billion semiconductor "mega site" with state-of-the-art factories, creating 10,000 new good-paying jobs and helping to build Silicon Valley.'Save the graph‚ÄãWe can also save and load the graph.graph.write_to_gml("graph.gml")from langchain.indexes.graph import NetworkxEntityGraphloaded_graph = NetworkxEntityGraph.from_gml("graph.gml")loaded_graph.get_triples() [('Intel', '$20 billion semiconductor "mega site"', 'is going to build'), ('Intel', 'state-of-the-art factories', 'is building'), ('Intel', '10,000 new good-paying jobs', 'is creating'), ('Intel', 'Silicon Valley', 'is helping build'), ('Field of dreams', "America's future will be built", 'is the ground on which')]PreviousNebulaGraphQAChainNextGraphSparqlQAChainCreate the graphQuerying the graphSave the graphCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
1,803	FalkorDBQAChain \| ü¶úÔ∏èüîó Langchain	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. ->: FalkorDBQAChain \| ü¶úÔ∏èüîó Langchain
1,804	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),
1,805	(val)-[:ACTED_IN]->(top), (anthony)-[:ACTED_IN]->(top), (meg)-[:ACTED_IN]->(top)""") []Creating FalkorDBQAChain‚Äãgraph.refresh_schema()print(graph.schema)import osos.environ['OPENAI_API_KEY']='API_KEY_HERE' Node properties: [[OrderedDict([('label', None), ('properties', ['name', 'birthDate', 'title'])])]] Relationships properties: [[OrderedDict([('type', None), ('properties', [])])]] Relationships: [['(:Person)-[:ACTED_IN]->(:Movie)']] chain = FalkorDBQAChain.from_llm( ChatOpenAI(temperature=0), graph=graph, verbose=True)Querying the graph‚Äãchain.run("Who played in Top Gun?") > Entering new FalkorDBQAChain chain... Generated Cypher: MATCH (p:Person)-[:ACTED_IN]->(m:Movie) WHERE m.title = 'Top Gun' RETURN p.name Full Context: [['Tom Cruise'], ['Val Kilmer'], ['Anthony Edwards'], ['Meg Ryan'], ['Tom Cruise'], ['Val Kilmer'], ['Anthony Edwards'], ['Meg Ryan']] > Finished chain. 'Tom Cruise, Val Kilmer, Anthony Edwards, and Meg Ryan played in Top Gun.'chain.run("Who is the oldest actor who played in The Godfather: Part II?") > Entering new FalkorDBQAChain chain... Generated Cypher: MATCH (p:Person)-[r:ACTED_IN]->(m:Movie) WHERE m.title = 'The Godfather: Part II' RETURN p.name ORDER BY p.birthDate ASC LIMIT 1 Full Context: [['Al Pacino']] > Finished chain. 'The oldest actor who played in The Godfather: Part II is Al Pacino.'chain.run("Robert De Niro played in which movies?") > Entering new FalkorDBQAChain chain... Generated Cypher: MATCH (p:Person {name: 'Robert De Niro'})-[:ACTED_IN]->(m:Movie) RETURN m.title Full Context: [['The Godfather: Part II'], ['The Godfather: Part II']] > Finished chain. 'Robert De Niro played in "The Godfather: Part II".'PreviousNeo4j DB QA chainNextHugeGraph QA ChainCreate a graph connection and insert some demo data.Creating FalkorDBQAChainQuerying the	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. ->: (val)-[:ACTED_IN]->(top), (anthony)-[:ACTED_IN]->(top), (meg)-[:ACTED_IN]->(top)""") []Creating FalkorDBQAChain‚Äãgraph.refresh_schema()print(graph.schema)import osos.environ['OPENAI_API_KEY']='API_KEY_HERE' Node properties: [[OrderedDict([('label', None), ('properties', ['name', 'birthDate', 'title'])])]] Relationships properties: [[OrderedDict([('type', None), ('properties', [])])]] Relationships: [['(:Person)-[:ACTED_IN]->(:Movie)']] chain = FalkorDBQAChain.from_llm( ChatOpenAI(temperature=0), graph=graph, verbose=True)Querying the graph‚Äãchain.run("Who played in Top Gun?") > Entering new FalkorDBQAChain chain... Generated Cypher: MATCH (p:Person)-[:ACTED_IN]->(m:Movie) WHERE m.title = 'Top Gun' RETURN p.name Full Context: [['Tom Cruise'], ['Val Kilmer'], ['Anthony Edwards'], ['Meg Ryan'], ['Tom Cruise'], ['Val Kilmer'], ['Anthony Edwards'], ['Meg Ryan']] > Finished chain. 'Tom Cruise, Val Kilmer, Anthony Edwards, and Meg Ryan played in Top Gun.'chain.run("Who is the oldest actor who played in The Godfather: Part II?") > Entering new FalkorDBQAChain chain... Generated Cypher: MATCH (p:Person)-[r:ACTED_IN]->(m:Movie) WHERE m.title = 'The Godfather: Part II' RETURN p.name ORDER BY p.birthDate ASC LIMIT 1 Full Context: [['Al Pacino']] > Finished chain. 'The oldest actor who played in The Godfather: Part II is Al Pacino.'chain.run("Robert De Niro played in which movies?") > Entering new FalkorDBQAChain chain... Generated Cypher: MATCH (p:Person {name: 'Robert De Niro'})-[:ACTED_IN]->(m:Movie) RETURN m.title Full Context: [['The Godfather: Part II'], ['The Godfather: Part II']] > Finished chain. 'Robert De Niro played in "The Godfather: Part II".'PreviousNeo4j DB QA chainNextHugeGraph QA ChainCreate a graph connection and insert some demo data.Creating FalkorDBQAChainQuerying the
1,806	demo data.Creating FalkorDBQAChainQuerying the graphCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.	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. ->: demo data.Creating FalkorDBQAChainQuerying the graphCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
1,807	Memgraph QA chain \| ü¶úÔ∏èüîó Langchain	This notebook shows how to use LLMs to provide a natural language interface to a Memgraph database. To complete this tutorial, you will need Docker and Python 3.x installed.	This notebook shows how to use LLMs to provide a natural language interface to a Memgraph database. To complete this tutorial, you will need Docker and Python 3.x installed. ->: Memgraph QA chain \| ü¶úÔ∏èüîó Langchain
1,808	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 queryingMemgraph QA chainOn this pageMemgraph QA chainThis notebook shows how to use LLMs to provide a natural language interface to a Memgraph database. To complete this tutorial, you will need Docker and Python 3.x installed.To follow along with this tutorial, ensure you have a running Memgraph instance. You can download and run it in a local Docker container by executing the following script:docker run \ -it \ -p 7687:7687 \ -p 7444:7444 \ -p 3000:3000 \ -e MEMGRAPH="--bolt-server-name-for-init=Neo4j/" \ -v mg_lib:/var/lib/memgraph memgraph/memgraph-platformYou will need to wait a few seconds for the database to start. If the process completes successfully, you should see something like this:mgconsole X.XConnected to 'memgraph://127.0.0.1:7687'Type :help for shell usageQuit the shell by typing Ctrl-D(eof) or :quitmemgraph>Now you can start playing with Memgraph!Begin by installing and importing all the necessary packages. We'll use the package manager called pip, along with the --user flag, to ensure proper permissions. If you've installed Python 3.4 or a later version, pip is included by default. You can install all the required packages using the following command:pip install langchain openai neo4j gqlalchemy --userYou can either run the provided code blocks in this notebook or use a separate Python file to experiment with Memgraph and LangChain.from langchain.chat_models import ChatOpenAIfrom langchain.chains import GraphCypherQAChainfrom	This notebook shows how to use LLMs to provide a natural language interface to a Memgraph database. To complete this tutorial, you will need Docker and Python 3.x installed.	This notebook shows how to use LLMs to provide a natural language interface to a Memgraph database. To complete this tutorial, you will need Docker and Python 3.x installed. ->: 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 queryingMemgraph QA chainOn this pageMemgraph QA chainThis notebook shows how to use LLMs to provide a natural language interface to a Memgraph database. To complete this tutorial, you will need Docker and Python 3.x installed.To follow along with this tutorial, ensure you have a running Memgraph instance. You can download and run it in a local Docker container by executing the following script:docker run \ -it \ -p 7687:7687 \ -p 7444:7444 \ -p 3000:3000 \ -e MEMGRAPH="--bolt-server-name-for-init=Neo4j/" \ -v mg_lib:/var/lib/memgraph memgraph/memgraph-platformYou will need to wait a few seconds for the database to start. If the process completes successfully, you should see something like this:mgconsole X.XConnected to 'memgraph://127.0.0.1:7687'Type :help for shell usageQuit the shell by typing Ctrl-D(eof) or :quitmemgraph>Now you can start playing with Memgraph!Begin by installing and importing all the necessary packages. We'll use the package manager called pip, along with the --user flag, to ensure proper permissions. If you've installed Python 3.4 or a later version, pip is included by default. You can install all the required packages using the following command:pip install langchain openai neo4j gqlalchemy --userYou can either run the provided code blocks in this notebook or use a separate Python file to experiment with Memgraph and LangChain.from langchain.chat_models import ChatOpenAIfrom langchain.chains import GraphCypherQAChainfrom
