feat: canonical intent taxonomy for AI operator (kb_answer -> intent_code)
deploy / deploy (push) Successful in 30s

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
This commit is contained in:
2026-08-31 00:17:51 +05:00
parent 3826f5704a
commit d2438b6954
14 changed files with 260 additions and 8 deletions
@@ -0,0 +1,2 @@
ALTER TABLE kb_articles ADD COLUMN IF NOT EXISTS intent_code TEXT;
CREATE INDEX IF NOT EXISTS idx_kb_articles_intent_code ON kb_articles(intent_code);
@@ -0,0 +1,2 @@
ALTER TABLE kb_articles ADD COLUMN intent_code TEXT;
CREATE INDEX IF NOT EXISTS idx_kb_articles_intent_code ON kb_articles(intent_code);
+17 -4
View File
@@ -9,7 +9,7 @@ import os
import re
import time
from threading import Lock
from typing import Any
from typing import Any, Iterable
import httpx
from fastapi import Depends, FastAPI, HTTPException, Query
@@ -23,6 +23,7 @@ from services.shared.ai_context_summary import (
update_context_summary_from_assistant_turn,
update_context_summary_from_user_turn,
)
from services.shared.intents import normalize_intent
from services.shared.kb_localization import normalize_kb_language
from services.shared.kb_search import search_kb_rows
from services.shared.models import (
@@ -2315,6 +2316,7 @@ def _openai_prompt(
"article_id": article.article_id,
"title": article.title,
"snippet": _article_snippet(article),
"intent_code": getattr(article, "intent_code", None),
}
for article in kb_results
]
@@ -2410,10 +2412,15 @@ def _openai_compatible_decision(
)
def _sanitize_decision(raw: dict[str, Any], *, fallback_language: str) -> dict[str, Any]:
def _sanitize_decision(
raw: dict[str, Any],
*,
fallback_language: str,
known_topic_codes: Iterable[str] = (),
) -> dict[str, Any]:
decision = {
"language": str(raw.get("language") or fallback_language or "ru"),
"intent": str(raw.get("intent") or "unknown"),
"intent": normalize_intent(raw.get("intent"), known_topic_codes=known_topic_codes),
"reply_text": str(raw.get("reply_text") or "").strip(),
"extracted_name": str(raw.get("extracted_name") or "").strip() or None,
"confidence": float(raw.get("confidence") or 0.0),
@@ -2611,7 +2618,13 @@ def _decide_reply(
raw["_model"] = _ai_model()
raw["_latency_ms"] = 1
raw["_finish_reason"] = "stop"
return _sanitize_decision(raw, fallback_language=language)
return _sanitize_decision(
raw,
fallback_language=language,
known_topic_codes=[
code for article in kb_results if (code := getattr(article, "intent_code", None))
],
)
def _update_ai_session_context_summary_from_user_turn(
@@ -179,4 +179,9 @@ def operator_system_prompt(*, language: str, channel_label: str, is_voice: bool,
f"{delivery_hint} "
"Return only a JSON object with keys: language, intent, reply_text, extracted_name, confidence, needs_handoff, "
"handoff_reason, case_action, kb_refs. case_action must be one of none, close, escalate, keep_open. "
"For `intent`: if your reply is grounded in one of the provided kb_results, set intent to that snippet's "
"intent_code exactly as given (do not translate, reformat, or invent your own code). If no kb_results were "
"used, use one of these fixed values as appropriate: identity_question, handoff_request, sensitive_request, "
"resolution_confirmed, clarification, kb_answer, unknown. Never invent a new intent value outside of these "
"two sources — the platform discards anything else."
)
+16 -3
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@@ -1359,7 +1359,13 @@ def _voice_v2_enabled(metadata: dict[str, Any] | None = None) -> bool:
return _voice_policy_mode() in {"v2_fast_conversational", "v2_streaming_duplex"}
def _voice_early_intent_bucket(text: str) -> str:
def _voice_ack_topic_bucket(text: str) -> str:
"""Coarse keyword heuristic used only to pick an ack phrase / early clarifying
question (see _voice_ack_kind_for_intent and _voice_early_plan) while the real,
KB-grounded decision is still in flight. This is NOT the canonical FAQ intent
(see services.shared.intents) and must never be echoed back as the final
decision's `intent` value.
