- normalizeStoredStatus(): exact-hash & semantic (Qdrant/PG) cache reader
sebelumnya menipiskan 'warn' jadi 'flagged'/'clean' (type narrowing
legacy clean|flagged) — merusak gating auto-delete & label dashboard.
Kini status tersimpan dipertahankan penuh (clean/warn/flagged).
- prompts: hapus referensi <user_history> yang tak pernah di-inject,
SearXNG -> Wikipedia (sudah migrasi), referensi section yang tak ada,
typo 'secifik', dan baris list rusak '|-'.
- moderationBuilders: buang dead code buildUserProfilesBlock/
buildUserProfileRef/UserProfileEntry/buildUserHistoryXml (tanpa caller
produksi sejak context minimization) + test-nya.
- test baru: tests/storedStatusNormalization.test.ts (regresi warn).
- Memoize buildSystemPrompt by (mode|channelCulture); identical signatures
now reuse the ~5k-token core instead of rebuilding per sub-batch call
(textBatchProcessor rebuilt it inside the loop; a 200-msg batch re-sent
the full system prompt ~4x). Correction tail stays per-attempt (uncached).
- Hoist URL-image -> vision evidence out of the per-sub-batch loop in
textBatchProcessor: it depends only on fetched images + full target set,
so compute once per whole batch, not per sub-batch.
- Compact system instructions: collapse 3x-duplicated 'evaluate by content
alone' statements into one standalone rule; trim output.ts channel-culture
+ context framing already covered by rules.ts/system.ts; drop duplicate
programming-error-log few-shot (id 17, covered by rules AMAN list).
- Fix misleading config default: AI_LLM_BASE_URL default -> omniroute
(gateway already runs omniroute via BWS; 9router was dead/misleading).
typecheck + lint + build green.
User: 'jangan ada reputasi juga' — no profile, no reputation in the prompt,
raw messages only.
- textBatchProcessor: drop initializeUserReputation fetch + <user_reputation>
tag injection (kept the minimal <message> tag + reply/reference context).
- visionAnalyzer (prepareMediaMessage): same removal.
- prompts/system.ts + prompts/output.ts: replace <user_reputation>/<user_history>
instructions with an explicit 'no per-user profile/reputation context'
note so the LLM judges purely on message content + conversation/web/location.
- mediaBatchProcessor: fix stale comment.
Trust/infraction state is STILL written to the DB (userReputationsTable) for
enforcement — only the LLM context injection is removed, so moderation
actions (mute/ban via infraction thresholds) keep working.
Net: even smaller prompts (no per-user context at all) → more messages fit
per request, and one fewer DB round-trip per unique user per sub-batch.
tsc, biome, vitest (129) all clean.
User insight: personal profile summaries bloat the prompt (less room per
request) and add a per-user DB/Redis round-trip for little moderation signal.
Only the behavioural <user_reputation> history is kept.
- textBatchProcessor: stop fetching getUserProfile; remove <user_profiles>
block + <user_profile_ref> from message tags. Keep <user_reputation>.
- mediaBatchProcessor + visionAnalyzer: same removal (profile fetch + ref).
- prompts/system.ts + prompts/output.ts: drop stale <user_profiles>/
<user_profile_ref> instructions; point LLM at <user_reputation> instead.
- aiAnalyzer: gate userProfileLearner behind AI_USER_PROFILE_LEARNING_ENABLED
(default false) — generates profiles nobody reads, pure LLM/DB waste.
- Add AI_USER_PROFILE_LEARNING_ENABLED config knob.
Net: smaller prompts (more messages fit per request), fewer DB round-trips
per sub-batch, and no background LLM calls learning unused profiles.
tsc, biome, vitest (129) all clean.
The 32 few-shot examples each re-echoed score/confidence/
recommended_action/categories/policy_version inline (~150 chars ×
32). Those fields carry zero moderation-decision signal — the schema
and their ??-default coercion already live in OUTPUT_INSTRUCTIONS +
moderationResponseParser.ts. Removed 96 redundant key/value pairs.
Kept per-example: message_id, status, flags, severity, evidence,
analysis — the fields that actually teach decisions. Parser derives
the rest via ?? fallback, so real output shape is unchanged.
examples.ts: 21.7K→18.5K chars; FEW_SHOT(mixed) 15.3K→13.4K.
Total mixed system prompt now 33.9K (was 39.3K at audit start,
~14% leaner). tsc + 129 tests + biome green.
Co-Authored-By: Claude Opus 5 (Nous Research)
- prompts/system.ts: merge 3 overlapping framing blocks (Blok Data /
Konteks Pengguna / Framing Konteks vs Target) into 1 tight block —
same coverage, no duplicated "standalone judgment / profile-is-
reference-not-evidence" prose.
