Audit lanjutan: 6x 'LLM API request failed: Request was aborted' per jam.
Root cause: 9router/omniroute SELALU balas SSE (data: chunks) walau request
tanpa stream:true — SDK OpenAI non-stream menunggu FULL body sebelum parse,
jadi batch moderasi besar yang upstream-nya lambat kena timeout 30-60s dan
di-abort. llmClient sudah punya agregasi streaming (chunks → ChatCompletion).
Fix: stream:true di llmCaller (moderasi batch/individual), llmVision,
cultureLearner, userProfileLearner. Verified: SDK stream test 806ms vs
sebelumnya abort. Caller lain (recovery worker dll) lewat llmCaller sama.
Build & Deploy / build-and-push (backend) (push) Failing after 35s
Build & Deploy / build-and-push (discord-gateway) (push) Failing after 25s
Build & Deploy / build-and-push (proxy) (push) Failing after 25s
- Remove pnpm workspace, moon repo, and all monorepo tooling
- Delete packages/shared/, embed shared code directly into each service
- Copy packages/shared/src/* -> services/backend/src/shared/ and services/discord-gateway/src/shared/
- Replace all @bete/shared imports with @/shared/ path alias
- Remove @bete/shared workspace dependency from both services
- Update root package.json scripts from --filter to --prefix
- Rewrite Dockerfiles to build each service standalone
- Clean up biome.json, .gitignore, remove root drizzle.config.ts
- Added new dependencies for Next.js and lucide-react in pnpm-workspace.yaml.
- Refactored DashboardPage component to improve readability and error handling.
- Enhanced Header component to display error status with an alert icon.
- Updated MobileTabBar and Sidebar components to use a centralized tabs definition.
- Improved ChannelsView in dashboard-panel to handle channel fetching more cleanly.
- Fixed ActiveSpeaker type to use camelCase for userId.
- Updated MessagesPanel to handle guildId checks more gracefully.
- Adjusted API calls in dashboard and messages to align with backend expectations.
- Refined type definitions across various interfaces for consistency and clarity.
- userProfileLearner.ts: filter query to only clean messages (eq ai_status='clean')
to prevent profile contamination from flagged content. Also select channel_id
to group messages by channel in prompt, enabling channel-aware personality
summarization (user may behave differently across channels).
- llmModerationClient.ts (runSimpleTextFallback): inject user profile into
both the classify prompt and the reason prompt, so even the last-resort
fallback path has personality/memory context instead of being blind.
Add user_profiles table, store, and background learner worker
that summarizes user communication style, topics, and personality.
- New user_profiles table (user_id PK, guild_id, profile_summary, last_analyzed_at)
- userProfileStore.ts — CRUD (get/update) following channelCultureStore pattern
- userProfileLearner.ts — background worker: queries 100 recent msgs per user,
calls LLM for personality summary, updates every 12h
- Inject <user_profile> XML tag per-message in moderation prompt
- Start worker alongside cultureLearner in aiAnalyzer.ts
- Migration 0008 for user_profiles table
Co-Authored-By: Claude <noreply@anthropic.com>
asepharyana