asepharyana
4cf5b87f2b
User insight: rather than many small per-batch API requests, pack many messages into ONE request so a burst is analyzed with far fewer calls. - AI_LLM_TEXT_BATCH_SIZE 20 -> 60 (one request now carries ~3x more messages). - AI_ANALYSIS_MAX_TARGET_TOKENS 4000 -> 14000 (the scheduler's token-budget gate was trimming pending messages to ~20 before they reached the sub-batch splitter; raising it lets ~60 messages through to a single LLM call). - AI_LLM_TEXT_ANALYSIS_TIMEOUT_MS 30000 -> 45000 (one larger call needs more headroom; gemini-flash-lite has a 1M-token context so 14k+8k is trivial). Net effect when ramai: a 60-message burst = 1-2 API calls instead of 3+, less semaphore contention, faster throughput.