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Refactor: Make system_prompt_path and content configurable in BaseTelegramInferenceBot

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cyclop-bot
2025-06-02 16:35:08 -05:00
parent 0ffafe3bc5
commit 75381f5f11
1 changed files with 49 additions and 23 deletions
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base_telegram_inference_bot.py
+49 -23
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@@ -7,26 +7,47 @@ from abc import ABC, abstractmethod
from tools.base_tool import BaseTool
class BaseTelegramInferenceBot(ABC):
def __init__(self):
def __init__(self, system_prompt_content: str | None = None, system_prompt_path: str | None = None): # MODIFIED
self.conversation_history = {}
self.processing_status = {}
self.system_prompt = self.load_system_prompt()
# MODIFIED to pass arguments
self.system_prompt = self.load_system_prompt(
direct_content=system_prompt_content,
file_path=system_prompt_path
)
self.tools, self.functions = self.load_functions()
logging.info(f'System Prompt: {os.environ.get("SYSTEM_PROMPT_PATH")}')
# Logging the actual source of the system prompt might be more complex now,
# but we can log the final prompt or indicate if it's custom/default.
# We'll also log the source of the prompt inside load_system_prompt.
logging.info(f'System Prompt (effective): {"Custom" if self.system_prompt != "You are a helpful AI assistant." else "Default"}')
logging.info(f'Github Repository: {os.environ.get("GITHUB_REPOSITORY")}')
def load_system_prompt(self):
system_prompt_path = os.getenv("SYSTEM_PROMPT_PATH")
if system_prompt_path and os.path.isfile(system_prompt_path):
try:
with open(system_prompt_path, "r", encoding="utf-8") as file:
return file.read().strip()
except IOError as e:
logging.warning(f"Could not read system prompt file {system_prompt_path}: {e}")
return "You are a helpful AI assistant."
def load_system_prompt(self, direct_content: str | None = None, file_path: str | None = None) -> str: # MODIFIED
default_prompt = "You are a helpful AI assistant."
if direct_content:
logging.info("Using direct content for system prompt.")
return direct_content.strip()
prompt_path_to_try = file_path or os.getenv("SYSTEM_PROMPT_PATH")
if prompt_path_to_try:
if os.path.isfile(prompt_path_to_try):
try:
with open(prompt_path_to_try, "r", encoding="utf-8") as file:
content = file.read().strip()
logging.info(f"Successfully loaded system prompt from {prompt_path_to_try}.")
return content
except IOError as e:
logging.warning(f"Could not read system prompt file {prompt_path_to_try}: {e}. Using default.")
return default_prompt
else:
# This condition now also covers if 'file_path' argument was given but invalid
logging.warning(f"System prompt file {prompt_path_to_try} not found. Using default system prompt.")
return default_prompt
else:
logging.warning("SYSTEM_PROMPT_PATH is not set or file does not exist. Using default system prompt.")
return "You are a helpful AI assistant."
logging.info("No system prompt path provided (argument or ENV) or direct content. Using default system prompt.")
return default_prompt
def load_functions(self):
tools = []
@@ -44,7 +65,7 @@ class BaseTelegramInferenceBot(ABC):
for name, obj in inspect.getmembers(module):
if inspect.isclass(obj) and issubclass(obj, BaseTool) and obj != BaseTool:
try:
tools.append(obj())
tools.append(obj()) # This instantiation might be an issue for tools needing config
except Exception as e:
logging.error(f"Error instantiating tool {name} from {filename}: {e}")
except Exception as e:
@@ -87,9 +108,9 @@ class BaseTelegramInferenceBot(ABC):
except json.JSONDecodeError as e:
logging.error(f"Error decoding function call arguments (string) for {function_call_name}: {e}. Arguments: {function_call_arguments}")
return f"Error: Malformed arguments for tool call: {e}"
else: # Handle cases where arguments might be None or other unexpected types
else:
if function_call_arguments is None:
function_args = {} # Default to empty dict if arguments are None
function_args = {}
else:
logging.error(f"Unexpected type for function_call_arguments for {function_call_name}: {type(function_call_arguments)}. Arguments: {function_call_arguments}")
return f"Error: Invalid argument type for tool call: {type(function_call_arguments)}"
@@ -98,7 +119,6 @@ class BaseTelegramInferenceBot(ABC):
for function in tool.get_functions():
if function["function"]["name"] == function_name:
try:
# Ensure function_args is a dictionary before unpacking
if not isinstance(function_args, dict):
logging.error(f"Internal error: function_args not a dict for {function_name} before execution. Args: {function_args}")
return f"Internal error preparing arguments for tool {function_name}."
@@ -110,16 +130,23 @@ class BaseTelegramInferenceBot(ABC):
return f"Error: Tool function {function_name} not found."
def get_system_prompt_description(self) -> str:
"""Returns a description of the system prompt being used."""
return f"System Prompt: {'Custom' if os.getenv('SYSTEM_PROMPT_PATH') else 'Default'}"
# This method could be updated to be more specific about the prompt source if needed.
# For now, it still reflects custom vs default based on the original ENV var logic's spirit.
# A more accurate reflection would require storing how the prompt was loaded.
# For simplicity, let's assume if it's not the default, it's "Custom".
if self.system_prompt != "You are a helpful AI assistant.":
return "System Prompt: Custom"
# Check original ENV var for backward compatibility in description only
elif os.getenv('SYSTEM_PROMPT_PATH'):
return "System Prompt: Custom (via ENV)"
return "System Prompt: Default"
@abstractmethod
def get_llm_description(self) -> str:
"""Returns a description of the LLM being used."""
pass
async def get_bot_status(self) -> str:
"""Provides a status message including prompt and LLM information."""
prompt_desc = self.get_system_prompt_description()
llm_desc = self.get_llm_description()
return f"{prompt_desc}\n{llm_desc}"
@@ -134,5 +161,4 @@ class BaseTelegramInferenceBot(ABC):
@abstractmethod
async def switch_model(self):
"""Switches the underlying model if supported by the bot."""
pass