1,809	langchain.chains import GraphCypherQAChainfrom langchain.graphs import MemgraphGraphfrom langchain.prompts import PromptTemplatefrom gqlalchemy import Memgraphimport osWe're utilizing the Python library GQLAlchemy to establish a connection between our Memgraph database and Python script. To execute queries, we can set up a Memgraph instance as follows:memgraph = Memgraph(host='127.0.0.1', port=7687)Populating the database‚ÄãYou can effortlessly populate your new, empty database using the Cypher query language. Don't worry if you don't grasp every line just yet, you can learn Cypher from the documentation here. Running the following script will execute a seeding query on the database, giving us data about a video game, including details like the publisher, available platforms, and genres. This data will serve as a basis for our work.# Creating and executing the seeding queryquery = """ MERGE (g:Game {name: "Baldur's Gate 3"}) WITH g, ["PlayStation 5", "Mac OS", "Windows", "Xbox Series X/S"] AS platforms, ["Adventure", "Role-Playing Game", "Strategy"] AS genres FOREACH (platform IN platforms \| MERGE (p:Platform {name: platform}) MERGE (g)-[:AVAILABLE_ON]->(p) ) FOREACH (genre IN genres \| MERGE (gn:Genre {name: genre}) MERGE (g)-[:HAS_GENRE]->(gn) ) MERGE (p:Publisher {name: "Larian Studios"}) MERGE (g)-[:PUBLISHED_BY]->(p);"""memgraph.execute(query)Refresh graph schema‚ÄãYou're all set to instantiate the Memgraph-LangChain graph using the following script. This interface will allow us to query our database using LangChain, automatically creating the required graph schema for generating Cypher queries through LLM.graph = MemgraphGraph(url="bolt://localhost:7687", username="", password="")If necessary, you can manually refresh the graph schema as follows.graph.refresh_schema()To familiarize yourself with the data and verify the updated graph schema, you can print it using the following	This notebook shows how to use LLMs to provide a natural language interface to a Memgraph database. To complete this tutorial, you will need Docker and Python 3.x installed.	This notebook shows how to use LLMs to provide a natural language interface to a Memgraph database. To complete this tutorial, you will need Docker and Python 3.x installed. ->: langchain.chains import GraphCypherQAChainfrom langchain.graphs import MemgraphGraphfrom langchain.prompts import PromptTemplatefrom gqlalchemy import Memgraphimport osWe're utilizing the Python library GQLAlchemy to establish a connection between our Memgraph database and Python script. To execute queries, we can set up a Memgraph instance as follows:memgraph = Memgraph(host='127.0.0.1', port=7687)Populating the database‚ÄãYou can effortlessly populate your new, empty database using the Cypher query language. Don't worry if you don't grasp every line just yet, you can learn Cypher from the documentation here. Running the following script will execute a seeding query on the database, giving us data about a video game, including details like the publisher, available platforms, and genres. This data will serve as a basis for our work.# Creating and executing the seeding queryquery = """ MERGE (g:Game {name: "Baldur's Gate 3"}) WITH g, ["PlayStation 5", "Mac OS", "Windows", "Xbox Series X/S"] AS platforms, ["Adventure", "Role-Playing Game", "Strategy"] AS genres FOREACH (platform IN platforms \| MERGE (p:Platform {name: platform}) MERGE (g)-[:AVAILABLE_ON]->(p) ) FOREACH (genre IN genres \| MERGE (gn:Genre {name: genre}) MERGE (g)-[:HAS_GENRE]->(gn) ) MERGE (p:Publisher {name: "Larian Studios"}) MERGE (g)-[:PUBLISHED_BY]->(p);"""memgraph.execute(query)Refresh graph schema‚ÄãYou're all set to instantiate the Memgraph-LangChain graph using the following script. This interface will allow us to query our database using LangChain, automatically creating the required graph schema for generating Cypher queries through LLM.graph = MemgraphGraph(url="bolt://localhost:7687", username="", password="")If necessary, you can manually refresh the graph schema as follows.graph.refresh_schema()To familiarize yourself with the data and verify the updated graph schema, you can print it using the following
1,810	schema, you can print it using the following statement.print(graph.schema)Node properties are the following:Node name: 'Game', Node properties: [{'property': 'name', 'type': 'str'}]Node name: 'Platform', Node properties: [{'property': 'name', 'type': 'str'}]Node name: 'Genre', Node properties: [{'property': 'name', 'type': 'str'}]Node name: 'Publisher', Node properties: [{'property': 'name', 'type': 'str'}]Relationship properties are the following:The relationships are the following:['(:Game)-[:AVAILABLE_ON]->(:Platform)']['(:Game)-[:HAS_GENRE]->(:Genre)']['(:Game)-[:PUBLISHED_BY]->(:Publisher)']Querying the database‚ÄãTo interact with the OpenAI API, you must configure your API key as an environment variable using the Python os package. This ensures proper authorization for your requests. You can find more information on obtaining your API key here.os.environ["OPENAI_API_KEY"] = "your-key-here"You should create the graph chain using the following script, which will be utilized in the question-answering process based on your graph data. While it defaults to GPT-3.5-turbo, you might also consider experimenting with other models like GPT-4 for notably improved Cypher queries and outcomes. We'll utilize the OpenAI chat, utilizing the key you previously configured. We'll set the temperature to zero, ensuring predictable and consistent answers. Additionally, we'll use our Memgraph-LangChain graph and set the verbose parameter, which defaults to False, to True to receive more detailed messages regarding query generation.chain = GraphCypherQAChain.from_llm( ChatOpenAI(temperature=0), graph=graph, verbose=True, model_name='gpt-3.5-turbo')Now you can start asking questions!response = chain.run("Which platforms is Baldur's Gate 3 available on?")print(response)> Entering new GraphCypherQAChain chain...Generated Cypher:MATCH (g:Game {name: 'Baldur\'s Gate 3'})-[:AVAILABLE_ON]->(p:Platform)RETURN p.nameFull Context:[{'p.name': 'PlayStation 5'}, {'p.name': 'Mac OS'},	This notebook shows how to use LLMs to provide a natural language interface to a Memgraph database. To complete this tutorial, you will need Docker and Python 3.x installed.	This notebook shows how to use LLMs to provide a natural language interface to a Memgraph database. To complete this tutorial, you will need Docker and Python 3.x installed. ->: schema, you can print it using the following statement.print(graph.schema)Node properties are the following:Node name: 'Game', Node properties: [{'property': 'name', 'type': 'str'}]Node name: 'Platform', Node properties: [{'property': 'name', 'type': 'str'}]Node name: 'Genre', Node properties: [{'property': 'name', 'type': 'str'}]Node name: 'Publisher', Node properties: [{'property': 'name', 'type': 'str'}]Relationship properties are the following:The relationships are the following:['(:Game)-[:AVAILABLE_ON]->(:Platform)']['(:Game)-[:HAS_GENRE]->(:Genre)']['(:Game)-[:PUBLISHED_BY]->(:Publisher)']Querying the database‚ÄãTo interact with the OpenAI API, you must configure your API key as an environment variable using the Python os package. This ensures proper authorization for your requests. You can find more information on obtaining your API key here.os.environ["OPENAI_API_KEY"] = "your-key-here"You should create the graph chain using the following script, which will be utilized in the question-answering process based on your graph data. While it defaults to GPT-3.5-turbo, you might also consider experimenting with other models like GPT-4 for notably improved Cypher queries and outcomes. We'll utilize the OpenAI chat, utilizing the key you previously configured. We'll set the temperature to zero, ensuring predictable and consistent answers. Additionally, we'll use our Memgraph-LangChain graph and set the verbose parameter, which defaults to False, to True to receive more detailed messages regarding query generation.chain = GraphCypherQAChain.from_llm( ChatOpenAI(temperature=0), graph=graph, verbose=True, model_name='gpt-3.5-turbo')Now you can start asking questions!response = chain.run("Which platforms is Baldur's Gate 3 available on?")print(response)> Entering new GraphCypherQAChain chain...Generated Cypher:MATCH (g:Game {name: 'Baldur\'s Gate 3'})-[:AVAILABLE_ON]->(p:Platform)RETURN p.nameFull Context:[{'p.name': 'PlayStation 5'}, {'p.name': 'Mac OS'},
1,811	'PlayStation 5'}, {'p.name': 'Mac OS'}, {'p.name': 'Windows'}, {'p.name': 'Xbox Series X/S'}]> Finished chain.Baldur's Gate 3 is available on PlayStation 5, Mac OS, Windows, and Xbox Series X/S.response = chain.run("Is Baldur's Gate 3 available on Windows?")print(response)> Entering new GraphCypherQAChain chain...Generated Cypher:MATCH (:Game {name: 'Baldur\'s Gate 3'})-[:AVAILABLE_ON]->(:Platform {name: 'Windows'})RETURN trueFull Context:[{'true': True}]> Finished chain.Yes, Baldur's Gate 3 is available on Windows.Chain modifiers‚ÄãTo modify the behavior of your chain and obtain more context or additional information, you can modify the chain's parameters.Return direct query results‚ÄãThe return_direct modifier specifies whether to return the direct results of the executed Cypher query or the processed natural language response.# Return the result of querying the graph directlychain = GraphCypherQAChain.from_llm( ChatOpenAI(temperature=0), graph=graph, verbose=True, return_direct=True)response = chain.run("Which studio published Baldur's Gate 3?")print(response)> Entering new GraphCypherQAChain chain...Generated Cypher:MATCH (:Game {name: 'Baldur\'s Gate 3'})-[:PUBLISHED_BY]->(p:Publisher)RETURN p.name> Finished chain.[{'p.name': 'Larian Studios'}]Return query intermediate steps‚ÄãThe return_intermediate_steps chain modifier enhances the returned response by including the intermediate steps of the query in addition to the initial query result.