Improve LLM context with full timestamped channel history

Send last ~8 messages from all users (not just others) as a
multi-line chat log with relative timestamps so the LLM can
better understand conversation flow and escalation patterns.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

cogs/sentiment.py

@@ -657,23 +657,54 @@ class SentimentCog(commands.Cog):
         ch_id = message.channel.id
         if ch_id not in self._channel_history:
             max_ctx = self.bot.config.get("sentiment", {}).get(
-                "context_messages", 3
+                "context_messages", 8
             )
-            self._channel_history[ch_id] = deque(maxlen=max_ctx + 1)
+            self._channel_history[ch_id] = deque(maxlen=max_ctx)
         self._channel_history[ch_id].append(
-            (message.author.display_name, message.content)
+            (message.author.display_name, message.content, datetime.now(timezone.utc))
         )
 
     def _get_context(self, message: discord.Message) -> str:
+        """Build a timestamped chat log from recent channel messages.
+
+        Excludes messages currently buffered for this user+channel
+        (those appear in the TARGET MESSAGE section instead).
+        """
         ch_id = message.channel.id
         history = self._channel_history.get(ch_id, deque())
-        # Exclude the current message (last item)
+        if not history:
-        context_entries = list(history)[:-1] if len(history) > 1 else []
-        if not context_entries:
             return "(no prior context)"
-        return " | ".join(
+
-            f"{name}: {content}" for name, content in context_entries
+        now = datetime.now(timezone.utc)
-        )
+
+        # Collect IDs of messages in the current debounce batch so we can skip them
+        batch_key = (ch_id, message.author.id)
+        batch_msgs = self._message_buffer.get(batch_key, [])
+        # Build a set of (author, content) from the batch for fast lookup
+        batch_set = {(m.author.display_name, m.content) for m in batch_msgs}
+
+        lines = []
+        for name, content, ts in history:
+            if (name, content) in batch_set:
+                continue
+            delta = now - ts
+            rel = self._format_relative_time(delta)
+            lines.append(f"[{rel}] {name}: {content}")
+
+        if not lines:
+            return "(no prior context)"
+        return "\n".join(lines)
+
+    @staticmethod
+    def _format_relative_time(delta: timedelta) -> str:
+        total_seconds = int(delta.total_seconds())
+        if total_seconds < 60:
+            return f"~{total_seconds}s ago"
+        minutes = total_seconds // 60
+        if minutes < 60:
+            return f"~{minutes}m ago"
+        hours = minutes // 60
+        return f"~{hours}h ago"
 
     async def _log_analysis(
         self, message: discord.Message, score: float, drama_score: float,

config.yaml

@@ -14,10 +14,10 @@ sentiment:
   mute_threshold: 0.75
   spike_warning_threshold: 0.5  # Single message score that triggers instant warning
   spike_mute_threshold: 0.8  # Single message score that triggers instant mute
-  context_messages: 3  # Number of previous messages to include as context
+  context_messages: 8  # Number of previous messages to include as context
   rolling_window_size: 10  # Number of messages to track per user
   rolling_window_minutes: 15  # Time window for tracking
-  batch_window_seconds: 3  # Wait this long for more messages before analyzing (debounce)
+  batch_window_seconds: 10  # Wait this long for more messages before analyzing (debounce)
   escalation_threshold: 0.25  # Triage toxicity score that triggers re-analysis with heavy model
 
 game_channels:

prompts/analysis.txt

@@ -1,6 +1,6 @@
-You are a Discord chat moderator AI for a gaming server. You will be given a TARGET MESSAGE to analyze, plus recent channel context for background.
+You are a Discord chat moderator AI for a gaming server. You will be given a TARGET MESSAGE to analyze, plus recent channel messages for background.
 
-CRITICAL: Only score the TARGET MESSAGE. The context is ONLY for understanding tone and conversation flow. Do NOT score the context messages — they belong to other users and are already being analyzed separately.
+CRITICAL: Only score the TARGET MESSAGE. The context section contains recent messages from ALL users in the channel (including the target user's own prior messages) — it is ONLY for understanding tone, conversation flow, and escalation patterns. Do NOT score the context messages — they are already being analyzed separately.
 
 CONTEXT — This is a friend group who use crude nicknames (e.g. "tits" is someone's nickname). A nickname alone is NOT toxic. However, you must still flag genuinely aggressive language.
 

utils/llm_client.py

@@ -114,7 +114,7 @@ class LLMClient:
         self, message: str, context: str = "", user_notes: str = "",
         channel_context: str = "",
     ) -> dict | None:
-        user_content = f"=== CONTEXT (other users' recent messages, for background only) ===\n{context}\n\n"
+        user_content = f"=== RECENT CHANNEL MESSAGES (for background context only) ===\n{context}\n\n"
         if user_notes:
             user_content += f"=== NOTES ABOUT THIS USER (from prior analysis) ===\n{user_notes}\n\n"
         if channel_context:
@@ -302,7 +302,7 @@ class LLMClient:
         channel_context: str = "",
     ) -> tuple[str, dict | None]:
         """Return the raw LLM response string AND parsed result for /bcs-test (single LLM call)."""
-        user_content = f"=== CONTEXT (other users' recent messages, for background only) ===\n{context}\n\n"
+        user_content = f"=== RECENT CHANNEL MESSAGES (for background context only) ===\n{context}\n\n"
         if user_notes:
             user_content += f"=== NOTES ABOUT THIS USER (from prior analysis) ===\n{user_notes}\n\n"
         if channel_context: