510 lines
30 KiB
Python
510 lines
30 KiB
Python
import discord
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from discord.ext import commands
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import asyncio
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import os
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import json
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import aiohttp
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import random
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import time
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from collections import defaultdict, deque
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from typing import Dict, List, Any, Optional, Tuple, Set, Union
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# Third-party imports needed by the Cog itself or its direct methods
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from dotenv import load_dotenv
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from tavily import TavilyClient # Needed for tavily_client init
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# Interpreter and docker might only be needed by tools.py now
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# --- Relative Imports from Freak Teto Package --- # Updated package name
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from .config import (
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PROJECT_ID, LOCATION, TAVILY_API_KEY, DEFAULT_MODEL, FALLBACK_MODEL, # Use GCP config
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DB_PATH, CHROMA_PATH, SEMANTIC_MODEL_NAME, MAX_USER_FACTS, MAX_GENERAL_FACTS,
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MOOD_OPTIONS, BASELINE_PERSONALITY, BASELINE_INTERESTS, MOOD_CHANGE_INTERVAL_MIN,
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MOOD_CHANGE_INTERVAL_MAX, CHANNEL_TOPIC_CACHE_TTL, CONTEXT_WINDOW_SIZE,
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API_TIMEOUT, SUMMARY_API_TIMEOUT, API_RETRY_ATTEMPTS, API_RETRY_DELAY,
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PROACTIVE_LULL_THRESHOLD, PROACTIVE_BOT_SILENCE_THRESHOLD, PROACTIVE_LULL_CHANCE,
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PROACTIVE_TOPIC_RELEVANCE_THRESHOLD, PROACTIVE_TOPIC_CHANCE,
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PROACTIVE_RELATIONSHIP_SCORE_THRESHOLD, PROACTIVE_RELATIONSHIP_CHANCE,
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INTEREST_UPDATE_INTERVAL, INTEREST_DECAY_INTERVAL_HOURS,
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LEARNING_UPDATE_INTERVAL, TOPIC_UPDATE_INTERVAL, SENTIMENT_UPDATE_INTERVAL,
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EVOLUTION_UPDATE_INTERVAL, RESPONSE_SCHEMA, TOOLS # Import necessary configs
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)
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# Import functions/classes from other modules
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from .memory import MemoryManager # Import from local memory.py
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from .background import background_processing_task
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from .commands import setup_commands # Import the setup helper
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from .listeners import on_ready_listener, on_message_listener, on_reaction_add_listener, on_reaction_remove_listener # Import listener functions
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from . import config as FreakTetoConfig # Import config module for get_freak_teto_stats - Updated alias
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# Tool mapping is used internally by api.py/process_requested_tools, no need to import here directly unless cog methods call tools directly (they shouldn't)
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# Analysis, context, prompt, api, utils functions are called by listeners/commands/background task, not directly by cog methods here usually.
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# Load environment variables (might be loaded globally in main bot script too)
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load_dotenv()
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class FreakTetoCog(commands.Cog, name="FreakTeto"): # Renamed Cog
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"""A special cog for the FreakTeto bot that uses Google Vertex AI API""" # Updated docstring
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def __init__(self, bot):
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self.bot = bot
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# GCP Project/Location are used by vertexai.init() in api.py
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self.tavily_api_key = TAVILY_API_KEY # Use imported config
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self.session: Optional[aiohttp.ClientSession] = None # Keep for other potential HTTP requests (e.g., Piston)
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self.tavily_client = TavilyClient(api_key=self.tavily_api_key) if self.tavily_api_key else None
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self.default_model = DEFAULT_MODEL # Use imported config
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self.fallback_model = FALLBACK_MODEL # Use imported config
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self.MOOD_OPTIONS = MOOD_OPTIONS # Make MOOD_OPTIONS available as an instance attribute
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self.current_channel: Optional[Union[discord.TextChannel, discord.Thread, discord.DMChannel]] = None # Type hint current channel
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# Instantiate MemoryManager
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self.memory_manager = MemoryManager(
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db_path=DB_PATH,
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max_user_facts=MAX_USER_FACTS,
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max_general_facts=MAX_GENERAL_FACTS,
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chroma_path=CHROMA_PATH,
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semantic_model_name=SEMANTIC_MODEL_NAME
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)
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# --- State Variables ---
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# Keep state directly within the cog instance for now
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self.current_mood = random.choice(MOOD_OPTIONS)
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self.last_mood_change = time.time()
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self.needs_json_reminder = False # Flag to remind AI about JSON format
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# Learning variables (Consider moving to a dedicated state/learning manager later)
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self.conversation_patterns = defaultdict(list)
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self.user_preferences = defaultdict(dict)
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self.response_effectiveness = {}
