refs #739: add Smilegate MCP operations guide

# Conflicts:
#	src/main/java/com/cloudhandson/vpdbackoffice/service/StructuredDataService.java
#	vpd-backoffice/src/main/java/com/cloudhandson/vpdbackoffice/service/SchemaMetadataService.java
#	vpd-backoffice/src/main/java/com/cloudhandson/vpdbackoffice/service/VectorKnowledgeService.java
#	vpd-backoffice/src/main/resources/mapper/MaskingRuleMapper.xml
#	vpd-backoffice/src/main/resources/templates/schema-metadata.html
#	vpd-backoffice/src/main/resources/templates/structured-data.html

.env.example

@@ -26,12 +26,25 @@ export BACKOFFICE_DB_PASSWORD="${ADB_PASSWORD}"
 # --- (2b) Spring Boot 백오피스 ---
 export BACKOFFICE_ADMIN_USER="admin"
 export BACKOFFICE_ADMIN_PASSWORD="admin"
+# 운영에서는 최초 한 번 생성한 {bcrypt} 해시를 .env에 보관합니다. 이를 사용하면
+# 지속 로그인 쿠키가 앱 재기동 후에도 유효합니다. 원문 비밀번호 값은 Git에 넣지 마세요.
+export BACKOFFICE_ADMIN_PASSWORD_HASH=""
+# 읽기 전용 조회 계정. 활성화하면 ROLE_VIEWER로 로그인 가능하며 POST/PUT/PATCH/DELETE는 차단됩니다.
+export BACKOFFICE_GUEST_ENABLED="false"
+export BACKOFFICE_GUEST_USER="guest"
+export BACKOFFICE_GUEST_PASSWORD=""
+export BACKOFFICE_GUEST_PASSWORD_HASH=""
 # 로컬 HTTP 개발 기본값. 외부 VM 배포 스크립트는 loopback/HTTPS 안전값으로 덮어씁니다.
 export BACKOFFICE_BIND_ADDRESS="0.0.0.0"
 export BACKOFFICE_FORWARD_HEADERS_STRATEGY="none"
 export BACKOFFICE_REQUIRE_HTTPS="false"
 export BACKOFFICE_SESSION_COOKIE_SECURE="false"
-export BACKOFFICE_ORDS_BASE_URL="https://yh0olybn5pqce4n-d8aukro81636mon0.adb.ap-seoul-1.oraclecloudapps.com/ords"
+# HTTPS에서만 사용. key는 배포 환경의 secret으로 32자 이상 임의값을 넣고 Git에 저장하지 않습니다.
+export BACKOFFICE_REMEMBER_ME_ENABLED="false"
+export BACKOFFICE_REMEMBER_ME_KEY=""
+export BACKOFFICE_REMEMBER_ME_DAYS="14"
+# HMM HR 질의는 HMM MCP/DBMS_CLOUD_AI_AGENT를 사용합니다. 기존 ORDS 운영 기능이 필요할 때만 설정합니다.
+export BACKOFFICE_ORDS_BASE_URL=""
 export BACKOFFICE_ORDS_TIMEOUT_SECONDS="10"
 # ORDS metadata 생성/수정 전용 계정. 비워두면 BACKOFFICE_DB_* 연결을 사용하므로
 # ADMIN으로 실행 중이면 Handler 생성은 막히고 소스 보기만 사용합니다.
@@ -39,18 +52,68 @@ export BACKOFFICE_ORDS_DB_URL="${BACKOFFICE_DB_URL}"
 export BACKOFFICE_ORDS_DB_USERNAME="CB_ORDS"
 export BACKOFFICE_ORDS_DB_PASSWORD=""
 
+# Select AI는 프로파일 소유 스키마로 별도 접속합니다.
+# 원문 비밀번호는 .env 또는 배포 환경 secret에만 두며 Git에 올리지 않습니다.
+export BACKOFFICE_SELECT_AI_DB_URL="${BACKOFFICE_DB_URL}"
+export BACKOFFICE_SELECT_AI_DB_USERNAME=""
+export BACKOFFICE_SELECT_AI_DB_PASSWORD=""
+export BACKOFFICE_SELECT_AI_PROFILE=""
+export BACKOFFICE_SELECT_AI_FEW_SHOT_ENABLED="true"
+export BACKOFFICE_SELECT_AI_FEW_SHOT_TOP_K="3"
+# Customer-owned DB view: game aliases, approved profile objects, and valid DB objects.
+export BACKOFFICE_GAME_SCOPE_ENABLED="false"
+export BACKOFFICE_GAME_SCOPE_VIEW=""
+export BACKOFFICE_GAME_SCOPE_MAX_SCOPES="8"
+
+# 공통 데이터 카탈로그. objects는 key/tableName/objectType/businessName/description JSON 배열입니다.
+# 배포 환경마다 반드시 실제 소유자와 허용 객체를 지정합니다.
+export BACKOFFICE_CATALOG_OWNER="APP_OWNER"
+export BACKOFFICE_CATALOG_OBJECTS='[{"key":"customers","tableName":"CUSTOMER","objectType":"TABLE","businessName":"고객","description":"고객 기본 정보"}]'
+export BACKOFFICE_PRODUCT_NAME="Data & AI Backoffice"
+export BACKOFFICE_PRODUCT_TITLE="Data & AI Backoffice"
+export BACKOFFICE_PRODUCT_DATA_LABEL="업무 데이터"
+export BACKOFFICE_MCP_TOOL_NAME="oracle.select_ai.data_text2sql"
+export BACKOFFICE_MCP_TOOL_LABEL="업무 데이터 Text2SQL"
+export BACKOFFICE_MCP_TOOL_DESCRIPTION="승인된 업무 데이터에 대해 읽기 전용 SQL을 생성하고 실행합니다."
+export BACKOFFICE_MCP_PROMPT_DESCRIPTION="업무 데이터에서 조회할 내용을 자연어로 입력합니다."
+export BACKOFFICE_MCP_SHOWPROMPT_TOOL_NAME="oracle.select_ai.data_showprompt"
+export BACKOFFICE_MCP_SHOWPROMPT_TOOL_LABEL="업무 데이터 SHOWPROMPT"
+export BACKOFFICE_MCP_SHOWPROMPT_TOOL_DESCRIPTION="Select AI가 SQL 생성에 사용한 prompt를 조회하는 읽기 전용 진단 도구입니다."
+# Select AI few-shot 예제 SQL 조회·저장 MCP. 운영 환경은 고객별 도구명과 안내문만 변경합니다.
+export BACKOFFICE_MCP_QA_VECTOR_SEARCH_TOOL_NAME="oracle.select_ai.qa_vector_search"
+export BACKOFFICE_MCP_QA_VECTOR_SEARCH_TOOL_LABEL="Select AI 예제 SQL 조회"
+export BACKOFFICE_MCP_QA_VECTOR_SEARCH_TOOL_DESCRIPTION="현재 질문에 사용할 유사 예제 SQL을 Select AI 실행 전에 조회합니다."
+export BACKOFFICE_MCP_QA_VECTOR_STORE_TOOL_NAME="oracle.select_ai.qa_vector_store"
+export BACKOFFICE_MCP_QA_VECTOR_STORE_TOOL_LABEL="Select AI 예제 SQL 저장"
+export BACKOFFICE_MCP_QA_VECTOR_STORE_TOOL_DESCRIPTION="검토된 Select AI 결과를 후속 Text2SQL 품질 향상용 예제 SQL로 저장합니다."
+export BACKOFFICE_MCP_GAME_SCOPE_TOOL_NAME="oracle.select_ai.game_scope_resolve"
+export BACKOFFICE_MCP_GAME_SCOPE_TOOL_LABEL="게임 조회 범위 확인"
+export BACKOFFICE_MCP_GAME_SCOPE_TOOL_DESCRIPTION="질문의 게임 별칭을 DB 범위 view로 확인하고, SUPPORTED 결과에만 Few-shot NL2SQL을 호출하도록 안내합니다."
+# 마스킹 관리 대상. objectName/policyName JSON 배열이며, 비우면 어떤 DB 정책도 관리하지 않습니다.
+export BACKOFFICE_MASKING_POLICIES=''
+# 보안 SQL 화면에 노출할 번들 SQL. fileName은 패키지의 sql/adb/ 아래 파일명만 허용됩니다.
+export BACKOFFICE_SECURITY_SQL_SCRIPTS=''
+
 # --- (2c) OpenAI 호환 AI 호출 (MCP-style Reasoning 탭) ---
 export BACKOFFICE_AI_ENABLED="false"
+export BACKOFFICE_AI_PROVIDER="openai"                          # openai | oci
 export BACKOFFICE_AI_BASE_URL=""                                # 예: https://inference.generativeai.us-chicago-1.oci.oraclecloud.com
 export BACKOFFICE_AI_MODEL=""                                   # 예: openai.gpt-5.4-mini
 export BACKOFFICE_AI_API_KEY=""
 export BACKOFFICE_AI_TIMEOUT_SECONDS="30"
+# provider=oci이면 API key를 환경 변수로 복제하지 않고 OCI config profile로 요청에
+# 서명합니다. POC3_LLM_GPT55_OCI_*/OCI_GENAI_*가 있으면 아래 BACKOFFICE_* 값보다
+# fallback으로 사용됩니다.
+export BACKOFFICE_AI_OCI_CONFIG_FILE=""
+export BACKOFFICE_AI_OCI_PROFILE="DEFAULT"
+export BACKOFFICE_AI_OCI_REGION=""
+export BACKOFFICE_AI_OCI_COMPARTMENT_ID=""
 
 # 로컬 키 별칭은 .env에만 저장하세요. .env.example에는 원문 키를 넣지 않습니다.
 export VPDTEST1_API_KEY=""
 export VPDTEST2_API_KEY=""
 
-# --- (3) 데모용 ADB 엔드유저 비밀번호 (sql/adb/07_end_users.sql 에서 사용) ---
+# --- (3) 데모용 ADB 엔드유저 비밀번호 (database/adb/07_end_users.sql 에서 사용) ---
 # ADB 비번 정책: 12자 이상, 대/소/숫자/특수 조합.
 # 4명의 데모 유저:
 #   vpduser_my   → MySQL view 만 SELECT 가능
@@ -63,7 +126,7 @@ export VPDUSER_BOTH_PASSWORD="RowFilter#Both26"
 export VPDUSER_NONE_PASSWORD="RowFilter#None26"
 
 # --- (3b) OPTIONAL — DDS 변형 데모용 엔드유저 비밀번호 ---
-# sql/adb/13_dds_variant.sql (Oracle 26ai Deep Data Security 재구현) 에서만 사용.
+# database/adb/13_dds_variant.sql (Oracle 26ai Deep Data Security 재구현) 에서만 사용.
 # 동일한 4-user 매트릭스를 declarative Data Grants 로 재현. VPD 데모와 공존.
 export DDSUSER_MY_PASSWORD="DdsGrant#My2026"
 export DDSUSER_PG_PASSWORD="DdsGrant#Pg2026"
@@ -71,8 +134,20 @@ export DDSUSER_BOTH_PASSWORD="DdsGrant#Both26"
 export DDSUSER_NONE_PASSWORD="DdsGrant#None26"
 export DDSUSER_TOKEN_PASSWORD="DdsGrant#Token26"
 
+# --- (3c) DDS MCP SSE · OCI IAM Confidential Application ---
+# 실제 값은 .env 또는 배포 환경의 secret store에만 둔다.
+export DDS_OCI_IAM_CLIENT_ID=""
+export DDS_OCI_IAM_CLIENT_SECRET=""
+export DDS_OCI_IAM_DOMAIN_URL=""       # 예: https://idcs-<unique>.identity.oraclecloud.com
+export DDS_OCI_IAM_DATABASE_SCOPE=""   # OCI IAM의 DB resource scope
+export DDS_OCI_IAM_TOKEN_URI=""         # 비우면 ${DDS_OCI_IAM_DOMAIN_URL}/oauth2/v1/token
+export DDS_OCI_IAM_TIMEOUT_SECONDS="10s"
+export DDS_OCI_IAM_REFRESH_SKEW_SECONDS="60s"
+export DDS_MCP_BASE_URL="http://127.0.0.1:8083"
+export DDS_MCP_TEST_BEARER=""           # 발급한 활성 CB_APP_USER Bearer만 로컬 테스트에 사용
+
 # --- (4) 원격 Postgres (AWS RDS, Cloud SQL, ...) ---
-# sql/source/postgres_setup.sql 가 여기로 customers 테이블/seed 생성.
+# database/source/postgres_setup.sql 가 여기로 customers 테이블/seed 생성.
 # ADB 의 RDS_POSTGRES_LINK 가 이 인스턴스를 가리킴.
 export PG_HOST=""                                                # 예: vpd-poc.xxxxx.ap-northeast-2.rds.amazonaws.com
 export PG_PORT="5432"

.gitignore

@@ -17,6 +17,13 @@ logs/
 # Java / Maven
 target/
 
+# Python / Streamlit
+__pycache__/
+*.py[cod]
+
+# Locally downloaded development tools (for example SQLcl)
+.tools/
+
 # OS
 .DS_Store
 Thumbs.db

README.md

@@ -32,7 +32,7 @@ End-to-End 데모입니다.
 행 자체는 보여주되 특정 컬럼만 가리는 통제입니다. 예: 영업팀은 고객 행은 다 보지만
 `email` 컬럼은 항상 `NULL` 로 반환.
 
-* VPD 경로: 별도의 `DBMS_REDACT` 정책 (`sql/adb/06a_redaction.sql`).
+* VPD 경로: 별도의 `DBMS_REDACT` 정책 (`database/adb/06a_redaction.sql`).
 * DDS 경로: 데이터 그랜트 안에 `(ALL COLUMNS EXCEPT email)` 한 줄로 흡수.
 
 ### 3. 두 가지 구현 — VPD 와 DDS
@@ -57,11 +57,47 @@ DDS 변형의 능력 (멀티테넌트, OAuth2 federated identity, MAC 모드,
 
 ---
 
+## 소스 위치와 Git 저장 기준
+
+현재 이 저장소에는 서로 독립적으로 빌드·실행되는 세 애플리케이션이 있다. 각 제품은
+최상위 폴더를 하나씩 사용하며 저장소 루트에 프레임워크 `src`를 두지 않는다.
+
+| 구분 | 저장 위치 | 역할 |
+|---|---|---|
+| VPD/ASO/FGA 관리 백오피스 | `vpd-backoffice/` | Spring Boot 기반 데이터 접근 제어 관리 콘솔 |
+| VPD 백오피스 화면 | `vpd-backoffice/src/main/resources/templates/` | Thymeleaf 서버 렌더링 화면 |
+| VPD 백오피스 CSS/JS | `vpd-backoffice/src/main/resources/static/` | 화면 동작과 스타일 |
+| VPD/ASO/FGA/ORDS/Select AI SQL | `database/adb/` | ADB 적용·검증 SQL 스크립트 |
+| 원격 원장 seed/검증 SQL | `database/source/` | PostgreSQL·MySQL 가상 원장과 HMM 선사 실적 데이터 |
+| DDS 백오피스 | `dds-backoffice/` | VPD와 분리된 DDS 전용 Spring Boot 데모 |
+| AI Web Agent Console | `ai-web-agent-console/` | Streamlit 기반 MCP 업무 에이전트 콘솔 |
+| AI Web Agent Console 진입점 | `ai-web-agent-console/app.py` | MCP discovery, 도구 호출, 답변 합성, 감사 증적 표시 |
+| AI Web Agent Console 패키지 | `ai-web-agent-console/ai_web_agent_console/` | 인증, 화면, MCP, 모델과 질의 계약 모듈 |
+| AI Web Agent Console 설정 | `ai-web-agent-console/config/` | 제품, MCP, 모델, 데모 사용자와 질문 JSON |
+
+Git 기준은 다음과 같다.
+
+| 항목 | 값 |
+|---|---|
+| 로컬 작업 디렉터리 | `/home/opc/workspaces/vpd-permission-poc-20260628213409` |
+| 운영 기준 remote | `gitea` |
+| 운영 기준 repository | `https://gittea.cloud-handson.com/joungmin/vpd-permission-poc.git` |
+| HMM 기준 branch | `hmm-backoffice` |
+
+주의:
+
+- HMM 운영·현행화 기준은 Gitea의 `hmm-backoffice` 브랜치다.
+- 날짜 스냅샷과 PoC 번호는 현재 소스 경로로 사용하지 않는다.
+- 실제 DB password, wallet password, Bearer token, AI key, 대화 SQLite DB는 Git에 넣지 않는다.
+
+---
+
 ## 구성요소
 
