AJ Isaacs
660086a500
Convert cogs/sentiment.py (1050 lines) into cogs/sentiment/ package: - __init__.py (656 lines): core SentimentCog with new _process_finding() that deduplicates the per-user finding loop from _process_buffered and _run_mention_scan (~90 lines each → single shared method) - actions.py: mute_user, warn_user - topic_drift.py: handle_topic_drift - channel_redirect.py: handle_channel_redirect, build_channel_context - coherence.py: handle_coherence_alert - log_utils.py: log_analysis, log_action, score_color - state.py: save_user_state, flush_dirty_states All extracted modules use plain async functions (not methods) receiving bot/config as parameters. Named log_utils.py to avoid shadowing stdlib logging. Also update CLAUDE.md with comprehensive project documentation. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
657 lines
26 KiB
Python
657 lines
26 KiB
Python
import asyncio
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import logging
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from datetime import datetime, timezone
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import discord
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from discord.ext import commands, tasks
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from cogs.sentiment.actions import mute_user, warn_user
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from cogs.sentiment.channel_redirect import build_channel_context, handle_channel_redirect
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from cogs.sentiment.coherence import handle_coherence_alert
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from cogs.sentiment.log_utils import log_analysis
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from cogs.sentiment.state import flush_dirty_states
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from cogs.sentiment.topic_drift import handle_topic_drift
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logger = logging.getLogger("bcs.sentiment")
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# How often to flush dirty user states to DB (seconds)
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STATE_FLUSH_INTERVAL = 300 # 5 minutes
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class SentimentCog(commands.Cog):
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def __init__(self, bot: commands.Bot):
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self.bot = bot
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# Track which user IDs have unsaved in-memory changes
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self._dirty_users: set[int] = set()
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# Per-user redirect cooldown: {user_id: last_redirect_datetime}
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self._redirect_cooldowns: dict[int, datetime] = {}
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# Debounce buffer: keyed by channel_id, stores list of messages from ALL users
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self._message_buffer: dict[int, list[discord.Message]] = {}
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# Pending debounce timer tasks (per-channel)
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self._debounce_tasks: dict[int, asyncio.Task] = {}
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# Mention scan tasks (separate from debounce)
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self._mention_scan_tasks: dict[int, asyncio.Task] = {}
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# Mention scan state
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self._mention_scan_cooldowns: dict[int, datetime] = {} # {channel_id: last_scan_time}
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self._mention_scan_results: dict[int, str] = {} # {trigger_message_id: findings_summary}
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self._analyzed_message_ids: set[int] = set() # Discord message IDs already analyzed
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self._max_analyzed_ids = 500
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async def cog_load(self):
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self._flush_states.start()
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async def cog_unload(self):
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self._flush_states.cancel()
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# Cancel all pending debounce timers and process remaining buffers
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for task in self._debounce_tasks.values():
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task.cancel()
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self._debounce_tasks.clear()
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for task in self._mention_scan_tasks.values():
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task.cancel()
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self._mention_scan_tasks.clear()
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for channel_id in list(self._message_buffer):
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await self._process_buffered(channel_id)
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# Final flush on shutdown
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await flush_dirty_states(self.bot, self._dirty_users)
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@commands.Cog.listener()
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async def on_message(self, message: discord.Message):
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logger.info("MSG from %s in #%s: %s", message.author, getattr(message.channel, 'name', 'DM'), message.content[:80] if message.content else "(empty)")
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# Ignore bots (including ourselves)
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if message.author.bot:
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return
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# Ignore DMs
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if not message.guild:
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return
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config = self.bot.config
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monitoring = config.get("monitoring", {})
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if not monitoring.get("enabled", True):
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return
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# Check if channel is monitored
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monitored_channels = monitoring.get("channels", [])
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if monitored_channels and message.channel.id not in monitored_channels:
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return
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# Check ignored users
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if message.author.id in monitoring.get("ignored_users", []):
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return
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# Check immune roles
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immune_roles = set(monitoring.get("immune_roles", []))
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if immune_roles and any(
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r.id in immune_roles for r in message.author.roles
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):
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return
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# Check per-user immunity
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if self.bot.drama_tracker.is_immune(message.author.id):
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return
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# Explicit @mention of the bot triggers a mention scan instead of scoring.