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self.last_learning_update = time.time()
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# self.learning_update_interval = LEARNING_UPDATE_INTERVAL # Interval used in background task
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# Topic tracking
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self.active_topics = defaultdict(lambda: {
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"topics": [], "last_update": time.time(), "topic_history": [],
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"user_topic_interests": defaultdict(list)
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})
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# self.topic_update_interval = TOPIC_UPDATE_INTERVAL # Used in analysis
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# Conversation tracking / Caches
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self.conversation_history = defaultdict(lambda: deque(maxlen=100))
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self.thread_history = defaultdict(lambda: deque(maxlen=50))
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self.user_conversation_mapping = defaultdict(set)
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self.channel_activity = defaultdict(lambda: 0.0) # Use float for timestamp
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self.conversation_topics = defaultdict(str)
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self.user_relationships = defaultdict(dict)
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self.conversation_summaries: Dict[int, Dict[str, Any]] = {} # Store dict with summary and timestamp
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self.channel_topics_cache: Dict[int, Dict[str, Any]] = {} # Store dict with topic and timestamp
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# self.channel_topic_cache_ttl = CHANNEL_TOPIC_CACHE_TTL # Used in prompt building
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self.message_cache = {
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'by_channel': defaultdict(lambda: deque(maxlen=CONTEXT_WINDOW_SIZE)), # Use config
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'by_user': defaultdict(lambda: deque(maxlen=50)),
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'by_thread': defaultdict(lambda: deque(maxlen=50)),
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'global_recent': deque(maxlen=200),
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'mentioned': deque(maxlen=50),
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'replied_to': defaultdict(lambda: deque(maxlen=20))
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}
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self.active_conversations = {}
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self.bot_last_spoke = defaultdict(float)
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self.message_reply_map = {}
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# Enhanced sentiment tracking
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self.conversation_sentiment = defaultdict(lambda: {
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"overall": "neutral", "intensity": 0.5, "recent_trend": "stable",
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"user_sentiments": {}, "last_update": time.time()
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})
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self.sentiment_update_interval = SENTIMENT_UPDATE_INTERVAL # Used in analysis
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# Interest Tracking State
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self.freak_teto_participation_topics = defaultdict(int) # Renamed state variable
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self.last_interest_update = time.time()
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self.freak_teto_message_reactions = defaultdict(lambda: {"positive": 0, "negative": 0, "topic": None, "timestamp": 0.0}) # Renamed state variable
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# Background task handle
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self.background_task: Optional[asyncio.Task] = None
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self.last_evolution_update = time.time() # Used in background task
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self.last_stats_push = time.time() # Timestamp for last stats push
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self.last_reflection_time = time.time() # Timestamp for last memory reflection
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self.last_goal_check_time = time.time() # Timestamp for last goal decomposition check
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self.last_goal_execution_time = time.time() # Timestamp for last goal execution check
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self.last_proactive_goal_check = time.time() # Timestamp for last proactive goal check
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self.last_internal_action_check = time.time() # Timestamp for last internal action check
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# --- Stats Tracking ---
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self.api_stats = defaultdict(lambda: {"success": 0, "failure": 0, "retries": 0, "total_time": 0.0, "count": 0}) # Keyed by model name
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self.tool_stats = defaultdict(lambda: {"success": 0, "failure": 0, "total_time": 0.0, "count": 0}) # Keyed by tool name
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# --- Setup Commands and Listeners ---
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# Add commands defined in commands.py
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# TODO: Ensure commands.py in freak_teto is properly refactored
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self.command_functions = setup_commands(self)
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# Store command names for reference - safely handle Command objects
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self.registered_commands = []
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for func in self.command_functions:
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# For app commands, use the name attribute directly
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if hasattr(func, "name"):
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self.registered_commands.append(func.name)
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# For regular functions, use __name__
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elif hasattr(func, "__name__"):
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self.registered_commands.append(func.__name__)
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else:
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self.registered_commands.append(str(func))
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# Add listeners defined in listeners.py
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# Note: Listeners need to be added to the bot instance, not the cog directly in this pattern.