 | 계층 | 객체 | 역할 |
 |---|---|---|
 | 원격 | `public.customers` (PG), `ecommerce_poc.customers` (MySQL) | 원본 데이터 |
+| 원격 HMM 데모 | `hmm_demo.carriers`, `hmm_demo.carrier_monthly_performance` (PG) | 가상 선사 기준정보와 최근 18개월 월간 KPI |
 | ADB - 연결 | `RDS_POSTGRES_LINK`, `RDS_LINK` | `DBMS_CLOUD_ADMIN.CREATE_DATABASE_LINK` 로 만든 heterogeneous DB Link |
 | ADB - 매핑 | `app_customer`, `app_user`, `app_group`, `user_group`, `db_source`, `permission` | 누가 어느 소스의 어느 region 을 볼 수 있는지 |
 | ADB - 컨텍스트 | `vpd_ctx` (Secure Application Context) + `ctx_pkg` | 로그인 시 권한을 세션 컨텍스트로 로딩 |
@@ -74,7 +110,7 @@ DDS 변형의 능력 (멀티테넌트, OAuth2 federated identity, MAC 모드,
 ## 빠른 시작 (One-Click)
 
 ```bash
-git clone https://github.com/<you>/vpd-permission-poc.git
+git clone --branch hmm-backoffice https://gittea.cloud-handson.com/joungmin/vpd-permission-poc.git
 cd vpd-permission-poc
 
 # 1) 환경값 채우기
@@ -132,7 +168,7 @@ $EDITOR .env
 
 ## 데모 시나리오 — 2×2 source access matrix
 
-`sql/adb/03_seed.sql` 의 매핑 (4 유저, 4 케이스):
+`database/adb/03_seed.sql` 의 매핑 (4 유저, 4 케이스):
 
 | DB 유저 | 그룹 | PG 뷰 | MySQL 뷰 | VPD 결과 |
 |---|---|---|---|---|
@@ -150,7 +186,7 @@ $EDITOR .env
   (`1=0` predicate) — **deny by default**.
 * 누구든 원본 테이블 직접 접근 시도 (`@RDS_POSTGRES_LINK` 등) → 권한 없음.
 
-`sql/adb/08_tests_user_my.sql` 가 우회 시도 5개 (원격 직접 SELECT, 컨텍스트
+`database/adb/08_tests_user_my.sql` 가 우회 시도 5개 (원격 직접 SELECT, 컨텍스트
 스푸핑, DBMS_RLS 변경, 매핑 테이블 SELECT) 를 시도하고 모두 ORA-xxxxx 로 실패하는 것을
 보여줍니다. 09/10/11 은 각 유저의 expected 행 수를 가볍게 확인합니다.
 
@@ -162,7 +198,7 @@ $EDITOR .env
 
 ## DDS 설정 핵심
 
-DDS 변형을 직접 셋업할 때 알아야 하는 최소 단위. `sql/adb/13_dds_variant.sql` 가 이
+DDS 변형을 직접 셋업할 때 알아야 하는 최소 단위. `database/adb/13_dds_variant.sql` 가 이
 순서로 돌아가며, 행/컬럼 통제가 모두 **선언형 DDL 한 줄** 로 끝납니다.
 
 ### 사전 조건
@@ -222,10 +258,23 @@ CREATE DATA GRANT admin.alice_apac_grant
 .
 ├── run.sh                       # 원클릭 엔트리포인트
 ├── .env.example
+├── vpd-backoffice/              # Spring Boot VPD 관리 백오피스
+│   ├── pom.xml
+│   └── src/
+├── ai-web-agent-console/        # Streamlit MCP 업무 에이전트
+│   ├── app.py
+│   ├── ai_web_agent_console/
+│   ├── config/
+│   └── tests/
+├── dds-backoffice/              # DDS 전용 Spring Boot 백오피스
+├── deploy/                      # 애플리케이션별 배포 설정
+├── tests/                       # 저장소 폴더 경계와 경로 규칙 검증
 ├── scripts/lib/common.sh        # log/ok/warn/die + env 검증 헬퍼
-├── sql/
+├── database/
 │   ├── source/
 │   │   ├── postgres_setup.sql   # 원격 PG: customers + 12 rows
+│   │   ├── postgres_hmm_carrier_performance.sql        # 원격 PG: HMM 가상 선사 8개 + 월간 KPI 144 rows
+│   │   ├── postgres_hmm_carrier_performance_verify.sql # HMM 선사 실적 건수·패턴·제약·TLS·권한 검증
 │   │   └── mysql_setup.sql      # 원격 MySQL: customers + 12 rows
 │   └── adb/
 │       ├── 00_cleanup.sql       # 멱등 teardown

ai-web-agent-console/.env.example

@@ -0,0 +1,48 @@
+# AI Web Agent Console local/VM configuration example.
+# Copy to .env and keep real credentials out of Git.
+
+# MCP
+HMM_MCP_BEARER_TOKEN=
+AI_WEB_AGENT_CONSOLE_MCP_TIMEOUT_SECONDS=45
+
+# OCI Generative AI SDK
+OCI_AUTH_TYPE=config_file
+OCI_CONFIG_FILE=~/.oci/config
+OCI_PROFILE=DEFAULT
+OCI_GENAI_COMPARTMENT_ID=
+AI_WEB_AGENT_CONSOLE_MODEL_PROFILE=gpt54_mini_oci
+AI_WEB_AGENT_CONSOLE_COMPLEX_REASONING_MODEL_PROFILE=gpt54_mini_oci
+
+# Optional model route overrides
+# AI_WEB_AGENT_CONSOLE_LLM_GPT54_MINI_OCI_MODEL_ID=openai.gpt-5.4-mini
+# AI_WEB_AGENT_CONSOLE_LLM_GPT54_MINI_OCI_REGION=us-chicago-1
+# AI_WEB_AGENT_CONSOLE_LLM_GPT54_MINI_OCI_ENDPOINT=https://inference.generativeai.us-chicago-1.oci.oraclecloud.com
+
+# Login gateway. Generate PBKDF2 hash and random cookie secret outside Git.
+AI_WEB_AGENT_CONSOLE_LOGIN_USER=demo-admin
+AI_WEB_AGENT_CONSOLE_LOGIN_PASSWORD_PBKDF2=
+AI_WEB_AGENT_CONSOLE_LOGIN_COOKIE_SECRET=
+PORTAL_AUTH_BIND_ADDRESS=127.0.0.1
+PORTAL_AUTH_PORT=8621
+PORTAL_AUTH_SESSION_SECONDS=43200
+PORTAL_AUTH_REMEMBER_SECONDS=604800
+
+# Product display
+AGENT_CONSOLE_NAME=HMM AI 업무 에이전트
+AGENT_CONSOLE_SHORT_NAME=HMM
+AGENT_CONSOLE_PAGE_TITLE=HMM AI 업무 에이전트
+AGENT_CONSOLE_PRIMARY_COLOR=#004b87
+
+# Local conversation store
+AI_WEB_AGENT_CONSOLE_CHAT_DB_PATH=data/ai_web_agent_console_chat.sqlite3
+
+# Optional audit DB. Password and Wallet remain outside Git.
+AI_WEB_AGENT_CONSOLE_AUDIT_DB_ENV_FILE=.env
+AI_WEB_AGENT_CONSOLE_AUDIT_DB_USER=ADMIN
+AI_WEB_AGENT_CONSOLE_AUDIT_DB_PASSWORD=
+AI_WEB_AGENT_CONSOLE_AUDIT_DSN=
+AI_WEB_AGENT_CONSOLE_AUDIT_WALLET_DIR=
+AI_WEB_AGENT_CONSOLE_AUDIT_WALLET_PASSWORD=
+
+# Optional query contract override
+AI_WEB_AGENT_CONSOLE_QUERY_CONTRACTS_PATH=config/hmm_hr_query_contracts.json

ai-web-agent-console/README.md

@@ -0,0 +1,57 @@
+# AI Web Agent Console
+
+HMM 업무 데이터를 MCP 도구로 조회하고 OCI Generative AI로 답변을 구성하는 Streamlit
+애플리케이션이다. 과거 PoC 번호나 소스 추출 날짜는 애플리케이션 이름과 패키지 경계로
+사용하지 않는다.
+
+## 구조
+
+```text
+ai-web-agent-console/
+├── app.py                       Streamlit 실행 진입점
+├── ai_web_agent_console/        인증, 화면, MCP, 모델, 질의 계약 모듈
+├── config/                      제품·모델·MCP·시나리오 JSON
+├── tests/                       Python 회귀 테스트
+├── .env.example                 환경변수 예시
+└── requirements.txt
+```
+
+## 실행
+
+```bash
+cd ai-web-agent-console
+python3 -m venv .venv
+.venv/bin/pip install -r requirements.txt
+.venv/bin/streamlit run app.py --server.address 0.0.0.0 --server.port 8622
+```
+
+인증 게이트웨이는 같은 디렉토리에서 다음과 같이 실행한다.
+
+```bash
+.venv/bin/python -m ai_web_agent_console.auth_gateway
+```
+
+## 설정
+
+- `config/app_profile.json`: 제품명, 설명, 아이콘, 색상
+- `config/mcp_servers.json`: MCP endpoint와 허용 도구
+- `config/model_profiles.json`: OCI Generative AI 모델 profile
+- `config/vpd_token_presets.json`: 데모 사용자와 토큰 환경변수 매핑
+- `config/hmm_demo_scenarios.json`: 화면의 예제 질문
+- `config/hmm_hr_query_contracts.json`: 질의별 필수 근거와 계산·시간 규칙
+
+실제 토큰, DB 비밀번호, Wallet, OCI private key는 Git에 넣지 않는다. 데모 사용자 JSON은
+`HMM_MCP_BEARER_TOKEN` 같은 환경변수 이름만 참조한다.
+
+## 검증
+
+```bash
+python3 -m compileall -q app.py ai_web_agent_console tests
+python3 -m unittest discover -s tests -p 'test_*.py'
+```
+
+## 운영 호환성
+
+운영 서버의 물리 경로 `/opt/hmm-poc4`와 기존 SQLite 테이블명은 대화 이력과 서비스 전환
+호환을 위해 유지할 수 있다. Git 폴더, Python 패키지와 실행 명령은 이 문서의 새 이름만
+사용한다.

ai-web-agent-console/ai_web_agent_console/__init__.py

@@ -0,0 +1 @@
+"""Reusable presentation shell for MCP-backed Streamlit agent consoles."""

ai-web-agent-console/ai_web_agent_console/audit.py

@@ -0,0 +1,190 @@
+"""Reusable audit-tab renderer with data loaders supplied by the application."""
+
+from __future__ import annotations
+
+from typing import Any, Callable
+
+
+AuditInventoryLoader = Callable[[], list[dict[str, Any]]]
+AuditEventsLoader = Callable[[int, int, str, str], list[dict[str, Any]]]
+
+
+def render_hmm_audit_tab(
+    st: Any,
+    inventory_loader: AuditInventoryLoader,
+    events_loader: AuditEventsLoader,
+    error_type: type[Exception],
+) -> None:
+    """Render HMM access audit data without owning DB connection details."""
+
+    st.markdown(
+        '<div class="kb-section-title input" role="heading" aria-level="3">'
+        '감사로그 ( <strong>HMM 접근 관리</strong> )'
+        '</div>',
+        unsafe_allow_html=True,
+    )
+    st.markdown(
+        '<div class="kb-audit-lead">'
+        'HMM 백오피스의 사용자·그룹·역할·토큰·접근 정책 변경 이력을 시간순으로 확인합니다. '
+        '이벤트 유형과 처리 상태로 필터링해 운영 변경의 성공·실패를 추적할 수 있습니다.'
+        '</div>',
+        unsafe_allow_html=True,
+    )
+    try:
+        inventory = inventory_loader()
+    except error_type as exc:
+        st.error(str(exc))
+        return
+
+    event_types = tuple(
+        str(item.get("event_type") or "").strip()
+        for item in inventory
+        if str(item.get("event_type") or "").strip()
+    )
+    st.markdown('<div class="kb-audit-heading">조회 조건</div>', unsafe_allow_html=True)
+    with st.container(key="poc4_hmm_audit_filters"):
+        event_column, status_column = st.columns(2)
+        with event_column:
+            selected_event_type = st.selectbox(
+                "이벤트 유형",
+                options=("", *event_types),
+                format_func=lambda value: "전체 이벤트" if not value else value,
+                key="poc4_hmm_audit_event_filter",
+            )
+        with status_column:
+            selected_status = st.selectbox(
+                "처리 상태",
+                options=("", "SUCCESS", "FAILURE", "DENIED"),
+                format_func=lambda value: "전체 상태" if not value else value,
+                key="poc4_hmm_audit_status_filter",
+            )
+        days_column, limit_column, refresh_column = st.columns([1.5, 1, 0.8])
+        with days_column:
+            days = st.slider(
+                "조회 기간",
+                min_value=1,
+                max_value=90,
+                value=7,
+                format="%d일",
+                key="poc4_hmm_audit_days",
+            )
+        with limit_column:
+            row_limit = st.number_input(
+                "최대 건수",
+                min_value=10,
+                max_value=500,
+                value=100,
+                step=10,
+                key="poc4_hmm_audit_row_limit",
+            )
+        with refresh_column:
+            st.markdown('<div style="height: 28px"></div>', unsafe_allow_html=True)
+            if st.button(
+                "새로고침",
+                icon=":material/refresh:",
+                width="stretch",
+                key="poc4_hmm_audit_refresh",
+            ):
+                inventory_loader.clear()
+                events_loader.clear()
+                st.rerun()
+
+    try:
+        events = events_loader(
+            int(days), int(row_limit), selected_event_type, selected_status
+        )
+    except error_type as exc:
+        st.error(str(exc))
+        return
+
+    success_count = sum(
+        1 for item in events if str(item.get("status") or "").upper() == "SUCCESS"
+    )
+    with st.container(key="poc4_hmm_audit_metrics"):
+        type_metric, event_metric, success_metric, failure_metric = st.columns(4)
+        type_metric.metric("이벤트 유형", len(inventory))
+        event_metric.metric("조회 이벤트", len(events))
+        success_metric.metric("성공", success_count)
+        failure_metric.metric("실패·거부", len(events) - success_count)
+
+    st.markdown(
+        '<div class="kb-audit-heading">이벤트 유형 현황</div>'
+        f'<div class="kb-audit-caption">현재 기록된 이벤트 유형 {len(inventory)}개 · '
+        '유형별 누적 건수와 최근 발생 시각을 확인합니다.</div>',
+        unsafe_allow_html=True,
+    )
+    with st.container(key="poc4_hmm_audit_inventory_panel"):
+        with st.expander("감사 이벤트 유형", expanded=True):
+            if inventory:
+                st.dataframe(
+                    [
+                        {
+                            "이벤트 유형": str(item.get("event_type") or ""),
+                            "누적 건수": int(item.get("event_count") or 0),
+                            "최근 발생(KST)": str(item.get("latest_event_time") or ""),
+                        }
+                        for item in inventory
+                    ],
+                    column_config={
+                        "이벤트 유형": st.column_config.TextColumn(width="large"),
+                        "누적 건수": st.column_config.NumberColumn(width="small"),
+                        "최근 발생(KST)": st.column_config.TextColumn(width="medium"),
+                    },
+                    hide_index=True,
+                    width="stretch",
+                    height=min(360, 72 + 36 * len(inventory)),
+                )
+            else:
+                st.caption("아직 기록된 HMM 접근 관리 이벤트가 없습니다.")
+
+    st.markdown(
+        '<div class="kb-audit-heading">감사 이벤트</div>'
+        '<div class="kb-audit-caption">최신 이벤트부터 표시합니다. '
+        '처리 상태와 대상 식별자, 오류 메시지를 먼저 확인하세요.</div>',
+        unsafe_allow_html=True,
+    )
+    show_details = st.toggle(
+        "상세 메시지 표시",
+        value=True,
+        key="poc4_hmm_audit_show_details",
+    )
+    if not events:
+        st.info("선택한 조건에 해당하는 HMM 접근 관리 이벤트가 없습니다.")
+        return
+
+    display_rows: list[dict[str, Any]] = []
+    for event in events:
+        row: dict[str, Any] = {
+            "감사 ID": int(event.get("audit_id") or 0),
+            "발생시각(KST)": str(event.get("event_time") or ""),
+            "이벤트 유형": str(event.get("event_type") or ""),
+            "상태": str(event.get("status") or ""),
+            "토큰 Key ID": event.get("key_id"),
+            "대상 Object ID": event.get("object_id"),
