class Config:
        """Configuration for this pydantic object."""
        arbitrary_types_allowed = True
    @validator("llm_chain")
    def validate_llm_chain_input_variables(  # pylint: disable=E0213
        cls, llm_chain: LLMChain
    ) -> LLMChain:
        """Make sure the LLM chain has the correct input variables."""
        for var in llm_chain.prompt.input_variables:
            if var not in ["tool_input", "tables", "schemas", "examples"]:
                raise ValueError(
                    "LLM chain for QueryPowerBITool must have input variables ['tool_input', 'tables', 'schemas', 'examples'], found %s",  # noqa: C0301 E501 # pylint: disable=C0301
                    llm_chain.prompt.input_variables,
                )
        return llm_chain
    def _check_cache(self, tool_input: str) -> Optional[str]:
        """Check if the input is present in the cache.
        If the value is a bad request, overwrite with the escalated version,
        if not present return None."""
        if tool_input not in self.session_cache:
            return None
        return self.session_cache[tool_input]
    def _run(
        self,
        tool_input: str,
        run_manager: Optional[CallbackManagerForToolRun] = None,
        **kwargs: Any,
    ) -> str:
        """Execute the query, return the results or an error message."""
        if cache := self._check_cache(tool_input):
            logger.debug("Found cached result for %s: %s", tool_input, cache)
            return cache
        try:
            logger.info("Running PBI Query Tool with input: %s", tool_input)
            query = self.llm_chain.predict(