# Return all the intermediate steps of query executionchain = GraphCypherQAChain.from_llm( ChatOpenAI(temperature=0), graph=graph, verbose=True, return_intermediate_steps=True)response = chain("Is Baldur's Gate 3 an Adventure game?")print(f"Intermediate steps: {response['intermediate_steps']}")print(f"Final response: {response['result']}")> Entering new GraphCypherQAChain chain...Generated Cypher:MATCH (g:Game {name: 'Baldur\'s Gate 3'})-[:HAS_GENRE]->(genre:Genre {name: 'Adventure'})RETURN g, genreFull Context:[{'g':	This notebook shows how to use LLMs to provide a natural language interface to a Memgraph database. To complete this tutorial, you will need Docker and Python 3.x installed.	This notebook shows how to use LLMs to provide a natural language interface to a Memgraph database. To complete this tutorial, you will need Docker and Python 3.x installed. ->: 'PlayStation 5'}, {'p.name': 'Mac OS'}, {'p.name': 'Windows'}, {'p.name': 'Xbox Series X/S'}]> Finished chain.Baldur's Gate 3 is available on PlayStation 5, Mac OS, Windows, and Xbox Series X/S.response = chain.run("Is Baldur's Gate 3 available on Windows?")print(response)> Entering new GraphCypherQAChain chain...Generated Cypher:MATCH (:Game {name: 'Baldur\'s Gate 3'})-[:AVAILABLE_ON]->(:Platform {name: 'Windows'})RETURN trueFull Context:[{'true': True}]> Finished chain.Yes, Baldur's Gate 3 is available on Windows.Chain modifiers‚ÄãTo modify the behavior of your chain and obtain more context or additional information, you can modify the chain's parameters.Return direct query results‚ÄãThe return_direct modifier specifies whether to return the direct results of the executed Cypher query or the processed natural language response.# Return the result of querying the graph directlychain = GraphCypherQAChain.from_llm( ChatOpenAI(temperature=0), graph=graph, verbose=True, return_direct=True)response = chain.run("Which studio published Baldur's Gate 3?")print(response)> Entering new GraphCypherQAChain chain...Generated Cypher:MATCH (:Game {name: 'Baldur\'s Gate 3'})-[:PUBLISHED_BY]->(p:Publisher)RETURN p.name> Finished chain.[{'p.name': 'Larian Studios'}]Return query intermediate steps‚ÄãThe return_intermediate_steps chain modifier enhances the returned response by including the intermediate steps of the query in addition to the initial query result.# Return all the intermediate steps of query executionchain = GraphCypherQAChain.from_llm( ChatOpenAI(temperature=0), graph=graph, verbose=True, return_intermediate_steps=True)response = chain("Is Baldur's Gate 3 an Adventure game?")print(f"Intermediate steps: {response['intermediate_steps']}")print(f"Final response: {response['result']}")> Entering new GraphCypherQAChain chain...Generated Cypher:MATCH (g:Game {name: 'Baldur\'s Gate 3'})-[:HAS_GENRE]->(genre:Genre {name: 'Adventure'})RETURN g, genreFull Context:[{'g':
1,812	'Adventure'})RETURN g, genreFull Context:[{'g': {'name': "Baldur's Gate 3"}, 'genre': {'name': 'Adventure'}}]> Finished chain.Intermediate steps: [{'query': "MATCH (g:Game {name: 'Baldur\\'s Gate 3'})-[:HAS_GENRE]->(genre:Genre {name: 'Adventure'})\nRETURN g, genre"}, {'context': [{'g': {'name': "Baldur's Gate 3"}, 'genre': {'name': 'Adventure'}}]}]Final response: Yes, Baldur's Gate 3 is an Adventure game.Limit the number of query results‚ÄãThe top_k modifier can be used when you want to restrict the maximum number of query results.# Limit the maximum number of results returned by querychain = GraphCypherQAChain.from_llm( ChatOpenAI(temperature=0), graph=graph, verbose=True, top_k=2)response = chain.run("What genres are associated with Baldur's Gate 3?")print(response)> Entering new GraphCypherQAChain chain...Generated Cypher:MATCH (:Game {name: 'Baldur\'s Gate 3'})-[:HAS_GENRE]->(g:Genre)RETURN g.nameFull Context:[{'g.name': 'Adventure'}, {'g.name': 'Role-Playing Game'}]> Finished chain.Baldur's Gate 3 is associated with the genres Adventure and Role-Playing Game.Advanced queryingAs the complexity of your solution grows, you might encounter different use-cases that require careful handling. Ensuring your application's scalability is essential to maintain a smooth user flow without any hitches.Let's instantiate our chain once again and attempt to ask some questions that users might potentially ask.chain = GraphCypherQAChain.from_llm( ChatOpenAI(temperature=0), graph=graph, verbose=True, model_name='gpt-3.5-turbo')response = chain.run("Is Baldur's Gate 3 available on PS5?")print(response)> Entering new GraphCypherQAChain chain...Generated Cypher:MATCH (g:Game {name: 'Baldur\'s Gate 3'})-[:AVAILABLE_ON]->(p:Platform {name: 'PS5'})RETURN g.name, p.nameFull Context:[]> Finished chain.I'm sorry, but I don't have the information to answer your question.The generated Cypher query looks fine, but we didn't receive any information in response. This illustrates a	This notebook shows how to use LLMs to provide a natural language interface to a Memgraph database. To complete this tutorial, you will need Docker and Python 3.x installed.	This notebook shows how to use LLMs to provide a natural language interface to a Memgraph database. To complete this tutorial, you will need Docker and Python 3.x installed. ->: 'Adventure'})RETURN g, genreFull Context:[{'g': {'name': "Baldur's Gate 3"}, 'genre': {'name': 'Adventure'}}]> Finished chain.Intermediate steps: [{'query': "MATCH (g:Game {name: 'Baldur\\'s Gate 3'})-[:HAS_GENRE]->(genre:Genre {name: 'Adventure'})\nRETURN g, genre"}, {'context': [{'g': {'name': "Baldur's Gate 3"}, 'genre': {'name': 'Adventure'}}]}]Final response: Yes, Baldur's Gate 3 is an Adventure game.Limit the number of query results‚ÄãThe top_k modifier can be used when you want to restrict the maximum number of query results.# Limit the maximum number of results returned by querychain = GraphCypherQAChain.from_llm( ChatOpenAI(temperature=0), graph=graph, verbose=True, top_k=2)response = chain.run("What genres are associated with Baldur's Gate 3?")print(response)> Entering new GraphCypherQAChain chain...Generated Cypher:MATCH (:Game {name: 'Baldur\'s Gate 3'})-[:HAS_GENRE]->(g:Genre)RETURN g.nameFull Context:[{'g.name': 'Adventure'}, {'g.name': 'Role-Playing Game'}]> Finished chain.Baldur's Gate 3 is associated with the genres Adventure and Role-Playing Game.Advanced queryingAs the complexity of your solution grows, you might encounter different use-cases that require careful handling. Ensuring your application's scalability is essential to maintain a smooth user flow without any hitches.Let's instantiate our chain once again and attempt to ask some questions that users might potentially ask.chain = GraphCypherQAChain.from_llm( ChatOpenAI(temperature=0), graph=graph, verbose=True, model_name='gpt-3.5-turbo')response = chain.run("Is Baldur's Gate 3 available on PS5?")print(response)> Entering new GraphCypherQAChain chain...Generated Cypher:MATCH (g:Game {name: 'Baldur\'s Gate 3'})-[:AVAILABLE_ON]->(p:Platform {name: 'PS5'})RETURN g.name, p.nameFull Context:[]> Finished chain.I'm sorry, but I don't have the information to answer your question.The generated Cypher query looks fine, but we didn't receive any information in response. This illustrates a
1,813	any information in response. This illustrates a common challenge when working with LLMs - the misalignment between how users phrase queries and how data is stored. In this case, the difference between user perception and the actual data storage can cause mismatches. Prompt refinement, the process of honing the model's prompts to better grasp these distinctions, is an efficient solution that tackles this issue. Through prompt refinement, the model gains increased proficiency in generating precise and pertinent queries, leading to the successful retrieval of the desired data.Prompt refinement‚ÄãTo address this, we can adjust the initial Cypher prompt of the QA chain. This involves adding guidance to the LLM on how users can refer to specific platforms, such as PS5 in our case. We achieve this using the LangChain PromptTemplate, creating a modified initial prompt. This modified prompt is then supplied as an argument to our refined Memgraph-LangChain instance.CYPHER_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.If the user asks about PS5, Play Station 5 or PS 5, that is the platform called PlayStation 5.The question is:{question}"""CYPHER_GENERATION_PROMPT = PromptTemplate( input_variables=["schema", "question"], template=CYPHER_GENERATION_TEMPLATE)chain = GraphCypherQAChain.from_llm( ChatOpenAI(temperature=0), cypher_prompt=CYPHER_GENERATION_PROMPT, graph=graph, verbose=True, model_name='gpt-3.5-turbo')response = chain.run("Is Baldur's Gate 3 available on PS5?")print(response)> Entering new GraphCypherQAChain	This notebook shows how to use LLMs to provide a natural language interface to a Memgraph database. To complete this tutorial, you will need Docker and Python 3.x installed.	This notebook shows how to use LLMs to provide a natural language interface to a Memgraph database. To complete this tutorial, you will need Docker and Python 3.x installed. ->: any information in response. This illustrates a common challenge when working with LLMs - the misalignment between how users phrase queries and how data is stored. In this case, the difference between user perception and the actual data storage can cause mismatches. Prompt refinement, the process of honing the model's prompts to better grasp these distinctions, is an efficient solution that tackles this issue. Through prompt refinement, the model gains increased proficiency in generating precise and pertinent queries, leading to the successful retrieval of the desired data.Prompt refinement‚ÄãTo address this, we can adjust the initial Cypher prompt of the QA chain. This involves adding guidance to the LLM on how users can refer to specific platforms, such as PS5 in our case. We achieve this using the LangChain PromptTemplate, creating a modified initial prompt. This modified prompt is then supplied as an argument to our refined Memgraph-LangChain instance.CYPHER_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.If the user asks about PS5, Play Station 5 or PS 5, that is the platform called PlayStation 5.The question is:{question}"""CYPHER_GENERATION_PROMPT = PromptTemplate( input_variables=["schema", "question"], template=CYPHER_GENERATION_TEMPLATE)chain = GraphCypherQAChain.from_llm( ChatOpenAI(temperature=0), cypher_prompt=CYPHER_GENERATION_PROMPT, graph=graph, verbose=True, model_name='gpt-3.5-turbo')response = chain.run("Is Baldur's Gate 3 available on PS5?")print(response)> Entering new GraphCypherQAChain