"""
normalized = " ".join(str(text or "").strip().lower().split())
if not normalized:
return "unknown"
@@ -1509,7 +1515,7 @@ def _voice_v2_metadata(
if not _voice_v2_enabled(request_metadata):
return {}
payload = request_metadata if isinstance(request_metadata, dict) else {}
early_intent = _voice_early_intent_bucket(transcript_text)
early_intent = _voice_ack_topic_bucket(transcript_text)
metadata: dict[str, Any] = {
"voice_v2_enabled": True,
"early_intent": early_intent,
@@ -1668,6 +1674,7 @@ def _voice_llm_prompt_messages(
"article_id": article.article_id,
"title": article.title,
"snippet": app._article_snippet(article, limit=240),
"intent_code": getattr(article, "intent_code", None),
}
for article in kb_results[:3]
]
@@ -1767,7 +1774,13 @@ def _voice_llm_decision(
)
except Exception:
return None
decision = app._sanitize_decision(raw, fallback_language=language)
decision = app._sanitize_decision(
raw,
fallback_language=language,
known_topic_codes=[
code for article in kb_results if (code := getattr(article, "intent_code", None))
],
)
if not str(decision.get("reply_text") or "").strip():
decision["reply_text"] = _voice_generic_prompt(language)
if decision.get("needs_handoff") and not decision.get("handoff_reason"):
+8
View File
@@ -40,6 +40,7 @@ def _article_out(row: KBArticleRow) -> KBArticleOut:
article_id=row.article_id,
category_id=row.category_id,
article_group_id=resolve_article_group_id(row.article_id, row.article_group_id),
intent_code=row.intent_code,
language=normalize_kb_language(row.language),
title=row.title,
body=row.body,
@@ -49,6 +50,10 @@ def _article_out(row: KBArticleRow) -> KBArticleOut:
)
def _normalize_intent_code(value: str | None) -> str | None:
return str(value or "").strip().upper() or None
def _article_group_expr():
return func.coalesce(KBArticleRow.article_group_id, KBArticleRow.article_id)
@@ -133,6 +138,7 @@ def create_article(
article_id=article_id,
category_id=payload.category_id,
article_group_id=article_group_id,
intent_code=_normalize_intent_code(payload.intent_code),
language=language,
title=payload.title,
body=payload.body,
@@ -196,6 +202,8 @@ def update_article(
if "article_group_id" in data:
row.article_group_id = target_group_id
if "intent_code" in data:
row.intent_code = _normalize_intent_code(data["intent_code"])
if "language" in data:
row.language = target_language
if "title" in data:
+39
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@@ -0,0 +1,39 @@
from __future__ import annotations
from typing import Iterable
# Fixed, code-level conversational signals — not FAQ topics. These strings are
# already used as intent literals across ai_orchestrator_service/app.py,
# voice.py, and asserted directly in tests; centralized here rather than
# renamed so every call site validates against the same set.
CONTROL_INTENTS: frozenset[str] = frozenset(
{
"identity_question",
"handoff_request",
"sensitive_request",
"resolution_confirmed",
"clarification",
"kb_answer",
"unknown",
}
)
UNKNOWN_INTENT = "unknown"
def normalize_intent(raw: str | None, *, known_topic_codes: Iterable[str] = ()) -> str:
"""Validate a model-produced intent against control intents and KB topic codes.
`known_topic_codes` are the `intent_code` values of the KB articles actually
shown to the model for this turn anything else the model invents collapses
to UNKNOWN_INTENT rather than being trusted verbatim.
