feat: Implement prompt/LLM status and refine tool handling (Gemini bot)

gemini_telegram_inference_bot.py

@@ -1,12 +1,11 @@
 import json
 import os
 import logging
-from base_telegram_inference_bot import BaseTelegramInferenceBot # Assuming this base class exists
+from base_telegram_inference_bot import BaseTelegramInferenceBot
-from telegram_helper import TelegramHelper # Assuming this helper class exists
+from telegram_helper import TelegramHelper # This import might be unused if main() is removed or TelegramHelper is not directly instantiated here.
 from openai import OpenAI
 
-# Ensure basic logging is configured if not done elsewhere
+# logging.basicConfig(level=logging.INFO) # Usually configured in main execution script
-# logging.basicConfig(level=logging.INFO) # Example: You might have a more sophisticated setup
 
 class GeminiTelegramInferenceBot(BaseTelegramInferenceBot):
     def __init__(self):
@@ -14,12 +13,12 @@ class GeminiTelegramInferenceBot(BaseTelegramInferenceBot):
         self.client = OpenAI(api_key=os.environ.get("GEMINI_API_KEY"), base_url=os.environ.get("GEMINI_API_BASE_URL"))
         
         self._configure_model_and_tokens(
-            os.environ.get("GEMINI_SMALL_MODEL"), # Default model
+            os.environ.get("GEMINI_SMALL_MODEL"),
-            os.environ.get("GEMINI_SMALL_MODEL_MAX_TOKENS") # Default tokens
+            os.environ.get("GEMINI_SMALL_MODEL_MAX_TOKENS")
         )
 
     def _configure_model_and_tokens(self, model_name, max_tokens_str, default_max_tokens=1000):
-        self.model = model_name
+        self.model = model_name if model_name else "default-gemini-model" # Ensure model has a default
         try:
             self.max_tokens = int(max_tokens_str) if max_tokens_str is not None else default_max_tokens
         except ValueError:
@@ -27,11 +26,23 @@ class GeminiTelegramInferenceBot(BaseTelegramInferenceBot):
             self.max_tokens = default_max_tokens
         logging.info(f"Configured to use model: {self.model} with max_tokens: {self.max_tokens}")
 
+    def get_system_prompt_description(self) -> str:
+        system_prompt_path = os.getenv("SYSTEM_PROMPT_PATH")
+        if system_prompt_path and os.path.isfile(system_prompt_path):
+            return f"System Prompt File: {os.path.basename(system_prompt_path)}"
+        elif system_prompt_path: # Path is set but file not found
+            return f"System Prompt File: {os.path.basename(system_prompt_path)} (Not found at path: {system_prompt_path})"
+        else: # Path not set
+            return "System Prompt File: Not configured (SYSTEM_PROMPT_PATH not set)."
+
+    def get_llm_description(self) -> str:
+        return f"LLM: {self.model}, Max Tokens: {self.max_tokens}"
+
     def get_chat_response(self, messages):
         try:
             response = self.client.chat.completions.create(
                 model=self.model,
-                messages=messages, # The system prompt is expected to be part of messages here
+                messages=messages,
                 tools=self.functions if hasattr(self, 'functions') and self.functions else None,
                 tool_choice="auto" if hasattr(self, 'functions') and self.functions else None,
                 max_tokens=self.max_tokens
@@ -39,6 +50,8 @@ class GeminiTelegramInferenceBot(BaseTelegramInferenceBot):
             return response
         except Exception as e:
             logging.error(f"Gemini API call failed: {e}")
+            # Return a more structured error or re-raise a custom exception
+            # For now, re-raising to be handled by the caller
             raise
 
     async def handle_message(self, user_id, user_message):
@@ -52,92 +65,125 @@ class GeminiTelegramInferenceBot(BaseTelegramInferenceBot):
 
         response = self.get_chat_response(messages)
         
-        tool_calls = []
+        # Ensure response.choices[0].message exists before appending
-    
+        if response.choices and response.choices[0].message:
-        for message_part in response.choices:
+            messages.append(response.choices[0].message) # Append the assistant's response message
-            if message_part.finish_reason == "tool_calls":
+        else:
-                tool_calls.extend(message_part.message.tool_calls)
+            logging.error("No valid response choice message from LLM.")
+            return "Error: Could not get a valid response from the LLM."
+
+        tool_calls_from_response = []
+        if response.choices[0].message.tool_calls:
+            tool_calls_from_response.extend(response.choices[0].message.tool_calls)
         
-        messages.append(response.choices[0].message)
-
         tool_use_count = 0
-        while len(tool_calls) > 0 and tool_use_count < 500:
+        MAX_TOOL_ITERATIONS = 5 # Define a max to prevent infinite loops more explicitly
-            tool_use_results = []
 
-            while len(tool_calls) > 0:
+        while tool_calls_from_response and tool_use_count < MAX_TOOL_ITERATIONS:
-                tool_call_message = tool_calls.pop(0)
+            tool_results_for_model = [] # Results to be sent back to the model
-                tool_call_id = tool_call_message.id
+
-                tool_call = tool_call_message.function
+            for tool_call in tool_calls_from_response:
-                tool_response = self.call_tool(tool_call.name, tool_call.arguments)
+                tool_call_id = tool_call.id
+                function_to_call = tool_call.function
+                
+                logging.info(f"Attempting to call tool: {function_to_call.name} with args: {function_to_call.arguments}")
                 try:
-                    tool_use_results.append({"role": "tool", "tool_call_id": tool_call_id, "name":tool_call.name, "content": str(tool_response) })
+                    tool_response_content = self.call_tool(function_to_call.name, function_to_call.arguments)
-                except (TypeError, ValueError) as e:
+                    # Ensure tool_response_content is a string for the API
-                    logging.error(f"Failed to serialize tool response: {e}")
+                    if not isinstance(tool_response_content, str):
-                    tool_use_results.append({"role": "function", "name": tool_call.name, "content": "Serialization error"})
+                        tool_response_content = json.dumps(tool_response_content) 
+                except Exception as e:
+                    logging.error(f"Error calling tool {function_to_call.name}: {e}")
+                    tool_response_content = f"Error executing tool {function_to_call.name}: {str(e)}"
+                
+                tool_results_for_model.append({
+                    "role": "tool",
+                    "tool_call_id": tool_call_id,
+                    "name": function_to_call.name,
+                    "content": tool_response_content
+                })
 
