Merge pull request #189 from bucolucas/refactor/bot-core-logic
Refactor Core Bot Logic: telegram_helper.py and *_inference_bot.py
This commit is contained in:
GitHub
@@ -1,45 +1,29 @@
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import os
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import json
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import logging
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from anthropic import Anthropic
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from anthropic import Anthropic, APIError, RateLimitError
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from base_telegram_inference_bot import BaseTelegramInferenceBot
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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 AnthropicTelegramInferenceBot(BaseTelegramInferenceBot):
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def __init__(self):
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super().__init__()
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self.anthropic_client = Anthropic(api_key=os.environ.get("ANTHROPIC_API_KEY"))
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# Note: default_headers for max_tokens with older models might be needed.
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# For Claude 3.5 Sonnet, max_tokens is a top-level param in messages.create
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# Configure model and tokens. Using Sonnet 3.5 as default.
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# ANTHROPIC_MODEL and ANTHROPIC_MAX_TOKENS would be new ENVs.
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self._configure_model_and_tokens(
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os.environ.get("ANTHROPIC_MODEL", "claude-3-5-sonnet-20240620"),
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os.environ.get("ANTHROPIC_MAX_TOKENS", "4096") # Default max tokens for Sonnet 3.5
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os.environ.get("ANTHROPIC_MAX_TOKENS", "4096")
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)
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def _configure_model_and_tokens(self, model_name, max_tokens_str, default_max_tokens=4096):
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self.model = model_name if model_name else "claude-3-5-sonnet-20240620"
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try:
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# Anthropic's max_tokens is an integer.
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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 Anthropic 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 Anthropic 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:
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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:
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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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@@ -66,9 +50,27 @@ class AnthropicTelegramInferenceBot(BaseTelegramInferenceBot):
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tool_choice={"type": "auto"} if anthropic_tools else None
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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"Anthropic API call failed: {e}")
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except (APIError, RateLimitError) as e:
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logging.error(f"Anthropic API error: {e}")
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raise
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except Exception as e:
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logging.error(f"An unexpected error occurred during Anthropic API call: {e}")
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raise
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def _format_tool_response_for_anthropic(self, tool_response_data):
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if isinstance(tool_response_data, str):
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return [{"type": "text", "text": tool_response_data}]
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elif isinstance(tool_response_data, (dict, list)):
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try:
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is_valid_block_list = isinstance(tool_response_data, list) and all(isinstance(item, dict) and "type" in item for item in tool_response_data)
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if is_valid_block_list:
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return tool_response_data
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else:
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return [{"type": "text", "text": json.dumps(tool_response_data)}]
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except (TypeError, json.JSONDecodeError):
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return [{"type": "text", "text": str(tool_response_data)}]
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else:
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return [{"type": "text", "text": str(tool_response_data)}]
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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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@@ -86,7 +88,7 @@ class AnthropicTelegramInferenceBot(BaseTelegramInferenceBot):
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if not response or not response.content:
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logging.error("No valid response content from Anthropic LLM.")
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self.conversation_history[user_id] = current_turn_messages # Persist what we have
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self.conversation_history[user_id] = current_turn_messages
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return "Error: Could not get a valid response from the LLM."
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assistant_current_turn_content_blocks = response.content
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@@ -114,22 +116,7 @@ class AnthropicTelegramInferenceBot(BaseTelegramInferenceBot):
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logging.info(f"Attempting to call Anthropic tool: {tool_name} with input: {tool_input}")
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try:
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tool_response_data = self.call_tool(tool_name, tool_input)
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if isinstance(tool_response_data, str):
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tool_result_content_block = [{"type": "text", "text": tool_response_data}]
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elif isinstance(tool_response_data, dict) or isinstance(tool_response_data, list):
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try:
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# If tool_response_data is already a list of Anthropic content blocks, use as is.
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# Otherwise, dump to JSON string and wrap in a text block.
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is_valid_block_list = isinstance(tool_response_data, list) and all(isinstance(item, dict) and "type" in item for item in tool_response_data)
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if is_valid_block_list:
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tool_result_content_block = tool_response_data
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else:
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tool_result_content_block = [{"type": "text", "text": json.dumps(tool_response_data)}]
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except (TypeError, json.JSONDecodeError): # Not easily serializable or not a valid block list
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tool_result_content_block = [{"type": "text", "text": str(tool_response_data)}]
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else: # bool, int, float, None, etc.