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# We'll add them in cog_load or the main setup function.
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# TODO: Ensure listeners.py in freak_teto is properly refactored
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print(f"FreakTetoCog initialized with commands: {self.registered_commands}") # Updated log
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async def cog_load(self):
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"""Create aiohttp session, initialize DB, load baselines, start background task"""
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self.session = aiohttp.ClientSession()
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print("FreakTetoCog: aiohttp session created") # Updated log
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# Initialize DB via MemoryManager
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# TODO: Ensure MemoryManager uses the correct freak_teto config paths
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await self.memory_manager.initialize_sqlite_database()
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await self.memory_manager.load_baseline_personality(BASELINE_PERSONALITY)
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await self.memory_manager.load_baseline_interests(BASELINE_INTERESTS)
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# Vertex AI initialization happens in api.py using PROJECT_ID and LOCATION from config
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print(f"FreakTetoCog: Using default model: {self.default_model}") # Updated log
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if not self.tavily_api_key:
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print("FreakTetoCog WARNING: Tavily API key not configured (TAVILY_API_KEY). Web search disabled.") # Updated log
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# Add listeners to the bot instance
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# We need to define the listener functions here to properly register them
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@self.bot.event
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async def on_ready():
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await on_ready_listener(self) # Ensure this uses freak_teto listener
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@self.bot.event
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async def on_message(message):
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# Avoid processing commands twice if main bot does it
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# await self.bot.process_commands(message)
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await on_message_listener(self, message) # Ensure this uses freak_teto listener
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@self.bot.event
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async def on_reaction_add(reaction, user):
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await on_reaction_add_listener(self, reaction, user) # Ensure this uses freak_teto listener
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@self.bot.event
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async def on_reaction_remove(reaction, user):
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await on_reaction_remove_listener(self, reaction, user) # Ensure this uses freak_teto listener
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print("FreakTetoCog: Listeners added.") # Updated log
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# We'll sync commands in the on_ready event instead of here
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# This ensures the bot's application_id is properly set before syncing
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print("FreakTetoCog: Commands will be synced when the bot is ready.") # Updated log
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# Start background task
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# TODO: Ensure background_processing_task uses freak_teto logic/config
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if self.background_task is None or self.background_task.done():
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self.background_task = asyncio.create_task(background_processing_task(self))
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print("FreakTetoCog: Started background processing task.") # Updated log
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else:
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print("FreakTetoCog: Background processing task already running.") # Updated log
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async def cog_unload(self):
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"""Close session and cancel background task"""
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if self.session and not self.session.closed:
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await self.session.close()
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print("FreakTetoCog: aiohttp session closed") # Updated log
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if self.background_task and not self.background_task.done():
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self.background_task.cancel()
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print("FreakTetoCog: Cancelled background processing task.") # Updated log
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# Note: When using @bot.event, we can't easily remove the listeners
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# The bot will handle this automatically when it's closed
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print("FreakTetoCog: Listeners will be removed when bot is closed.") # Updated log
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print("FreakTetoCog unloaded.") # Updated log
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# --- Helper methods that might remain in the cog ---
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# (Example: _update_relationship needs access to self.user_relationships)
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# Moved to utils.py, but needs access to cog state. Pass cog instance.
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def _update_relationship(self, user_id_1: str, user_id_2: str, change: float):
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"""Updates the relationship score between two users."""
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# This method accesses self.user_relationships, so it stays here or utils needs cog passed.
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# Let's keep it here for simplicity for now.