+            "처리 행": event.get("row_count"),
+            "오류 코드": str(event.get("error_code") or ""),
+        }
+        if show_details:
+            row["메시지"] = str(event.get("message") or "")
+        display_rows.append(row)
+
+    column_config: dict[str, Any] = {
+        "감사 ID": st.column_config.NumberColumn(width="small"),
+        "발생시각(KST)": st.column_config.TextColumn(width="medium"),
+        "이벤트 유형": st.column_config.TextColumn(width="large"),
+        "상태": st.column_config.TextColumn(width="small"),
+        "토큰 Key ID": st.column_config.NumberColumn(width="small"),
+        "대상 Object ID": st.column_config.NumberColumn(width="small"),
+        "처리 행": st.column_config.NumberColumn(width="small"),
+        "오류 코드": st.column_config.TextColumn(width="medium"),
+    }
+    if show_details:
+        column_config["메시지"] = st.column_config.TextColumn(width="large")
+    with st.container(key="poc4_hmm_audit_event_panel"):
+        st.dataframe(
+            display_rows,
+            column_config=column_config,
+            hide_index=True,
+            width="stretch",
+            height=min(640, 104 + 38 * len(display_rows)),
+        )

ai-web-agent-console/ai_web_agent_console/auth_gateway.py

@@ -0,0 +1,563 @@
+"""Small localhost authentication service for the HMM Streamlit portal.
+
+Nginx owns the public security boundary.  This module validates the existing
+PBKDF2 login, issues a signed HttpOnly cookie, and answers Nginx auth_request
+subrequests.  Authentication values are never accepted from a URL.
+"""
+
+from __future__ import annotations
+
+import base64
+import binascii
+from collections import defaultdict, deque
+from dataclasses import dataclass
+from datetime import datetime, timezone
+from http import HTTPStatus
+from http.cookies import SimpleCookie
+from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
+import hashlib
+import hmac
+import html
+import json
+import logging
+import os
+import secrets
+import threading
+import time
+from typing import Deque
+from urllib.parse import parse_qs, urlsplit
+
+
+def _environment_value(*names: str, default: str = "") -> str:
+    for name in names:
+        value = os.environ.get(name)
+        if value is not None and value.strip():
+            return value.strip()
+    return default
+
+
+LOG = logging.getLogger("hmm_portal_auth")
+SESSION_COOKIE_NAME = "__Host-HMM_PORTAL_SESSION"
+CSRF_COOKIE_NAME = "__Host-HMM_LOGIN_CSRF"
+SESSION_TOKEN_VERSION = 2
+MAX_REQUEST_BYTES = 8_192
+MAX_FIELD_CHARS = 200
+
+
+@dataclass(frozen=True)
+class AuthConfig:
+    username: str
+    password_pbkdf2: str
+    cookie_secret: str
+    bind_address: str = "127.0.0.1"
+    port: int = 8621
+    session_seconds: int = 12 * 60 * 60
+    remember_seconds: int = 7 * 24 * 60 * 60
+    product_name: str = "HMM AI 업무 에이전트"
+    login_title: str = "HMM AI 업무 에이전트"
+    login_description: str = "사용자 인증 후 AI 업무 질의 기능을 이용할 수 있습니다."
+    login_footer: str = "승인된 사용자만 접속할 수 있습니다."
+    primary_color: str = "#004b87"
+
+    @classmethod
+    def from_environment(cls) -> "AuthConfig":
+        config = cls(
+            username=_environment_value(
+                "AI_WEB_AGENT_CONSOLE_LOGIN_USER", "POC4_LOGIN_USER"
+            ),
+            password_pbkdf2=_environment_value(
+                "AI_WEB_AGENT_CONSOLE_LOGIN_PASSWORD_PBKDF2",
+                "POC4_LOGIN_PASSWORD_PBKDF2",
+            ),
+            cookie_secret=_environment_value(
+                "AI_WEB_AGENT_CONSOLE_LOGIN_COOKIE_SECRET",
+                "POC4_LOGIN_COOKIE_SECRET",
+            ),
+            bind_address=os.environ.get(
+                "PORTAL_AUTH_BIND_ADDRESS", "127.0.0.1"
+            ).strip(),
+            port=int(os.environ.get("PORTAL_AUTH_PORT", "8621")),
+            session_seconds=int(
+                os.environ.get("PORTAL_AUTH_SESSION_SECONDS", str(12 * 60 * 60))
+            ),
+            remember_seconds=int(
+                os.environ.get(
+                    "PORTAL_AUTH_REMEMBER_SECONDS", str(7 * 24 * 60 * 60)
+                )
+            ),
+            product_name=os.environ.get(
+                "AGENT_CONSOLE_NAME", "HMM AI 업무 에이전트"
+            ).strip(),
+            login_title=os.environ.get(
+                "AGENT_CONSOLE_LOGIN_TITLE", "HMM AI 업무 에이전트"
+            ).strip(),
+            login_description=os.environ.get(
+                "AGENT_CONSOLE_LOGIN_DESCRIPTION",
+                "사용자 인증 후 AI 업무 질의 기능을 이용할 수 있습니다.",
+            ).strip(),
+            login_footer=os.environ.get(
+                "AGENT_CONSOLE_LOGIN_FOOTER",
+                "승인된 사용자만 접속할 수 있습니다.",
+            ).strip(),
+            primary_color=os.environ.get(
+                "AGENT_CONSOLE_PRIMARY_COLOR", "#004b87"
+            ).strip(),
+        )
+        config.validate()
+        return config
+
+    def validate(self) -> None:
+        if not self.username or not self.password_pbkdf2:
+            raise ValueError("AI web agent console login credentials are not configured")
+        if len(self.cookie_secret.encode("utf-8")) < 32:
+            raise ValueError(
+                "AI_WEB_AGENT_CONSOLE_LOGIN_COOKIE_SECRET must be at least 32 bytes"
+            )
+        if self.bind_address not in {"127.0.0.1", "::1"}:
+            raise ValueError("Portal authentication service must bind to loopback")
+        if not 1 <= self.port <= 65535:
+            raise ValueError("PORTAL_AUTH_PORT is invalid")
+        if not 300 <= self.session_seconds <= 24 * 60 * 60:
+            raise ValueError("PORTAL_AUTH_SESSION_SECONDS is outside the safe range")
+        if not self.session_seconds <= self.remember_seconds <= 30 * 24 * 60 * 60:
+            raise ValueError("PORTAL_AUTH_REMEMBER_SECONDS is outside the safe range")
+
+
+@dataclass(frozen=True)
+class AuthenticatedSession:
+    username: str
+    expires_at: int
+
+
+class SessionTokenCodec:
+    def __init__(self, secret: str):
+        self._secret = secret.encode("utf-8")
+
+    def issue(self, username: str, lifetime_seconds: int, now: int | None = None) -> str:
+        issued_at = int(time.time()) if now is None else now
+        payload = {
+            "v": SESSION_TOKEN_VERSION,
+            "u": username,
+            "i": issued_at,
+            "e": issued_at + lifetime_seconds,
+            "n": secrets.token_urlsafe(18),
+        }
+        encoded = _base64url_encode(
+            json.dumps(payload, separators=(",", ":")).encode("utf-8")
+        )
+        signature = hmac.new(
+            self._secret, encoded.encode("ascii"), hashlib.sha256
+        ).hexdigest()
+        return f"{encoded}.{signature}"
+
+    def verify(self, token: str, expected_username: str, now: int | None = None) -> AuthenticatedSession | None:
+        if not token or len(token) > 2048:
+            return None
+        current_time = int(time.time()) if now is None else now
+        try:
+            encoded, supplied_signature = token.split(".", 1)
+            expected_signature = hmac.new(
+                self._secret, encoded.encode("ascii"), hashlib.sha256
+            ).hexdigest()
+            if not hmac.compare_digest(supplied_signature, expected_signature):
+                return None
+            payload = json.loads(_base64url_decode(encoded).decode("utf-8"))
+            version = int(payload["v"])
+            username = str(payload["u"])
+            issued_at = int(payload["i"])
+            expires_at = int(payload["e"])
+        except (
+            binascii.Error,
+            KeyError,
+            TypeError,
+            ValueError,
+            UnicodeDecodeError,
+            json.JSONDecodeError,
+        ):
+            return None
+        if version != SESSION_TOKEN_VERSION:
+            return None
+        if issued_at > current_time + 30 or expires_at <= current_time:
+            return None
+        if expires_at - issued_at > 30 * 24 * 60 * 60:
+            return None
+        if not hmac.compare_digest(username, expected_username):
+            return None
+        return AuthenticatedSession(username=username, expires_at=expires_at)
+
+
+class LoginAttemptLimiter:
+    def __init__(self, maximum_failures: int = 5, window_seconds: int = 300):
+        self._maximum_failures = maximum_failures
+        self._window_seconds = window_seconds
+        self._failures: dict[str, Deque[float]] = defaultdict(deque)
+        self._lock = threading.Lock()
+
+    def blocked(self, key: str, now: float | None = None) -> bool:
+        current_time = time.monotonic() if now is None else now
+        with self._lock:
+            failures = self._failures[key]
+            self._prune(failures, current_time)
+            return len(failures) >= self._maximum_failures
+
+    def record_failure(self, key: str, now: float | None = None) -> None:
+        current_time = time.monotonic() if now is None else now
+        with self._lock:
+            failures = self._failures[key]
+            self._prune(failures, current_time)
+            failures.append(current_time)
+
+    def reset(self, key: str) -> None:
+        with self._lock:
+            self._failures.pop(key, None)
+
+    def _prune(self, failures: Deque[float], now: float) -> None:
+        cutoff = now - self._window_seconds
+        while failures and failures[0] < cutoff:
+            failures.popleft()
+
+
+def password_matches(password: str, encoded_password: str) -> bool:
+    try:
+        scheme, iterations_text, salt_hex, expected_hex = encoded_password.split(
+            "$", 3
+        )
+        iterations = int(iterations_text)
+        salt = bytes.fromhex(salt_hex)
+        expected = bytes.fromhex(expected_hex)
+    except (TypeError, ValueError):
+        return False
+    if scheme != "pbkdf2_sha256" or not 100_000 <= iterations <= 2_000_000:
+        return False
+    candidate = hashlib.pbkdf2_hmac(
+        "sha256", password.encode("utf-8"), salt, iterations
+    )
+    return hmac.compare_digest(candidate, expected)
+
+
+def session_cookie_header(token: str, max_age: int | None) -> str:
+    attributes = [
+        f"{SESSION_COOKIE_NAME}={token}",
+        "Path=/",
+        "Secure",
+        "HttpOnly",
+        "SameSite=Lax",
+    ]
+    if max_age is not None:
+        attributes.append(f"Max-Age={max_age}")
+    return "; ".join(attributes)
+
+
+def clear_session_cookie_header() -> str:
+    return (
+        f"{SESSION_COOKIE_NAME}=; Path=/; Max-Age=0; "
+        "Secure; HttpOnly; SameSite=Lax"
+    )
+
+
+def csrf_cookie_header(value: str, max_age: int = 600) -> str:
+    return (
+        f"{CSRF_COOKIE_NAME}={value}; Path=/; Max-Age={max_age}; "
+        "Secure; HttpOnly; SameSite=Strict"
+    )
+
+
+def clear_csrf_cookie_header() -> str:
+    return (
+        f"{CSRF_COOKIE_NAME}=; Path=/; Max-Age=0; "
+        "Secure; HttpOnly; SameSite=Strict"
+    )
+
+
+def _base64url_encode(value: bytes) -> str:
+    return base64.urlsafe_b64encode(value).decode("ascii").rstrip("=")
+
+
+def _base64url_decode(value: str) -> bytes:
+    padded = value + "=" * (-len(value) % 4)
+    return base64.urlsafe_b64decode(padded)
+
+
+def _cookie_value(cookie_header: str, name: str) -> str:
+    try:
+        cookies = SimpleCookie()
+        cookies.load(cookie_header)
+        morsel = cookies.get(name)
+        return morsel.value if morsel is not None else ""
+    except (KeyError, TypeError):
+        return ""
+
+
+def _login_page(config: AuthConfig, csrf_value: str, error: str = "") -> bytes:
+    error_html = (
+        f'<div class="error" role="alert">{html.escape(error)}</div>'
+        if error
+        else ""
+    )
+    return f"""<!doctype html>
+<html lang="ko">
+<head>
+  <meta charset="utf-8">
+  <meta name="viewport" content="width=device-width,initial-scale=1">
+  <title>{html.escape(config.product_name)}</title>
+  <style>
+    :root {{ --primary:{html.escape(config.primary_color)}; --text:#17232d;
+      --muted:#60717f; --border:#d9e0e5; }}
+    * {{ box-sizing:border-box; }}
+    body {{ margin:0; background:#fff; color:var(--text);
+      font-family:"Noto Sans KR","Malgun Gothic",sans-serif; }}
+    main {{ width:min(420px,calc(100% - 40px)); margin:12vh auto 0; }}
+    .wordmark {{ color:var(--primary); font-size:1.25rem; font-weight:800;
+      letter-spacing:.08em; }}