"""
candidate = str(raw or "").strip()
if not candidate:
return UNKNOWN_INTENT
if candidate in CONTROL_INTENTS:
return candidate
normalized_topic_codes = {str(code or "").strip().upper() for code in known_topic_codes if str(code or "").strip()}
if candidate.upper() in normalized_topic_codes:
return candidate.upper()
return UNKNOWN_INTENT
+1
View File
@@ -25,6 +25,7 @@ class KBSearchRow(Protocol):
title: str
body: str
tags_json: str
intent_code: str | None
T = TypeVar("T", bound=KBSearchRow)
+2
View File
@@ -983,6 +983,7 @@ class KBCategoryOut(KBCategoryCreate):
class KBArticleCreate(BaseModel):
category_id: str
article_group_id: str | None = None
intent_code: str | None = None
language: str = "ru"
title: str
body: str
@@ -991,6 +992,7 @@ class KBArticleCreate(BaseModel):
class KBArticleUpdate(BaseModel):
article_group_id: str | None = None
intent_code: str | None = None
language: str | None = None
title: str | None = None
body: str | None = None
+8
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@@ -583,6 +583,7 @@ def _apply_runtime_schema_compatibility() -> None:
if "kb_articles" in table_names:
columns = _table_columns(inspector, "kb_articles")
_add_column_if_missing(conn, columns, "kb_articles", "article_group_id", "VARCHAR(64)")
_add_column_if_missing(conn, columns, "kb_articles", "intent_code", "VARCHAR(64)")
_add_column_if_missing(conn, columns, "kb_articles", "language", "VARCHAR(8) DEFAULT 'ru'")
if "language" in columns:
conn.execute(
@@ -619,6 +620,13 @@ def _apply_runtime_schema_compatibility() -> None:
"ON kb_articles(language)"
)
)
if "idx_kb_articles_intent_code" not in indexes:
conn.execute(
text(
"CREATE INDEX IF NOT EXISTS idx_kb_articles_intent_code "
"ON kb_articles(intent_code)"
)
)
if "sales_automation_tasks" in table_names:
columns = _table_columns(inspector, "sales_automation_tasks")
+1
View File
@@ -700,6 +700,7 @@ class KBArticleRow(Base):
article_id: Mapped[str] = mapped_column(String(64), unique=True, index=True)
category_id: Mapped[str] = mapped_column(String(64), index=True)
article_group_id: Mapped[str | None] = mapped_column(String(64), nullable=True, index=True)
intent_code: Mapped[str | None] = mapped_column(String(64), nullable=True, index=True)
language: Mapped[str] = mapped_column(String(8), index=True, default="ru")
title: Mapped[str] = mapped_column(String(512), index=True)
body: Mapped[str] = mapped_column(Text)
+84
View File
@@ -414,6 +414,7 @@ def seed_kb_article(
*,
language: str = "ru",
article_group_id: str | None = None,
intent_code: str | None = None,
) -> dict[str, str]:
session = get_session()
try:
@@ -434,6 +435,7 @@ def seed_kb_article(
article_id=article_id,
category_id=category_id,
article_group_id=resolved_group_id,
intent_code=intent_code,
language=language,
title=title,
body=body,
@@ -1347,6 +1349,88 @@ def test_voice_llm_guarded_decision_uses_operator_style_without_ai_or_kb(monkeyp
assert "Do not say or imply that you are an AI" in system_prompt
def test_voice_llm_decision_echoes_kb_article_intent_code(monkeypatch):
monkeypatch.setenv("AI_VOICE_POLICY_MODE", "llm_guarded")
monkeypatch.setattr(ai_module, "_ai_provider", lambda: "openai_compatible")
def _fake_structured(messages, **kwargs):
del messages
return {
"language": "ru",
"intent": "voucher_activation",
"reply_text": "Подтвердите СМС с номера 1414 командой 21*1, затем завершите активацию в eGov.",
"confidence": 0.9,
"needs_handoff": False,
"handoff_reason": None,
"case_action": "keep_open",
"kb_refs": ["kba_voucher_1"],
"_model": "gpt-test",
"_latency_ms": 30,
"_finish_reason": "stop",
}
monkeypatch.setattr(ai_module, "_request_structured_model_decision", _fake_structured)
kb_article = SimpleNamespace(
article_id="kba_voucher_1",
title="Активация ваучера",
body="Подтвердите СМС 1414 командой 21*1, затем перейдите по ссылке и завершите в eGov Mobile.",
intent_code="VOUCHER_ACTIVATION",
)
decision = voice_module._voice_decision(
language="ru",
customer=None,
interaction=SimpleNamespace(interaction_id="int_voice_voucher", customer_id=None, status="new", queue_id="que_voice", subject="voucher"),
transcript_text="Что делать с СМС от 1414?",
transcript_window=[SimpleNamespace(speaker="caller", text="Что делать с СМС от 1414?", sequence_no=1, source_type="voice_asr", barge_in_interrupted=False, created_at=utc_now_iso())],
kb_results=[kb_article],
disclosure_required=False,