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tool_result_content_block = [{"type": "text", "text": str(tool_response_data)}]
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tool_result_content_block = self._format_tool_response_for_anthropic(tool_response_data)
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tool_results_for_model.append({
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"type": "tool_result",
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@@ -157,11 +144,10 @@ class AnthropicTelegramInferenceBot(BaseTelegramInferenceBot):
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if len(self.conversation_history[user_id]) > 20:
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self.conversation_history[user_id] = self.conversation_history[user_id][-20:]
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if assistant_response_content: # Text from the last successful assistant turn (or before max iterations)
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if assistant_response_content:
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return assistant_response_content
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else: # Fallback if no text content was generated by assistant (e.g. initial error, or only tool use)
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else:
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if current_turn_messages:
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# Try to get the *very last* text block from the *very last* assistant message in history.
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last_message_in_turn = current_turn_messages[-1]
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if last_message_in_turn.get("role") == "assistant" and isinstance(last_message_in_turn.get("content"), list):
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for block in reversed(last_message_in_turn["content"]):
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@@ -173,17 +159,17 @@ class AnthropicTelegramInferenceBot(BaseTelegramInferenceBot):
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async def start(self):
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logging.info("Anthropic Bot started")
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async def clear(self, user_id):
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super().clear_conversation(user_id)
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async def clear_conversation_history(self, user_id):
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super().clear_conversation_history(user_id)
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logging.info(f"Cleared conversation history for Anthropic bot, user {user_id}")
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async def abort_processing(self, user_id):
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if user_id in self.processing_status:
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self.processing_status[user_id]["processing"] = False
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await self.clear(user_id)
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await self.clear_conversation_history(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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await self.clear_conversation_history(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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@@ -2,6 +2,7 @@ import importlib
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import os
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import json
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import inspect
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import logging
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from abc import ABC, abstractmethod
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from tools.base_tool import BaseTool
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@@ -11,32 +12,44 @@ class BaseTelegramInferenceBot(ABC):
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self.processing_status = {}
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self.system_prompt = self.load_system_prompt()
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self.tools, self.functions = self.load_functions()
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print(f'System Prompt: {os.environ.get("SYSTEM_PROMPT_PATH")}')
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print(f'Github Repository: {os.environ.get("GITHUB_REPOSITORY")}')
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logging.info(f'System Prompt: {os.environ.get("SYSTEM_PROMPT_PATH")}')
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logging.info(f'Github Repository: {os.environ.get("GITHUB_REPOSITORY")}')
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@staticmethod
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def load_system_prompt():
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def load_system_prompt(self):
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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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with open(system_prompt_path, "r", encoding="utf-8") as file:
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return file.read().strip()
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try:
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with open(system_prompt_path, "r", encoding="utf-8") as file:
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return file.read().strip()
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except IOError as e:
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logging.warning(f"Could not read system prompt file {system_prompt_path}: {e}")
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return "You are a helpful AI assistant."
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else:
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raise FileNotFoundError("SYSTEM_PROMPT_PATH is not set or file does not exist.")
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logging.warning("SYSTEM_PROMPT_PATH is not set or file does not exist. Using default system prompt.")
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return "You are a helpful AI assistant."
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@staticmethod
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def load_functions():
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def load_functions(self):
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tools = []
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functions = []
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tools_dir = os.path.join(os.path.dirname(__file__), 'tools')
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if not os.path.exists(tools_dir):
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logging.warning(f"Tools directory not found: {tools_dir}")
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return [], []
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for filename in os.listdir(tools_dir):
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if filename.endswith('.py') and filename != '__init__.py' and filename != 'base_tool.py':
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module_name = f'tools.{filename[:-3]}'
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module = importlib.import_module(module_name)
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for name, obj in inspect.getmembers(module):
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if inspect.isclass(obj) and issubclass(obj, BaseTool) and obj != BaseTool:
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tools.append(obj())
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try:
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module = importlib.import_module(module_name)
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for name, obj in inspect.getmembers(module):
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if inspect.isclass(obj) and issubclass(obj, BaseTool) and obj != BaseTool:
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try:
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tools.append(obj())
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except Exception as e:
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logging.error(f"Error instantiating tool {name} from {filename}: {e}")
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except Exception as e:
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logging.error(f"Error importing module {module_name}: {e}")
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# Collect all function definitions
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functions = []
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for tool in tools:
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functions.extend(tool.get_functions())
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return tools, functions
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@@ -49,37 +62,53 @@ class BaseTelegramInferenceBot(ABC):
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async def handle_message(self, user_id, user_message):
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pass
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def clear_conversation(self, user_id):
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def clear_conversation_history(self, user_id):
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if user_id in self.conversation_history:
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del self.conversation_history[user_id]
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# Assuming tool.clear() is for global state or doesn't need user_id
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for tool in self.tools:
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tool.clear()
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def set_processing_status(self, user_id: int, message_id: int):
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self.processing_status[user_id] = {"processing": True, "message_id": message_id}
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def clear_processing_status(self, user_id: int):
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if user_id in self.processing_status:
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del self.processing_status[user_id]
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def call_tool(self, function_call_name, function_call_arguments):
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function_name = function_call_name
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function_args = json.loads(function_call_arguments if function_call_arguments is not None else "{}")
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try:
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function_args = json.loads(function_call_arguments if function_call_arguments is not None else "{}")
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except json.JSONDecodeError as e:
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logging.error(f"Error decoding function call arguments for {function_call_name}: {e}. Arguments: {function_call_arguments}")
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return f"Error: Malformed arguments for tool call: {e}"
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for tool in self.tools:
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for function in tool.get_functions():
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if function["function"]["name"] == function_name:
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return tool.execute(function_name, **function_args)
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try:
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return tool.execute(function_name, **function_args)
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except Exception as e:
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logging.error(f"Error executing tool {function_name} with args {function_args}: {e}")
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return f"Error executing tool {function_name}: {e}"
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logging.warning(f"Tool function {function_name} not found.")
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return f"Error: Tool function {function_name} not found."
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@abstractmethod
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def get_system_prompt_description(self) -> str:
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"""Returns a description of the system prompt being used."""
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pass
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return f"System Prompt: {'Custom' if os.getenv('SYSTEM_PROMPT_PATH') else 'Default'}"
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@abstractmethod
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def get_llm_description(self) -> str:
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"""Returns a description of the LLM being used."""
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pass
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async def status(self) -> str: # Changed from abstract to concrete
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async def get_bot_status(self) -> str:
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"""Provides a status message including prompt and LLM information."""
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prompt_desc = self.get_system_prompt_description()
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llm_desc = self.get_llm_description()
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# Consider potential async calls if get_... methods were async
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# For now, assuming they are synchronous as per design
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return f"{prompt_desc}\n{llm_desc}"
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@abstractmethod
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@@ -87,9 +116,10 @@ class BaseTelegramInferenceBot(ABC):
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pass
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@abstractmethod
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async def clear(self, user_id):
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async def abort_processing(self, user_id):
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pass
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@abstractmethod
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async def abort_processing(self, user_id):
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async def switch_model(self):
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"""Switches the underlying model if supported by the bot."""
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pass
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@@ -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({
|
||||
"role": "tool",
|
||||
"tool_call_id": tool_call_id,
|
||||
"name": function_to_call.name,
|
||||
"content": tool_response_content
|
||||
})
|
||||
|
||||
messages.extend(tool_results_for_model)
|
||||
|
||||
response = self.get_chat_response(messages)
|
||||
if not (response.choices and response.choices[0].message):
|
||||
logging.error("No valid response choice message from LLM after tool call.")
|
||||
return "Error: Could not get a valid response from the LLM after tool call."
|
||||
|
||||
messages.append(response.choices[0].message)
|
||||
|
||||
tool_calls_from_response = []
|
||||
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.")
|
||||
|
||||
if len(self.conversation_history[user_id]) > 20: # This limit seems small, consider increasing
|
||||
self.conversation_history[user_id] = self.conversation_history[user_id][-20:]
|
||||
|
||||
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("ChatGPT Bot started")
|
||||
# super().start() if Base class start() has common logic
|
||||
|
||||
async def clear(self, user_id):
|
||||
super().clear_conversation(user_id)
|
||||
|
||||
# status() method is inherited from BaseTelegramInferenceBot
|
||||
|
||||
async def abort_processing(self, user_id):
|
||||
if user_id in self.processing_status: # Relies on processing_status from Base
|
||||
self.processing_status[user_id]["processing"] = False
|
||||
await self.clear(user_id)
|
||||
return "Processing aborted and conversation cleared."