+    h1 {{ margin:16px 0 8px; font-size:1.75rem; }}
+    .description,.footer {{ color:var(--muted); line-height:1.55; }}
+    form {{ margin-top:28px; }}
+    label {{ display:block; margin:0 0 18px; font-weight:700; }}
+    input[type="text"],input[type="password"] {{ width:100%; margin-top:8px;
+      padding:12px 13px; border:1px solid var(--border); border-radius:5px;
+      font:inherit; color:var(--text); background:#fff; }}
+    .remember {{ display:flex; align-items:center; gap:8px; font-weight:500; }}
+    .remember input {{ width:17px; height:17px; }}
+    button {{ width:100%; padding:12px; border:1px solid var(--primary);
+      border-radius:5px; background:var(--primary); color:#fff; font:inherit;
+      font-weight:800; cursor:pointer; }}
+    .error {{ margin:18px 0 0; padding:11px 12px; border:1px solid #d99898;
+      border-radius:5px; color:#8a2222; background:#fff7f7; }}
+    .footer {{ margin-top:22px; font-size:.9rem; }}
+  </style>
+</head>
+<body>
+<main>
+  <div class="wordmark">HMM</div>
+  <h1>{html.escape(config.login_title)}</h1>
+  <p class="description">{html.escape(config.login_description)}</p>
+  {error_html}
+  <form action="/auth/login" method="post" autocomplete="on">
+    <input type="hidden" name="csrf" value="{html.escape(csrf_value)}">
+    <label>사용자 ID
+      <input name="username" type="text" maxlength="80" autocomplete="username"
+        required autofocus>
+    </label>
+    <label>비밀번호
+      <input name="password" type="password" maxlength="200"
+        autocomplete="current-password" required>
+    </label>
+    <label class="remember">
+      <input name="remember" type="checkbox" value="yes"> 로그인 유지 (7일)
+    </label>
+    <button type="submit">로그인</button>
+  </form>
+  <p class="footer">{html.escape(config.login_footer)}</p>
+</main>
+</body>
+</html>""".encode("utf-8")
+
+
+def build_handler(config: AuthConfig) -> type[BaseHTTPRequestHandler]:
+    codec = SessionTokenCodec(config.cookie_secret)
+    limiter = LoginAttemptLimiter()
+
+    class PortalAuthHandler(BaseHTTPRequestHandler):
+        server_version = "HMMPortalAuth/1.0"
+        sys_version = ""
+
+        def do_HEAD(self) -> None:
+            self._route(send_body=False)
+
+        def do_GET(self) -> None:
+            self._route(send_body=True)
+
+        def do_POST(self) -> None:
+            path = urlsplit(self.path).path
+            if path == "/auth/login":
+                self._login()
+            elif path == "/auth/logout":
+                self._logout()
+            else:
+                self._send_text(HTTPStatus.NOT_FOUND, "Not found")
+
+        def _route(self, send_body: bool) -> None:
+            path = urlsplit(self.path).path
+            if path == "/auth/check":
+                self._check()
+            elif path == "/auth/login":
+                self._show_login(send_body=send_body)
+            elif path == "/auth/logout":
+                self._logout()
+            elif path == "/auth/healthz":
+                self._send_text(HTTPStatus.OK, "ok", send_body=send_body)
+            else:
+                self._send_text(HTTPStatus.NOT_FOUND, "Not found", send_body=send_body)
+
+        def _check(self) -> None:
+            session = self._session()
+            if session is None:
+                self._send_empty(HTTPStatus.UNAUTHORIZED)
+                return
+            self.send_response(HTTPStatus.NO_CONTENT)
+            self._security_headers()
+            self.send_header("X-Auth-User", session.username)
+            self.send_header("X-Auth-Expires", str(session.expires_at))
+            self.end_headers()
+
+        def _show_login(self, send_body: bool = True, error: str = "") -> None:
+            if self._session() is not None and not error:
+                self._redirect("/")
+                return
+            csrf_value = secrets.token_urlsafe(32)
+            body = _login_page(config, csrf_value, error)
+            self.send_response(HTTPStatus.OK)
+            self._security_headers()
+            self.send_header("Content-Type", "text/html; charset=utf-8")
+            self.send_header("Content-Length", str(len(body)))
+            self.send_header("Set-Cookie", csrf_cookie_header(csrf_value))
+            self.end_headers()
+            if send_body:
+                self.wfile.write(body)
+
+        def _login(self) -> None:
+            client_key = self._client_key()
+            if limiter.blocked(client_key):
+                self._show_login(error="로그인 시도가 잠시 제한되었습니다. 잠시 후 다시 시도해 주세요.")
+                return
+            try:
+                content_length = int(self.headers.get("Content-Length", "0"))
+            except ValueError:
+                content_length = 0
+            if not 1 <= content_length <= MAX_REQUEST_BYTES:
+                self._send_text(HTTPStatus.BAD_REQUEST, "Invalid request")
+                return
+            raw_body = self.rfile.read(content_length)
+            try:
+                form = parse_qs(
+                    raw_body.decode("utf-8"),
+                    keep_blank_values=True,
+                    strict_parsing=False,
+                    max_num_fields=8,
+                )
+            except (UnicodeDecodeError, ValueError):
+                self._send_text(HTTPStatus.BAD_REQUEST, "Invalid request")
+                return
+            username = _form_value(form, "username")
+            password = _form_value(form, "password")
+            csrf_form = _form_value(form, "csrf")
+            csrf_cookie = _cookie_value(
+                self.headers.get("Cookie", ""), CSRF_COOKIE_NAME
+            )
+            if (
+                not csrf_form
+                or not csrf_cookie
+                or not hmac.compare_digest(csrf_form, csrf_cookie)
+            ):
+                self._send_text(HTTPStatus.BAD_REQUEST, "Invalid request")
+                return
+            valid_credentials = (
+                len(username) <= 80
+                and len(password) <= MAX_FIELD_CHARS
+                and hmac.compare_digest(username.strip(), config.username)
+                and password_matches(password, config.password_pbkdf2)
+            )
+            if not valid_credentials:
+                limiter.record_failure(client_key)
+                self._show_login(error="사용자 ID 또는 비밀번호를 확인해 주세요.")
+                return
+            limiter.reset(client_key)
+            remember = _form_value(form, "remember") == "yes"
+            lifetime = (
+                config.remember_seconds if remember else config.session_seconds
+            )
+            token = codec.issue(config.username, lifetime)
+            self.send_response(HTTPStatus.SEE_OTHER)
+            self._security_headers()
+            self.send_header("Location", "/")
+            self.send_header(
+                "Set-Cookie",
+                session_cookie_header(token, lifetime if remember else None),
+            )
+            self.send_header("Set-Cookie", clear_csrf_cookie_header())
+            self.end_headers()
+
+        def _logout(self) -> None:
+            self.send_response(HTTPStatus.SEE_OTHER)
+            self._security_headers()
+            self.send_header("Location", "/auth/login")
+            self.send_header("Set-Cookie", clear_session_cookie_header())
+            self.send_header("Set-Cookie", clear_csrf_cookie_header())
+            self.end_headers()
+
+        def _session(self) -> AuthenticatedSession | None:
+            token = _cookie_value(
+                self.headers.get("Cookie", ""), SESSION_COOKIE_NAME
+            )
+            return codec.verify(token, config.username)
+
+        def _client_key(self) -> str:
+            forwarded = self.headers.get("X-Real-IP", "").strip()
+            return forwarded or self.client_address[0]
+
+        def _redirect(self, location: str) -> None:
+            self.send_response(HTTPStatus.SEE_OTHER)
+            self._security_headers()
+            self.send_header("Location", location)
+            self.end_headers()
+
+        def _send_empty(self, status: HTTPStatus) -> None:
+            self.send_response(status)
+            self._security_headers()
+            self.send_header("Content-Length", "0")
+            self.end_headers()
+
+        def _send_text(
+            self,
+            status: HTTPStatus,
+            message: str,
+            send_body: bool = True,
+        ) -> None:
+            body = message.encode("utf-8")
+            self.send_response(status)
+            self._security_headers()
+            self.send_header("Content-Type", "text/plain; charset=utf-8")
+            self.send_header("Content-Length", str(len(body)))
+            self.end_headers()
+            if send_body:
+                self.wfile.write(body)
+
+        def _security_headers(self) -> None:
+            self.send_header("Cache-Control", "no-store")
+            self.send_header("Pragma", "no-cache")
+            self.send_header("X-Content-Type-Options", "nosniff")
+            self.send_header("X-Frame-Options", "DENY")
+            self.send_header("Referrer-Policy", "no-referrer")
+            self.send_header(
+                "Content-Security-Policy",
+                "default-src 'none'; style-src 'unsafe-inline'; "
+                "form-action 'self'; base-uri 'none'; frame-ancestors 'none'",
+            )
+
+        def log_message(self, _format: str, *args: object) -> None:
+            # Do not log query strings, cookies, form bodies, or tokens.
+            LOG.info("%s %s", self.command, urlsplit(self.path).path)
+
+    return PortalAuthHandler
+
+
+def _form_value(form: dict[str, list[str]], name: str) -> str:
+    values = form.get(name)
+    return values[0] if values else ""
+
+
+def main() -> None:
+    logging.basicConfig(
+        level=os.environ.get("PORTAL_AUTH_LOG_LEVEL", "INFO"),
+        format="%(asctime)s %(levelname)s %(name)s %(message)s",
+    )
+    config = AuthConfig.from_environment()
+    server = ThreadingHTTPServer(
+        (config.bind_address, config.port), build_handler(config)
+    )
+    LOG.info("HMM portal authentication service listening on loopback port %s", config.port)
+    server.serve_forever()
+
+
+if __name__ == "__main__":
+    main()

ai-web-agent-console/ai_web_agent_console/mcp_result.py

@@ -0,0 +1,80 @@
+"""Pure helpers for MCP result envelopes used by the Streamlit console."""
+
+from __future__ import annotations
+
+from collections.abc import Mapping
+from typing import Any
+
+
+def response_payload(value: Any) -> Mapping[str, Any]:
+    """Return the business payload from a direct or nested MCP response."""
+
+    if not isinstance(value, Mapping):
+        return {}
+    nested = value.get("response")
+    return nested if isinstance(nested, Mapping) else value
+
+
+def text_result(value: Any) -> str:
+    """Return a textual `result` field without stringifying other structures."""
+
+    result = response_payload(value).get("result")
+    return result.strip() if isinstance(result, str) else ""
+
+
+def status_result_summary(value: Any, *, excerpt_chars: int = 900) -> dict[str, Any]:
+    """Build a safe UI summary for status/result-style compatibility tools."""
+
+    payload = response_payload(value)
+    summary: dict[str, Any] = {}
+    for key in ("status", "success", "error", "errorCode", "errorMessage"):
+        item = payload.get(key)
+        if item not in (None, "", []):
+            summary[key] = item
+    result = text_result(value)
+    if result:
+        summary["result_chars"] = len(result)
+        summary["result_excerpt"] = result[:excerpt_chars] + (
+            "..." if len(result) > excerpt_chars else ""
+        )
+    return summary
+
+
+def status_result_evidence(value: Any, *, max_chars: int = 7000) -> dict[str, Any]:
+    """Preserve bounded textual policy/data evidence for final answer synthesis."""
+
+    payload = response_payload(value)
+    evidence: dict[str, Any] = {}
+    for key in ("status", "success", "error", "errorCode", "errorMessage"):
+        item = payload.get(key)
+        if item not in (None, "", []):
+            evidence[key] = item
+    result = text_result(value)
+    if result:
+        evidence["result"] = result[:max_chars] + (
+            "..." if len(result) > max_chars else ""
