tools/gemini_tool.py

from langchain.tools import BaseTool
from typing import Type, Optional, Any
from pydantic import BaseModel, Field
import google.generativeai as genai
from config.settings import settings
from services.logger import app_logger

class GeminiInput(BaseModel):
    query: str = Field(description="The query or prompt to send to Google Gemini.")

class GeminiTool(BaseTool):
    name: str = "google_gemini_chat"
    description: str = (
        "Useful for when you need to answer questions or generate text using Google Gemini. "
        "Use this for general knowledge, creative text generation, or complex reasoning tasks "
        "that might benefit from a powerful large language model."
    )
    args_schema: Type[BaseModel] = GeminiInput
    # return_direct: bool = True # If you want the agent to return Gemini's output directly

    def _run(self, query: str) -> str:
        if not settings.GEMINI_API_KEY:
            app_logger.error("GEMINI_API_KEY not configured.")
            return "Error: Gemini API key not configured."
        try:
            genai.configure(api_key=settings.GEMINI_API_KEY)
            model = genai.GenerativeModel('gemini-pro')
            response = model.generate_content(query)
            return response.text
        except Exception as e:
            app_logger.error(f"Error calling Gemini API: {e}")
            return f"Error interacting with Gemini: {str(e)}"

    async def _arun(self, query: str) -> str:
        # Asynchronous version (optional, implement if needed)
        # For simplicity, using the synchronous version for now.
        # You might need to use an async client for genai if available or run sync in thread.
        if not settings.GEMINI_API_KEY:
            app_logger.error("GEMINI_API_KEY not configured.")
            return "Error: Gemini API key not configured."
        try:
            genai.configure(api_key=settings.GEMINI_API_KEY)
            model = genai.GenerativeModel('gemini-pro')
            # For async, genai might have an async client or you'd use `loop.run_in_executor`
            response = await model.generate_content_async(query) # Assuming an async method
            return response.text
        except Exception as e:
            app_logger.error(f"Error calling Gemini API asynchronously: {e}")
            return f"Error interacting with Gemini: {str(e)}"