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feat: add relevance-gated proactive replies

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Replace random-only proactive reply logic with LLM relevance check.
The bot now evaluates recent conversation context and user memory
before deciding to jump in, then applies reply_chance as a second
gate. Bump reply_chance values higher since the relevance filter
prevents most irrelevant replies.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
AJ Isaacs
2026-03-01 11:34:53 -05:00
parent 175c7ad219
commit 787b083e00
3 changed files with 109 additions and 10 deletions
Download Patch File Download Diff File Expand all files Collapse all files

52
cogs/chat.py
View File

@@ -226,16 +226,58 @@ class ChatCog(commands.Cog):
ch_id = message.channel.id
self._messages_since_reply[ch_id] = self._messages_since_reply.get(ch_id, 0) + 1
cooldown = self.bot.config.get("modes", {}).get("proactive_cooldown_messages", 5)
reply_chance = mode_config.get("reply_chance", 0.0)
if (
self._messages_since_reply[ch_id] >= cooldown
and reply_chance > 0
and random.random() < reply_chance
and message.content and message.content.strip()
):
should_reply = True
is_proactive = True
# Gather recent messages for relevance check
recent_for_check = []
try:
async for msg in message.channel.history(limit=5, before=message):
if msg.content and msg.content.strip() and not msg.author.bot:
recent_for_check.append(
f"{msg.author.display_name}: {msg.content[:200]}"
)
except discord.HTTPException:
pass
recent_for_check.reverse()
recent_for_check.append(
f"{message.author.display_name}: {message.content[:200]}"
)
# Build memory context for users in recent messages
memory_parts = []
seen_users = set()
for line in recent_for_check:
name = line.split(":")[0]
if name not in seen_users and message.guild:
seen_users.add(name)
member = discord.utils.find(
lambda m, n=name: m.display_name == n,
message.guild.members,
)
if member:
profile = self.bot.drama_tracker.get_user_notes(member.id)
if profile:
memory_parts.append(f"{name}: {profile}")
memory_ctx = "\n".join(memory_parts) if memory_parts else ""
is_relevant = await self.bot.llm.check_reply_relevance(
recent_for_check, memory_ctx,
)
if is_relevant:
reply_chance = mode_config.get("reply_chance", 0.0)
if reply_chance > 0 and random.random() < reply_chance:
should_reply = True
is_proactive = True
else:
# Not relevant — partially reset cooldown so we check again sooner
self._messages_since_reply[ch_id] = max(
0, self._messages_since_reply[ch_id] - 3
)
if not should_reply:
return

10
config.yaml
View File

@@ -83,7 +83,7 @@ modes:
description: "Friendly chat participant"
prompt_file: "personalities/chat_chatty.txt"
proactive_replies: true
reply_chance: 0.10
reply_chance: 0.40
moderation: relaxed
relaxed_thresholds:
warning_threshold: 0.80
@@ -96,7 +96,7 @@ modes:
description: "Savage roast mode"
prompt_file: "personalities/chat_roast.txt"
proactive_replies: true
reply_chance: 0.20
reply_chance: 0.60
moderation: relaxed
relaxed_thresholds:
warning_threshold: 0.85
@@ -109,7 +109,7 @@ modes:
description: "Your biggest fan"
prompt_file: "personalities/chat_hype.txt"
proactive_replies: true
reply_chance: 0.15
reply_chance: 0.50
moderation: relaxed
relaxed_thresholds:
warning_threshold: 0.80
@@ -122,7 +122,7 @@ modes:
description: "Had a few too many"
prompt_file: "personalities/chat_drunk.txt"
proactive_replies: true
reply_chance: 0.20
reply_chance: 0.60
moderation: relaxed
relaxed_thresholds:
warning_threshold: 0.85
@@ -135,7 +135,7 @@ modes:
description: "Insufferable grammar nerd mode"
prompt_file: "personalities/chat_english_teacher.txt"
proactive_replies: true
reply_chance: 0.20
reply_chance: 0.60
moderation: relaxed
relaxed_thresholds:
warning_threshold: 0.85

57
utils/llm_client.py
View File

@@ -804,6 +804,63 @@ class LLMClient:
self._log_llm("pick_reaction", elapsed, False, message_text[:200], error=str(e))
return None
async def check_reply_relevance(
self, recent_messages: list[str], memory_context: str = "",
) -> bool:
"""Check if the bot would naturally want to jump into a conversation.
Returns True if the conversation is something worth replying to.
"""
prompt = (
"You're a regular member of a Discord gaming server. You're reading chat and deciding "
"whether you'd naturally want to jump in and say something.\n\n"
"Say YES if:\n"
"- Someone said something you'd have a strong reaction to\n"
"- You know something relevant about these people (see memory context)\n"
"- Someone is wrong or has a hot take you'd want to respond to\n"
"- The conversation is funny or interesting enough to comment on\n"
"- Someone mentioned something you have an opinion on\n\n"
"Say NO if:\n"
"- It's mundane/boring small talk\n"
"- You'd have nothing interesting to add\n"
"- People are just chatting normally and don't need interruption\n\n"
"Reply with EXACTLY one word: YES or NO."
)
convo_text = "\n".join(recent_messages[-5:])
user_content = ""
if memory_context:
user_content += f"{memory_context}\n\n"
user_content += f"Recent chat:\n{convo_text}"
t0 = time.monotonic()
async with self._semaphore:
try:
temp_kwargs = {"temperature": 0.3} if self._supports_temperature else {}
response = await self._client.chat.completions.create(
model=self.model,
messages=[
{"role": "system", "content": prompt},
{"role": "user", "content": user_content[:1000]},
],
**temp_kwargs,
max_completion_tokens=16,
)
elapsed = int((time.monotonic() - t0) * 1000)
content = (response.choices[0].message.content or "").strip().lower()
is_relevant = "yes" in content
self._log_llm(
"check_relevance", elapsed, True,
user_content[:300], content,
)
logger.debug("Relevance check: %s", content)
return is_relevant
except Exception as e:
elapsed = int((time.monotonic() - t0) * 1000)
logger.error("Relevance check error: %s", e)
self._log_llm("check_relevance", elapsed, False, user_content[:300], error=str(e))
return False
async def extract_memories(
self,
conversation: list[dict[str, str]],