Didar KozhikovandClaude Sonnet 5
49489f89b5
Two independent latency fixes for the voice-assistant reply pipeline, both scoped to the parts of the flow that run regardless of whether voice_v2 is enabled for a queue: 1. media_runtime._process_utterance: the v1/fallback turn path (used by any queue not covered by AI_VOICE_V2_QUEUE_CODES) silently awaited the full LLM decision with no audio playing at all, unlike the v2 path which already has a decision-timeout ack. Give v1 the same behavior: wait up to 600ms (_v1_ack_wait_seconds) for the decision, and if it's still not ready, play a short "Секунду." filler via the existing _emit_early_ack before the real reply, instead of leaving the caller in silence for the full LLM+TTS round trip. Reuses the same ack selection/playback code path v2 already exercises, so no new failure modes - just an added timeout branch mirroring the existing v2 one. 2. ai_orchestrator_service._kb_search: every voice/chat turn re-ran a full-table scan of kb_articles (all columns, including body text) and rescored every row in Python, even though the KB rarely changes mid-conversation. Added an in-process cache keyed by language, gated on a cheap content fingerprint (row count + max id + max updated_at + summed title/body/tags length, all computed server-side without transferring the text columns). A fingerprint mismatch always triggers a fresh fetch, so this can never serve stale results after an insert/update/delete - unlike a naive TTL cache, which would have been be wrong the moment a test (or a real KB edit) changed the table within the cache window. Note the first fingerprint design (count + max id + max updated_at only) was insufficient: utc_now_iso() truncates to whole seconds and SQLite reuses primary keys after a full-table delete, so two different row sets written in the same wall-clock second could share a fingerprint. Caught this via a real test failure (test_ai_whatsapp_relaxed_kb_search_answers_phrase_query breaking only when run after test_ai_orchestrator_service.py in the same process) before it could reach production; the summed content-length term closes the gap. Added test_media_runtime_plays_filler_ack_when_v1_decision_is_slow (asserts greeting -> ack -> reply delivery order when process_turn is slow) and verified the KB cache against the full test_ai_orchestrator_service.py + test_ai_whatsapp_orchestrator_service.py suite plus a wider kb/orchestrator/whatsapp/telegram/voice-filtered run: only the same pre-existing, already-documented failures remain (unrelated sales_service test-isolation ordering, one known persona-prompt assertion) - no new failures from either change. Streaming the LLM decision itself (start speaking reply_text before the full structured JSON response finishes generating) was scoped but deliberately deferred: it needs incremental JSON parsing on top of SSE streaming to detect when just the reply_text field is complete, shared across both voice and text-channel decision paths - a separate, higher-risk change that deserves its own PR and testing pass rather than being bundled here. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>