1,814	Entering new GraphCypherQAChain chain...Generated Cypher:MATCH (g:Game {name: 'Baldur\'s Gate 3'})-[:AVAILABLE_ON]->(p:Platform {name: 'PlayStation 5'})RETURN g.name, p.nameFull Context:[{'g.name': "Baldur's Gate 3", 'p.name': 'PlayStation 5'}]> Finished chain.Yes, Baldur's Gate 3 is available on PlayStation 5.Now, with the revised initial Cypher prompt that includes guidance on platform naming, we are obtaining accurate and relevant results that align more closely with user queries. This approach allows for further improvement of your QA chain. You can effortlessly integrate extra prompt refinement data into your chain, thereby enhancing the overall user experience of your app.PreviousKuzuQAChainNextNebulaGraphQAChainPopulating the databaseRefresh graph schemaQuerying the databaseChain modifiersPrompt refinementCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.	This notebook shows how to use LLMs to provide a natural language interface to a Memgraph database. To complete this tutorial, you will need Docker and Python 3.x installed.	This notebook shows how to use LLMs to provide a natural language interface to a Memgraph database. To complete this tutorial, you will need Docker and Python 3.x installed. ->: Entering new GraphCypherQAChain chain...Generated Cypher:MATCH (g:Game {name: 'Baldur\'s Gate 3'})-[:AVAILABLE_ON]->(p:Platform {name: 'PlayStation 5'})RETURN g.name, p.nameFull Context:[{'g.name': "Baldur's Gate 3", 'p.name': 'PlayStation 5'}]> Finished chain.Yes, Baldur's Gate 3 is available on PlayStation 5.Now, with the revised initial Cypher prompt that includes guidance on platform naming, we are obtaining accurate and relevant results that align more closely with user queries. This approach allows for further improvement of your QA chain. You can effortlessly integrate extra prompt refinement data into your chain, thereby enhancing the overall user experience of your app.PreviousKuzuQAChainNextNebulaGraphQAChainPopulating the databaseRefresh graph schemaQuerying the databaseChain modifiersPrompt refinementCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
1,815	HugeGraph QA Chain \| ü¶úÔ∏èüîó Langchain	This notebook shows how to use LLMs to provide a natural language interface to HugeGraph database.	This notebook shows how to use LLMs to provide a natural language interface to HugeGraph database. ->: HugeGraph QA Chain \| ü¶úÔ∏èüîó Langchain
1,816	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 queryingHugeGraph QA ChainOn this pageHugeGraph QA ChainThis notebook shows how to use LLMs to provide a natural language interface to HugeGraph database.You will need to have a running HugeGraph instance.	This notebook shows how to use LLMs to provide a natural language interface to HugeGraph database.	This notebook shows how to use LLMs to provide a natural language interface to HugeGraph 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 queryingHugeGraph QA ChainOn this pageHugeGraph QA ChainThis notebook shows how to use LLMs to provide a natural language interface to HugeGraph database.You will need to have a running HugeGraph instance.
1,817	You can run a local docker container by running the executing the following script:docker run \ --name=graph \ -itd \ -p 8080:8080 \ hugegraph/hugegraphIf we want to connect HugeGraph in the application, we need to install python sdk:pip3 install hugegraph-pythonIf you are using the docker container, you need to wait a couple of second for the database to start, and then we need create schema and write graph data for the database.from hugegraph.connection import PyHugeGraphclient = PyHugeGraph("localhost", "8080", user="admin", pwd="admin", graph="hugegraph")First, we create the schema for a simple movie database:"""schema"""schema = client.schema()schema.propertyKey("name").asText().ifNotExist().create()schema.propertyKey("birthDate").asText().ifNotExist().create()schema.vertexLabel("Person").properties( "name", "birthDate").usePrimaryKeyId().primaryKeys("name").ifNotExist().create()schema.vertexLabel("Movie").properties("name").usePrimaryKeyId().primaryKeys( "name").ifNotExist().create()schema.edgeLabel("ActedIn").sourceLabel("Person").targetLabel( "Movie").ifNotExist().create() 'create EdgeLabel success, Detail: "b\'{"id":1,"name":"ActedIn","source_label":"Person","target_label":"Movie","frequency":"SINGLE","sort_keys":[],"nullable_keys":[],"index_labels":[],"properties":[],"status":"CREATED","ttl":0,"enable_label_index":true,"user_data":{"~create_time":"2023-07-04 10:48:47.908"}}\'"'Then we can insert some data."""graph"""g = client.graph()g.addVertex("Person", {"name": "Al Pacino", "birthDate": "1940-04-25"})g.addVertex("Person", {"name": "Robert De Niro", "birthDate": "1943-08-17"})g.addVertex("Movie", {"name": "The Godfather"})g.addVertex("Movie", {"name": "The Godfather Part II"})g.addVertex("Movie", {"name": "The Godfather Coda The Death of Michael Corleone"})g.addEdge("ActedIn", "1:Al Pacino", "2:The Godfather", {})g.addEdge("ActedIn", "1:Al Pacino", "2:The Godfather Part II", {})g.addEdge( "ActedIn", "1:Al Pacino", "2:The	This notebook shows how to use LLMs to provide a natural language interface to HugeGraph database.	This notebook shows how to use LLMs to provide a natural language interface to HugeGraph database. ->: You can run a local docker container by running the executing the following script:docker run \ --name=graph \ -itd \ -p 8080:8080 \ hugegraph/hugegraphIf we want to connect HugeGraph in the application, we need to install python sdk:pip3 install hugegraph-pythonIf you are using the docker container, you need to wait a couple of second for the database to start, and then we need create schema and write graph data for the database.from hugegraph.connection import PyHugeGraphclient = PyHugeGraph("localhost", "8080", user="admin", pwd="admin", graph="hugegraph")First, we create the schema for a simple movie database:"""schema"""schema = client.schema()schema.propertyKey("name").asText().ifNotExist().create()schema.propertyKey("birthDate").asText().ifNotExist().create()schema.vertexLabel("Person").properties( "name", "birthDate").usePrimaryKeyId().primaryKeys("name").ifNotExist().create()schema.vertexLabel("Movie").properties("name").usePrimaryKeyId().primaryKeys( "name").ifNotExist().create()schema.edgeLabel("ActedIn").sourceLabel("Person").targetLabel( "Movie").ifNotExist().create() 'create EdgeLabel success, Detail: "b\'{"id":1,"name":"ActedIn","source_label":"Person","target_label":"Movie","frequency":"SINGLE","sort_keys":[],"nullable_keys":[],"index_labels":[],"properties":[],"status":"CREATED","ttl":0,"enable_label_index":true,"user_data":{"~create_time":"2023-07-04 10:48:47.908"}}\'"'Then we can insert some data."""graph"""g = client.graph()g.addVertex("Person", {"name": "Al Pacino", "birthDate": "1940-04-25"})g.addVertex("Person", {"name": "Robert De Niro", "birthDate": "1943-08-17"})g.addVertex("Movie", {"name": "The Godfather"})g.addVertex("Movie", {"name": "The Godfather Part II"})g.addVertex("Movie", {"name": "The Godfather Coda The Death of Michael Corleone"})g.addEdge("ActedIn", "1:Al Pacino", "2:The Godfather", {})g.addEdge("ActedIn", "1:Al Pacino", "2:The Godfather Part II", {})g.addEdge( "ActedIn", "1:Al Pacino", "2:The