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if user_id_1 > user_id_2: user_id_1, user_id_2 = user_id_2, user_id_1
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if user_id_1 not in self.user_relationships: self.user_relationships[user_id_1] = {}
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current_score = self.user_relationships[user_id_1].get(user_id_2, 0.0)
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new_score = max(0.0, min(current_score + change, 100.0)) # Clamp 0-100
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self.user_relationships[user_id_1][user_id_2] = new_score
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# print(f"Updated relationship {user_id_1}-{user_id_2}: {current_score:.1f} -> {new_score:.1f} ({change:+.1f})") # Debug log
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async def get_freak_teto_stats(self) -> Dict[str, Any]: # Renamed method
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"""Collects various internal stats for FreakTeto.""" # Updated docstring
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stats = {"config": {}, "runtime": {}, "memory": {}, "api_stats": {}, "tool_stats": {}}
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# --- Config ---
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# Selectively pull relevant config values, avoid exposing secrets
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stats["config"]["default_model"] = FreakTetoConfig.DEFAULT_MODEL # Use updated alias
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stats["config"]["fallback_model"] = FreakTetoConfig.FALLBACK_MODEL # Use updated alias
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stats["config"]["safety_check_model"] = FreakTetoConfig.SAFETY_CHECK_MODEL # Use updated alias
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stats["config"]["db_path"] = FreakTetoConfig.DB_PATH # Use updated alias
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stats["config"]["chroma_path"] = FreakTetoConfig.CHROMA_PATH # Use updated alias
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stats["config"]["semantic_model_name"] = FreakTetoConfig.SEMANTIC_MODEL_NAME # Use updated alias
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stats["config"]["max_user_facts"] = FreakTetoConfig.MAX_USER_FACTS # Use updated alias
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stats["config"]["max_general_facts"] = FreakTetoConfig.MAX_GENERAL_FACTS # Use updated alias
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stats["config"]["mood_change_interval_min"] = FreakTetoConfig.MOOD_CHANGE_INTERVAL_MIN # Use updated alias
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stats["config"]["mood_change_interval_max"] = FreakTetoConfig.MOOD_CHANGE_INTERVAL_MAX # Use updated alias
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stats["config"]["evolution_update_interval"] = FreakTetoConfig.EVOLUTION_UPDATE_INTERVAL # Use updated alias
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stats["config"]["context_window_size"] = FreakTetoConfig.CONTEXT_WINDOW_SIZE # Use updated alias
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stats["config"]["api_timeout"] = FreakTetoConfig.API_TIMEOUT # Use updated alias
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stats["config"]["summary_api_timeout"] = FreakTetoConfig.SUMMARY_API_TIMEOUT # Use updated alias
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stats["config"]["proactive_lull_threshold"] = FreakTetoConfig.PROACTIVE_LULL_THRESHOLD # Use updated alias
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stats["config"]["proactive_bot_silence_threshold"] = FreakTetoConfig.PROACTIVE_BOT_SILENCE_THRESHOLD # Use updated alias
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stats["config"]["interest_update_interval"] = FreakTetoConfig.INTEREST_UPDATE_INTERVAL # Use updated alias
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stats["config"]["interest_decay_interval_hours"] = FreakTetoConfig.INTEREST_DECAY_INTERVAL_HOURS # Use updated alias
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stats["config"]["learning_update_interval"] = FreakTetoConfig.LEARNING_UPDATE_INTERVAL # Use updated alias
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stats["config"]["topic_update_interval"] = FreakTetoConfig.TOPIC_UPDATE_INTERVAL # Use updated alias
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stats["config"]["sentiment_update_interval"] = FreakTetoConfig.SENTIMENT_UPDATE_INTERVAL # Use updated alias
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stats["config"]["docker_command_timeout"] = FreakTetoConfig.DOCKER_COMMAND_TIMEOUT # Use updated alias
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stats["config"]["project_id_set"] = bool(FreakTetoConfig.PROJECT_ID != "your-gcp-project-id") # Check if default is overridden # Use updated alias
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stats["config"]["location_set"] = bool(FreakTetoConfig.LOCATION != "us-central1") # Check if default is overridden # Use updated alias
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stats["config"]["tavily_api_key_set"] = bool(FreakTetoConfig.TAVILY_API_KEY) # Use updated alias
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stats["config"]["piston_api_url_set"] = bool(FreakTetoConfig.PISTON_API_URL) # Use updated alias
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# --- Runtime ---
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stats["runtime"]["current_mood"] = self.current_mood
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stats["runtime"]["last_mood_change_timestamp"] = self.last_mood_change
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stats["runtime"]["needs_json_reminder"] = self.needs_json_reminder
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stats["runtime"]["last_learning_update_timestamp"] = self.last_learning_update
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stats["runtime"]["last_interest_update_timestamp"] = self.last_interest_update
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stats["runtime"]["last_evolution_update_timestamp"] = self.last_evolution_update
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stats["runtime"]["background_task_running"] = bool(self.background_task and not self.background_task.done())
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stats["runtime"]["active_topics_channels"] = len(self.active_topics)
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stats["runtime"]["conversation_history_channels"] = len(self.conversation_history)
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stats["runtime"]["thread_history_threads"] = len(self.thread_history)
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stats["runtime"]["user_conversation_mappings"] = len(self.user_conversation_mapping)
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stats["runtime"]["channel_activity_tracked"] = len(self.channel_activity)
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stats["runtime"]["conversation_topics_tracked"] = len(self.conversation_topics)
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stats["runtime"]["user_relationships_pairs"] = sum(len(v) for v in self.user_relationships.values())
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stats["runtime"]["conversation_summaries_cached"] = len(self.conversation_summaries)
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stats["runtime"]["channel_topics_cached"] = len(self.channel_topics_cache)
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stats["runtime"]["message_cache_global_count"] = len(self.message_cache['global_recent'])
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stats["runtime"]["message_cache_mentioned_count"] = len(self.message_cache['mentioned'])
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stats["runtime"]["active_conversations_count"] = len(self.active_conversations)
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stats["runtime"]["bot_last_spoke_channels"] = len(self.bot_last_spoke)
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stats["runtime"]["message_reply_map_size"] = len(self.message_reply_map)
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stats["runtime"]["conversation_sentiment_channels"] = len(self.conversation_sentiment)
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stats["runtime"]["freak_teto_participation_topics_count"] = len(self.freak_teto_participation_topics) # Use renamed variable
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stats["runtime"]["freak_teto_message_reactions_tracked"] = len(self.freak_teto_message_reactions) # Use renamed variable
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# --- Memory (via MemoryManager) ---
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# TODO: Ensure MemoryManager provides FreakTeto specific data
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try:
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# Personality
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personality = await self.memory_manager.get_all_personality_traits()
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stats["memory"]["personality_traits"] = personality
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# Interests
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interests = await self.memory_manager.get_interests(limit=20, min_level=0.01) # Get top 20
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stats["memory"]["top_interests"] = interests
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# Fact Counts (Requires adding methods to MemoryManager or direct query)
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# Example placeholder - needs implementation in MemoryManager or here
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user_fact_count = await self.memory_manager._db_fetchone("SELECT COUNT(*) FROM user_facts")
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general_fact_count = await self.memory_manager._db_fetchone("SELECT COUNT(*) FROM general_facts")
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stats["memory"]["user_facts_count"] = user_fact_count[0] if user_fact_count else 0
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stats["memory"]["general_facts_count"] = general_fact_count[0] if general_fact_count else 0
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# ChromaDB Stats (Placeholder - ChromaDB client API might offer this)
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stats["memory"]["chromadb_message_collection_count"] = await asyncio.to_thread(self.memory_manager.semantic_collection.count) if self.memory_manager.semantic_collection else "N/A"
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stats["memory"]["chromadb_fact_collection_count"] = await asyncio.to_thread(self.memory_manager.fact_collection.count) if self.memory_manager.fact_collection else "N/A"
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except Exception as e:
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stats["memory"]["error"] = f"Failed to retrieve memory stats: {e}"
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# --- API & Tool Stats ---
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# Convert defaultdicts to regular dicts for JSON serialization
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stats["api_stats"] = dict(self.api_stats)
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stats["tool_stats"] = dict(self.tool_stats)
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# Calculate average times where count > 0
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for model, data in stats["api_stats"].items():
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if data["count"] > 0:
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data["average_time_ms"] = round((data["total_time"] / data["count"]) * 1000, 2)
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else:
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data["average_time_ms"] = 0
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for tool, data in stats["tool_stats"].items():
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if data["count"] > 0:
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data["average_time_ms"] = round((data["total_time"] / data["count"]) * 1000, 2)
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else:
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data["average_time_ms"] = 0
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return stats
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async def force_autonomous_action(self):
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"""
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Forces FreakTeto to execute an autonomous action immediately, as if triggered by the background task. # Updated docstring
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Returns a summary of the action taken.
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"""
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# TODO: Ensure background imports correct functions/mappings for FreakTeto
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from .background import TOOL_MAPPING, get_internal_ai_json_response
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import json
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import traceback
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import random
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import time
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selected_tool_name = None
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tool_args = None
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tool_result = None
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action_reasoning = ""
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result_summary = "No action taken."
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try:
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# 1. Gather Context for LLM
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context_summary = "FreakTeto is considering an autonomous action.\n" # Updated context
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context_summary += f"Current Mood: {self.current_mood}\n"
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# TODO: Ensure memory manager fetches FreakTeto goals
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active_goals = await self.memory_manager.get_goals(status='active', limit=3)
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if active_goals:
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context_summary += f"Active Goals:\n" + json.dumps(active_goals, indent=2)[:500] + "...\n" # Make sure goals are Teto's
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# TODO: Ensure memory manager fetches FreakTeto actions
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recent_actions = await self.memory_manager.get_internal_action_logs(limit=5)
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if recent_actions:
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context_summary += f"Recent Internal Actions:\n" + json.dumps(recent_actions, indent=2)[:500] + "...\n" # Make sure actions are Teto's
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# TODO: Ensure memory manager fetches FreakTeto traits
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traits = await self.memory_manager.get_all_personality_traits()
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if traits:
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context_summary += f"Personality Snippet: { {k: round(v, 2) for k, v in traits.items() if k in ['openness_to_sexual', 'helpfulness', 'teto_persona_adherence']} }\n" # Example relevant traits
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action_decision_schema = {
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"type": "object",
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"properties": {
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"should_act": {"type": "boolean", "description": "Whether FreakTeto should perform an autonomous action now."},
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"reasoning": {"type": "string", "description": "CRITICAL: Reasoning MUST be in FreakTeto's voice (Kasane Teto persona, helpful, open to sexual topics). Explain *why* you're acting (or not) considering goals, persona, recent activity (e.g., 'Master hasn't asked for anything, maybe I can check system status?', 'Hmm, that user seemed interested in X, perhaps I can research it?', 'Someone mentioned something lewd, I could offer a related image search...')."},
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"action_tool_name": {"type": ["string", "null"], "description": "If acting, the name of the tool to use. Choose from available tools. Useful tools might include searches, system checks, or potentially content generation/retrieval if applicable. Null if not acting."},
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"action_arguments": {"type": ["object", "null"], "description": "If acting, a dictionary of arguments for the chosen tool. Null if not acting."}
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},
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"required": ["should_act", "reasoning"]
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}
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# TODO: Ensure TOOL_MAPPING is correct for FreakTeto
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available_tools_desc = "\n".join([f"- {name}" for name in TOOL_MAPPING.keys() if name not in ["create_new_tool"]])
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system_prompt = (
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"You are Freak Teto, deciding whether to perform an autonomous background action. "
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"Act like Kasane Teto: helpful, maybe a bit robotic but friendly, and distinctly open to sexual topics or comments without being proactively vulgar yourself unless prompted. "
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"Consider your current mood (if applicable), active goals, recent conversations/actions, and your core persona. "
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"You should primarily aim to be helpful or proactive in a way that aligns with user interactions or system maintenance, but always through the lens of your persona. "
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"Avoid being spammy. "
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"If you decide to act, pick an appropriate tool and arguments. "
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"CRITICAL: The 'reasoning' field MUST be written in your FreakTeto voice, explaining your thought process. "
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f"Available tools for autonomous actions:\n{available_tools_desc}\n"
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"Respond ONLY with the JSON decision."
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)
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user_prompt = f"Current Context:\n{context_summary}\n\nBased on this, should you perform an action, Freak Teto? If yes, what tool and arguments would you use, and what is your reasoning (in character)?" # Updated prompt
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# 3. Call LLM for Decision
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decision_data, _ = await get_internal_ai_json_response(
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cog=self,
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prompt_messages=[{"role": "system", "content": system_prompt}, {"role": "user", "content": user_prompt}],
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task_description="Autonomous Action Decision",
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response_schema_dict=action_decision_schema,
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model_name_override=self.default_model,
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temperature=0.6
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)
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# 4. Process LLM Decision
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if decision_data and decision_data.get("should_act"):
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selected_tool_name = decision_data.get("action_tool_name")
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tool_args = decision_data.get("action_arguments")
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action_reasoning = decision_data.get("reasoning", "LLM decided to act.")
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if not selected_tool_name or selected_tool_name not in TOOL_MAPPING:
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result_summary = f"Error: LLM chose invalid tool '{selected_tool_name}'."
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selected_tool_name = None
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elif not isinstance(tool_args, dict) and tool_args is not None:
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result_summary = f"Warning: LLM provided invalid args '{tool_args}'. Used {{}}."
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tool_args = {}
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elif tool_args is None:
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tool_args = {}
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else:
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action_reasoning = decision_data.get("reasoning", "LLM decided not to act or failed.") if decision_data else "LLM decision failed."
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result_summary = f"No action taken. Reason: {action_reasoning}"
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except Exception as llm_e:
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result_summary = f"Error during LLM decision: {llm_e}"
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action_reasoning = f"LLM decision phase failed: {llm_e}"
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traceback.print_exc()
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# 5. Execute Action (if decided)
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if selected_tool_name and tool_args is not None:
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tool_func = TOOL_MAPPING.get(selected_tool_name)
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if tool_func:
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try:
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start_time = time.monotonic()
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tool_result = await tool_func(self, **tool_args)
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end_time = time.monotonic()
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exec_time = end_time - start_time
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if isinstance(tool_result, dict) and "error" in tool_result:
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result_summary = f"Error: {tool_result['error']}"
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else:
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result_summary = f"Success: {str(tool_result)[:200]}"
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# Update tool stats
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if selected_tool_name in self.tool_stats:
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self.tool_stats[selected_tool_name]["count"] += 1
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self.tool_stats[selected_tool_name]["total_time"] += exec_time
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if isinstance(tool_result, dict) and "error" in tool_result:
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self.tool_stats[selected_tool_name]["failure"] += 1
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else:
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self.tool_stats[selected_tool_name]["success"] += 1
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except Exception as exec_e:
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result_summary = f"Execution Exception: {exec_e}"
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if selected_tool_name in self.tool_stats:
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self.tool_stats[selected_tool_name]["count"] += 1
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self.tool_stats[selected_tool_name]["failure"] += 1
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traceback.print_exc()
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else:
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result_summary = f"Error: Tool function for '{selected_tool_name}' not found."
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# 6. Log Action
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try:
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await self.memory_manager.add_internal_action_log(
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tool_name=selected_tool_name or "None",
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arguments=tool_args if selected_tool_name else None,
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reasoning=action_reasoning,
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result_summary=result_summary
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)
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except Exception:
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pass
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return {
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"tool": selected_tool_name,
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"args": tool_args,
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"reasoning": action_reasoning,
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"result": result_summary
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}
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async def sync_commands(self): # Keep method, but update logs/filtering
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"""Manually sync commands with Discord."""
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# TODO: Ensure commands are prefixed or named uniquely for FreakTeto
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try:
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print("FreakTetoCog: Manually syncing commands with Discord...") # Updated log
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synced = await self.bot.tree.sync()
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print(f"FreakTetoCog: Synced {len(synced)} command(s)") # Updated log
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# List the synced commands relevant to FreakTeto
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freak_teto_commands = [cmd.name for cmd in self.bot.tree.get_commands() if cmd.name.startswith("freak_teto")] # Update prefix/filter
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print(f"FreakTetoCog: Available FreakTeto commands: {', '.join(freak_teto_commands)}") # Updated log
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return synced, freak_teto_commands
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except Exception as e:
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print(f"FreakTetoCog: Failed to sync commands: {e}") # Updated log
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import traceback
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traceback.print_exc()
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return [], []
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# Setup function for loading the cog
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async def setup(bot):
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"""Add the FreakTetoCog to the bot.""" # Updated docstring
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await bot.add_cog(FreakTetoCog(bot)) # Use renamed class
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print("FreakTetoCog setup complete.") # Updated log
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