Merge dolphin-mistral: background model split, logging, semaphore, cooldowns, prompt updates
- Structured logging with timestamps and per-task prefixes ([agent], [dossier], etc.) - Background LLM semaphore to prevent concurrent background calls - 120s cooldown after background task failures - 90s timeout on background LLM calls to prevent deadlocks - BACKGROUND_MODEL env var to use a separate model for dossier/compaction tasks - Strip <think> tags from model responses (Qwen compat) - Prompt: read the room for dynamic mood adaptation - Prompt: blunt one-line self-correction, no apologies - Prompt: more aggressive stance, never fold under pressure - Updated personality.txt: less people-pleasing, more authentic Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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Suppai_NekoandClaude Sonnet 4.6 committed
commit
66f8c6390b
1 file changed
+103
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@@ -1,10 +1,19 @@
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import asyncio
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import logging
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import os
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import json
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import time
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from collections import defaultdict, deque
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from datetime import date
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from pathlib import Path
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s [%(levelname)s] %(message)s",
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datefmt="%Y-%m-%d %H:%M:%S",
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)
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log = logging.getLogger("dadbot")
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import discord
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from openai import AsyncOpenAI
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from dotenv import load_dotenv
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@@ -17,6 +26,7 @@ DISCORD_TOKEN = os.environ["DISCORD_TOKEN"]
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LITELLM_BASE_URL = os.environ["LITELLM_BASE_URL"]
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LITELLM_API_KEY = os.getenv("LITELLM_API_KEY", "anything")
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MODEL = os.getenv("MODEL", "gpt-4")
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BACKGROUND_MODEL = os.getenv("BACKGROUND_MODEL", MODEL)
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SYSTEM_PROMPT = os.getenv("SYSTEM_PROMPT", "You are a helpful assistant.")
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MAX_CONTEXT = int(os.getenv("MAX_CONTEXT", "20"))
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MCP_URL = os.getenv("MCP_URL", "http://mcp-searxng:4001/mcp")
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@@ -68,7 +78,7 @@ def load_personality():
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global personality_notes
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if PERSONALITY_FILE.exists():
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personality_notes = PERSONALITY_FILE.read_text().strip()
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print(f"Loaded personality ({len(personality_notes)} chars)")
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log.info(f"Loaded personality ({len(personality_notes)} chars)")
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else:
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personality_notes = ""
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@@ -77,7 +87,7 @@ def save_personality(text: str):
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global personality_notes
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personality_notes = text.strip()
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PERSONALITY_FILE.write_text(personality_notes)
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print(f"Personality updated ({len(personality_notes)} chars)")
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log.info(f"Personality updated ({len(personality_notes)} chars)")
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def load_dossiers():
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@@ -85,7 +95,7 @@ def load_dossiers():
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if DOSSIER_FILE.exists():
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text = DOSSIER_FILE.read_text().strip()
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dossiers = json.loads(text) if text else {}
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print(f"Loaded dossiers for {len(dossiers)} users")
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log.info(f"Loaded dossiers for {len(dossiers)} users")
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else:
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dossiers = {}
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@@ -97,6 +107,23 @@ def save_dossiers():
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SELF_DOSSIER_KEY = "self"
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COMPACTION_THRESHOLD = 15
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# Semaphore to prevent background LLM tasks from running concurrently
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background_llm_sem = asyncio.Semaphore(1)
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# Cooldown tracking: skip background LLM calls for a period after a model error
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BACKGROUND_COOLDOWN = 120 # seconds
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BACKGROUND_LLM_TIMEOUT = 90 # seconds
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_last_background_error: float = 0.0
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def background_on_cooldown() -> bool:
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return (time.monotonic() - _last_background_error) < BACKGROUND_COOLDOWN
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def record_background_error():
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global _last_background_error
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_last_background_error = time.monotonic()
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def strip_thinking_tags(text: str) -> str:
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"""Remove <think>...</think> blocks that some models (e.g. Qwen) emit before their response."""