|
||||
else:
|
||||
await self.clear(user_id)
|
||||
return "No active processing found to abort. Conversation cleared."
|
||||
|
||||
async def switch_model(self):
|
||||
# Ensure environment variables for model names are set for this to work meaningfully
|
||||
current_small_model = os.environ.get("OPENAI_SMALL_MODEL", "gpt-3.5-turbo")
|
||||
current_large_model = os.environ.get("OPENAI_LARGE_MODEL", "gpt-4") # Example large model
|
||||
current_large_model = os.environ.get("OPENAI_LARGE_MODEL", "gpt-4")
|
||||
|
||||
# Default to small model if current model is not recognized or if it's the large one
|
||||
if self.model == current_large_model or self.model != current_small_model :
|
||||
if self.model == current_large_model or self.model != current_small_model:
|
||||
target_model = current_small_model
|
||||
target_max_tokens = os.environ.get("OPENAI_SMALL_MODEL_MAX_TOKENS")
|
||||
else: # Current is small (or default), switch to large
|
||||
else:
|
||||
target_model = current_large_model
|
||||
target_max_tokens = os.environ.get("OPENAI_LARGE_MODEL_MAX_TOKENS")
|
||||
|
||||
@@ -163,7 +34,6 @@ def main():
|
||||
logging.error("FATAL: OPENAI_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 = ChatGPTTelegramInferenceBot()
|
||||
|
||||
@@ -1,166 +1,27 @@
|
||||
import json
|
||||
import os
|
||||
import logging
|
||||
from base_telegram_inference_bot import BaseTelegramInferenceBot
|
||||
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
|
||||
from openai_compatible_inference_bot import OpenAICompatibleInferenceBot
|
||||
from telegram_helper import TelegramHelper
|
||||
|
||||
# logging.basicConfig(level=logging.INFO) # Usually configured in main execution script
|
||||
|
||||
class GeminiTelegramInferenceBot(BaseTelegramInferenceBot):
|
||||
class GeminiTelegramInferenceBot(OpenAICompatibleInferenceBot):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
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"),
|
||||
os.environ.get("GEMINI_SMALL_MODEL", "gemini-pro"),
|
||||
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 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:
|
||||
logging.error(f"Invalid value for max_tokens: {max_tokens_str}. Using default {default_max_tokens}.")
|
||||
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,
|
||||
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
|
||||
)
|
||||
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):
|
||||
if user_id not in self.conversation_history:
|
||||
self.conversation_history[user_id] = []
|
||||
if hasattr(self, 'system_prompt') and self.system_prompt:
|
||||
self.conversation_history[user_id].append({"role": "system", "content": self.system_prompt})
|
||||
|
||||
self.conversation_history[user_id].append({"role": "user", "content": user_message})
|
||||
messages = self.conversation_history[user_id]
|
||||
|
||||
response = self.get_chat_response(messages)
|
||||
|
||||
# Ensure response.choices[0].message exists before appending
|
||||
if response.choices and response.choices[0].message:
|
||||
messages.append(response.choices[0].message) # Append the assistant's response message
|
||||
else:
|
||||
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)
|
||||
|
||||
tool_use_count = 0
|
||||
MAX_TOOL_ITERATIONS = 5 # Define a max to prevent infinite loops more explicitly
|
||||
|
||||
while tool_calls_from_response and tool_use_count < MAX_TOOL_ITERATIONS:
|
||||
tool_results_for_model = [] # Results to be sent back to the model
|
||||
|
||||
for tool_call in tool_calls_from_response:
|
||||
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_response_content = self.call_tool(function_to_call.name, function_to_call.arguments)
|
||||
# Ensure tool_response_content is a string for the API
|
||||
if not isinstance(tool_response_content, str):
|
||||
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_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):
|
||||
logging.error("No valid response choice message from LLM after tool call.")
|
||||
return "Error: Could not get a valid response from the LLM after tool call."
