add google

application/llm/google_ai.py

@@ -72,29 +72,38 @@ class GoogleLLM(BaseLLM):
         messages,
         stream=False,
         tools=None,
+        formatting="openai",
         **kwargs
     ):  
-        import google.generativeai as genai
-        genai.configure(api_key=self.api_key)
+        from google import genai
+        from google.genai import types
+        client = genai.Client(api_key=self.api_key)
+
 
         config = {
         }
         model = 'gemini-2.0-flash-exp'
-        
-        model = genai.GenerativeModel(
-            model_name=model,
-            generation_config=config,
-            system_instruction=messages[0]["content"],
-            tools=self._clean_tools_format(tools)
+        if formatting=="raw":
+            response = client.models.generate_content(
+                model=model,
+                contents=messages
             )
-        chat_session = model.start_chat(
-            history=self._clean_messages_google(messages)[:-1]
-        )
-        response = chat_session.send_message(
-            self._clean_messages_google(messages)[-1]
-        )
-        logging.info(response)
-        return response.text
+        
+        else:
+            model = genai.GenerativeModel(
+                model_name=model,
+                generation_config=config,
+                system_instruction=messages[0]["content"],
+                tools=self._clean_tools_format(tools)
+                )
+            chat_session = model.start_chat(
+                history=self._clean_messages_google(messages)[:-1]
+            )
+            response = chat_session.send_message(
+                self._clean_messages_google(messages)[-1]
+            )
+            logging.info(response)
+            return response.text
 
     def _raw_gen_stream(
         self,

application/tools/agent.py

@@ -1,4 +1,5 @@
 import json
+import logging
 
 from application.core.mongo_db import MongoDB
 from application.llm.llm_creator import LLMCreator
@@ -79,6 +80,25 @@ class Agent:
         print(f"Executing tool: {action_name} with args: {call_args}")
         return tool.execute_action(action_name, **call_args), call_id
 
+    def _execute_tool_action_google(self, tools_dict, call):
+        call_args = json.loads(call.args)
+        tool_id = call.name.split("_")[-1]
+        action_name = call.name.rsplit("_", 1)[0]
+
+        tool_data = tools_dict[tool_id]
+        action_data = next(
+            action for action in tool_data["actions"] if action["name"] == action_name
+        )
+
+        for param, details in action_data["parameters"]["properties"].items():
+            if param not in call_args and "value" in details:
+                call_args[param] = details["value"]
+
+        tm = ToolManager(config={})
+        tool = tm.load_tool(tool_data["name"], tool_config=tool_data["config"])
+        print(f"Executing tool: {action_name} with args: {call_args}")
+        return tool.execute_action(action_name, **call_args)
+
     def _simple_tool_agent(self, messages):
         tools_dict = self._get_user_tools()
         self._prepare_tools(tools_dict)
@@ -91,8 +111,18 @@ class Agent:
         if resp.message.content:
             yield resp.message.content
             return
+        # check if self.llm class is GoogleLLM
+        while self.llm.__class__.__name__ == "GoogleLLM" and resp.content.parts[0].function_call:
+            from google.genai import types
 
-        while resp.finish_reason == "tool_calls":
+            function_call_part = resp.candidates[0].content.parts[0]
+            tool_response = self._execute_tool_action_google(tools_dict, function_call_part.function_call)
+            function_response_part = types.Part.from_function_response(
+                name=function_call_part.function_call.name,
+                response=tool_response
+            )
+
+        while self.llm.__class__.__name__ == "OpenAILLM" and resp.finish_reason == "tool_calls":
             message = json.loads(resp.model_dump_json())["message"]
             keys_to_remove = {"audio", "function_call", "refusal"}
             filtered_data = {