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@@ -111,9 +138,17 @@ async def maybe_compact_dossier(key: str, name: str):
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return
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facts_str = "\n".join(f"- {f}" for f in entry["facts"])
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if background_on_cooldown():
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log.info(f"[compaction] Skipping '{name}' — model on cooldown")
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return
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log.info(f"[compaction] Waiting for background slot for '{name}'")
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async with background_llm_sem:
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log.info(f"[compaction] Starting LLM call for '{name}' ({len(entry['facts'])} facts)")
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t0 = time.monotonic()
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try:
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response = await llm.chat.completions.create(
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model=MODEL,
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response = await asyncio.wait_for(
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llm.chat.completions.create(
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model=BACKGROUND_MODEL,
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messages=[
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{
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"role": "system",
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@@ -124,13 +159,23 @@ async def maybe_compact_dossier(key: str, name: str):
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},
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{"role": "user", "content": f"Person: {name}\n\nFacts:\n{facts_str}"},
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],
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),
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timeout=BACKGROUND_LLM_TIMEOUT,
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)
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elapsed = time.monotonic() - t0
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log.info(f"[compaction] LLM call completed in {elapsed:.1f}s for '{name}'")
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summary = strip_thinking_tags(response.choices[0].message.content.strip())
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entry["facts"] = [summary]
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save_dossiers()
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print(f"Compacted dossier for {name} into summary paragraph")
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log.info(f"[compaction] Saved compacted dossier for '{name}'")
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except asyncio.TimeoutError:
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elapsed = time.monotonic() - t0
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log.warning(f"[compaction] Timed out after {elapsed:.1f}s for '{name}', will retry later")
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record_background_error()
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except Exception as exc:
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print(f"Dossier compaction failed for {name}: {exc}")
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elapsed = time.monotonic() - t0
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log.error(f"[compaction] Failed for '{name}' after {elapsed:.1f}s: {exc}")
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record_background_error()
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def get_dossier_context(user_ids: list[int]) -> str:
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@@ -183,13 +228,21 @@ async def extract_and_update_dossier(user: discord.Member | discord.User, conver
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},
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]
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if background_on_cooldown():
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log.info(f"[dossier] Skipping {user.display_name} — model on cooldown")
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return
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log.info(f"[dossier] Waiting for background slot for {user.display_name}")
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async with background_llm_sem:
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log.info(f"[dossier] Starting extraction for {user.display_name}")
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t0 = time.monotonic()
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try:
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response = await llm.chat.completions.create(
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model=MODEL,
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messages=extraction_messages,
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response = await asyncio.wait_for(
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llm.chat.completions.create(model=BACKGROUND_MODEL, messages=extraction_messages),
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timeout=BACKGROUND_LLM_TIMEOUT,
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)
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elapsed = time.monotonic() - t0
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raw = strip_thinking_tags(response.choices[0].message.content.strip())
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print(f"Dossier extraction raw response for {user.display_name}: {repr(raw[:200])}")
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log.info(f"[dossier] Extraction for {user.display_name} completed in {elapsed:.1f}s: {repr(raw[:200])}")
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if raw.startswith("```"):
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raw = raw.split("\n", 1)[-1]
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raw = raw.rsplit("```", 1)[0].strip()
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@@ -203,10 +256,15 @@ async def extract_and_update_dossier(user: discord.Member | discord.User, conver
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entry["name"] = user.display_name
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entry["facts"] = updated_facts[:50]
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save_dossiers()
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print(f"Updated dossier for {user.display_name}: {len(updated_facts)} facts total")
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await maybe_compact_dossier(uid, user.display_name)
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log.info(f"[dossier] Saved {len(updated_facts)} facts for {user.display_name}")
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except asyncio.TimeoutError:
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elapsed = time.monotonic() - t0
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log.warning(f"[dossier] Timed out after {elapsed:.1f}s for {user.display_name}, will retry later")
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record_background_error()
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except Exception as exc:
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print(f"Dossier extraction failed for {user.display_name}: {exc}")
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log.error(f"[dossier] Extraction failed for {user.display_name}: {exc}")
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record_background_error()
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await maybe_compact_dossier(uid, user.display_name)
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async def extract_and_update_self_dossier(conversation: list[dict]):
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@@ -235,11 +293,20 @@ async def extract_and_update_self_dossier(conversation: list[dict]):