+        )
+        evidence["result_chars"] = len(result)
+    return evidence
+
+
+def has_actionable_text_result(value: Any) -> bool:
+    """Return whether a textual result contains evidence worth stopping on."""
+
+    result = text_result(value)
+    if not result:
+        return False
+    normalized = " ".join(result.casefold().split())
+    return not any(
+        marker in normalized
+        for marker in ("no data found", "no evidence found", "error:")
+    )
+
+
+__all__ = [
+    "has_actionable_text_result",
+    "response_payload",
+    "status_result_evidence",
+    "status_result_summary",
+    "text_result",
+]

ai-web-agent-console/ai_web_agent_console/mcp_tool_router.py

@@ -0,0 +1,196 @@
+"""LLM-based MCP tool routing.
+
+The router receives only the user question and the discovered MCP tool
+descriptors.  It never receives MCP bearer tokens or provider credentials.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+import json
+from typing import Any, Mapping
+
+from ai_web_agent_console.oci_genai_sdk import (
+    build_oci_genai_completion_client,
+    temperature_for_model_profile,
+)
+from ai_web_agent_console.model_registry import resolve_model_profile
+
+
+@dataclass(frozen=True)
+class McpTool:
+    name: str
+    description: str
+    schema: Mapping[str, Any]
+    read_only: bool
+
+
+@dataclass(frozen=True)
+class RoutedMcpTool:
+    server_id: str
+    tool: McpTool
+
+
+class McpToolRouterError(RuntimeError):
+    """Safe routing error. Must not contain secrets or provider traces."""
+
+
+def route_mcp_tool_across_servers_with_llm(
+    tools: list[RoutedMcpTool],
+    question: str,
+    *,
+    router_model_profile: str,
+) -> RoutedMcpTool:
+    """Select one discovered MCP server/tool pair with OCI GenAI."""
+
+    candidates = list(tools)
+    if not candidates:
+        raise McpToolRouterError("라우팅 가능한 MCP tool이 없습니다.")
+
+    by_key = {
+        "{}::{}".format(candidate.server_id, candidate.tool.name): candidate
+        for candidate in candidates
+    }
+    route_keys = list(by_key)
+    try:
+        profile = resolve_model_profile(router_model_profile)
+        client = build_oci_genai_completion_client(
+            profile.model_id,
+            profile.answer_model_region,
+            profile.answer_model_endpoint,
+        )
+        tool_catalog = [
+            {
+                "route_key": "{}::{}".format(candidate.server_id, candidate.tool.name),
+                "server_id": candidate.server_id,
+                "tool_name": candidate.tool.name,
+                "description": candidate.tool.description[:1000],
+                "input_properties": sorted(
+                    (
+                        candidate.tool.schema.get("properties", {})
+                        if isinstance(
+                            candidate.tool.schema.get("properties"), Mapping
+                        )
+                        else {}
+                    ).keys()
+                ),
+                "read_only": candidate.tool.read_only,
+            }
+            for candidate in candidates
+        ]
+        text = client.complete(
+            system_prompt=(
+                "You are an MCP server and tool router. Choose exactly one "
+                "server/tool route for the user question from the discovered "
+                "routes. Return only JSON that matches the schema. Never "
+                "request or expose bearer tokens. Do not invent server ids or "
+                "tool names."
+            ),
+            user_prompt=json.dumps(
+                {
+                    "question": question,
+                    "routes": tool_catalog,
+                },
+                ensure_ascii=False,
+            ),
+            response_schema={
+                "type": "object",
+                "additionalProperties": False,
+                "required": ["route_key"],
+                "properties": {
+                    "route_key": {
+                        "type": "string",
+                        "enum": route_keys,
+                    }
+                },
+            },
+            max_tokens=256,
+            temperature=temperature_for_model_profile(profile),
+        )
+        routed = json.loads(text)
+    except Exception:
+        raise McpToolRouterError("LLM tool router 호출에 실패했습니다.") from None
+
+    if not isinstance(routed, Mapping):
+        raise McpToolRouterError("LLM tool router 응답 형식이 올바르지 않습니다.")
+    selected_key = str(routed.get("route_key") or "").strip()
+    selected = by_key.get(selected_key)
+    if selected is None:
+        raise McpToolRouterError("LLM tool router가 허용되지 않은 route를 선택했습니다.")
+    return selected
+
+
+def route_mcp_tool_with_llm(
+    tools: list[McpTool],
+    question: str,
+    *,
+    preferred_tool: str,
+    tool_allowlist: tuple[str, ...],
+    router_model_profile: str,
+) -> McpTool:
+    """Backward-compatible single-server routing helper."""
+
+    candidates = [
+        tool for tool in tools if not tool_allowlist or tool.name in tool_allowlist
+    ]
+    routed = route_mcp_tool_across_servers_with_llm(
+        [RoutedMcpTool(server_id="default", tool=tool) for tool in candidates],
+        question,
+        router_model_profile=router_model_profile,
+    )
+    return routed.tool
+
+
+def build_mcp_tool_arguments(
+    tool: McpTool,
+    question: str,
+    limit: int,
+    *,
+    preferred_tool: str,
+) -> dict[str, Any]:
+    """Build bounded tool arguments from the selected tool schema."""
+
+    properties = tool.schema.get("properties")
+    if not isinstance(properties, Mapping):
+        properties = {}
+
+    # A server's default/preferred tool still has to obey its discovered schema.
+    # HMM tools use `query` and `term`; forcing the legacy `prompt`/`limit` shape
+    # makes an otherwise valid tool fail argument validation.
+    del preferred_tool
+    input_name = next(
+        (name for name in ("prompt", "question", "query", "term", "text") if name in properties),
+        "",
+    )
+    if not input_name:
+        required = tool.schema.get("required")
+        if isinstance(required, list):
+            input_name = next(
+                (
+                    str(name)
+                    for name in required
+                    if isinstance(properties.get(str(name)), Mapping)
+                    and properties[str(name)].get("type") == "string"
+                ),
+                "",
+            )
+    if input_name:
+        args: dict[str, Any] = {input_name: question}
+        if "max_evidence" in properties:
+            args["max_evidence"] = min(limit, 10)
+        elif "max_rows" in properties:
+            args["max_rows"] = limit
+        elif "limit" in properties:
+            args["limit"] = limit
+        return args
+    return {}
+
+
+__all__ = [
+    "McpTool",
+    "McpToolRouterError",
+    "RoutedMcpTool",
+    "build_mcp_tool_arguments",
+    "route_mcp_tool_across_servers_with_llm",
+    "route_mcp_tool_with_llm",
+]

ai-web-agent-console/ai_web_agent_console/model_registry.py

@@ -0,0 +1,434 @@
+"""8512/8513 전용 AI Web Agent Console model profile registry.
+
+이 registry는 모델 metadata만 관리한다. ``provider=oci``는 모델의 출처를 뜻하며
+``AI_WEB_AGENT_CONSOLE_MCP_PROVIDER``와 독립적이다. 따라서 기본 model profile이 GPT-5.5여도 현재
+MCP 실행 경로는 계속 ``mock``일 수 있다.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field, replace
+import json
+import os
+from pathlib import Path
+import re
+from typing import Any, Mapping, Optional
+
+
+ROOT = Path(__file__).resolve().parents[1]
+REGISTRY_PATH = ROOT / "config" / "model_profiles.json"
+DEFAULT_MODEL_PROFILE_KEY = "gpt55_oci"
+MODEL_PROFILE_ENV = "AI_WEB_AGENT_CONSOLE_MODEL_PROFILE"
+MODEL_PROFILE_DEFAULT_ENV = "AI_WEB_AGENT_CONSOLE_MODEL_PROFILE_DEFAULT"
+EXISTING_MODEL_PROFILE_KEYS = ("grok43", "llama4_maverick", "llama33_70b")
+MODEL_PROFILE_ALIASES = {
+    "gpt54_mini": "gpt54_mini_oci",
+    "llama33": "llama33_70b",
+}
+EXPECTED_SOURCE_TAG = "poc_2-gpt55-oci-partial"
+EXPECTED_SOURCE_COMMIT = "7a3b37f175b65ed5eab1d8bf37c9bf6114e7558f"
+_EXPECTED_ANSWER_MODEL_ROUTES = {
+    "gpt55_oci": (
+        "openai.gpt-5.5",
+        "us-chicago-1",
+        "OPENAI_GPT_5_5_CHAT",
+        "OCI_REGIONAL_DEFAULT",
+    ),
+    "gpt54_mini_oci": (
+        "openai.gpt-5.4-mini",
+        "us-chicago-1",
+        "OPENAI_GPT_5_4_MINI_CHAT",
+        "OCI_REGIONAL_DEFAULT",
+    ),
+    "grok43": (
+        "xai.grok-4.3",
+        "us-chicago-1",
+        "XAI_GROK_4_3_CHAT",
+        "OCI_REGIONAL_DEFAULT",
+    ),
+    "llama4_maverick": (
+        "meta.llama-4-maverick-17b-128e-instruct-fp8",
+        "us-chicago-1",
+        "META_LLAMA_4_MAVERICK_CHAT",
+        "OCI_REGIONAL_DEFAULT",
+    ),
+    "llama33_70b": (
+        "meta.llama-3.3-70b-instruct",
+        "us-chicago-1",
+        "META_LLAMA_3_3_70B_CHAT",
+        "OCI_REGIONAL_DEFAULT",
+    ),
+}
+
+_MODEL_KEY = re.compile(r"^[a-z][a-z0-9_]{1,63}$")
+_MODEL_ID = re.compile(r"^[A-Za-z0-9][A-Za-z0-9._:-]{1,127}$")
+_ANSWER_MODEL_ID_ALIAS = re.compile(r"^[A-Z][A-Z0-9_]{1,127}$")
+_OCI_REGION = re.compile(r"^[a-z]{2}-[a-z0-9-]+-[1-9][0-9]*$")
+_OCI_REGIONAL_ENDPOINT = re.compile(
+    r"^https://inference\.generativeai\."
+    r"(?P<region>[a-z]{2}-[a-z0-9-]+-[1-9][0-9]*)\.oci\.oraclecloud\.com$"
+)
+_ORACLE_IDENTIFIER = re.compile(r"^[A-Z][A-Z0-9_$#]{0,127}$")
+_SOURCE_TAG = re.compile(r"^[A-Za-z0-9][A-Za-z0-9._-]{1,127}$")
+_VERIFICATION_STATUSES = frozenset(
+    {"VERIFIED", "PARTIAL_VERIFIED", "VERIFIED_WITH_WARNINGS"}
+)
+_REQUIRED_PROFILE_FIELDS = (
+    "model_key",
+    "display_name",
+    "provider",
+    "model_id",
+    "answer_model_id_alias",
+    "answer_model_region",
+    "answer_model_endpoint_mode",
+    "poc2_select_ai_profile",
+    "poc2_native_agent_team",
+    "verification_status",
+    "default_for_console",
+    "source_tag",
+    "notes",
+)
+
+_PROFILE_ROUTE_ENV_KEYS = {
+    "gpt55_oci": (
+        "AI_WEB_AGENT_CONSOLE_LLM_GPT55_OCI_MODEL_ID",
+        "AI_WEB_AGENT_CONSOLE_LLM_GPT55_OCI_REGION",
+        "AI_WEB_AGENT_CONSOLE_LLM_GPT55_OCI_ENDPOINT",
+    ),
+    "gpt54_mini_oci": (
+        "AI_WEB_AGENT_CONSOLE_LLM_GPT54_MINI_OCI_MODEL_ID",
+        "AI_WEB_AGENT_CONSOLE_LLM_GPT54_MINI_OCI_REGION",
+        "AI_WEB_AGENT_CONSOLE_LLM_GPT54_MINI_OCI_ENDPOINT",
+    ),
+    "grok43": (
+        "AI_WEB_AGENT_CONSOLE_LLM_GROK43_MODEL_ID",
+        "AI_WEB_AGENT_CONSOLE_LLM_GROK43_REGION",
+        "AI_WEB_AGENT_CONSOLE_LLM_GROK43_ENDPOINT",
+    ),
+    "llama4_maverick": (
+        "AI_WEB_AGENT_CONSOLE_LLM_LLAMA4_MAVERICK_MODEL_ID",
+        "AI_WEB_AGENT_CONSOLE_LLM_LLAMA4_MAVERICK_REGION",
+        "AI_WEB_AGENT_CONSOLE_LLM_LLAMA4_MAVERICK_ENDPOINT",
+    ),
+    "llama33_70b": (
+        "AI_WEB_AGENT_CONSOLE_LLM_LLAMA33_70B_MODEL_ID",
+        "AI_WEB_AGENT_CONSOLE_LLM_LLAMA33_70B_REGION",
+        "AI_WEB_AGENT_CONSOLE_LLM_LLAMA33_70B_ENDPOINT",
+    ),
+}
+
+
+def _regional_endpoint(region: str) -> str:
+    return "https://inference.generativeai.%s.oci.oraclecloud.com" % region
+
+
+def _endpoint_host_alias(region: str) -> str:
+    return "OCI_GENAI_INFERENCE_%s" % region.upper().replace("-", "_")
+
+
+def _env_override(
+    environ: Mapping[str, str],
+    key: str,
+    default: str,
+) -> str:
+    if key not in environ:
+        return default
+    value = environ.get(key)
+    if not isinstance(value, str) or not value.strip():
+        raise ValueError("answer model route override is invalid")
+    return value.strip()
+
+
+@dataclass(frozen=True)
+class ModelProfile:
+    """UI와 workflow가 공유하는 비밀값 없는 model metadata."""
+
+    model_key: str
+    display_name: str
+    provider: str
+    model_id: str
+    answer_model_id_alias: str
+    answer_model_region: str
+    answer_model_endpoint_mode: str
+    answer_model_endpoint: str = field(repr=False)
+    poc2_select_ai_profile: str
+    poc2_native_agent_team: str
+    verification_status: str
+    default_for_console: bool
+    source_tag: str
+    notes: str
+    display_order: int = 999
+
+    @classmethod
+    def from_mapping(cls, value: Mapping[str, Any]) -> "ModelProfile":
+        missing = [name for name in _REQUIRED_PROFILE_FIELDS if name not in value]
+        if missing:
+            raise ValueError("AI Web Agent Console model profile fields are missing")