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# Reply-pings (Discord auto-adds replied-to user to mentions) should NOT
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# trigger scans — and reply-to-bot messages should still be scored normally
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# so toxic replies to bot warnings aren't silently skipped.
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bot_mentioned_in_text = (
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f"<@{self.bot.user.id}>" in (message.content or "")
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or f"<@!{self.bot.user.id}>" in (message.content or "")
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)
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if bot_mentioned_in_text:
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mention_config = config.get("mention_scan", {})
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if mention_config.get("enabled", True):
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await self._maybe_start_mention_scan(message, mention_config)
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return
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# Skip if empty
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if not message.content or not message.content.strip():
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return
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# Buffer the message and start/reset debounce timer (per-channel)
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channel_id = message.channel.id
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if channel_id not in self._message_buffer:
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self._message_buffer[channel_id] = []
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self._message_buffer[channel_id].append(message)
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# Cancel existing debounce timer for this channel
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existing_task = self._debounce_tasks.get(channel_id)
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if existing_task and not existing_task.done():
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existing_task.cancel()
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batch_window = config.get("sentiment", {}).get("batch_window_seconds", 3)
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self._debounce_tasks[channel_id] = asyncio.create_task(
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self._debounce_then_process(channel_id, batch_window)
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)
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async def _debounce_then_process(self, channel_id: int, delay: float):
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"""Sleep for the debounce window, then process the buffered messages."""
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try:
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await asyncio.sleep(delay)
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await self._process_buffered(channel_id)
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except asyncio.CancelledError:
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pass # Timer was reset by a new message — expected
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def _resolve_thresholds(self) -> dict:
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"""Resolve effective moderation thresholds based on current mode."""
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config = self.bot.config
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sentiment_config = config.get("sentiment", {})
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mode_config = self.bot.get_mode_config()
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moderation_level = mode_config.get("moderation", "full")
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if moderation_level == "relaxed" and "relaxed_thresholds" in mode_config:
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rt = mode_config["relaxed_thresholds"]
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return {
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"warning": rt.get("warning_threshold", 0.80),
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"mute": rt.get("mute_threshold", 0.85),
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"spike_warn": rt.get("spike_warning_threshold", 0.70),
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"spike_mute": rt.get("spike_mute_threshold", 0.85),
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}
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return {
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"warning": sentiment_config.get("warning_threshold", 0.6),
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"mute": sentiment_config.get("mute_threshold", 0.75),
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"spike_warn": sentiment_config.get("spike_warning_threshold", 0.5),
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"spike_mute": sentiment_config.get("spike_mute_threshold", 0.8),
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}
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async def _apply_moderation(
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self,
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message: discord.Message,
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user_id: int,
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score: float,
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drama_score: float,
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categories: list[str],
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thresholds: dict,
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db_message_id: int | None,
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) -> None:
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"""Issue a warning or mute based on scores and thresholds."""
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mute_threshold = self.bot.drama_tracker.get_mute_threshold(user_id, thresholds["mute"])
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user_data = self.bot.drama_tracker.get_user(user_id)
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if drama_score >= mute_threshold or score >= thresholds["spike_mute"]:
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effective_score = max(drama_score, score)
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if user_data.warned_since_reset:
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await mute_user(self.bot, message, effective_score, categories, db_message_id, self._dirty_users)
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else:
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logger.info("Downgrading mute to warning for %s (no prior warning)", message.author)
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await warn_user(self.bot, message, effective_score, db_message_id, self._dirty_users)
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elif drama_score >= thresholds["warning"] or score >= thresholds["spike_warn"]:
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effective_score = max(drama_score, score)
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await warn_user(self.bot, message, effective_score, db_message_id, self._dirty_users)
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@staticmethod
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def _build_user_lookup(messages: list[discord.Message]) -> dict[str, tuple[int, discord.Message, list[discord.Message]]]:
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"""Build username -> (user_id, ref_msg, [messages]) mapping."""
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lookup: dict[str, tuple[int, discord.Message, list[discord.Message]]] = {}
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for msg in messages:
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name = msg.author.display_name
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if name not in lookup:
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lookup[name] = (msg.author.id, msg, [])
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lookup[name][2].append(msg)
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return lookup
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def _build_user_notes_map(self, messages: list[discord.Message]) -> dict[str, str]:
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"""Build username -> LLM notes mapping for users in the message list."""
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notes_map: dict[str, str] = {}
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for msg in messages:
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name = msg.author.display_name
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if name not in notes_map:
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notes = self.bot.drama_tracker.get_user_notes(msg.author.id)
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if notes:
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notes_map[name] = notes
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return notes_map
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@staticmethod
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def _build_conversation(
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messages: list[discord.Message],
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) -> list[tuple[str, str, datetime, str | None]]:
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"""Convert a list of Discord messages to conversation tuples with reply resolution."""
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msg_id_to_author = {m.id: m.author.display_name for m in messages}
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conversation = []
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for msg in messages:
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reply_to = None
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if msg.reference and msg.reference.message_id:
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reply_to = msg_id_to_author.get(msg.reference.message_id)
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if not reply_to:
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ref = msg.reference.cached_message
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if ref:
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reply_to = ref.author.display_name
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conversation.append((
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msg.author.display_name,
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msg.content,
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msg.created_at,
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reply_to,
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))
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return conversation
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# -- Shared finding processor --
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async def _process_finding(
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self,
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finding: dict,
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user_lookup: dict,
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*,
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sentiment_config: dict,
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dry_run: bool,
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thresholds: dict,
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db_content: str,
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db_topic_category: str,
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db_topic_reasoning: str,
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db_coherence_score: float | None,
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db_coherence_flag: str | None,
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game_channels: dict | None = None,
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coherence_config: dict | None = None,
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) -> tuple[str, float, float, list[str]] | None:
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"""Process a single user finding.
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Returns (username, score, drama_score, categories) or None if skipped.
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When game_channels is not None, topic drift, game redirect, and coherence
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handlers are active (buffered analysis mode). When None, they are skipped
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(mention scan mode).
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"""
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username = finding["username"]
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lookup = user_lookup.get(username)
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if not lookup:
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return None
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user_id, user_ref_msg, user_msgs = lookup
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score = finding["toxicity_score"]
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categories = finding["categories"]
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reasoning = finding["reasoning"]
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off_topic = finding.get("off_topic", False)
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note_update = finding.get("note_update")
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# Track in DramaTracker
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self.bot.drama_tracker.add_entry(user_id, score, categories, reasoning)
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escalation_boost = sentiment_config.get("escalation_boost", 0.04)
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drama_score = self.bot.drama_tracker.get_drama_score(user_id, escalation_boost=escalation_boost)
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logger.info(
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"User %s (%d) | msg_score=%.2f | drama_score=%.2f | categories=%s | %s",
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username, user_id, score, drama_score, categories, reasoning,
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)
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# Save to DB
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db_message_id = await self.bot.db.save_message_and_analysis(
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guild_id=user_ref_msg.guild.id,
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channel_id=user_ref_msg.channel.id,
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user_id=user_id,
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username=username,
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content=db_content,
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message_ts=user_ref_msg.created_at.replace(tzinfo=timezone.utc),
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toxicity_score=score,
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drama_score=drama_score,
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categories=categories,
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reasoning=reasoning,
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off_topic=off_topic,
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topic_category=db_topic_category,
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topic_reasoning=db_topic_reasoning,
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coherence_score=db_coherence_score,
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coherence_flag=db_coherence_flag,
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)
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# Feature handlers — only active during buffered analysis (game_channels set)
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if game_channels is not None:
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if off_topic:
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await handle_topic_drift(
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self.bot, user_ref_msg, db_topic_category, db_topic_reasoning,
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db_message_id, self._dirty_users,
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)
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detected_game = finding.get("detected_game")
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if detected_game and game_channels and not dry_run:
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await handle_channel_redirect(
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self.bot, user_ref_msg, detected_game, game_channels,
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db_message_id, self._redirect_cooldowns,
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)
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if coherence_config is not None and coherence_config.get("enabled", True):
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coherence_score = finding.get("coherence_score", 0.85)
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coherence_flag = finding.get("coherence_flag", "normal")
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degradation = self.bot.drama_tracker.update_coherence(
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user_id=user_id,
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score=coherence_score,
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flag=coherence_flag,
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drop_threshold=coherence_config.get("drop_threshold", 0.3),
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absolute_floor=coherence_config.get("absolute_floor", 0.5),
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cooldown_minutes=coherence_config.get("cooldown_minutes", 30),
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)
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if degradation and not dry_run:
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await handle_coherence_alert(
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self.bot, user_ref_msg, degradation, coherence_config,
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db_message_id, self._dirty_users,
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)
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# Note update
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if note_update:
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self.bot.drama_tracker.update_user_notes(user_id, note_update)
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self._dirty_users.add(user_id)
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self._dirty_users.add(user_id)
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# Log analysis
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await log_analysis(
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user_ref_msg, score, drama_score, categories, reasoning,
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off_topic, db_topic_category,
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)
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# Moderation
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if not dry_run:
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await self._apply_moderation(
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user_ref_msg, user_id, score, drama_score, categories, thresholds, db_message_id,
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)
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return (username, score, drama_score, categories)
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# -- Buffered analysis --
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async def _process_buffered(self, channel_id: int):
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"""Collect buffered messages, build conversation block, and run analysis."""
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messages = self._message_buffer.pop(channel_id, [])
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self._debounce_tasks.pop(channel_id, None)
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if not messages:
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return
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# Use the last message as reference for channel/guild
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ref_message = messages[-1]
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channel = ref_message.channel
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config = self.bot.config
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sentiment_config = config.get("sentiment", {})
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game_channels = config.get("game_channels", {})
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# Fetch some history before the buffered messages for leading context
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context_count = sentiment_config.get("context_messages", 8)
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oldest_buffered = messages[0]
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history_messages: list[discord.Message] = []
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try:
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async for msg in channel.history(limit=context_count + 5, before=oldest_buffered):
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if msg.author.bot:
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continue
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if not msg.content or not msg.content.strip():
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continue
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history_messages.append(msg)
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if len(history_messages) >= context_count:
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break
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except discord.HTTPException:
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pass
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history_messages.reverse() # chronological order
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# Combine: history (context) + buffered (new messages to analyze)
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new_message_start = len(history_messages)
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all_messages = history_messages + messages
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conversation = self._build_conversation(all_messages)
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if not conversation:
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return
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user_notes_map = self._build_user_notes_map(messages)
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channel_context = build_channel_context(ref_message, game_channels)
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logger.info(
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"Channel analysis: %d new messages (+%d context) in #%s",
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len(messages), len(history_messages),
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getattr(channel, 'name', 'unknown'),
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)
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# TRIAGE: Lightweight model — conversation-level analysis
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result = await self.bot.llm.analyze_conversation(
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conversation,
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channel_context=channel_context,
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user_notes_map=user_notes_map,
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new_message_start=new_message_start,
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)
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if result is None:
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return
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# ESCALATION: Re-analyze with heavy model if any finding warrants it
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escalation_threshold = sentiment_config.get("escalation_threshold", 0.25)
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needs_escalation = any(
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f["toxicity_score"] >= escalation_threshold
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or f.get("off_topic", False)
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or f.get("coherence_score", 1.0) < 0.6
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for f in result.get("user_findings", [])
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)
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if needs_escalation:
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heavy_result = await self.bot.llm_heavy.analyze_conversation(
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conversation,
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channel_context=channel_context,
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user_notes_map=user_notes_map,
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new_message_start=new_message_start,
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)
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if heavy_result is not None:
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logger.info(
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"Escalated to heavy model for #%s",
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getattr(channel, 'name', 'unknown'),
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)
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result = heavy_result
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user_lookup = self._build_user_lookup(messages)
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# Mark all buffered messages as analyzed (for mention scan dedup)
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for m in messages:
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self._mark_analyzed(m.id)
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dry_run = config.get("monitoring", {}).get("dry_run", False)
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thresholds = self._resolve_thresholds()
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coherence_config = config.get("coherence", {})
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# Process per-user findings
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for finding in result.get("user_findings", []):
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username = finding["username"]
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lookup = user_lookup.get(username)
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if not lookup:
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continue
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_, _, user_msgs = lookup
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combined_content = "\n".join(
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m.content for m in user_msgs if m.content and m.content.strip()
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)[:4000]
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await self._process_finding(
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finding, user_lookup,
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sentiment_config=sentiment_config,
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dry_run=dry_run,
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thresholds=thresholds,
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db_content=combined_content,
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db_topic_category=finding.get("topic_category", "general_chat"),
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db_topic_reasoning=finding.get("topic_reasoning", ""),
|
|
db_coherence_score=finding.get("coherence_score", 0.85),
|
|
db_coherence_flag=finding.get("coherence_flag", "normal"),
|
|
game_channels=game_channels,
|
|
coherence_config=coherence_config,
|
|
)
|
|
|
|
# -- Mention scan methods --
|
|
|
|
def _mark_analyzed(self, discord_message_id: int):
|
|
"""Track a Discord message ID as already analyzed."""
|
|
self._analyzed_message_ids.add(discord_message_id)
|
|
if len(self._analyzed_message_ids) > self._max_analyzed_ids:
|
|
sorted_ids = sorted(self._analyzed_message_ids)
|
|
self._analyzed_message_ids = set(sorted_ids[len(sorted_ids) // 2:])
|
|
|
|
async def _maybe_start_mention_scan(
|
|
self, trigger_message: discord.Message, mention_config: dict
|
|
):
|
|
"""Check cooldown and kick off a mention-triggered scan of recent messages."""
|
|
channel_id = trigger_message.channel.id
|
|
cooldown_seconds = mention_config.get("cooldown_seconds", 60)
|
|
|
|
now = datetime.now(timezone.utc)
|
|
last_scan = self._mention_scan_cooldowns.get(channel_id)
|
|
if last_scan and (now - last_scan).total_seconds() < cooldown_seconds:
|
|
logger.info(
|
|
"Mention scan cooldown active for #%s, skipping.",
|
|
getattr(trigger_message.channel, "name", "unknown"),
|
|
)
|
|
return
|
|
|
|
self._mention_scan_cooldowns[channel_id] = now
|
|
|
|
# Extract the user's concern (strip the bot ping from the message)
|
|
mention_text = trigger_message.content
|
|
for fmt in (f"<@{self.bot.user.id}>", f"<@!{self.bot.user.id}>"):
|
|
mention_text = mention_text.replace(fmt, "")
|
|
mention_text = mention_text.strip() or "(user pinged bot without specific concern)"
|
|
|
|
# Store as a mention scan task (separate from debounce)
|
|
existing_task = self._mention_scan_tasks.get(channel_id)
|
|
if existing_task and not existing_task.done():
|
|
existing_task.cancel()
|
|
|
|
self._mention_scan_tasks[channel_id] = asyncio.create_task(
|
|
self._run_mention_scan(trigger_message, mention_text, mention_config)
|
|
)
|
|
|
|
async def _run_mention_scan(
|
|
self,
|
|
trigger_message: discord.Message,
|
|
mention_text: str,
|
|
mention_config: dict,
|
|
):
|
|
"""Scan recent channel messages with ONE conversation-level LLM call."""
|
|
channel = trigger_message.channel
|
|
scan_count = mention_config.get("scan_messages", 30)
|
|
|
|
config = self.bot.config
|
|
sentiment_config = config.get("sentiment", {})
|
|
game_channels = config.get("game_channels", {})
|
|
|
|
# Fetch recent messages (before the trigger, skip bots/empty)
|
|
raw_messages: list[discord.Message] = []
|
|
try:
|
|
async for msg in channel.history(limit=scan_count + 10, before=trigger_message):
|
|
if msg.author.bot:
|
|
continue
|
|
if not msg.content or not msg.content.strip():
|
|
continue
|
|
raw_messages.append(msg)
|
|
if len(raw_messages) >= scan_count:
|
|
break
|
|
except discord.HTTPException:
|
|
logger.warning("Failed to fetch history for mention scan in #%s",
|
|
getattr(channel, "name", "unknown"))
|
|
return
|
|
|
|
raw_messages.reverse() # chronological order
|
|
|
|
if not raw_messages:
|
|
self._mention_scan_results[trigger_message.id] = "No recent messages found to analyze."