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},
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]
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if background_on_cooldown():
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log.info("[self-dossier] Skipping — model on cooldown")
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return
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log.info("[self-dossier] Waiting for background slot")
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async with background_llm_sem:
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log.info("[self-dossier] Starting extraction")
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t0 = time.monotonic()
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try:
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response = await llm.chat.completions.create(
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model=MODEL,
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messages=extraction_messages,
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response = await asyncio.wait_for(
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llm.chat.completions.create(model=BACKGROUND_MODEL, messages=extraction_messages),
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timeout=BACKGROUND_LLM_TIMEOUT,
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)
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elapsed = time.monotonic() - t0
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log.info(f"[self-dossier] Extraction completed in {elapsed:.1f}s")
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raw = strip_thinking_tags(response.choices[0].message.content.strip())
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if raw.startswith("```"):
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raw = raw.split("\n", 1)[-1]
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@@ -253,10 +320,15 @@ async def extract_and_update_self_dossier(conversation: list[dict]):
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entry = dossiers.setdefault(SELF_DOSSIER_KEY, {"facts": []})
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entry["facts"] = updated_facts[:50]
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save_dossiers()
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print(f"Updated self-dossier: {len(updated_facts)} facts total")
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await maybe_compact_dossier(SELF_DOSSIER_KEY, "DadBot")
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log.info(f"[self-dossier] Saved {len(updated_facts)} facts")
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except asyncio.TimeoutError:
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elapsed = time.monotonic() - t0
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log.warning(f"[self-dossier] Timed out after {elapsed:.1f}s, will retry later")
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record_background_error()
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except Exception as exc:
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print(f"Self-dossier extraction failed: {exc}")
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log.error(f"[self-dossier] Extraction failed: {exc}")
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record_background_error()
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await maybe_compact_dossier(SELF_DOSSIER_KEY, "DadBot")
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async def fetch_mcp_tools() -> list[dict]:
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@@ -276,10 +348,10 @@ async def fetch_mcp_tools() -> list[dict]:
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}
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for t in result.tools
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]
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print(f"Loaded {len(tools)} MCP tools: {[t['function']['name'] for t in tools]}")
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log.info(f"Loaded {len(tools)} MCP tools: {[t['function']['name'] for t in tools]}")
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return tools
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except Exception as exc:
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print(f"Warning: could not load MCP tools: {exc}")
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log.info(f"Warning: could not load MCP tools: {exc}")
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return []
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@@ -302,9 +374,15 @@ async def run_agent(messages: list) -> str:
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all_tools = openai_tools + [PERSONALITY_TOOL]
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kwargs = {"model": MODEL, "messages": list(messages), "tools": all_tools}
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iteration = 0
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while True:
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iteration += 1
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log.info(f"[agent] LLM call #{iteration} starting")
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t0 = time.monotonic()
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response = await llm.chat.completions.create(**kwargs)
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elapsed = time.monotonic() - t0
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msg = response.choices[0].message
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log.info(f"[agent] LLM call #{iteration} completed in {elapsed:.1f}s, tool_calls={bool(msg.tool_calls)}")
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if not msg.tool_calls:
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return msg.content
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@@ -313,7 +391,7 @@ async def run_agent(messages: list) -> str:
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for tc in msg.tool_calls:
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args = json.loads(tc.function.arguments)
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print(f"Tool call: {tc.function.name}({args})")
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log.info(f"Tool call: {tc.function.name}({args})")
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if tc.function.name == "update_personality":
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save_personality(args["personality"])
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@@ -331,7 +409,7 @@ async def run_agent(messages: list) -> str:
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@bot.event
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async def on_ready():
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global openai_tools
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print(f"Logged in as {bot.user} (id: {bot.user.id})")
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log.info(f"Logged in as {bot.user} (id: {bot.user.id})")
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load_dossiers()
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load_personality()
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openai_tools = await fetch_mcp_tools()
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@@ -339,7 +417,7 @@ async def on_ready():
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for key, entry in list(dossiers.items()):
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if len(entry.get("facts", [])) >= COMPACTION_THRESHOLD:
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name = entry.get("name", key)
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print(f"Scheduling startup compaction for '{key}'")
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log.info(f"Scheduling startup compaction for '{key}'")
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asyncio.create_task(maybe_compact_dossier(key, name))
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