|
||||
|
||||
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
|
||||
|
||||
# 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 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("Gemini Bot started")
|
||||
# super().start() if Base class start() has common logic
|
||||
|
||||
async def clear(self, user_id):
|
||||
super().clear_conversation(user_id) # Calls base class method
|
||||
|
||||
# status() method is inherited from BaseTelegramInferenceBot
|
||||
|
||||
async def abort_processing(self, user_id):
|
||||
if user_id in self.processing_status:
|
||||
self.processing_status[user_id]["processing"] = False
|
||||
# It's good practice to also clear the conversation for an aborted state
|
||||
await self.clear(user_id)
|
||||
return "Processing aborted and conversation cleared."
|
||||
else:
|
||||
# If no specific status, clearing conversation is a safe default
|
||||
await self.clear(user_id)
|
||||
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")
|
||||
current_small_model = os.environ.get("GEMINI_SMALL_MODEL", "gemini-pro")
|
||||
current_large_model = os.environ.get("GEMINI_LARGE_MODEL", "gemini-1.5-pro-latest")
|
||||
|
||||
# Default to small model if current model is not recognized or if it's the large one
|
||||
if self.model == current_large_model or self.model != current_small_model :
|
||||
target_model = current_small_model
|
||||
target_max_tokens = os.environ.get("GEMINI_SMALL_MODEL_MAX_TOKENS")
|
||||
else: # Current is small, switch to large
|
||||
else:
|
||||
target_model = current_large_model
|
||||
target_max_tokens = os.environ.get("GEMINI_LARGE_MODEL_MAX_TOKENS")
|
||||
|
||||
@@ -168,20 +29,14 @@ class GeminiTelegramInferenceBot(BaseTelegramInferenceBot):
|
||||
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():
|
||||
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()
|
||||
|
||||
|
||||
@@ -0,0 +1,133 @@
|
||||
import json
|
||||
import os
|
||||
import logging
|
||||
from abc import abstractmethod
|
||||
from base_telegram_inference_bot import BaseTelegramInferenceBot
|
||||
from openai import OpenAI
|
||||
|
||||
class OpenAICompatibleInferenceBot(BaseTelegramInferenceBot):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
# Client and model configuration will be handled by subclasses
|
||||
self.client = None
|
||||
self.model = None
|
||||
self.max_tokens = None
|
||||
|
||||
def _configure_model_and_tokens(self, model_name, max_tokens_str, default_max_tokens=1000):
|
||||
self.model = model_name if model_name else "default-model"
|
||||
try:
|
||||
self.max_tokens = int(max_tokens_str) if max_tokens_str is not None else default_max_tokens
|
||||
except ValueError:
|
||||
logging.error(f"Invalid value for max_tokens: {max_tokens_str}. Using default {default_max_tokens}.")
|
||||
self.max_tokens = default_max_tokens
|
||||
logging.info(f"Configured to use model: {self.model} with max_tokens: {self.max_tokens}")
|
||||
|
||||
def get_llm_description(self) -> str:
|
||||
return f"LLM: {self.model}, Max Tokens: {self.max_tokens}"
|
||||
|
||||
def get_chat_response(self, messages):
|
||||
if not self.client:
|
||||
raise ValueError("OpenAI client not initialized. Subclasses must initialize it.")
|
||||
try:
|
||||
response = self.client.chat.completions.create(
|
||||
model=self.model,
|
||||
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
|
||||
)
|
||||
return response
|
||||
except Exception as e:
|
||||
logging.error(f"API call failed: {e}")
|
||||
raise
|
||||
|
||||
async def handle_message(self, user_id, user_message):
|
||||
if user_id not in self.conversation_history:
|
||||
self.conversation_history[user_id] = []
|
||||
if hasattr(self, 'system_prompt') and self.system_prompt:
|
||||
self.conversation_history[user_id].append({"role": "system", "content": self.system_prompt})
|
||||
|
||||
self.conversation_history[user_id].append({"role": "user", "content": user_message})
|
||||
messages = self.conversation_history[user_id]
|
||||
|
||||
response = self.get_chat_response(messages)
|
||||
|
||||
if not (response.choices and response.choices[0].message):
|
||||
logging.error("No valid response choice message from LLM.")
|
||||
return "Error: Could not get a valid response from the LLM."
