max_aql_generation_attempts: int = 3
    @property
    def input_keys(self) -> List[str]:
        return [self.input_key]
    @property
    def output_keys(self) -> List[str]:
        return [self.output_key]
    @property
    def _chain_type(self) -> str:
        return "graph_aql_chain"
[docs]    @classmethod
    def from_llm(
        cls,
        llm: BaseLanguageModel,
        *,
        qa_prompt: BasePromptTemplate = AQL_QA_PROMPT,
        aql_generation_prompt: BasePromptTemplate = AQL_GENERATION_PROMPT,
        aql_fix_prompt: BasePromptTemplate = AQL_FIX_PROMPT,
        **kwargs: Any,
    ) -> ArangoGraphQAChain:
        """Initialize from LLM."""
        qa_chain = LLMChain(llm=llm, prompt=qa_prompt)
        aql_generation_chain = LLMChain(llm=llm, prompt=aql_generation_prompt)
        aql_fix_chain = LLMChain(llm=llm, prompt=aql_fix_prompt)
        return cls(
            qa_chain=qa_chain,
            aql_generation_chain=aql_generation_chain,
            aql_fix_chain=aql_fix_chain,
            **kwargs,
        )
    def _call(
        self,
        inputs: Dict[str, Any],
        run_manager: Optional[CallbackManagerForChainRun] = None,
    ) -> Dict[str, Any]:
        """
        Generate an AQL statement from user input, use it retrieve a response
        from an ArangoDB Database instance, and respond to the user input
        in natural language.
        Users can modify the following ArangoGraphQAChain Class Variables: