Refactor: Inject dependencies in ChatGPTTelegramInferenceBot
This commit is contained in:
cyclop-bot
@@ -1,43 +1,105 @@
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import os
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import logging
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from openai import OpenAI
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from openai import OpenAI # Keep for type hinting and default client creation
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from openai_compatible_inference_bot import OpenAICompatibleInferenceBot
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from telegram_helper import TelegramHelper
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from telegram_helper import TelegramHelper # Used in main
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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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DEFAULT_SMALL_MODEL_NAME = "gpt-3.5-turbo"
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DEFAULT_LARGE_MODEL_NAME = "gpt-4"
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# Default max tokens can be None, relying on parent or API defaults
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DEFAULT_SMALL_MODEL_MAX_TOKENS = None
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DEFAULT_LARGE_MODEL_MAX_TOKENS = None
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def __init__(
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self,
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client: OpenAI | None = None, # Accepts an OpenAI client
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api_key: str | None = None,
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small_model_name: str | None = None,
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small_model_max_tokens: str | None = None, # Kept as str for consistency with env vars
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large_model_name: str | None = None,
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large_model_max_tokens: str | None = None,
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system_prompt_content: str | None = None,
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system_prompt_path: str | None = None,
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base_url: str | None = None, # For OpenAI compatible, though direct OpenAI client doesn't use it here
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):
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# Initialize model names and tokens before calling super, as super might use them via _configure_model_and_tokens
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self.small_model_name = small_model_name or os.environ.get("OPENAI_SMALL_MODEL") or self.DEFAULT_SMALL_MODEL_NAME
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self.small_model_max_tokens_str = small_model_max_tokens or os.environ.get("OPENAI_SMALL_MODEL_MAX_TOKENS") or self.DEFAULT_SMALL_MODEL_MAX_TOKENS
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self._configure_model_and_tokens(
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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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self.large_model_name = large_model_name or os.environ.get("OPENAI_LARGE_MODEL") or self.DEFAULT_LARGE_MODEL_NAME
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self.large_model_max_tokens_str = large_model_max_tokens or os.environ.get("OPENAI_LARGE_MODEL_MAX_TOKENS") or self.DEFAULT_LARGE_MODEL_MAX_TOKENS
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# The actual client and active model configuration will be handled by OpenAICompatibleInferenceBot's __init__
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# We pass the specific OpenAI client or parameters to create one.
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# If a client is passed, api_key and base_url might be ignored by super if super prioritizes existing client.
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super().__init__(
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client=client,
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api_key=api_key,
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model_name=self.small_model_name, # Initial model
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max_tokens_str=self.small_model_max_tokens_str,
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system_prompt_content=system_prompt_content,
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system_prompt_path=system_prompt_path,
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base_url=base_url # Pass base_url, though for standard OpenAI it's fixed
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)
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# Ensure client is of type OpenAI for this specific class, if not already set by super with a compatible one.
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# This check is more of an assertion, as OpenAICompatibleInferenceBot should handle client creation.
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if not isinstance(self.client, OpenAI):
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# If super() didn't create a vanilla OpenAI client (e.g. if base_url was for Azure)
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# we might need to recreate it here if this class *must* use a non-Azure OpenAI client.
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# However, the current structure of OpenAICompatibleInferenceBot handles this.
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# This is more about ensuring type correctness if code specific to OpenAI (non-compatible) methods were added here.
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_api_key = api_key or os.environ.get("OPENAI_API_KEY")
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if not self.client or (base_url and not isinstance(self.client, OpenAI)):
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# If superclass initialized with a generic client due to base_url, re-init for OpenAI specifically if needed.
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# For now, assume superclass correctly initializes based on absence of Azure env vars for this path.
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# This logic might be simplified once OpenAICompatibleInferenceBot is fully refactored.
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if not _api_key: # Ensure API key is available if we need to create a client
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raise ValueError("OpenAI API key must be provided for ChatGPTTelegramInferenceBot if no client is passed.")
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self.client = OpenAI(api_key=_api_key)
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logging.info("Client re-initialized to standard OpenAI client for ChatGPTTelegramInferenceBot.")
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async def switch_model(self):
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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")
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# Uses instance variables for model names set in __init__
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if not self.small_model_name or not self.large_model_name:
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logging.warning("Small or Large model names for OpenAI are not defined. Cannot switch model.")
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return f"Model switching not fully configured. Currently using {self.model}."
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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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current_is_small = self.model == self.small_model_name
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current_is_large = self.model == self.large_model_name
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if current_is_large:
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target_model = self.small_model_name
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target_max_tokens_str = self.small_model_max_tokens_str
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elif current_is_small:
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target_model = self.large_model_name
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target_max_tokens_str = self.large_model_max_tokens_str
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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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# Current model is neither the designated small nor large for this bot,
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# switch to this bot's default small model as a reset.
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logging.warning(f"Current model {self.model} is unrecognized for ChatGPT bot. Switching to default small model: {self.small_model_name}.")
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target_model = self.small_model_name
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target_max_tokens_str = self.small_model_max_tokens_str
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self._configure_model_and_tokens(target_model, target_max_tokens)
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logging.info(f"Switched to model: {self.model}")
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return f"Switched to model: {self.model}"
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self._configure_model_and_tokens(target_model, target_max_tokens_str)
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# self.model and self.max_tokens are updated by _configure_model_and_tokens
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logging.info(f"Switched to OpenAI model: {self.model}")
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return f"Switched to OpenAI model: {self.model} (Max Tokens: {self.max_tokens if self.max_tokens is not None else 'API default'})"
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def main():
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if not os.environ.get("OPENAI_API_KEY"):
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logging.error("FATAL: OPENAI_API_KEY environment variable not set.")
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return
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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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telegram_helper = TelegramHelper(bot)
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try:
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# Example: api_key from env, other params default or from env via constructor logic
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bot = ChatGPTTelegramInferenceBot(api_key=os.environ.get("OPENAI_API_KEY"))
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except ValueError as e:
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logging.error(f"FATAL: {e}")
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return
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except Exception as e:
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logging.error(f"An unexpected error occurred during bot initialization: {e}")
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return
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telegram_helper = TelegramHelper(bot)
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telegram_helper.run()
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if __name__ == '__main__':
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