|
||||
|
||||
messages.append(response.choices[0].message) # Append the assistant's response message
|
||||
|
||||
tool_calls_from_response = []
|
||||
if response.choices[0].message.tool_calls:
|
||||
tool_calls_from_response.extend(response.choices[0].message.tool_calls)
|
||||
|
||||
tool_use_count = 0
|
||||
MAX_TOOL_ITERATIONS = 5
|
||||
|
||||
while tool_calls_from_response and tool_use_count < MAX_TOOL_ITERATIONS:
|
||||
tool_results_for_model = []
|
||||
|
||||
for tool_call in tool_calls_from_response:
|
||||
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_response_content = self.call_tool(function_to_call.name, function_to_call.arguments)
|
||||
if not isinstance(tool_response_content, str):
|
||||
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_results_for_model)
|
||||
|
||||
response = self.get_chat_response(messages)
|
||||
if not (response.choices and response.choices[0].message):
|
||||
logging.error("No valid response choice message from LLM after tool call.")
|
||||
return "Error: Could not get a valid response from the LLM after tool call."
|
||||
|
||||
messages.append(response.choices[0].message)
|
||||
|
||||
tool_calls_from_response = []
|
||||
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.")
|
||||
|
||||
# Conversation history management
|
||||
# This limit should be reviewed and potentially made configurable
|
||||
if len(self.conversation_history[user_id]) > 20: # Example limit, adjust as needed
|
||||
self.conversation_history[user_id] = self.conversation_history[user_id][-20:]
|
||||
|
||||
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(f"{self.__class__.__name__} started.")
|
||||
|
||||
async def clear(self, user_id):
|
||||
super().clear_conversation_history(user_id)
|
||||
|
||||
async def abort_processing(self, user_id):
|
||||
if user_id in self.processing_status:
|
||||
self.processing_status[user_id]["processing"] = False
|
||||
await self.clear(user_id)
|
||||
return "Processing aborted and conversation cleared."
|
||||
else:
|
||||
await self.clear(user_id)
|
||||
return "No active processing found to abort. Conversation cleared."
|
||||
|
||||
@abstractmethod
|
||||
async def switch_model(self):
|
||||
pass
|
||||
+21
-21
@@ -3,16 +3,21 @@ import logging
|
||||
import sys
|
||||
import asyncio
|
||||
import time
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||||
import git
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||||
from telegram import Update, InlineKeyboardButton, InlineKeyboardMarkup
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||||
from telegram.ext import Application, CommandHandler, MessageHandler, filters, ContextTypes, CallbackQueryHandler
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||||
from browse_command import browse_command, button_callback
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||||
class TelegramHelper:
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||||
# --- Constants for configurable paths and magic strings ---
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||||
REBOOT_CLAUDE_FILE = '.reboot_claude'
|
||||
REBOOT_FILE = '.doreboot'
|
||||
CLAUDE_REBOOT_TARGET = 'claude'
|
||||
HTML_QUOTE_BLOCK_START = '<blockquote expandable><b>Thinking...</b>'
|
||||
HTML_QUOTE_BLOCK_END = '</blockquote>'
|
||||
|
||||
def __init__(self, bot):
|
||||
self.bot = bot
|
||||
self.telegram_bot_token = os.getenv('TELEGRAM_BOT_TOKEN')
|
||||
self.repo = git.Repo(".")
|
||||
self.start_time = time.time()
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||||
|
||||
async def start(self, update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
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||||
@@ -23,11 +28,11 @@ class TelegramHelper:
|
||||
|
||||
async def clear(self, update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
|
||||
user_id = update.effective_user.id
|
||||
await self.bot.clear(user_id)
|
||||
await self.bot.clear_conversation_history(user_id)
|
||||
await update.message.reply_text("Conversation history cleared. Let's start fresh!")