1,818	{})g.addEdge( "ActedIn", "1:Al Pacino", "2:The Godfather Coda The Death of Michael Corleone", {})g.addEdge("ActedIn", "1:Robert De Niro", "2:The Godfather Part II", {}) 1:Robert De Niro--ActedIn-->2:The Godfather Part IICreating HugeGraphQAChain‚ÄãWe can now create the HugeGraph and HugeGraphQAChain. To create the HugeGraph we simply need to pass the database object to the HugeGraph constructor.from langchain.chat_models import ChatOpenAIfrom langchain.chains import HugeGraphQAChainfrom langchain.graphs import HugeGraphgraph = HugeGraph( username="admin", password="admin", address="localhost", port=8080, graph="hugegraph",)Refresh graph schema information‚ÄãIf the schema of database changes, you can refresh the schema information needed to generate Gremlin statements.# graph.refresh_schema()print(graph.get_schema) Node properties: [name: Person, primary_keys: ['name'], properties: ['name', 'birthDate'], name: Movie, primary_keys: ['name'], properties: ['name']] Edge properties: [name: ActedIn, properties: []] Relationships: ['Person--ActedIn-->Movie'] Querying the graph‚ÄãWe can now use the graph Gremlin QA chain to ask question of the graphchain = HugeGraphQAChain.from_llm(ChatOpenAI(temperature=0), graph=graph, verbose=True)chain.run("Who played in The Godfather?") > Entering new chain... Generated gremlin: g.V().has('Movie', 'name', 'The Godfather').in('ActedIn').valueMap(true) Full Context: [{'id': '1:Al Pacino', 'label': 'Person', 'name': ['Al Pacino'], 'birthDate': ['1940-04-25']}] > Finished chain. 'Al Pacino played in The Godfather.'PreviousFalkorDBQAChainNextKuzuQAChainCreating HugeGraphQAChainRefresh graph schema informationQuerying the graphCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.	This notebook shows how to use LLMs to provide a natural language interface to HugeGraph database.	This notebook shows how to use LLMs to provide a natural language interface to HugeGraph database. ->: {})g.addEdge( "ActedIn", "1:Al Pacino", "2:The Godfather Coda The Death of Michael Corleone", {})g.addEdge("ActedIn", "1:Robert De Niro", "2:The Godfather Part II", {}) 1:Robert De Niro--ActedIn-->2:The Godfather Part IICreating HugeGraphQAChain‚ÄãWe can now create the HugeGraph and HugeGraphQAChain. To create the HugeGraph we simply need to pass the database object to the HugeGraph constructor.from langchain.chat_models import ChatOpenAIfrom langchain.chains import HugeGraphQAChainfrom langchain.graphs import HugeGraphgraph = HugeGraph( username="admin", password="admin", address="localhost", port=8080, graph="hugegraph",)Refresh graph schema information‚ÄãIf the schema of database changes, you can refresh the schema information needed to generate Gremlin statements.# graph.refresh_schema()print(graph.get_schema) Node properties: [name: Person, primary_keys: ['name'], properties: ['name', 'birthDate'], name: Movie, primary_keys: ['name'], properties: ['name']] Edge properties: [name: ActedIn, properties: []] Relationships: ['Person--ActedIn-->Movie'] Querying the graph‚ÄãWe can now use the graph Gremlin QA chain to ask question of the graphchain = HugeGraphQAChain.from_llm(ChatOpenAI(temperature=0), graph=graph, verbose=True)chain.run("Who played in The Godfather?") > Entering new chain... Generated gremlin: g.V().has('Movie', 'name', 'The Godfather').in('ActedIn').valueMap(true) Full Context: [{'id': '1:Al Pacino', 'label': 'Person', 'name': ['Al Pacino'], 'birthDate': ['1940-04-25']}] > Finished chain. 'Al Pacino played in The Godfather.'PreviousFalkorDBQAChainNextKuzuQAChainCreating HugeGraphQAChainRefresh graph schema informationQuerying the graphCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
1,819	Neo4j DB QA chain \| ü¶úÔ∏èüîó Langchain	This notebook shows how to use LLMs to provide a natural language interface to a graph database you can query with the Cypher query language.	This notebook shows how to use LLMs to provide a natural language interface to a graph database you can query with the Cypher query language. ->: Neo4j DB QA chain \| ü¶úÔ∏èüîó Langchain
1,820	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 queryingNeo4j 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.	This notebook shows how to use LLMs to provide a natural language interface to a graph database you can query with the Cypher query language.	This notebook shows how to use LLMs to provide a natural language interface to a graph database you can query with the Cypher query language. ->: 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 queryingNeo4j 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.
1,821	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‚ÄãAssuming 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‚ÄãIf 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‚ÄãWe 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?")	This notebook shows how to use LLMs to provide a natural language interface to a graph database you can query with the Cypher query language.	This 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 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‚ÄãAssuming 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‚ÄãIf 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‚ÄãWe 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?")
1,822	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‚ÄãYou can limit the number of results from the Cypher QA Chain using the top_k parameter.	This notebook shows how to use LLMs to provide a natural language interface to a graph database you can query with the Cypher query language.	This notebook shows how to use LLMs to provide a natural language interface to a graph database you can query with the Cypher query language. ->: 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‚ÄãYou can limit the number of results from the Cypher QA Chain using the top_k parameter.
1,823	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‚ÄãYou 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‚ÄãYou 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':	This notebook shows how to use LLMs to provide a natural language interface to a graph database you can query with the Cypher query language.	This notebook shows how to use LLMs to provide a natural language interface to a graph database you can query with the Cypher query language. ->: 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‚ÄãYou 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‚ÄãYou 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':
1,824	{'a.name': 'Val Kilmer'}, {'a.name': 'Anthony Edwards'}, {'a.name': 'Meg Ryan'}]Add examples in the Cypher generation prompt‚ÄãYou 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‚ÄãYou 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	This notebook shows how to use LLMs to provide a natural language interface to a graph database you can query with the Cypher query language.	This notebook shows how to use LLMs to provide a natural language interface to a graph database you can query with the Cypher query language. ->: {'a.name': 'Val Kilmer'}, {'a.name': 'Anthony Edwards'}, {'a.name': 'Meg Ryan'}]Add examples in the Cypher generation prompt‚ÄãYou 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‚ÄãYou 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
1,825	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	This notebook shows how to use LLMs to provide a natural language interface to a graph database you can query with the Cypher query language.	This notebook shows how to use LLMs to provide a natural language interface to a graph database you can query with the Cypher query language. ->: 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
1,826	the Cypher generation promptUse separate LLMs for Cypher and answer generationCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.	This notebook shows how to use LLMs to provide a natural language interface to a graph database you can query with the Cypher query language.	This notebook shows how to use LLMs to provide a natural language interface to a graph database you can query with the Cypher query language. ->: the Cypher generation promptUse separate LLMs for Cypher and answer generationCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
1,827	ArangoDB QA chain \| ü¶úÔ∏èüîó Langchain	Open In Colab	Open In Colab ->: ArangoDB QA chain \| ü¶úÔ∏èüîó Langchain
1,828	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 queryingArangoDB QA chainOn this pageArangoDB QA chainThis notebook shows how to use LLMs to provide a natural language interface to an ArangoDB database.You can get a local ArangoDB instance running via the ArangoDB Docker image: docker run -p 8529:8529 -e ARANGO_ROOT_PASSWORD= arangodb/arangodbAn alternative is to use the ArangoDB Cloud Connector package to get a temporary cloud instance running:pip install python-arango # The ArangoDB Python Driverpip install adb-cloud-connector # The ArangoDB Cloud Instance provisionerpip install openaipip install langchain# Instantiate ArangoDB Databaseimport jsonfrom arango import ArangoClientfrom adb_cloud_connector import get_temp_credentialscon = get_temp_credentials()db = ArangoClient(hosts=con["url"]).db( con["dbName"], con["username"], con["password"], verify=True)print(json.dumps(con, indent=2)) Log: requesting new credentials... Succcess: new credentials acquired { "dbName": "TUT3sp29s3pjf1io0h4cfdsq", "username": "TUTo6nkwgzkizej3kysgdyeo8", "password": "TUT9vx0qjqt42i9bq8uik4v9", "hostname": "tutorials.arangodb.cloud", "port": 8529, "url": "https://tutorials.arangodb.cloud:8529" }# Instantiate the ArangoDB-LangChain Graphfrom langchain.graphs import ArangoGraphgraph = ArangoGraph(db)Populating the Database‚ÄãWe will rely on the Python Driver to import our GameOfThrones data into our database.if db.has_graph("GameOfThrones"): db.delete_graph("GameOfThrones",	Open In Colab	Open In Colab ->: 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 queryingArangoDB QA chainOn this pageArangoDB QA chainThis notebook shows how to use LLMs to provide a natural language interface to an ArangoDB database.You can get a local ArangoDB instance running via the ArangoDB Docker image: docker run -p 8529:8529 -e ARANGO_ROOT_PASSWORD= arangodb/arangodbAn alternative is to use the ArangoDB Cloud Connector package to get a temporary cloud instance running:pip install python-arango # The ArangoDB Python Driverpip install adb-cloud-connector # The ArangoDB Cloud Instance provisionerpip install openaipip install langchain# Instantiate ArangoDB Databaseimport jsonfrom arango import ArangoClientfrom adb_cloud_connector import get_temp_credentialscon = get_temp_credentials()db = ArangoClient(hosts=con["url"]).db( con["dbName"], con["username"], con["password"], verify=True)print(json.dumps(con, indent=2)) Log: requesting new credentials... Succcess: new credentials acquired { "dbName": "TUT3sp29s3pjf1io0h4cfdsq", "username": "TUTo6nkwgzkizej3kysgdyeo8", "password": "TUT9vx0qjqt42i9bq8uik4v9", "hostname": "tutorials.arangodb.cloud", "port": 8529, "url": "https://tutorials.arangodb.cloud:8529" }# Instantiate the ArangoDB-LangChain Graphfrom langchain.graphs import ArangoGraphgraph = ArangoGraph(db)Populating the Database‚ÄãWe will rely on the Python Driver to import our GameOfThrones data into our database.if db.has_graph("GameOfThrones"): db.delete_graph("GameOfThrones",
