444 lines
22 KiB
Python
444 lines
22 KiB
Python
import importlib
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import json
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import os
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import logging
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import inspect
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from abc import abstractmethod
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from openai import OpenAI
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from tools.base_tool import BaseTool
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from telegram_helper import TelegramHelper
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import argparse
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from inference_bot import InferenceBot
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import tiktoken # Added this import
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class OpenAICompatibleInferenceBot(InferenceBot):
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def __init__(
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self,
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api_key: str | None = None,
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base_url: str | None = None,
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small_model_name: str | None = None,
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small_model_max_tokens: str | None = None,
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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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allowed_function_tags: list[str] | None = None,
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system_prompt_path: str | None = None,
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use_large_model: bool = False
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):
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self.model_config = {
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"small_model_name": small_model_name,
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"small_model_max_tokens": small_model_max_tokens,
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"large_model_name": large_model_name,
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"large_model_max_tokens": large_model_max_tokens
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}
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self.allowed_function_tags = allowed_function_tags if allowed_function_tags else None
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self.conversation_history = {}
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self._processing_status = {}
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self.system_prompt_path = system_prompt_path
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self.system_prompt = self.load_system_prompt(
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file_path=system_prompt_path
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)
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self.tools, self.functions = self.load_functions()
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self.client = OpenAI(api_key=api_key, base_url=base_url)
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log_msg = f"Initialized OpenAI compatible client. Target URL: {base_url if base_url else 'OpenAI default'}."
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logging.info(log_msg)
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# Load inference token limits
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self.small_model_max_inference_tokens = int(os.getenv("_SMALL_MODEL_MAX_INFERENCE_TOKENS", "32768"))
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self.large_model_max_inference_tokens = int(os.getenv("_LARGE_MODEL_MAX_INFERENCE_TOKENS", "32768"))
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# Configure the actual model name and max_tokens for API calls
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if use_large_model:
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self._configure_model_and_tokens(
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self.model_config["large_model_name"],
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self.model_config["large_model_max_tokens"]
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)
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else:
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self._configure_model_and_tokens(
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self.model_config["small_model_name"],
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self.model_config["small_model_max_tokens"]
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)
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@property
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def processing_status(self):
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return self._processing_status
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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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for tool in self.tools:
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tool.clear()
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def _configure_model_and_tokens(self, model_name: str | None, max_tokens_str: str | None):
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self.model = model_name
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try:
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if max_tokens_str and max_tokens_str.lower() not in ["none", "", "null"]:
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self.max_tokens = int(max_tokens_str)
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else:
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self.max_tokens = None
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except ValueError:
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logging.warning(f"Invalid value for max_tokens: {max_tokens_str}. Using API default (None)")
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self.max_tokens = None
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logging.info(f"Configured to use model: {self.model} with max_tokens: {self.max_tokens if self.max_tokens is not None else 'API default'}")
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def get_llm_description(self) -> str:
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client_type = type(self.client).__name__
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return f"Client: {client_type}, LLM: {self.model}, Max Tokens: {self.max_tokens if self.max_tokens is not None else 'API default'}"
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def _count_tokens(self, messages, model):
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"""Returns the number of tokens in a list of messages."""
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try:
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encoding = tiktoken.encoding_for_model(model)
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except KeyError:
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encoding = tiktoken.get_encoding("cl100k_base") # Fallback for unknown models
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logging.warning(f"Warning: model {model} not found. Using cl100k_base encoding.")
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num_tokens = 0
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for message in messages:
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num_tokens += 4
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if hasattr(message, "items"):
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for key, value in message.items():
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if isinstance(value, str):
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num_tokens += len(encoding.encode(value))
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if key == "name":
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num_tokens += 1
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num_tokens += 2
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return num_tokens
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def get_chat_response(self, messages):
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if not self.client:
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logging.error("OpenAI client not initialized before get_chat_response.")
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raise ValueError("OpenAI client not initialized.")
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try:
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cleaned_tools = None
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if hasattr(self, 'functions') and self.functions:
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cleaned_tools = []
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for func in self.functions:
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include_function = False
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if not hasattr(self, 'allowed_function_tags') or self.allowed_function_tags is None:
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include_function = True
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else:
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tags = func.get("_tags", [])
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if any(tag in self.allowed_function_tags for tag in tags):
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include_function = True
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if include_function:
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func_copy = {k: v for k, v in func.items() if k != "_tags"}
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cleaned_tools.append(func_copy)
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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=cleaned_tools,
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tool_choice="auto" if cleaned_tools 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"API call to model {self.model} failed: {e}")
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raise
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def get_bot_status(self):
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"""
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Returns a message with the currently enabled model and the system prompt path being used.