+        if not isinstance(value.get("default_for_console"), bool):
+            raise ValueError("default_for_console must be boolean")
+        order = value.get("display_order", 999)
+        if isinstance(order, bool) or not isinstance(order, int) or order < 0:
+            raise ValueError("model profile display_order is invalid")
+
+        answer_model_region = str(value["answer_model_region"]).strip().lower()
+        profile = cls(
+            model_key=str(value["model_key"]).strip().lower(),
+            display_name=str(value["display_name"]).strip(),
+            provider=str(value["provider"]).strip().lower(),
+            model_id=str(value["model_id"]).strip(),
+            answer_model_id_alias=str(value["answer_model_id_alias"])
+            .strip()
+            .upper(),
+            answer_model_region=answer_model_region,
+            answer_model_endpoint_mode=str(
+                value["answer_model_endpoint_mode"]
+            ).strip().upper(),
+            answer_model_endpoint=_regional_endpoint(answer_model_region),
+            poc2_select_ai_profile=str(value["poc2_select_ai_profile"])
+            .strip()
+            .upper(),
+            poc2_native_agent_team=str(value["poc2_native_agent_team"])
+            .strip()
+            .upper(),
+            verification_status=str(value["verification_status"]).strip().upper(),
+            default_for_console=value["default_for_console"],
+            source_tag=str(value["source_tag"]).strip(),
+            notes=str(value["notes"]).strip(),
+            display_order=order,
+        )
+        if not _MODEL_KEY.fullmatch(profile.model_key):
+            raise ValueError("model profile key is invalid")
+        if not profile.display_name or len(profile.display_name) > 128:
+            raise ValueError("model profile display name is invalid")
+        if profile.provider != "oci":
+            raise ValueError("unsupported model provider")
+        if not _MODEL_ID.fullmatch(profile.model_id):
+            raise ValueError("model id is invalid")
+        if not _ANSWER_MODEL_ID_ALIAS.fullmatch(profile.answer_model_id_alias):
+            raise ValueError("answer model id alias is invalid")
+        if not _OCI_REGION.fullmatch(profile.answer_model_region):
+            raise ValueError("answer model region is invalid")
+        if profile.answer_model_endpoint_mode != "OCI_REGIONAL_DEFAULT":
+            raise ValueError("answer model endpoint mode is invalid")
+        endpoint_match = _OCI_REGIONAL_ENDPOINT.fullmatch(
+            profile.answer_model_endpoint
+        )
+        if (
+            endpoint_match is None
+            or endpoint_match.group("region") != profile.answer_model_region
+        ):
+            raise ValueError("answer model endpoint is invalid")
+        if not _ORACLE_IDENTIFIER.fullmatch(profile.poc2_select_ai_profile):
+            raise ValueError("PoC_2 Select AI profile mapping is invalid")
+        if not _ORACLE_IDENTIFIER.fullmatch(profile.poc2_native_agent_team):
+            raise ValueError("PoC_2 Native Agent team mapping is invalid")
+        if profile.verification_status not in _VERIFICATION_STATUSES:
+            raise ValueError("model verification status is invalid")
+        if not _SOURCE_TAG.fullmatch(profile.source_tag):
+            raise ValueError("model profile source tag is invalid")
+        if not profile.notes or len(profile.notes) > 1_000:
+            raise ValueError("model profile notes are invalid")
+        return profile
+
+    def with_answer_route_overrides(
+        self,
+        environ: Mapping[str, str],
+    ) -> "ModelProfile":
+        """Apply only this profile's validated, non-secret OCI route settings."""
+
+        keys = _PROFILE_ROUTE_ENV_KEYS.get(self.model_key)
+        if keys is None:
+            raise ValueError("answer model route is not registered")
+        model_id = _env_override(environ, keys[0], self.model_id)
+        region = _env_override(environ, keys[1], self.answer_model_region).lower()
+        endpoint = _env_override(
+            environ,
+            keys[2],
+            _regional_endpoint(region),
+        )
+        if not _MODEL_ID.fullmatch(model_id):
+            raise ValueError("answer model route override is invalid")
+        if not _OCI_REGION.fullmatch(region):
+            raise ValueError("answer model route override is invalid")
+        endpoint_match = _OCI_REGIONAL_ENDPOINT.fullmatch(endpoint)
+        if endpoint_match is None or endpoint_match.group("region") != region:
+            raise ValueError("answer model route override is invalid")
+        return replace(
+            self,
+            model_id=model_id,
+            answer_model_region=region,
+            answer_model_endpoint=endpoint,
+        )
+
+    def public_metadata(self) -> dict[str, object]:
+        """System Details에 투영 가능한 비밀값 없는 metadata를 반환한다."""
+
+        return {
+            "model_key": self.model_key,
+            "display_name": self.display_name,
+            "provider": self.provider,
+            "answer_model_id_alias": self.answer_model_id_alias,
+            "answer_model_region": self.answer_model_region,
+            "answer_model_endpoint_mode": self.answer_model_endpoint_mode,
+            "answer_model_endpoint_host_alias": _endpoint_host_alias(
+                self.answer_model_region
+            ),
+            "poc2_select_ai_profile": self.poc2_select_ai_profile,
+            "poc2_native_agent_team": self.poc2_native_agent_team,
+            "verification_status": self.verification_status,
+            "default_for_console": self.default_for_console,
+            "source_tag": self.source_tag,
+            "notes": self.notes,
+        }
+
+
+@dataclass(frozen=True)
+class ModelProfileRegistry:
+    """검증된 AI Web Agent Console model profile 집합."""
+
+    profiles: tuple[ModelProfile, ...]
+    default_model_profile: str
+    registry_name: str
+    source_commit: str
+    schema_version: int = 1
+
+    def by_key(self, model_key: object) -> ModelProfile:
+        candidate = str(model_key or "").strip().lower()
+        candidate = MODEL_PROFILE_ALIASES.get(candidate, candidate)
+        for profile in self.profiles:
+            if profile.model_key == candidate:
+                return profile
+        # 사용자 입력이나 환경변수 원문을 오류에 반사하지 않는다.
+        raise ValueError("model profile is not registered")
+
+    @property
+    def default_profile(self) -> ModelProfile:
+        return self.by_key(self.default_model_profile)
+
+    def selector_options(self) -> tuple[ModelProfile, ...]:
+        return tuple(sorted(self.profiles, key=lambda item: item.display_order))
+
+    def resolve(
+        self,
+        requested: object = None,
+        *,
+        environ: Optional[Mapping[str, str]] = None,
+    ) -> ModelProfile:
+        """명시 요청은 엄격히 검증하고 환경 기본값은 안전하게 fallback한다."""
+
+        source = os.environ if environ is None else environ
+        if requested is not None and str(requested).strip():
+            return self.by_key(requested).with_answer_route_overrides(source)
+
+        for name in (MODEL_PROFILE_ENV, MODEL_PROFILE_DEFAULT_ENV):
+            candidate = source.get(name)
+            if not candidate or not candidate.strip():
+                continue
+            try:
+                profile = self.by_key(candidate)
+            except ValueError:
+                continue
+            return profile.with_answer_route_overrides(source)
+        return self.default_profile.with_answer_route_overrides(source)
+
+
+def load_model_registry(path: Path = REGISTRY_PATH) -> ModelProfileRegistry:
+    """JSON registry를 매 호출마다 검증해 반환한다."""
+
+    try:
+        payload = json.loads(path.read_text(encoding="utf-8"))
+    except (OSError, json.JSONDecodeError) as exc:
+        raise ValueError("AI Web Agent Console model profile registry cannot be loaded") from exc
+    if not isinstance(payload, Mapping):
+        raise ValueError("AI Web Agent Console model profile registry must be an object")
+    raw_profiles = payload.get("profiles")
+    if not isinstance(raw_profiles, list) or not raw_profiles:
+        raise ValueError("AI Web Agent Console model profile registry has no profiles")
+    profiles = tuple(
+        ModelProfile.from_mapping(item)
+        for item in raw_profiles
+        if isinstance(item, Mapping)
+    )
+    if len(profiles) != len(raw_profiles):
+        raise ValueError("AI Web Agent Console model profile registry contains an invalid profile")
+    keys = tuple(item.model_key for item in profiles)
+    if len(set(keys)) != len(keys):
+        raise ValueError("AI Web Agent Console model profile keys must be unique")
+    if len({item.display_name for item in profiles}) != len(profiles):
+        raise ValueError("AI Web Agent Console model profile display names must be unique")
+    defaults = tuple(item.model_key for item in profiles if item.default_for_console)
+    configured_default = str(payload.get("default_model_profile") or "").strip().lower()
+    if defaults != (configured_default,):
+        raise ValueError("AI Web Agent Console model profile default is inconsistent")
+    if configured_default != DEFAULT_MODEL_PROFILE_KEY:
+        raise ValueError("AI Web Agent Console GPT-5.5 default contract is not satisfied")
+    if not set(EXISTING_MODEL_PROFILE_KEYS).issubset(keys):
+        raise ValueError("existing AI Web Agent Console selector models are missing")
+    actual_answer_routes = {
+        item.model_key: (
+            item.model_id,
+            item.answer_model_region,
+            item.answer_model_id_alias,
+            item.answer_model_endpoint_mode,
+        )
+        for item in profiles
+    }
+    if actual_answer_routes != _EXPECTED_ANSWER_MODEL_ROUTES:
+        raise ValueError("AI Web Agent Console answer model route mapping is inconsistent")
+    if str(payload.get("source_commit") or "").strip() != EXPECTED_SOURCE_COMMIT:
+        raise ValueError("PoC_2 source commit is inconsistent")
+    if any(item.source_tag != EXPECTED_SOURCE_TAG for item in profiles):
+        raise ValueError("PoC_2 source tag is inconsistent")
+    if payload.get("schema_version") != 1:
+        raise ValueError("unsupported AI Web Agent Console model profile registry schema")
+    registry_name = str(payload.get("registry_name") or "").strip()
+    if not registry_name:
+        raise ValueError("AI Web Agent Console model profile registry name is missing")
+    return ModelProfileRegistry(
+        profiles=profiles,
+        default_model_profile=configured_default,
+        registry_name=registry_name,
+        source_commit=EXPECTED_SOURCE_COMMIT,
+    )
+
+
+def resolve_model_profile(
+    requested: object = None,
+    *,
+    environ: Optional[Mapping[str, str]] = None,
+) -> ModelProfile:
+    return load_model_registry().resolve(requested, environ=environ)
+
+
+def resolve_model_profile_key(
+    requested: object = None,
+    *,
+    environ: Optional[Mapping[str, str]] = None,
+) -> str:
+    return resolve_model_profile(requested, environ=environ).model_key
+
+
+def is_registered_model_profile(value: object) -> bool:
+    try:
+        load_model_registry().by_key(value)
+    except ValueError:
+        return False
+    return True
+
+
+__all__ = [
+    "DEFAULT_MODEL_PROFILE_KEY",
+    "EXISTING_MODEL_PROFILE_KEYS",
+    "EXPECTED_SOURCE_COMMIT",
+    "EXPECTED_SOURCE_TAG",
+    "MODEL_PROFILE_DEFAULT_ENV",
+    "MODEL_PROFILE_ENV",
+    "MODEL_PROFILE_ALIASES",
+    "ModelProfile",
+    "ModelProfileRegistry",
+    "REGISTRY_PATH",
+    "is_registered_model_profile",
+    "load_model_registry",
+    "resolve_model_profile",
+    "resolve_model_profile_key",
+]

ai-web-agent-console/ai_web_agent_console/oci_genai_sdk.py

@@ -0,0 +1,277 @@
+"""Common OCI Generative AI chat-completion SDK boundary.
+
+This module is intentionally small: callers provide a validated model route
+and a JSON schema, and this boundary performs one OCI GenAI chat call.  It
+does not know about MCP, Streamlit, business payloads, or bearer tokens.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Mapping, Sequence
+from dataclasses import dataclass
+from functools import lru_cache
+import os
+from pathlib import Path