1,829	db.delete_graph("GameOfThrones", drop_collections=True)db.create_graph( "GameOfThrones", edge_definitions=[ { "edge_collection": "ChildOf", "from_vertex_collections": ["Characters"], "to_vertex_collections": ["Characters"], }, ],)documents = [ { "_key": "NedStark", "name": "Ned", "surname": "Stark", "alive": True, "age": 41, "gender": "male", }, { "_key": "CatelynStark", "name": "Catelyn", "surname": "Stark", "alive": False, "age": 40, "gender": "female", }, { "_key": "AryaStark", "name": "Arya", "surname": "Stark", "alive": True, "age": 11, "gender": "female", }, { "_key": "BranStark", "name": "Bran", "surname": "Stark", "alive": True, "age": 10, "gender": "male", },]edges = [ {"_to": "Characters/NedStark", "_from": "Characters/AryaStark"}, {"_to": "Characters/NedStark", "_from": "Characters/BranStark"}, {"_to": "Characters/CatelynStark", "_from": "Characters/AryaStark"}, {"_to": "Characters/CatelynStark", "_from": "Characters/BranStark"},]db.collection("Characters").import_bulk(documents)db.collection("ChildOf").import_bulk(edges) {'error': False, 'created': 4, 'errors': 0, 'empty': 0, 'updated': 0, 'ignored': 0, 'details': []}Getting & Setting the ArangoDB Schema‚ÄãAn initial ArangoDB Schema is generated upon instantiating the ArangoDBGraph object. Below are the schema's getter & setter methods should you be interested in viewing or modifying the schema:# The schema should be empty here,# since `graph` was initialized prior to ArangoDB Data ingestion (see above).import jsonprint(json.dumps(graph.schema, indent=4)) { "Graph Schema": [], "Collection Schema": [] }graph.set_schema()# We can now view the generated schemaimport jsonprint(json.dumps(graph.schema,	Open In Colab	Open In Colab ->: db.delete_graph("GameOfThrones", drop_collections=True)db.create_graph( "GameOfThrones", edge_definitions=[ { "edge_collection": "ChildOf", "from_vertex_collections": ["Characters"], "to_vertex_collections": ["Characters"], }, ],)documents = [ { "_key": "NedStark", "name": "Ned", "surname": "Stark", "alive": True, "age": 41, "gender": "male", }, { "_key": "CatelynStark", "name": "Catelyn", "surname": "Stark", "alive": False, "age": 40, "gender": "female", }, { "_key": "AryaStark", "name": "Arya", "surname": "Stark", "alive": True, "age": 11, "gender": "female", }, { "_key": "BranStark", "name": "Bran", "surname": "Stark", "alive": True, "age": 10, "gender": "male", },]edges = [ {"_to": "Characters/NedStark", "_from": "Characters/AryaStark"}, {"_to": "Characters/NedStark", "_from": "Characters/BranStark"}, {"_to": "Characters/CatelynStark", "_from": "Characters/AryaStark"}, {"_to": "Characters/CatelynStark", "_from": "Characters/BranStark"},]db.collection("Characters").import_bulk(documents)db.collection("ChildOf").import_bulk(edges) {'error': False, 'created': 4, 'errors': 0, 'empty': 0, 'updated': 0, 'ignored': 0, 'details': []}Getting & Setting the ArangoDB Schema‚ÄãAn initial ArangoDB Schema is generated upon instantiating the ArangoDBGraph object. Below are the schema's getter & setter methods should you be interested in viewing or modifying the schema:# The schema should be empty here,# since `graph` was initialized prior to ArangoDB Data ingestion (see above).import jsonprint(json.dumps(graph.schema, indent=4)) { "Graph Schema": [], "Collection Schema": [] }graph.set_schema()# We can now view the generated schemaimport jsonprint(json.dumps(graph.schema,
1,830	schemaimport jsonprint(json.dumps(graph.schema, indent=4)) { "Graph Schema": [ { "graph_name": "GameOfThrones", "edge_definitions": [ { "edge_collection": "ChildOf", "from_vertex_collections": [ "Characters" ], "to_vertex_collections": [ "Characters" ] } ] } ], "Collection Schema": [ { "collection_name": "ChildOf", "collection_type": "edge", "edge_properties": [ { "name": "_key", "type": "str" }, { "name": "_id", "type": "str" }, { "name": "_from", "type": "str" }, { "name": "_to", "type": "str" }, { "name": "_rev", "type": "str" } ], "example_edge": { "_key": "266218884025", "_id": "ChildOf/266218884025", "_from": "Characters/AryaStark", "_to": "Characters/NedStark", "_rev": "_gVPKGSq---" } }, { "collection_name": "Characters", "collection_type": "document", "document_properties": [ { "name": "_key", "type": "str" }, { "name": "_id", "type": "str"	Open In Colab	Open In Colab ->: schemaimport jsonprint(json.dumps(graph.schema, indent=4)) { "Graph Schema": [ { "graph_name": "GameOfThrones", "edge_definitions": [ { "edge_collection": "ChildOf", "from_vertex_collections": [ "Characters" ], "to_vertex_collections": [ "Characters" ] } ] } ], "Collection Schema": [ { "collection_name": "ChildOf", "collection_type": "edge", "edge_properties": [ { "name": "_key", "type": "str" }, { "name": "_id", "type": "str" }, { "name": "_from", "type": "str" }, { "name": "_to", "type": "str" }, { "name": "_rev", "type": "str" } ], "example_edge": { "_key": "266218884025", "_id": "ChildOf/266218884025", "_from": "Characters/AryaStark", "_to": "Characters/NedStark", "_rev": "_gVPKGSq---" } }, { "collection_name": "Characters", "collection_type": "document", "document_properties": [ { "name": "_key", "type": "str" }, { "name": "_id", "type": "str"
1,831	"type": "str" }, { "name": "_rev", "type": "str" }, { "name": "name", "type": "str" }, { "name": "surname", "type": "str" }, { "name": "alive", "type": "bool" }, { "name": "age", "type": "int" }, { "name": "gender", "type": "str" } ], "example_document": { "_key": "NedStark", "_id": "Characters/NedStark", "_rev": "_gVPKGPi---", "name": "Ned", "surname": "Stark", "alive": true, "age": 41, "gender": "male" } } ] }Querying the ArangoDB Database‚ÄãWe can now use the ArangoDB Graph QA Chain to inquire about our dataimport osos.environ["OPENAI_API_KEY"] = "your-key-here"from langchain.chat_models import ChatOpenAIfrom langchain.chains import ArangoGraphQAChainchain = ArangoGraphQAChain.from_llm( ChatOpenAI(temperature=0), graph=graph, verbose=True)chain.run("Is Ned Stark alive?") > Entering new ArangoGraphQAChain chain... AQL Query (1): WITH Characters FOR character IN Characters FILTER character.name == "Ned" AND character.surname == "Stark" RETURN character.alive AQL Result: [True] > Finished chain. 'Yes, Ned Stark is alive.'chain.run("How old is Arya Stark?") > Entering new ArangoGraphQAChain chain... AQL Query (1): WITH Characters FOR character IN	Open In Colab	Open In Colab ->: "type": "str" }, { "name": "_rev", "type": "str" }, { "name": "name", "type": "str" }, { "name": "surname", "type": "str" }, { "name": "alive", "type": "bool" }, { "name": "age", "type": "int" }, { "name": "gender", "type": "str" } ], "example_document": { "_key": "NedStark", "_id": "Characters/NedStark", "_rev": "_gVPKGPi---", "name": "Ned", "surname": "Stark", "alive": true, "age": 41, "gender": "male" } } ] }Querying the ArangoDB Database‚ÄãWe can now use the ArangoDB Graph QA Chain to inquire about our dataimport osos.environ["OPENAI_API_KEY"] = "your-key-here"from langchain.chat_models import ChatOpenAIfrom langchain.chains import ArangoGraphQAChainchain = ArangoGraphQAChain.from_llm( ChatOpenAI(temperature=0), graph=graph, verbose=True)chain.run("Is Ned Stark alive?") > Entering new ArangoGraphQAChain chain... AQL Query (1): WITH Characters FOR character IN Characters FILTER character.name == "Ned" AND character.surname == "Stark" RETURN character.alive AQL Result: [True] > Finished chain. 'Yes, Ned Stark is alive.'chain.run("How old is Arya Stark?") > Entering new ArangoGraphQAChain chain... AQL Query (1): WITH Characters FOR character IN
