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Phase 3: Unified Cheshire Cat integration with WebSocket-based per-user isolation

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Key changes:
- CatAdapter (bot/utils/cat_client.py): WebSocket /ws/{user_id} for chat
  queries instead of HTTP POST (fixes per-user memory isolation when no
  API keys are configured — HTTP defaults all users to user_id='user')
- Memory management API: 8 endpoints for status, stats, facts, episodic
  memories, consolidation trigger, multi-step delete with confirmation
- Web UI: Memory tab (tab9) with collection stats, fact/episodic browser,
  manual consolidation trigger, and 3-step delete flow requiring exact
  confirmation string
- Bot integration: Cat-first response path with query_llama fallback for
  both text and embed responses, server mood detection
- Discord bridge plugin: fixed .pop() to .get() (UserMessage is a Pydantic
  BaseModelDict, not a raw dict), metadata extraction via extra attributes
- Unified docker-compose: Cat + Qdrant services merged into main compose,
  bot depends_on Cat healthcheck
- All plugins (discord_bridge, memory_consolidation, miku_personality)
  consolidated into cat-plugins/ for volume mount
- query_llama deprecated but functional for compatibility
This commit is contained in:
koko210Serve
2026-02-07 20:22:03 +02:00
parent edb88e9ede
commit 14e1a8df51
14 changed files with 1382 additions and 70 deletions
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83
bot/bot.py
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@@ -512,14 +512,34 @@ async def on_message(message):
guild_id = message.guild.id if message.guild else None
response_type = "dm_response" if is_dm else "server_response"
author_name = message.author.display_name
response = await query_llama(
enhanced_prompt,
user_id=str(message.author.id),
guild_id=guild_id,
response_type=response_type,
author_name=author_name
)
# Phase 3: Try Cat pipeline first for embed responses too
response = None
if globals.USE_CHESHIRE_CAT:
try:
from utils.cat_client import cat_adapter
response = await cat_adapter.query(
text=enhanced_prompt,
user_id=str(message.author.id),
guild_id=str(guild_id) if guild_id else None,
author_name=author_name,
mood=globals.DM_MOOD,
response_type=response_type,
)
if response:
logger.info(f"🐱 Cat embed response for {author_name}")
except Exception as e:
logger.warning(f"🐱 Cat embed error, fallback: {e}")
response = None
if not response:
response = await query_llama(
enhanced_prompt,
user_id=str(message.author.id),
guild_id=guild_id,
response_type=response_type,
author_name=author_name
)
if is_dm:
logger.info(f"💌 DM embed response to {message.author.display_name} (using DM mood: {globals.DM_MOOD})")
@@ -570,13 +590,46 @@ async def on_message(message):
guild_id = message.guild.id if message.guild else None
response_type = "dm_response" if is_dm else "server_response"
author_name = message.author.display_name
response = await query_llama(
prompt,
user_id=str(message.author.id),
guild_id=guild_id,
response_type=response_type,
author_name=author_name
)
# Phase 3: Try Cheshire Cat pipeline first (memory-augmented response)
# Falls back to query_llama if Cat is unavailable or disabled
response = None
if globals.USE_CHESHIRE_CAT:
try:
from utils.cat_client import cat_adapter
current_mood = globals.DM_MOOD
if guild_id:
try:
from server_manager import server_manager
sc = server_manager.get_server_config(guild_id)
if sc:
current_mood = sc.current_mood_name
except Exception:
pass
response = await cat_adapter.query(
text=prompt,
user_id=str(message.author.id),
guild_id=str(guild_id) if guild_id else None,
author_name=author_name,
mood=current_mood,
response_type=response_type,
)
if response:
logger.info(f"🐱 Cat response for {author_name} (mood: {current_mood})")
except Exception as e:
logger.warning(f"🐱 Cat pipeline error, falling back to query_llama: {e}")
response = None
# Fallback to direct LLM query if Cat didn't respond
if not response:
response = await query_llama(
prompt,
user_id=str(message.author.id),
guild_id=guild_id,
response_type=response_type,
author_name=author_name
)
if is_dm:
logger.info(f"💌 DM response to {message.author.display_name} (using DM mood: {globals.DM_MOOD})")