)
assert decision["intent"] == "VOUCHER_ACTIVATION"
def test_voice_llm_decision_rejects_invented_intent_not_in_kb_results(monkeypatch):
monkeypatch.setenv("AI_VOICE_POLICY_MODE", "llm_guarded")
monkeypatch.setattr(ai_module, "_ai_provider", lambda: "openai_compatible")
def _fake_structured(messages, **kwargs):
del messages
return {
"language": "ru",
"intent": "totally_made_up_intent",
"reply_text": "Подтвердите СМС с номера 1414 командой 21*1.",
"confidence": 0.9,
"needs_handoff": False,
"handoff_reason": None,
"case_action": "keep_open",
"kb_refs": ["kba_voucher_2"],
"_model": "gpt-test",
"_latency_ms": 30,
"_finish_reason": "stop",
}
monkeypatch.setattr(ai_module, "_request_structured_model_decision", _fake_structured)
kb_article = SimpleNamespace(
article_id="kba_voucher_2",
title="Активация ваучера",
body="Подтвердите СМС 1414 командой 21*1.",
intent_code="VOUCHER_ACTIVATION",
)
decision = voice_module._voice_decision(
language="ru",
customer=None,
interaction=SimpleNamespace(interaction_id="int_voice_voucher_2", customer_id=None, status="new", queue_id="que_voice", subject="voucher"),
transcript_text="Куда отправлять 21*1?",
transcript_window=[SimpleNamespace(speaker="caller", text="Куда отправлять 21*1?", sequence_no=1, source_type="voice_asr", barge_in_interrupted=False, created_at=utc_now_iso())],
kb_results=[kb_article],
disclosure_required=False,
)
assert decision["intent"] == "unknown"
def test_voice_v2_fast_conversational_adds_ack_metadata_and_compacts_reply(monkeypatch):
monkeypatch.setenv("AI_VOICE_POLICY_MODE", "v2_fast_conversational")
monkeypatch.setattr(ai_module, "_ai_provider", lambda: "openai_compatible")
+34
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@@ -0,0 +1,34 @@
from services.shared.intents import CONTROL_INTENTS, UNKNOWN_INTENT, normalize_intent
def test_control_intent_passes_through_unchanged():
assert normalize_intent("handoff_request") == "handoff_request"
assert normalize_intent("kb_answer", known_topic_codes=["VOUCHER_ACTIVATION"]) == "kb_answer"
def test_matching_topic_code_passes_through_case_insensitively():
assert normalize_intent("voucher_activation", known_topic_codes=["VOUCHER_ACTIVATION"]) == "VOUCHER_ACTIVATION"
assert normalize_intent(" Voucher_Activation ", known_topic_codes=["voucher_activation"]) == "VOUCHER_ACTIVATION"
def test_unknown_topic_code_falls_back_to_unknown():
assert normalize_intent("made_up_intent", known_topic_codes=["VOUCHER_ACTIVATION"]) == UNKNOWN_INTENT
assert normalize_intent("voucher_activation", known_topic_codes=[]) == UNKNOWN_INTENT
def test_empty_or_missing_intent_falls_back_to_unknown():
assert normalize_intent(None) == UNKNOWN_INTENT
assert normalize_intent("") == UNKNOWN_INTENT
assert normalize_intent(" ") == UNKNOWN_INTENT
def test_control_intents_frozenset_matches_documented_values():
assert CONTROL_INTENTS == {
"identity_question",
"handoff_request",
"sensitive_request",
"resolution_confirmed",
"clarification",
"kb_answer",
"unknown",
}
+40
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@@ -40,6 +40,46 @@ def test_kb_lite_search():
assert len(search.json()) >= 1
def test_kb_article_intent_code_round_trips_through_create_get_update():
client = TestClient(kb_app)
headers = {"X-User": "analyst", "X-Role": "analyst"}
cat = client.post(
"/knowledge/categories",
json={"name": "Vouchers", "description": "Voucher help"},
headers=headers,
)
assert cat.status_code == 200
category_id = cat.json()["category_id"]
created = client.post(
"/knowledge/articles",
json={
"category_id": category_id,
"title": "Активация ваучера",
"body": "Подтвердите СМС 1414 командой 21*1.",
"tags": ["voucher"],
"intent_code": "voucher_activation",
},
headers=headers,
)
assert created.status_code == 200
assert created.json()["intent_code"] == "VOUCHER_ACTIVATION"
article_id = created.json()["article_id"]
fetched = client.get(f"/knowledge/articles/{article_id}")
assert fetched.status_code == 200
assert fetched.json()["intent_code"] == "VOUCHER_ACTIVATION"
updated = client.patch(
f"/knowledge/articles/{article_id}",
json={"intent_code": "voucher_activation_v2"},
headers=headers,
)
assert updated.status_code == 200
assert updated.json()["intent_code"] == "VOUCHER_ACTIVATION_V2"
def test_kb_lite_search_ranks_title_over_body_only_matches():
client = TestClient(kb_app)
headers = {"X-User": "analyst", "X-Role": "analyst"}