1,832	Query (1): WITH Characters FOR character IN Characters FILTER character.name == "Arya" && character.surname == "Stark" RETURN character.age AQL Result: [11] > Finished chain. 'Arya Stark is 11 years old.'chain.run("Are Arya Stark and Ned Stark related?") > Entering new ArangoGraphQAChain chain... AQL Query (1): WITH Characters, ChildOf FOR v, e, p IN 1..1 OUTBOUND 'Characters/AryaStark' ChildOf FILTER p.vertices[-1]._key == 'NedStark' RETURN p AQL Result: [{'vertices': [{'_key': 'AryaStark', '_id': 'Characters/AryaStark', '_rev': '_gVPKGPi--B', 'name': 'Arya', 'surname': 'Stark', 'alive': True, 'age': 11, 'gender': 'female'}, {'_key': 'NedStark', '_id': 'Characters/NedStark', '_rev': '_gVPKGPi---', 'name': 'Ned', 'surname': 'Stark', 'alive': True, 'age': 41, 'gender': 'male'}], 'edges': [{'_key': '266218884025', '_id': 'ChildOf/266218884025', '_from': 'Characters/AryaStark', '_to': 'Characters/NedStark', '_rev': '_gVPKGSq---'}], 'weights': [0, 1]}] > Finished chain. 'Yes, Arya Stark and Ned Stark are related. According to the information retrieved from the database, there is a relationship between them. Arya Stark is the child of Ned Stark.'chain.run("Does Arya Stark have a dead parent?") > Entering new ArangoGraphQAChain chain... AQL Query (1): WITH Characters, ChildOf FOR v, e IN 1..1 OUTBOUND 'Characters/AryaStark' ChildOf FILTER v.alive == false RETURN e AQL Result: [{'_key': '266218884027', '_id': 'ChildOf/266218884027', '_from': 'Characters/AryaStark', '_to': 'Characters/CatelynStark', '_rev': '_gVPKGSu---'}] > Finished chain. 'Yes, Arya Stark has a dead parent. The parent is Catelyn Stark.'Chain Modifiers‚ÄãYou can alter the values of the following ArangoDBGraphQAChain class variables to modify the behaviour of your chain results# Specify the maximum number of AQL Query Results to returnchain.top_k = 10# Specify whether or not	Open In Colab	Open In Colab ->: Query (1): WITH Characters FOR character IN Characters FILTER character.name == "Arya" && character.surname == "Stark" RETURN character.age AQL Result: [11] > Finished chain. 'Arya Stark is 11 years old.'chain.run("Are Arya Stark and Ned Stark related?") > Entering new ArangoGraphQAChain chain... AQL Query (1): WITH Characters, ChildOf FOR v, e, p IN 1..1 OUTBOUND 'Characters/AryaStark' ChildOf FILTER p.vertices[-1]._key == 'NedStark' RETURN p AQL Result: [{'vertices': [{'_key': 'AryaStark', '_id': 'Characters/AryaStark', '_rev': '_gVPKGPi--B', 'name': 'Arya', 'surname': 'Stark', 'alive': True, 'age': 11, 'gender': 'female'}, {'_key': 'NedStark', '_id': 'Characters/NedStark', '_rev': '_gVPKGPi---', 'name': 'Ned', 'surname': 'Stark', 'alive': True, 'age': 41, 'gender': 'male'}], 'edges': [{'_key': '266218884025', '_id': 'ChildOf/266218884025', '_from': 'Characters/AryaStark', '_to': 'Characters/NedStark', '_rev': '_gVPKGSq---'}], 'weights': [0, 1]}] > Finished chain. 'Yes, Arya Stark and Ned Stark are related. According to the information retrieved from the database, there is a relationship between them. Arya Stark is the child of Ned Stark.'chain.run("Does Arya Stark have a dead parent?") > Entering new ArangoGraphQAChain chain... AQL Query (1): WITH Characters, ChildOf FOR v, e IN 1..1 OUTBOUND 'Characters/AryaStark' ChildOf FILTER v.alive == false RETURN e AQL Result: [{'_key': '266218884027', '_id': 'ChildOf/266218884027', '_from': 'Characters/AryaStark', '_to': 'Characters/CatelynStark', '_rev': '_gVPKGSu---'}] > Finished chain. 'Yes, Arya Stark has a dead parent. The parent is Catelyn Stark.'Chain Modifiers‚ÄãYou can alter the values of the following ArangoDBGraphQAChain class variables to modify the behaviour of your chain results# Specify the maximum number of AQL Query Results to returnchain.top_k = 10# Specify whether or not
1,833	to returnchain.top_k = 10# Specify whether or not to return the AQL Query in the output dictionarychain.return_aql_query = True# Specify whether or not to return the AQL JSON Result in the output dictionarychain.return_aql_result = True# Specify the maximum amount of AQL Generation attempts that should be madechain.max_aql_generation_attempts = 5# Specify a set of AQL Query Examples, which are passed to# the AQL Generation Prompt Template to promote few-shot-learning.# Defaults to an empty string.chain.aql_examples = """# Is Ned Stark alive?RETURN DOCUMENT('Characters/NedStark').alive# Is Arya Stark the child of Ned Stark?FOR e IN ChildOf FILTER e._from == "Characters/AryaStark" AND e._to == "Characters/NedStark" RETURN e"""chain.run("Is Ned Stark alive?")# chain("Is Ned Stark alive?") # Returns a dictionary with the AQL Query & AQL Result > Entering new ArangoGraphQAChain chain... AQL Query (1): RETURN DOCUMENT('Characters/NedStark').alive AQL Result: [True] > Finished chain. 'Yes, according to the information in the database, Ned Stark is alive.'chain.run("Is Bran Stark the child of Ned Stark?") > Entering new ArangoGraphQAChain chain... AQL Query (1): FOR e IN ChildOf FILTER e._from == "Characters/BranStark" AND e._to == "Characters/NedStark" RETURN e AQL Result: [{'_key': '266218884026', '_id': 'ChildOf/266218884026', '_from': 'Characters/BranStark', '_to': 'Characters/NedStark', '_rev': '_gVPKGSq--_'}] > Finished chain. 'Yes, according to the information in the ArangoDB database, Bran Stark is indeed the child of Ned Stark.'PreviousDiffbot Graph TransformerNextNeo4j DB QA chainPopulating the DatabaseGetting & Setting the ArangoDB SchemaQuerying the ArangoDB DatabaseChain ModifiersCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.	Open In Colab	Open In Colab ->: to returnchain.top_k = 10# Specify whether or not to return the AQL Query in the output dictionarychain.return_aql_query = True# Specify whether or not to return the AQL JSON Result in the output dictionarychain.return_aql_result = True# Specify the maximum amount of AQL Generation attempts that should be madechain.max_aql_generation_attempts = 5# Specify a set of AQL Query Examples, which are passed to# the AQL Generation Prompt Template to promote few-shot-learning.# Defaults to an empty string.chain.aql_examples = """# Is Ned Stark alive?RETURN DOCUMENT('Characters/NedStark').alive# Is Arya Stark the child of Ned Stark?FOR e IN ChildOf FILTER e._from == "Characters/AryaStark" AND e._to == "Characters/NedStark" RETURN e"""chain.run("Is Ned Stark alive?")# chain("Is Ned Stark alive?") # Returns a dictionary with the AQL Query & AQL Result > Entering new ArangoGraphQAChain chain... AQL Query (1): RETURN DOCUMENT('Characters/NedStark').alive AQL Result: [True] > Finished chain. 'Yes, according to the information in the database, Ned Stark is alive.'chain.run("Is Bran Stark the child of Ned Stark?") > Entering new ArangoGraphQAChain chain... AQL Query (1): FOR e IN ChildOf FILTER e._from == "Characters/BranStark" AND e._to == "Characters/NedStark" RETURN e AQL Result: [{'_key': '266218884026', '_id': 'ChildOf/266218884026', '_from': 'Characters/BranStark', '_to': 'Characters/NedStark', '_rev': '_gVPKGSq--_'}] > Finished chain. 'Yes, according to the information in the ArangoDB database, Bran Stark is indeed the child of Ned Stark.'PreviousDiffbot Graph TransformerNextNeo4j DB QA chainPopulating the DatabaseGetting & Setting the ArangoDB SchemaQuerying the ArangoDB DatabaseChain ModifiersCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
1,834	Diffbot Graph Transformer \| ü¶úÔ∏èüîó Langchain	Open In Colab	Open In Colab ->: Diffbot Graph Transformer \| ü¶úÔ∏èüîó Langchain
1,835	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 queryingDiffbot Graph TransformerOn this pageDiffbot Graph TransformerUse case‚ÄãText data often contain rich relationships and insights that can be useful for various analytics, recommendation engines, or knowledge management applications.Diffbot's NLP API allows for the extraction of entities, relationships, and semantic meaning from unstructured text data.By coupling Diffbot's NLP API with Neo4j, a graph database, you can create powerful, dynamic graph structures based on the information extracted from text. These graph structures are fully queryable and can be integrated into various applications.This combination allows for use cases such as:Building knowledge graphs from textual documents, websites, or social media feeds.Generating recommendations based on semantic relationships in the data.Creating advanced search features that understand the relationships between entities.Building analytics dashboards that allow users to explore the hidden relationships in data.Overview‚ÄãLangChain provides tools to interact with Graph Databases:Construct knowledge graphs from text using graph transformer and store integrations Query a graph database using chains for query creation and executionInteract with a graph database using agents for robust and flexible querying Quickstart‚ÄãFirst, get required packages and set environment variables:pip install langchain langchain-experimental openai neo4j wikipediaDiffbot NLP Service‚ÄãDiffbot's NLP service is a tool for extracting entities,	Open In Colab	Open In Colab ->: 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 queryingDiffbot Graph TransformerOn this pageDiffbot Graph TransformerUse case‚ÄãText data often contain rich relationships and insights that can be useful for various analytics, recommendation engines, or knowledge management applications.Diffbot's NLP API allows for the extraction of entities, relationships, and semantic meaning from unstructured text data.By coupling Diffbot's NLP API with Neo4j, a graph database, you can create powerful, dynamic graph structures based on the information extracted from text. These graph structures are fully queryable and can be integrated into various applications.This combination allows for use cases such as:Building knowledge graphs from textual documents, websites, or social media feeds.Generating recommendations based on semantic relationships in the data.Creating advanced search features that understand the relationships between entities.Building analytics dashboards that allow users to explore the hidden relationships in data.Overview‚ÄãLangChain provides tools to interact with Graph Databases:Construct knowledge graphs from text using graph transformer and store integrations Query a graph database using chains for query creation and executionInteract with a graph database using agents for robust and flexible querying Quickstart‚ÄãFirst, get required packages and set environment variables:pip install langchain langchain-experimental openai neo4j wikipediaDiffbot NLP Service‚ÄãDiffbot's NLP service is a tool for extracting entities,
1,836	NLP service is a tool for extracting entities, relationships, and semantic context from unstructured text data.	Open In Colab	Open In Colab ->: NLP service is a tool for extracting entities, relationships, and semantic context from unstructured text data.