-            messages.extend(tool_use_results)
+            messages.extend(tool_results_for_model) # Add tool responses to message history
 
+            # Get new response from model based on tool execution results
             response = self.get_chat_response(messages)
-            
+            if not (response.choices and response.choices[0].message):
-            for message_part in response.choices:
+                logging.error("No valid response choice message from LLM after tool call.")
-                if message_part.finish_reason == "tool_calls":
+                return "Error: Could not get a valid response from the LLM after tool call."
-                    tool_calls.extend(message_part.message.tool_calls)
-            
-            messages.append(response.choices[0].message)
 
+            messages.append(response.choices[0].message) # Append new assistant message
+
+            # Check for new tool calls
+            tool_calls_from_response = [] # Reset for this iteration
+            if response.choices[0].message.tool_calls:
+                tool_calls_from_response.extend(response.choices[0].message.tool_calls)
+            
             tool_use_count += 1
+            if tool_use_count >= MAX_TOOL_ITERATIONS and tool_calls_from_response:
+                logging.warning(f"Max tool iterations ({MAX_TOOL_ITERATIONS}) reached. Returning last assistant message.")
+                # May need to return a message indicating this to user
 
-        if len(self.conversation_history[user_id]) > 2000:
+        # Conversation history management
+        if len(self.conversation_history[user_id]) > 2000: # Assuming this limit is for messages, not tokens
             self.conversation_history[user_id] = self.conversation_history[user_id][-2000:]
 
-        return messages[-1].content
+        # Return the latest assistant content
+        final_assistant_message = messages[-1]
+        return final_assistant_message.content if final_assistant_message.role == "assistant" and final_assistant_message.content else "No content in final message."
+
 
     async def start(self):
-        logging.info("Bot started")
+        logging.info("Gemini Bot started")
-        # Potentially call super().start() if it exists and does something
+        # super().start() if Base class start() has common logic
 
     async def clear(self, user_id):
-        super().clear_conversation(user_id)
+        super().clear_conversation(user_id) # Calls base class method
 
-
+    # status() method is inherited from BaseTelegramInferenceBot
-    async def status(self):
-        return f"Currently using: {self.model}, Max Tokens: {self.max_tokens}"
 
     async def abort_processing(self, user_id):
-        # This depends on how processing_status is managed, likely in BaseTelegramInferenceBot
+        if user_id in self.processing_status:
-        if hasattr(self, 'processing_status') and user_id in self.processing_status:
+            self.processing_status[user_id]["processing"] = False
-            self.processing_status[user_id]["processing"] = False # Example
+            # It's good practice to also clear the conversation for an aborted state
-            await self.clear(user_id) # Clearing conversation on abort might be desired
+            await self.clear(user_id) 
             return "Processing aborted and conversation cleared."
         else:
-            # If not tracking processing_status here, just clear for safety
+            # If no specific status, clearing conversation is a safe default
             await self.clear(user_id)
-            return "No specific active processing to abort, cleared conversation for safety."
+            return "No active processing found to abort. Conversation cleared."
 
     async def switch_model(self):
         current_small_model = os.environ.get("GEMINI_SMALL_MODEL")
         current_large_model = os.environ.get("GEMINI_LARGE_MODEL")
 
-        if self.model == current_small_model:
+        # Default to small model if current model is not recognized or if it's the large one
-            target_model = current_large_model
+        if self.model == current_large_model or self.model != current_small_model :
-            target_max_tokens = os.environ.get("GEMINI_LARGE_MODEL_MAX_TOKENS")
-        else:
             target_model = current_small_model
             target_max_tokens = os.environ.get("GEMINI_SMALL_MODEL_MAX_TOKENS")
+        else: # Current is small, switch to large
+            target_model = current_large_model
+            target_max_tokens = os.environ.get("GEMINI_LARGE_MODEL_MAX_TOKENS")
         
         self._configure_model_and_tokens(target_model, target_max_tokens)
+        logging.info(f"Switched to model: {self.model}")
         return f"Switched to model: {self.model}"
 
+# The main() function and if __name__ == '__main__': block are for standalone execution.
+# If this bot is imported as a module, these might not be necessary or might be handled differently.
+# For now, keeping them as they were.
 def main():
-    # Ensure GEMINI_API_KEY and other environment variables are set
     if not os.environ.get("GEMINI_API_KEY"):
         logging.error("FATAL: GEMINI_API_KEY environment variable not set.")
         return
 
+    # Configure logging here if it's the main entry point
+    logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+    
     bot = GeminiTelegramInferenceBot()
+    # The instantiation of TelegramHelper and running it implies this file can be an entry point.
+    # If it's purely a module, this main() would be removed.
     telegram_helper = TelegramHelper(bot) 
     telegram_helper.run()
 
 if __name__ == '__main__':
-    logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+    main()
-    main()