Refactor chatgpt_telegram_inference_bot.py to use OpenAICompatibleInferenceBot.
This commit is contained in:
cyclop-bot
@@ -1,156 +1,27 @@
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import json
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import os
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import logging
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from base_telegram_inference_bot import BaseTelegramInferenceBot
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from telegram_helper import TelegramHelper
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from openai import OpenAI
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from openai_compatible_inference_bot import OpenAICompatibleInferenceBot
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from telegram_helper import TelegramHelper
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# logging.basicConfig(level=logging.INFO) # Usually configured in main execution script
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class ChatGPTTelegramInferenceBot(BaseTelegramInferenceBot):
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class ChatGPTTelegramInferenceBot(OpenAICompatibleInferenceBot):
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def __init__(self):
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super().__init__()
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self.client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
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self._configure_model_and_tokens(
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os.environ.get("OPENAI_SMALL_MODEL", "gpt-3.5-turbo"), # Default to a common small model
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os.environ.get("OPENAI_SMALL_MODEL", "gpt-3.5-turbo"),
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os.environ.get("OPENAI_SMALL_MODEL_MAX_TOKENS")
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)
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def _configure_model_and_tokens(self, model_name, max_tokens_str, default_max_tokens=1000):
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self.model = model_name if model_name else "gpt-3.5-turbo" # Ensure model has a default
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try:
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self.max_tokens = int(max_tokens_str) if max_tokens_str is not None else default_max_tokens
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except ValueError:
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logging.error(f"Invalid value for max_tokens: {max_tokens_str}. Using default {default_max_tokens}.")
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self.max_tokens = default_max_tokens
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logging.info(f"Configured to use model: {self.model} with max_tokens: {self.max_tokens}")
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def get_system_prompt_description(self) -> str:
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system_prompt_path = os.getenv("SYSTEM_PROMPT_PATH")
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if system_prompt_path and os.path.isfile(system_prompt_path):
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return f"System Prompt File: {os.path.basename(system_prompt_path)}"
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elif system_prompt_path: # Path is set but file not found
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return f"System Prompt File: {os.path.basename(system_prompt_path)} (Not found at path: {system_prompt_path})"
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else: # Path not set
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return "System Prompt File: Not configured (SYSTEM_PROMPT_PATH not set)."
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def get_llm_description(self) -> str:
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return f"LLM: {self.model}, Max Tokens: {self.max_tokens}"
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def get_chat_response(self, messages):
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try:
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response = self.client.chat.completions.create(
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model=self.model,
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messages=messages,
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tools=self.functions if hasattr(self, 'functions') and self.functions else None,
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tool_choice="auto" if hasattr(self, 'functions') and self.functions else None,
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max_tokens=self.max_tokens
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)
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return response
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except Exception as e:
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logging.error(f"OpenAI API call failed: {e}")
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raise
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async def handle_message(self, user_id, user_message):
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if user_id not in self.conversation_history:
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self.conversation_history[user_id] = []
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if hasattr(self, 'system_prompt') and self.system_prompt:
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self.conversation_history[user_id].append({"role": "system", "content": self.system_prompt})
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self.conversation_history[user_id].append({"role": "user", "content": user_message})
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messages = self.conversation_history[user_id]
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response = self.get_chat_response(messages)
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if not (response.choices and response.choices[0].message):
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logging.error("No valid response choice message from LLM.")
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return "Error: Could not get a valid response from the LLM."
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messages.append(response.choices[0].message) # Append the assistant's response message
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tool_calls_from_response = []
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if response.choices[0].message.tool_calls:
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tool_calls_from_response.extend(response.choices[0].message.tool_calls)
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tool_use_count = 0
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MAX_TOOL_ITERATIONS = 5
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while tool_calls_from_response and tool_use_count < MAX_TOOL_ITERATIONS:
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tool_results_for_model = []
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for tool_call in tool_calls_from_response:
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tool_call_id = tool_call.id
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function_to_call = tool_call.function
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logging.info(f"Attempting to call tool: {function_to_call.name} with args: {function_to_call.arguments}")
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try:
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tool_response_content = self.call_tool(function_to_call.name, function_to_call.arguments)
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if not isinstance(tool_response_content, str):
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tool_response_content = json.dumps(tool_response_content)
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except Exception as e:
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logging.error(f"Error calling tool {function_to_call.name}: {e}")
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tool_response_content = f"Error executing tool {function_to_call.name}: {str(e)}"
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tool_results_for_model.append({
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"role": "tool",
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"tool_call_id": tool_call_id,
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"name": function_to_call.name,
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"content": tool_response_content
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})
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messages.extend(tool_results_for_model)
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response = self.get_chat_response(messages)
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if not (response.choices and response.choices[0].message):
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logging.error("No valid response choice message from LLM after tool call.")
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return "Error: Could not get a valid response from the LLM after tool call."
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messages.append(response.choices[0].message)
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tool_calls_from_response = []
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if response.choices[0].message.tool_calls:
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tool_calls_from_response.extend(response.choices[0].message.tool_calls)
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tool_use_count += 1
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if tool_use_count >= MAX_TOOL_ITERATIONS and tool_calls_from_response:
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logging.warning(f"Max tool iterations ({MAX_TOOL_ITERATIONS}) reached. Returning last assistant message.")
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if len(self.conversation_history[user_id]) > 20: # This limit seems small, consider increasing
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self.conversation_history[user_id] = self.conversation_history[user_id][-20:]
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final_assistant_message = messages[-1]
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return final_assistant_message.content if final_assistant_message.role == "assistant" and final_assistant_message.content else "No content in final message."
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async def start(self):
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logging.info("ChatGPT Bot started")
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# super().start() if Base class start() has common logic
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async def clear(self, user_id):
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super().clear_conversation(user_id)
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# status() method is inherited from BaseTelegramInferenceBot
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async def abort_processing(self, user_id):
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if user_id in self.processing_status: # Relies on processing_status from Base
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self.processing_status[user_id]["processing"] = False
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await self.clear(user_id)
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return "Processing aborted and conversation cleared."
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else:
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await self.clear(user_id)
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return "No active processing found to abort. Conversation cleared."
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async def switch_model(self):
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# Ensure environment variables for model names are set for this to work meaningfully
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current_small_model = os.environ.get("OPENAI_SMALL_MODEL", "gpt-3.5-turbo")
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current_large_model = os.environ.get("OPENAI_LARGE_MODEL", "gpt-4") # Example large model
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current_large_model = os.environ.get("OPENAI_LARGE_MODEL", "gpt-4")
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# Default to small model if current model is not recognized or if it's the large one
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if self.model == current_large_model or self.model != current_small_model:
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target_model = current_small_model
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target_max_tokens = os.environ.get("OPENAI_SMALL_MODEL_MAX_TOKENS")
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else: # Current is small (or default), switch to large
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else:
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target_model = current_large_model
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target_max_tokens = os.environ.get("OPENAI_LARGE_MODEL_MAX_TOKENS")
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@@ -163,7 +34,6 @@ def main():
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logging.error("FATAL: OPENAI_API_KEY environment variable not set.")
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return
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# Configure logging here if it's the main entry point
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
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bot = ChatGPTTelegramInferenceBot()
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