Update tools/gemini_tool.py

tools/gemini_tool.py

@@ -0,0 +1,49 @@
+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)}"