|
||||
|
||||
async def status(self, update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
|
||||
status_message = await self.bot.status()
|
||||
status_message = await self.bot.get_bot_status()
|
||||
await update.message.reply_text(status_message)
|
||||
|
||||
async def switch(self, update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
|
||||
@@ -56,21 +61,20 @@ class TelegramHelper:
|
||||
|
||||
logging.info(f"Message from user {user_id}: {user_message}")
|
||||
|
||||
status_message = await update.message.reply_text("Processing your request...", reply_markup=InlineKeyboardMarkup([[InlineKeyboardButton("Abort", callback_data='abort')]]))
|
||||
self.bot.processing_status[user_id] = {"processing": True, "message_id": status_message.message_id}
|
||||
status_message = await update.message.reply_text("Processing your request...", reply_markup=InlineKeyboardMarkup([[InlineKeyboardButton("Abort", callback_data='abort')]]))\
|
||||
await self.bot.set_processing_status(user_id, status_message.message_id)
|
||||
|
||||
response = await self.bot.handle_message(user_id, user_message)
|
||||
|
||||
await context.bot.delete_message(chat_id=update.effective_chat.id, message_id=status_message.message_id)
|
||||
del self.bot.processing_status[user_id]
|
||||
response = response.replace("<think>", "<blockquote expandable><b>Thinking...</b>").replace("</think>", "</blockquote>")
|
||||
# Return response as html message
|
||||
await self.bot.clear_processing_status(user_id)
|
||||
|
||||
response = response.replace("<think>", self.HTML_QUOTE_BLOCK_START).replace("</think>", self.HTML_QUOTE_BLOCK_END)
|
||||
|
||||
if len(response) > 4096:
|
||||
# If the response is too long, split it into chunks
|
||||
chunks = [response[i:i + 4096] for i in range(0, len(response), 4096)]
|
||||
for chunk in chunks:
|
||||
await update.message.reply_text(chunk)
|
||||
# Add a small delay to avoid flooding
|
||||
await asyncio.sleep(0.1)
|
||||
else:
|
||||
await update.message.reply_text(response)
|
||||
@@ -88,21 +92,21 @@ class TelegramHelper:
|
||||
await query.edit_message_text(text=result)
|
||||
|
||||
async def reboot(self, update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
|
||||
user_message = update.message.text.split() # Split the message to check for 'claude'
|
||||
if len(user_message) > 1 and user_message[1].lower() == 'claude':
|
||||
open('./.reboot_claude', 'w').close() # Create an empty file
|
||||
user_message = update.message.text.split()
|
||||
if len(user_message) > 1 and user_message[1].lower() == self.CLAUDE_REBOOT_TARGET:
|
||||
open(self.REBOOT_CLAUDE_FILE, 'w').close()
|
||||
|
||||
if update:
|
||||
await update.message.reply_text("Rebooting the bot...")
|
||||
logging.info("Received reboot command. Exiting process...")
|
||||
reboot_file_path = "./.doreboot"
|
||||
reboot_file_path = self.REBOOT_FILE
|
||||
if not os.path.exists(reboot_file_path):
|
||||
with open(reboot_file_path, 'w') as f:
|
||||
f.write(str(update.effective_chat.id) if update else "")
|
||||
sys.exit(0)
|
||||
|
||||
async def check_doreboot_file(self, application: Application):
|
||||
reboot_file_path = "./.doreboot"
|
||||
reboot_file_path = self.REBOOT_FILE
|
||||
if os.path.exists(reboot_file_path):
|
||||
with open(reboot_file_path, 'r') as f:
|
||||
chat_id = f.read().strip()
|
||||
@@ -122,16 +126,12 @@ class TelegramHelper:
|
||||
application.add_handler(CommandHandler("status", self.status))
|
||||
application.add_handler(CommandHandler("reboot", self.reboot))
|
||||
application.add_handler(CommandHandler("browse", self.browse))
|
||||
application.add_handler(MessageHandler(filters.TEXT & ~filters.COMMAND, self.handle_message))
|
||||
application.add_handler(MessageHandler(filters.TEXT & ~filters.COMMAND, self.handle_message))\
|
||||
application.add_handler(CallbackQueryHandler(self.abort_processing, pattern='^abort$'))
|
||||
application.add_handler(CallbackQueryHandler(button_callback, pattern='^(browse|file):'))
|
||||
|
||||
logging.info("Bot is running...")
|
||||
|
||||
# Check for .doreboot file and send message if it exists
|
||||
asyncio.get_event_loop().create_task(self.check_doreboot_file(application))
|
||||
|
||||
# Commenting out the commit checking task
|
||||
# asyncio.get_event_loop().create_task(self.check_for_new_commits())
|
||||
|
||||
application.run_polling()
|
||||
Reference in New Issue
Block a user