Align generic grounding agent's interface with uitars grounding agent

The generic grounding agent has not been tested properly but at least
it should be aligned with the interface being used by the ui-tars
grounding agent which has been tested.

src/khoj/processor/operator/grounding_agent.py

@@ -185,7 +185,7 @@ class GroundingAgent:
             },
         ]
 
-    async def act(self, instruction: str, current_state: EnvState) -> AgentActResult:
+    async def act(self, instruction: str, current_state: EnvState) -> tuple[str, list[OperatorAction]]:
         """Call the grounding LLM to get the next action based on the current state and instruction."""
         # Format the message for the API call
         messages_for_api = self._format_message_for_api(instruction, current_state)
@@ -204,7 +204,7 @@ class GroundingAgent:
 
             # Parse tool calls
             grounding_message = grounding_response.choices[0].message
-            action_results = self._parse_action(grounding_message, instruction, current_state)
+            rendered_response, actions = self._parse_action(grounding_message, instruction, current_state)
 
             # Update usage by grounding model
             self.tracer["usage"] = get_chat_usage_metrics(
@@ -215,10 +215,10 @@ class GroundingAgent:
             )
         except Exception as e:
             logger.error(f"Error calling Grounding LLM: {e}")
-            rendered_response = f"**Thought (Vision)**: {instruction}\n- **Error**: Error contacting Grounding LLM: {e}"
-            action_results = AgentActResult(actions=[], action_results=[], rendered_response=rendered_response)
+            rendered_response = f"**Error**: Error contacting Grounding LLM: {e}"
+            actions = []
 
-        return action_results
+        return rendered_response, actions
 
     def _format_message_for_api(self, instruction: str, current_state: EnvState) -> List:
         """Format the message for the API call."""
@@ -264,14 +264,13 @@ back() # Use this to go back to the previous page.
 
     def _parse_action(
         self, grounding_message: ChatCompletionMessage, instruction: str, current_state: EnvState
-    ) -> AgentActResult:
+    ) -> tuple[str, list[OperatorAction]]:
         """Parse the tool calls from the grounding LLM response and convert them to action objects."""
         actions: List[OperatorAction] = []
         action_results: List[dict] = []
 
         if grounding_message.tool_calls:
-            # Start rendering with vision output
-            rendered_parts = [f"**Thought (Vision)**: {instruction}"]
+            rendered_parts = []
             for tool_call in grounding_message.tool_calls:
                 function_name = tool_call.function.name
                 try:
@@ -336,17 +335,10 @@ back() # Use this to go back to the previous page.
         else:
             # Grounding LLM responded but didn't call a tool
             logger.warning("Grounding LLM did not produce a tool call.")
-            rendered_response = f"**Thought (Vision)**: {instruction}\n- **Response (Grounding)**: {grounding_message.content or '[No tool call]'}"
+            rendered_response = f"{grounding_message.content or 'No action required.'}"
 
         # Render the response
-        return AgentActResult(
-            actions=actions,
-            action_results=action_results,
-            rendered_response={
-                "text": rendered_response,
-                "image": f"data:image/webp;base64,{current_state.screenshot}",
-            },
-        )
+        return rendered_response, actions
 
     def reset(self):
         """Reset the agent state."""