"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