langchain_google_genai.chat_models.ChatGoogleGenerativeAIError: Invalid argument provided to Gemini: 400 * GenerateContentRequest.tools[0].function_declarations[0].parameters.properties[action].type: must be specified

[Arunprakaash]

pydantic model

class Response(BaseModel):
    """Response to user."""

    response: str

class Act(BaseModel):
    """Action to perform."""

    action: Union[Response, Plan] = Field(
        description="Action to perform. If you want to respond to user, use Response. "
        "If you need to further use tools to get the answer, use Plan."
    )

def call_replan(state: AgentState, config: RunnableConfig):
    model.with_structured_output(convert_to_openai_function(Act))
    // throws error with/without converting pydantic to dict schema using convert_to_openai_function
        

llm = ChatGoogleGenerativeAI(
    temperature=0, model="gemini-1.5-flash")

as well as on tool calling arg schema

class RequestsGetToolInput(BaseModel):
    url: str = Field(description="The URL to send the GET request to")
    params: Optional[dict[str, str]] = Field(
        # default_factory=dict,
        default={},
        description="Query parameters for the GET request"
    )
    output_instructions: str = Field(description="Instructions on what information to extract from the response")


class RequestsGetToolWithParsing(BaseRequestsTool, BaseTool):
    """Requests GET tool with LLM-instructed extraction of truncated responses."""

    name: str = "requests_get"
    description: str = REQUESTS_GET_TOOL_DESCRIPTION
    args_schema: type[BaseModel] = RequestsGetToolInput
    response_length: int = MAX_RESPONSE_LENGTH
    llm_chain: Any = Field(
        default_factory=_get_default_llm_chain_factory(PARSING_GET_PROMPT)
    )

    def _run(
        self,
        url: str,
        output_instructions: str,
        params: dict[str, str] = {},
        run_manager: Optional[CallbackManagerForToolRun] = None
    ) -> str:
        response: str = cast(
            str, self.requests_wrapper.get(url, params=params)
        )
        response = response[: self.response_length]
        return self.llm_chain.invoke({
            "response": response, "instructions": output_instructions
        }).strip()

    async def _arun(
        self,
        url: str,
        output_instructions: str,
        params: dict[str, str] = {},
        run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
    ) -> str:
        """Use the tool asynchronously."""
        return self._run(url, output_instructions, params, run_manager=run_manager.get_sync() if run_manager else None)

langchain_google_genai.chat_models.ChatGoogleGenerativeAIError: Invalid argument provided to Gemini: 400 * GenerateContentRequest.tools[0].function_declarations[0].parameters.properties[params].properties: should be non-empty for OBJECT type