|
|
return
|
|
|
|
logger.info(
|
|
"Mention scan triggered by %s in #%s: %d messages (single LLM call). Focus: %s",
|
|
trigger_message.author.display_name,
|
|
getattr(channel, "name", "unknown"),
|
|
len(raw_messages),
|
|
mention_text[:80],
|
|
)
|
|
|
|
conversation = self._build_conversation(raw_messages)
|
|
user_notes_map = self._build_user_notes_map(raw_messages)
|
|
|
|
channel_context = build_channel_context(raw_messages[0], game_channels)
|
|
mention_context = (
|
|
f"A user flagged this conversation and said: \"{mention_text}\"\n"
|
|
f"Pay special attention to whether this concern is valid."
|
|
)
|
|
|
|
# Single LLM call
|
|
result = await self.bot.llm.analyze_conversation(
|
|
conversation,
|
|
mention_context=mention_context,
|
|
channel_context=channel_context,
|
|
user_notes_map=user_notes_map,
|
|
)
|
|
|
|
if result is None:
|
|
logger.warning("Conversation analysis failed for mention scan.")
|
|
self._mention_scan_results[trigger_message.id] = "Analysis failed."
|
|
return
|
|
|
|
user_lookup = self._build_user_lookup(raw_messages)
|
|
findings: list[str] = []
|
|
dry_run = config.get("monitoring", {}).get("dry_run", False)
|
|
thresholds = self._resolve_thresholds()
|
|
|
|
for finding in result.get("user_findings", []):
|
|
username = finding["username"]
|
|
lookup = user_lookup.get(username)
|
|
if not lookup:
|
|
logger.warning("Mention scan: LLM returned unknown user '%s', skipping.", username)
|
|
continue
|
|
|
|
user_id, ref_msg, user_msgs = lookup
|
|
|
|
# Skip if all their messages were already analyzed
|
|
if all(m.id in self._analyzed_message_ids for m in user_msgs):
|
|
continue
|
|
|
|
# Mark their messages as analyzed
|
|
for m in user_msgs:
|
|
self._mark_analyzed(m.id)
|
|
|
|
worst_msg = finding.get("worst_message")
|
|
content = f"[Mention scan] {worst_msg}" if worst_msg else "[Mention scan] See conversation"
|
|
off_topic = finding.get("off_topic", False)
|
|
|
|
result_tuple = await self._process_finding(
|
|
finding, user_lookup,
|
|
sentiment_config=sentiment_config,
|
|
dry_run=dry_run,
|
|
thresholds=thresholds,
|
|
db_content=content,
|
|
db_topic_category="personal_drama" if off_topic else "gaming",
|
|
db_topic_reasoning=finding.get("reasoning", ""),
|
|
db_coherence_score=None,
|
|
db_coherence_flag=None,
|
|
)
|
|
if result_tuple:
|
|
_, score, _, categories = result_tuple
|
|
if score >= 0.3:
|
|
cat_str = ", ".join(c for c in categories if c != "none") or "none"
|
|
findings.append(f"{username}: {score:.2f} ({cat_str})")
|
|
|
|
# Build summary for ChatCog
|
|
convo_summary = result.get("conversation_summary", "")
|
|
if findings:
|
|
summary = f"Scanned {len(raw_messages)} msgs. {convo_summary} Notable: " + "; ".join(findings[:5])
|
|
else:
|
|
summary = f"Scanned {len(raw_messages)} msgs. {convo_summary}"
|
|
|
|
# Prune old scan results
|
|
if len(self._mention_scan_results) > 20:
|
|
oldest = sorted(self._mention_scan_results.keys())[:len(self._mention_scan_results) - 10]
|
|
for k in oldest:
|
|
del self._mention_scan_results[k]
|
|
|
|
self._mention_scan_results[trigger_message.id] = summary
|
|
|
|
logger.info(
|
|
"Mention scan complete in #%s: 1 LLM call, %d messages, %d users flagged",
|
|
getattr(channel, "name", "unknown"),
|
|
len(raw_messages),
|
|
len(findings),
|
|
)
|
|
|
|
# -- State flush loop --
|
|
|
|
@tasks.loop(seconds=STATE_FLUSH_INTERVAL)
|
|
async def _flush_states(self):
|
|
await flush_dirty_states(self.bot, self._dirty_users)
|
|
|
|
@_flush_states.before_loop
|
|
async def _before_flush(self):
|
|
await self.bot.wait_until_ready()
|
|
|
|
|
|
async def setup(bot: commands.Bot):
|
|
await bot.add_cog(SentimentCog(bot))
|