classmethod from_llm(llm: BaseLanguageModel, *, qa_prompt: BasePromptTemplate = PromptTemplate(input_variables=['adb_schema', 'user_input', 'aql_query', 'aql_result'], template="Task: Generate a natural language `Summary` from the results of an ArangoDB Query Language query.\n\nYou are an ArangoDB Query Language (AQL) expert responsible for creating a well-written `Summary` from the `User Input` and associated `AQL Result`.\n\nA user has executed an ArangoDB Query Language query, which has returned the AQL Result in JSON format.\nYou are responsible for creating an `Summary` based on the AQL Result.\n\nYou are given the following information:\n- `ArangoDB Schema`: contains a schema representation of the user's ArangoDB Database.\n- `User Input`: the original question/request of the user, which has been translated into an AQL Query.\n- `AQL Query`: the AQL equivalent of the `User Input`, translated by another AI Model. Should you deem it to be incorrect, suggest a different AQL Query.\n- `AQL Result`: the JSON output returned by executing the `AQL Query` within the ArangoDB Database.\n\nRemember to think step by step.\n\nYour `Summary` should sound like it is a response to the `User Input`.\nYour `Summary` should not include any mention of the `AQL Query` or the `AQL Result`.\n\nArangoDB Schema:\n{adb_schema}\n\nUser Input:\n{user_input}\n\nAQL Query:\n{aql_query}\n\nAQL Result:\n{aql_result}\n"), aql_generation_prompt: BasePromptTemplate = PromptTemplate(input_variables=['adb_schema', 'aql_examples', 'user_input'], template="Task: Generate an ArangoDB