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"""
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model_name = self.model if hasattr(self, 'model') else None
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prompt_path = self.system_prompt_path or os.getenv("SYSTEM_PROMPT_PATH") or "(default prompt in use)"
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return f"Current model: {model_name}\nSystem prompt path: {prompt_path}"
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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 or not self.conversation_history[user_id]:
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self.conversation_history[user_id] = []
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if 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 = list(self.conversation_history[user_id])
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# Pre-inference token limit check
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current_model_is_small = self.model == self.model_config["small_model_name"]
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current_model_is_large = self.model == self.model_config["large_model_name"]
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inference_token_limit = None
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if current_model_is_small:
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inference_token_limit = self.small_model_max_inference_tokens
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elif current_model_is_large:
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inference_token_limit = self.large_model_max_inference_tokens
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else:
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logging.warning(f"Could not determine inference token limit for model: {self.model}. Proceeding without check.")
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if inference_token_limit is not None:
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token_count = self._count_tokens(messages, self.model)
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if token_count > inference_token_limit:
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logging.warning(f"Request for user {user_id} exceeds inference token limit ({token_count}/{inference_token_limit}).")
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# Do not persist this message in history as it was not processed by LLM
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# Remove the last user message from history before returning, to prevent accumulation
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if self.conversation_history[user_id] and self.conversation_history[user_id][-1]["role"] == "user" and self.conversation_history[user_id][-1]["content"] == user_message:
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self.conversation_history[user_id].pop()
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return "Request exceeds inference token limit. Please use the /clear command, or implement RAG in your application."
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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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self.conversation_history[user_id] = messages
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return "Error: Could not get a valid response from the LLM."
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assistant_message = response.choices[0].message
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messages.append(assistant_message)
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tool_calls_from_response = list(assistant_message.tool_calls) if assistant_message.tool_calls else []
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tool_use_count = 0
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MAX_TOOL_ITERATIONS = 200
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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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function_name = function_to_call.name
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function_args_str = function_to_call.arguments
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logging.info(f"Attempting to call tool: {function_name} with args: {function_args_str}")
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if function_name not in [f["function"]["name"] for f in self.functions]:
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logging.warning(f"Tool function {function_name} not found in available functions.")
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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_name,
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"content": f"Error: Tool function {function_name} not found."
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})
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continue
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try:
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tool_response_content = self.call_tool(function_name, function_args_str)
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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_name}: {e}")
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tool_response_content = f"Error executing tool {function_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_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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self.conversation_history[user_id] = messages
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return "Error: Could not get a valid response from the LLM after tool call."
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assistant_message = response.choices[0].message
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messages.append(assistant_message)
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tool_calls_from_response = list(assistant_message.tool_calls) if assistant_message.tool_calls else []
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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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break
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self.conversation_history[user_id] = messages
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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 is not None else "Assistant did not provide a textual response."
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async def start(self):
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logging.info(f"{self.__class__.__name__} (Model: {self.model}) started.")
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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.clear_processing_status(user_id)
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logging.info(f"Processing aborted for user {user_id}.")
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return "Processing aborted. You can send a new message or /clear the conversation."
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else:
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return "No active processing found to abort. If you wish, /clear the conversation history."
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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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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()) # This instantiation might be an issue for tools needing config
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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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for tool in tools:
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functions.extend(tool.get_functions())
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return tools, functions
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def load_system_prompt(self, direct_content: str | None = None, file_path: str | None = None) -> str:
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default_prompt = "You are a helpful AI assistant."
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if direct_content:
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logging.info("Using direct content for system prompt.")
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return direct_content.strip()
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prompt_path_to_try = file_path or os.getenv("SYSTEM_PROMPT_PATH")
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if prompt_path_to_try:
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if os.path.isfile(prompt_path_to_try):
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try:
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with open(prompt_path_to_try, "r", encoding="utf-8") as file:
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content = file.read().strip()
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logging.info(f"Successfully loaded system prompt from {prompt_path_to_try}.")
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return content
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except IOError as e:
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logging.warning(f"Could not read system prompt file {prompt_path_to_try}: {e}. Using default.")
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return default_prompt
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else:
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logging.warning(f"System prompt file {prompt_path_to_try} not found. Using default system prompt.")
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return default_prompt
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else:
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logging.info("No system prompt path provided (argument or ENV) or direct content. Using default system prompt.")
