Image messages previously blocked the whole analysis pipeline:
- conversationProcessing was a single lock per conversation; processBatch
awaited BOTH text and media worker jobs before releasing it, so a fast
text verdict sat unused until the slow vision/media batch finished
- one global LLM semaphore (AI_LLM_MAX_CONCURRENT) was shared by text and
media, so a vision backlog could starve text inference
- recovery worker gated on conversationProcessing.size
Now the queue is split into independent text/media lanes:
- conversationProcessing maps key -> Partial<Record<lane, startedAt>>;
each lane holds its own lock and frees it the moment ITS worker job
resolves (ownership-guarded clear prevents stale timers clearing newer
slots)
- two LLM semaphores: AI_LLM_MAX_CONCURRENT (text, default 8) and
AI_LLM_MEDIA_MAX_CONCURRENT (media, default 4) via
withLlmConcurrency(fn, { lane })
- batchScheduler schedules per conversation+lane (timer keys
'<key>::<lane>'); splitMessagesByLane/laneOfMessage moved to pure
analysisLanes.ts (unit-testable without Piscina)
- ai-analysis-worker batch jobs carry a lane field; per-lane active
request gauges (active_text_requests / active_media_requests)
- added tests/analysisLaneLock.test.ts (7 tests: independent lane locks,
preserving other-lane lock, clear-all, ownership guard, lane split)
Docs: ARCHITECTURE.md + AGENTS.md concurrency model updated.
typecheck/lint/test(138)/build all green.
asepharyana
e34dcd6bc6