1,837	This extracted information can be used to construct a knowledge graph. To use their service, you'll need to obtain an API key from Diffbot.from langchain_experimental.graph_transformers.diffbot import DiffbotGraphTransformerdiffbot_api_key = "DIFFBOT_API_KEY"diffbot_nlp = DiffbotGraphTransformer(diffbot_api_key=diffbot_api_key)This code fetches Wikipedia articles about "Warren Buffett" and then uses DiffbotGraphTransformer to extract entities and relationships. The DiffbotGraphTransformer outputs a structured data GraphDocument, which can be used to populate a graph database.	Open In Colab	Open In Colab ->: This extracted information can be used to construct a knowledge graph. To use their service, you'll need to obtain an API key from Diffbot.from langchain_experimental.graph_transformers.diffbot import DiffbotGraphTransformerdiffbot_api_key = "DIFFBOT_API_KEY"diffbot_nlp = DiffbotGraphTransformer(diffbot_api_key=diffbot_api_key)This code fetches Wikipedia articles about "Warren Buffett" and then uses DiffbotGraphTransformer to extract entities and relationships. The DiffbotGraphTransformer outputs a structured data GraphDocument, which can be used to populate a graph database.
1,838	Note that text chunking is avoided due to Diffbot's character limit per API request.from langchain.document_loaders import WikipediaLoaderquery = "Warren Buffett"raw_documents = WikipediaLoader(query=query).load()graph_documents = diffbot_nlp.convert_to_graph_documents(raw_documents)Loading the data into a knowledge graph‚ÄãYou 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.graphs import Neo4jGraphurl="bolt://localhost:7687"username="neo4j"password="pleaseletmein"graph = Neo4jGraph( url=url, username=username, password=password)The GraphDocuments can be loaded into a knowledge graph using the add_graph_documents method.graph.add_graph_documents(graph_documents)Refresh graph schema information‚ÄãIf the schema of database changes, you can refresh the schema information needed to generate Cypher statementsgraph.refresh_schema()Querying the graph‚ÄãWe can now use the graph cypher QA chain to ask question of the graph. It is advisable to use gpt-4 to construct Cypher queries to get the best experience.from langchain.chains import GraphCypherQAChainfrom langchain.chat_models import ChatOpenAIchain = GraphCypherQAChain.from_llm( cypher_llm=ChatOpenAI(temperature=0, model_name="gpt-4"), qa_llm=ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo"), graph=graph, verbose=True, )chain.run("Which university did Warren Buffett attend?") > Entering new GraphCypherQAChain chain... Generated Cypher: MATCH	Open In Colab	Open In Colab ->: Note that text chunking is avoided due to Diffbot's character limit per API request.from langchain.document_loaders import WikipediaLoaderquery = "Warren Buffett"raw_documents = WikipediaLoader(query=query).load()graph_documents = diffbot_nlp.convert_to_graph_documents(raw_documents)Loading the data into a knowledge graph‚ÄãYou 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.graphs import Neo4jGraphurl="bolt://localhost:7687"username="neo4j"password="pleaseletmein"graph = Neo4jGraph( url=url, username=username, password=password)The GraphDocuments can be loaded into a knowledge graph using the add_graph_documents method.graph.add_graph_documents(graph_documents)Refresh graph schema information‚ÄãIf the schema of database changes, you can refresh the schema information needed to generate Cypher statementsgraph.refresh_schema()Querying the graph‚ÄãWe can now use the graph cypher QA chain to ask question of the graph. It is advisable to use gpt-4 to construct Cypher queries to get the best experience.from langchain.chains import GraphCypherQAChainfrom langchain.chat_models import ChatOpenAIchain = GraphCypherQAChain.from_llm( cypher_llm=ChatOpenAI(temperature=0, model_name="gpt-4"), qa_llm=ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo"), graph=graph, verbose=True, )chain.run("Which university did Warren Buffett attend?") > Entering new GraphCypherQAChain chain... Generated Cypher: MATCH
1,839	chain... Generated Cypher: MATCH (p:Person {name: "Warren Buffett"})-[:EDUCATED_AT]->(o:Organization) RETURN o.name Full Context: [{'o.name': 'New York Institute of Finance'}, {'o.name': 'Alice Deal Junior High School'}, {'o.name': 'Woodrow Wilson High School'}, {'o.name': 'University of Nebraska'}] > Finished chain. 'Warren Buffett attended the University of Nebraska.'chain.run("Who is or was working at Berkshire Hathaway?") > Entering new GraphCypherQAChain chain... Generated Cypher: MATCH (p:Person)-[r:EMPLOYEE_OR_MEMBER_OF]->(o:Organization) WHERE o.name = 'Berkshire Hathaway' RETURN p.name Full Context: [{'p.name': 'Charlie Munger'}, {'p.name': 'Oliver Chace'}, {'p.name': 'Howard Buffett'}, {'p.name': 'Howard'}, {'p.name': 'Susan Buffett'}, {'p.name': 'Warren Buffett'}] > Finished chain. 'Charlie Munger, Oliver Chace, Howard Buffett, Susan Buffett, and Warren Buffett are or were working at Berkshire Hathaway.'PreviousGraph queryingNextArangoDB QA chainUse caseOverviewQuickstartDiffbot NLP ServiceLoading the data into a knowledge graphRefresh graph schema informationQuerying the graphCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.	Open In Colab	Open In Colab ->: chain... Generated Cypher: MATCH (p:Person {name: "Warren Buffett"})-[:EDUCATED_AT]->(o:Organization) RETURN o.name Full Context: [{'o.name': 'New York Institute of Finance'}, {'o.name': 'Alice Deal Junior High School'}, {'o.name': 'Woodrow Wilson High School'}, {'o.name': 'University of Nebraska'}] > Finished chain. 'Warren Buffett attended the University of Nebraska.'chain.run("Who is or was working at Berkshire Hathaway?") > Entering new GraphCypherQAChain chain... Generated Cypher: MATCH (p:Person)-[r:EMPLOYEE_OR_MEMBER_OF]->(o:Organization) WHERE o.name = 'Berkshire Hathaway' RETURN p.name Full Context: [{'p.name': 'Charlie Munger'}, {'p.name': 'Oliver Chace'}, {'p.name': 'Howard Buffett'}, {'p.name': 'Howard'}, {'p.name': 'Susan Buffett'}, {'p.name': 'Warren Buffett'}] > Finished chain. 'Charlie Munger, Oliver Chace, Howard Buffett, Susan Buffett, and Warren Buffett are or were working at Berkshire Hathaway.'PreviousGraph queryingNextArangoDB QA chainUse caseOverviewQuickstartDiffbot NLP ServiceLoading the data into a knowledge graphRefresh graph schema informationQuerying the graphCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.