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return default_prompt
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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 = None
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if isinstance(function_call_arguments, dict):
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function_args = function_call_arguments
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elif isinstance(function_call_arguments, str):
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try:
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function_args = json.loads(function_call_arguments)
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except json.JSONDecodeError as e:
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logging.error(f"Error decoding function call arguments (string) 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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else:
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if function_call_arguments is None:
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function_args = {}
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else:
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logging.error(f"Unexpected type for function_call_arguments for {function_name}: {type(function_call_arguments)}. Arguments: {function_call_arguments}")
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return f"Error: Invalid argument type for tool call: {type(function_call_arguments)}"
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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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try:
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if not isinstance(function_args, dict):
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logging.error(f"Internal error: function_args not a dict for {function_name} before execution. Args: {function_args}")
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return f"Internal error preparing arguments for tool {function_name}."
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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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async def switch_model(self):
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if not self.model_config["small_model_name"] or not self.model_config["large_model_name"]:
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logging.warning("Small or Large model names 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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current_is_small = self.model == self.model_config["small_model_name"]
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current_is_large = self.model == self.model_config["large_model_name"]
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if current_is_large:
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target_model = self.model_config["small_model_name"]
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target_max_tokens_str = self.model_config["small_model_max_tokens"]
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elif current_is_small:
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target_model = self.model_config["large_model_name"]
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target_max_tokens_str = self.model_config["large_model_max_tokens"]
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else:
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logging.warning(f"Current model {self.model} is unrecognized. Switching to default small model: {self.model_config['small_model_name']}.")
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target_model = self.model_config["small_model_name"]
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target_max_tokens_str = self.model_config["small_model_max_tokens"]
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self._configure_model_and_tokens(target_model, target_max_tokens_str)
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return f"Switched model to {self.model}. Max tokens set to {self.max_tokens if self.max_tokens is not None else 'API default'}."
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def main():
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
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bot = None
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try:
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parser = argparse.ArgumentParser(description='OpenAI Compatible Inference Bot')
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parser.add_argument('--config', type=str, help='Configuration Prepend (i.e. gemini, openai, etc)', default="Telegram")
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parser.add_argument('--messenger', type=str, help='Messenger type (i.e. telegram)', required=True)
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parser.add_argument('--persona', type=str, help='Path to system prompt file', required=False)
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parser.add_argument('--tools', nargs='+', help='List of allowed function tags', required=False)
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parser.add_argument('--use-large-model', action='store_true', help='Use the large model instead of the small model')
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# Add these to launch.json arguments if you want to limit the toolset available: "--tools", "read", "communicate"
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# Parse command line arguments
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args = parser.parse_args()
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if args.persona:
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logging.info(f"Using custom persona from: {args.persona}")
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system_prompt_path=args.persona if args.persona else None
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allowed_function_tags=args.tools if args.tools else None
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config_prepend = args.config if args.config else None
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messenger = args.messenger if args.messenger else None
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use_large_model = args.use_large_model
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# Initialize model and max tokens based on the config prepend
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if config_prepend:
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api_key = os.environ.get(f"{config_prepend.upper()}_API_KEY")
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baseurl = os.environ.get(f"{config_prepend.upper()}_API_BASE_URL", "")
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small_model_name = os.environ.get(f"{config_prepend.upper()}_SMALL_MODEL")
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large_model_name = os.environ.get(f"{config_prepend.upper()}_LARGE_MODEL")
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small_model_max_tokens = os.environ.get(f"{config_prepend.upper()}_SMALL_MODEL_MAX_TOKENS")
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large_model_max_tokens = os.environ.get(f"{config_prepend.upper()}_LARGE_MODEL_MAX_TOKENS")
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bot = OpenAICompatibleInferenceBot(
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api_key=api_key,
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base_url=baseurl,
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small_model_name=small_model_name,
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small_model_max_tokens=small_model_max_tokens,
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large_model_name=large_model_name,
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large_model_max_tokens=large_model_max_tokens,
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system_prompt_path=system_prompt_path,
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allowed_function_tags=allowed_function_tags,
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use_large_model=use_large_model
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)
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full_code_file = importlib.import_module(f'{messenger.lower()}_helper')
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messenger_helper_class_name = f"{messenger.capitalize()}Helper"
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if not hasattr(full_code_file, messenger_helper_class_name):
|
|
messenger_helper_class_name = f"{messenger.upper()}Helper"
|
|
if not hasattr(full_code_file, messenger_helper_class_name):
|
|
raise ValueError(f"Messenger helper class {messenger_helper_class_name} not found in {full_code_file.__name__}.")
|
|
helper_class = getattr(full_code_file, messenger_helper_class_name)
|
|
|
|
helper = helper_class(bot)
|
|
helper.run()
|
|
except ValueError as e:
|
|
logging.error(f"FATAL: {e}")
|
|
return
|
|
except Exception as e: # Catch any other init errors
|
|
logging.error(f"An unexpected error occurred during bot initialization: {e}")
|
|
return
|
|
|
|
if __name__ == '__main__':
|
|
main()
|