Centralizes fixed control intents and adds a data-driven intent_code
field on kb_articles so many phrasings of the same FAQ question
resolve to one stable code (e.g. VOUCHER_ACTIVATION) instead of a
free-form, unvalidated string the LLM invented on the fly.
- services/shared/intents.py: CONTROL_INTENTS + normalize_intent()
- kb_articles.intent_code column (ORM + dev/sqlite runtime compat +
migrations/sql/0034_* for postgres/sqlite)
- kb_service CRUD exposes intent_code
- orchestrator surfaces intent_code to the LLM and validates its
intent output against control intents + the KB codes shown that turn
- voice.py: _voice_early_intent_bucket renamed to _voice_ack_topic_bucket
to stop it being conflated with the canonical FAQ intent
Both services/ai_orchestrator_service/voice.py and the aimaq persona/DOMAIN
SCOPE prompt were bind-mounted straight from the host on the aimaq stack,
bypassing git and CI entirely since they were first hand-edited in prod.
voice.py: merged the host's live business logic (gas/aimaq domain keyword
list, off-domain Kazakh/Russian replies, disabled re-correction of an already
obtained name) with the timeout_seconds fix from 09bcf74 that never reached
aimaq because the bind mount blocked it.
aimaq.env.production: replaced the AI_OPERATOR_* env values (which were never
interpolated -- {agent_name}/{company_name} would have been read literally)
with the final resolved Zhanna/Kazakgaz Aimaq text including the DOMAIN SCOPE
clause, matching what was actually live on the host.
RuntimeConfiguredTTSProvider (the live 'dynamic' TTS provider, resolves
voice from DB-backed config) never overrode cache_fingerprint(), so it
fell back to the base class's empty string. PrebakedAckBank keys its
on-disk cache on that fingerprint, so short filler phrases like
'Секунду' kept serving audio baked with the previous ElevenLabs voice
even after a voice change, while full LLM replies (cached inside the
resolved provider itself, keyed on its own voice id) already used the
new voice — explaining why callers heard two different voices in the
same call. Fix: delegate cache_fingerprint() to the resolved provider.
Also extend the voice delivery_hint so the model transliterates website
addresses and English words/abbreviations into spoken Cyrillic instead
of leaving raw Latin text for the TTS engine to mangle (egov.kz was
coming out as 'эговкз').
TTS reads raw text digit-by-digit with no number/date normalization layer.
Short numbers like 1414 were read as a single 4-digit number (tysyacha
chetyresta chetyrnadtsat) instead of a spoken code, and dates like '25
chisla' were read in the wrong grammatical case (dvadtsat pyat chislo
instead of dvadtsat pyatogo chisla). Extend the voice delivery_hint with
explicit spell-out rules so the model itself produces already-correct
spoken-form text.
Add sync_ai_operator_config_from_code() which overwrites the DB-cached
ai_operator_settings row from ai_operator_default_config() on startup
of ai_orchestrator_service and ai_voice_runtime_service. Greeting and
system prompt changes now go through git + deploy instead of manual
psql/API edits to prod. Also adds a root README pointing to existing docs.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
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>
- docs: longread.md — deep architecture/flow review of the whole
platform (services, event bus reality vs docs, AI/ML stack honesty
check, tech debt inventory)
- ai_orchestrator_service/voice.py: drop _voice_decision_legacy
(unreferenced) and the shadowed first _voice_decision definition
(silently overwritten by the real one, dead code)
- ui/analyst/app.js: drop duplicate dead definitions of
loadSavedAnalyticsViews/saveAnalyticsView/deleteAnalyticsView and
the first loadAnalyticsTrend implementation, all shadowed by later
declarations in the same file; kept the intentional AI-mode
drilldown wrapper layer (openAnalyticsDrilldown/exportAnalyticsDrilldownCsv/etc.)
since that duplication is deliberate delegation, not dead code
- ui/operator/vendor/sip-0.21.2.min.js: remove byte-identical orphaned
duplicate of ui/operator/sip-0.21.2.min.js (unreferenced anywhere)
Verified via full pytest run: identical set of 97 pre-existing
failures before and after (sales_* test-isolation ordering issue and
one known persona-prompt test), no new regressions.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
- Remove duplicate function definitions with hardcoded "AI-оператор" strings
(ai_voice_runtime, ai_orchestrator, voice_name_config, voice.py)
- Remove unreachable dead code after return in ai_voice_runtime
- Add SQL LIMIT to 17 unbounded queries across 12 services to prevent OOM
- Move Python-side filtering to SQL WHERE in reporting_service
- Downgrade 19 logger.warning to logger.info for normal-flow events in media_runtime
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
When a new voice session starts and the customer already has a trusted
display_name on file (e.g. from a previous call), automatically set
name_status to 'name_obtained' so the AI greets them by name instead
of asking again.
- Block old _voice_downstream_name_update from overwriting name_obtained status
unless user explicitly says 'меня зовут X' (explicit_candidate only)
- Move _persist_voice_name_state to run AFTER LLM extraction, not before,
so LLM always gets a chance to override the regex result
- Fixes bug where phrases like 'можешь назвать их' were incorrectly
captured as customer name, overwriting the real name
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