from typing import Dict, Optional, Type, Union

from langchain_core.callbacks import (
    AsyncCallbackManagerForToolRun,
    CallbackManagerForToolRun,
)
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_core.tools import BaseTool


class DummyToolInput(BaseModel):
    """Input for the Placeholder tool."""
    # Define the inputs for your tool. For a placeholder, we'll just use a simple query.
    query: str = Field(description="Placeholder query input")


class DummyToolTool(BaseTool):
    """A simple placeholder tool for demonstration purposes."""

    name: str = "placeholder_tool"
    description: str = "A tool that serves as a placeholder for future development."
    # If your tool requires initialization parameters, define them here.
    # For a placeholder, we might not need any, but you can add as necessary.
    args_schema: Type[BaseModel] = DummyToolInput

    def _run(
            self,
            query: str,
            run_manager: Optional[CallbackManagerForToolRun] = None,
    ) -> Union[Dict, str]:
        """Synchronously run the tool."""
        # Placeholder logic. Replace with your tool's functionality.
        try:
            # Simulate processing the query and returning a result.
            result = {"message": f"Processed query: {query}"}
            return result
        except Exception as e:
            return repr(e)

    async def _arun(
            self,
            query: str,
            run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
    ) -> Union[Dict, str]:
        """Asynchronously run the tool."""
        # Placeholder logic for asynchronous operation. Replace as needed.
        try:
            # Simulate asynchronous processing of the query.
            result = {"message": f"Processed query asynchronously: {query}"}
            return result
        except Exception as e:
            return repr(e)