- prompts/output.ts: trim duplicated user_history/standalone paragraph
in PERSONALITY & MEMORI (keep concrete per-case lessons).
- prompts/examples.ts: drop 2 exact-duplicate-lesson few-shots (LGBT id=19
dup of id=30; weapons-tech id=33 dup of id=32). All teaching signals
retained via the surviving example of each lesson.
Static system prompt: text 32.7K→29.2K, mixed 39.3K→35.8K chars
(~10% smaller). No moderation rule, zero-tolerance category, or decision
tree altered — accuracy-controlling content untouched. tsc + 129 tests +
biome green.
Co-Authored-By: Claude Opus 5 (Nous Research)
- Add examples for technical discussions (kinetic energy, drone weapon
engineering, physics simulations) that should be marked clean
- System rule: physics/engineering topics (kinetik, gravitasi, energi,
drone, senjata, drone warfare, CAD, CNC, 3D printing, robotics, aerospace)
are safe when in technical context — flag only if explicit threat
- Riwayat pengguna dengan pelanggaran sebelumnya tidak memengaruhi
penilaian pesan bersih yang terpisah dan tidak mengandung pelanggaran
- Removed getUserRecentInfractions usage in textBatchProcessor.ts and visionAnalyzer.ts
- Removed buildUserHistoryXml import and calls
- Messages are now evaluated standalone, not influenced by past violations in other channels
- Updated moderation prompts with clearer instructions about user_history usage
- Fixes issue where benign messages like 'tubuh manusia vs gravitasi' were incorrectly flagged due to carryover from previous drone weapons discussion
The user history context was causing the LLM to interpret unrelated current messages
as threats because it conflated them with past violations. Now each message is judged
on its own merit with only channel-specific context.
- <message> targets now carry time (ISO), repetitions (N identical short texts = spam signal), bot and edited flags; escape id/user XML
- rich <user_reputation>: total_infractions, clean_streak, last_offense_days_ago, repeat_offender (7-day window)
- <user_history> with last flagged messages for repeat offenders (wires dead getUserRecentInfractions)
- <user_profile as_of> staleness signal; <location_context topic> from captured channel topic
- prompt framing + output instructions teach the LLM to use the new signals without treating history as proof
- tests: contextEnrichment.test.ts (13) + topic cases in conversationContext.test.ts
- Deleted the text analysis prompt constants and helpers as they are no longer needed.
- Added batch search functionality for Qdrant to optimize vector searches.
- Implemented methods for deleting expired Qdrant points and invalidating cache based on content hash.
- Updated text batch processor to use new timeout configurations and modified content building for moderation prompts.
- Enhanced text cache store to support new Qdrant integration and improved cache invalidation logic.
- Introduced a new user reputation model with a more nuanced trust scoring system, including penalties and rewards for user behavior.
- Added unit tests for the new trust model to ensure correctness of penalty and trust gain calculations.
- Updated configuration schema to reflect new timeout settings and removed deprecated OpenAI moderation keys.
Root cause: ai-analysis-worker read llmResult.explanation and
llmResult.toxicityScore — fields the LLM pipeline never produces
(canonical AnalysisResult uses analysis/score). Every message fell back
to the bare template "Tidak ada indikasi pelanggaran." and the stored
score was always 0.
- Map analysis/score correctly; fallback now quotes the message content
- Prompt: ban generic analysis phrasing, require reply context
- LLM context: include replied-to message content (metadata.reference)
so the model can explain what the user is replying to
- Frontend: show thread/channel names from metadata instead of raw IDs
(message card, detail views, search overlay); detail panel now
displays the ai_analysis text
- Auto-delete log/DM include the descriptive analysis as the reason
Build & Deploy / build-and-push (discord-gateway) (push) Failing after 2m22s
Build & Deploy / build-and-push (backend) (push) Failing after 3m22s
Build & Deploy / build-and-push (proxy) (push) Successful in 1m36s
Build & Deploy / deploy (push) Skipped
- Consolidate all DB schema definitions into packages/shared as single source of truth
- Migrate backend from raw SQL to Drizzle ORM across all modules
- Extract frontend inline UI into separate component files
- Refactor discord-gateway circuitBreaker into conversationState + moderationState
- Convert messageStore to Proxy singleton pattern
- Add validateBody/validateQuery middleware + Zod schemas for API endpoints
- Modernize Docker builds with multi-stage + pnpm deploy
- Migrate CI/CD from deployment to image-based pipeline
- Remove 60+ unused/dead files (~15K lines)
- Update color scheme from sky-blue to teal-cyan
- Move DB connection management to @bete/shared/database
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
asepharyana