+import re
+from typing import Dict, Optional, Protocol
+
+
+ROOT = Path(__file__).resolve().parents[1]
+DOTENV_PATH = ROOT / ".env"
+ALLOWED_OCI_SETTINGS = frozenset(
+    {
+        "OCI_AUTH_TYPE",
+        "OCI_CONFIG_FILE",
+        "OCI_GENAI_COMPARTMENT_ID",
+        "OCI_PROFILE",
+    }
+)
+# OCI permits the tenancy OCID when the root compartment is selected.
+_COMPARTMENT_OR_ROOT_ID = re.compile(
+    r"^ocid1\.(?:compartment|tenancy)\.[A-Za-z0-9._-]+$"
+)
+
+
+class CompletionClient(Protocol):
+    """Minimal completion client contract shared by app layers."""
+
+    def complete(
+        self,
+        system_prompt: str,
+        user_prompt: str,
+        response_schema: Mapping[str, object],
+        max_tokens: int,
+        temperature: Optional[float],
+    ) -> str:
+        """Return the assistant message text."""
+
+
+def read_allowed_dotenv(path: Optional[Path] = None) -> Dict[str, str]:
+    """Read only non-secret OCI routing/auth-mode settings from .env."""
+
+    selected_path = DOTENV_PATH if path is None else path
+    try:
+        lines = selected_path.read_text(encoding="utf-8").splitlines()
+    except OSError:
+        return {}
+    values: Dict[str, str] = {}
+    for raw_line in lines:
+        line = raw_line.strip()
+        if not line or line.startswith("#"):
+            continue
+        if line.startswith("export "):
+            line = line[7:].lstrip()
+        key, separator, raw_value = line.partition("=")
+        key = key.strip()
+        if not separator or key not in ALLOWED_OCI_SETTINGS:
+            continue
+        value = raw_value.strip()
+        if len(value) >= 2 and value[0] == value[-1] and value[0] in {"'", '"'}:
+            value = value[1:-1]
+        if "\x00" not in value and "\n" not in value and "\r" not in value:
+            values[key] = value
+    return values
+
+
+@dataclass(frozen=True, repr=False)
+class OCISettings:
+    auth_type: str
+    config_file: str
+    profile: str
+    compartment_id: str
+
+
+def load_oci_settings() -> OCISettings:
+    """Resolve OCI GenAI settings from safe .env keys and environment."""
+
+    values = read_allowed_dotenv()
+    for key in ALLOWED_OCI_SETTINGS:
+        value = os.environ.get(key)
+        if isinstance(value, str) and value.strip():
+            values[key] = value.strip()
+
+    auth_type = values.get("OCI_AUTH_TYPE", "config_file").strip().casefold()
+    auth_type = auth_type.replace("-", "_")
+    if auth_type in {"api_key", "config", "config_file"}:
+        auth_type = "config_file"
+    elif auth_type not in {"instance_principal", "resource_principal"}:
+        raise ValueError("unsupported OCI authentication mode")
+
+    compartment_id = values.get("OCI_GENAI_COMPARTMENT_ID", "").strip()
+    if not _COMPARTMENT_OR_ROOT_ID.fullmatch(compartment_id):
+        raise ValueError("OCI Generative AI compartment is not configured")
+    return OCISettings(
+        auth_type=auth_type,
+        config_file=values.get("OCI_CONFIG_FILE", "~/.oci/config").strip(),
+        profile=values.get("OCI_PROFILE", "DEFAULT").strip() or "DEFAULT",
+        compartment_id=compartment_id,
+    )
+
+
+class OCICompletionClient:
+    """Minimal OCI GenericChatRequest adapter."""
+
+    def __init__(
+        self,
+        settings: OCISettings,
+        model_id: str,
+        region: str,
+        endpoint: str,
+    ) -> None:
+        try:
+            import oci
+            from oci.generative_ai_inference import GenerativeAiInferenceClient
+        except (ImportError, AttributeError):
+            raise RuntimeError("OCI SDK is unavailable") from None
+
+        kwargs: Dict[str, object] = {}
+        if settings.auth_type == "config_file":
+            try:
+                config = oci.config.from_file(
+                    file_location=os.path.expandvars(
+                        os.path.expanduser(settings.config_file)
+                    ),
+                    profile_name=settings.profile,
+                )
+            except Exception:
+                raise RuntimeError("OCI SDK configuration is unavailable") from None
+            config["region"] = region
+        elif settings.auth_type == "instance_principal":
+            try:
+                signer = oci.auth.signers.InstancePrincipalsSecurityTokenSigner()
+            except Exception:
+                raise RuntimeError("OCI signer is unavailable") from None
+            config = {"region": region}
+            kwargs["signer"] = signer
+        else:
+            try:
+                signer = oci.auth.signers.get_resource_principals_signer()
+            except Exception:
+                raise RuntimeError("OCI signer is unavailable") from None
+            config = {"region": region}
+            kwargs["signer"] = signer
+        if not config.get("region"):
+            raise RuntimeError("OCI region is unavailable")
+        kwargs["service_endpoint"] = endpoint
+        try:
+            self._client = GenerativeAiInferenceClient(config, **kwargs)
+        except Exception:
+            raise RuntimeError("OCI Generative AI client is unavailable") from None
+        self._compartment_id = settings.compartment_id
+        self._model_id = model_id
+
+    def complete(
+        self,
+        system_prompt: str,
+        user_prompt: str,
+        response_schema: Mapping[str, object],
+        max_tokens: int,
+        temperature: Optional[float],
+    ) -> str:
+        try:
+            from oci.generative_ai_inference.models import (
+                ChatDetails,
+                GenericChatRequest,
+                JsonSchemaResponseFormat,
+                OnDemandServingMode,
+                ResponseJsonSchema,
+                SystemMessage,
+                TextContent,
+                UserMessage,
+            )
+
+            schema = ResponseJsonSchema(
+                name="oci_genai_json_response",
+                description="Strict JSON response",
+                schema=dict(response_schema),
+                is_strict=True,
+            )
+            request_options: Dict[str, object] = {
+                "api_format": "GENERIC",
+                "messages": [
+                    SystemMessage(content=[TextContent(text=system_prompt)]),
+                    UserMessage(content=[TextContent(text=user_prompt)]),
+                ],
+                "max_completion_tokens": max_tokens,
+                "is_stream": False,
+                "response_format": JsonSchemaResponseFormat(json_schema=schema),
+            }
+            if temperature is not None:
+                request_options["temperature"] = temperature
+            request = GenericChatRequest(**request_options)
+            details = ChatDetails(
+                compartment_id=self._compartment_id,
+                serving_mode=OnDemandServingMode(model_id=self._model_id),
+                chat_request=request,
+            )
+            response = self._client.chat(details)
+            data = getattr(response, "data", None)
+            chat_response = getattr(data, "chat_response", None)
+            choices = getattr(chat_response, "choices", None)
+            if not isinstance(choices, Sequence) or not choices:
+                raise RuntimeError("OCI response has no choice")
+            message = getattr(choices[0], "message", None)
+            content = getattr(message, "content", None)
+            if not isinstance(content, Sequence) or isinstance(
+                content, (str, bytes, bytearray)
+            ) or not content:
+                raise RuntimeError("OCI response has no content")
+            text = getattr(content[0], "text", None)
+            if not isinstance(text, str):
+                raise RuntimeError("OCI response content is invalid")
+            return text
+        except Exception:
+            raise RuntimeError("OCI GenAI completion call failed") from None
+
+
+def build_oci_genai_completion_client(
+    model_id: str,
+    region: str,
+    endpoint: str,
+) -> CompletionClient:
+    """Build a completion client for one validated model route."""
+
+    return _cached_oci_genai_completion_client(
+        load_oci_settings(),
+        model_id,
+        region,
+        endpoint,
+    )
+
+
+@lru_cache(maxsize=16)
+def _cached_oci_genai_completion_client(
+    settings: OCISettings,
+    model_id: str,
+    region: str,
+    endpoint: str,
+) -> CompletionClient:
+    """Reuse OCI GenAI clients within one Python process."""
+
+    return OCICompletionClient(settings, model_id, region, endpoint)
+
+
+def temperature_for_model_key(model_key: object) -> Optional[float]:
+    """Return provider-compatible temperature for one registered model key."""
+
+    key = str(model_key or "").strip().lower()
+    if key.startswith("gpt"):
+        return None
+    return 0.1
+
+
+def temperature_for_model_profile(profile: object) -> Optional[float]:
+    return temperature_for_model_key(getattr(profile, "model_key", profile))
+
+
+__all__ = [
+    "ALLOWED_OCI_SETTINGS",
+    "CompletionClient",
+    "OCICompletionClient",
+    "OCISettings",
+    "build_oci_genai_completion_client",
+    "load_oci_settings",
+    "read_allowed_dotenv",
+    "temperature_for_model_key",
+    "temperature_for_model_profile",
+]

ai-web-agent-console/ai_web_agent_console/presentation.py

@@ -0,0 +1,176 @@
+"""Shared, intentionally small Streamlit presentation primitives."""
+
+from __future__ import annotations
+
+from html import escape
+from typing import Any
+
+from .profile import AppProfile
+
+
+def apply_console_theme(st: Any, profile: AppProfile) -> None:
+    """Apply one predictable light theme from the product profile."""
+
+    st.markdown(
+        f"""
+        <style>
+        :root {{ color-scheme: light !important; --console-primary: {escape(profile.primary_color)};
+          --console-text: {escape(profile.text_color)}; --console-muted: {escape(profile.muted_color)};
+          --console-border: {escape(profile.border_color)}; }}
+        html, body, [data-testid="stAppViewContainer"], .stApp {{ background:#fff !important;
+          color:var(--console-text) !important; color-scheme:light !important;
+          font-family:"Noto Sans KR","Malgun Gothic",sans-serif; }}
+        header[data-testid="stHeader"] {{ display:none !important; }}
+        .block-container {{ max-width:1180px; padding:2rem 3rem 4rem; }}
+        section[data-testid="stSidebar"], section[data-testid="stSidebar"] > div {{ background:#fff !important; }}
+        section[data-testid="stSidebar"] {{ border-right:1px solid var(--console-border); }}
+        [data-testid="stAppViewContainer"] p, [data-testid="stAppViewContainer"] span,
+        [data-testid="stAppViewContainer"] label, [data-testid="stAppViewContainer"] h1,
+        [data-testid="stAppViewContainer"] h2, [data-testid="stAppViewContainer"] h3,
+        [data-testid="stAppViewContainer"] li, [data-testid="stAppViewContainer"] dt,
+        [data-testid="stAppViewContainer"] dd, [data-testid="stAppViewContainer"] blockquote,
+        [data-testid="stAppViewContainer"] input, [data-testid="stAppViewContainer"] textarea,
+        section[data-testid="stSidebar"] * {{ color:var(--console-text) !important;
+          -webkit-text-fill-color:var(--console-text) !important; }}
+        input, textarea, [data-baseweb="select"] > div, [data-testid="stSidebar"] button {{
+          background:#fff !important; border:1px solid var(--console-border) !important;
+          border-radius:4px !important; box-shadow:none !important; }}
+        /* Streamlit JSON uses an independently styled code surface.  Keep the
+           MCP detail payload readable even when the browser/system prefers a
+           dark code theme. */
+        [data-testid="stJson"], [data-testid="stJson"] > div,
+        [data-testid="stJson"] .react-json-view, [data-testid="stJson"] pre {{
+          background:#f8fafc !important; color:var(--console-text) !important;
+          border-color:var(--console-border) !important; color-scheme:light !important; }}
+        [data-testid="stJson"] *, [data-testid="stJson"] pre *,
+        [data-testid="stJson"] code {{
+          background:transparent !important; color:var(--console-text) !important;
+          -webkit-text-fill-color:var(--console-text) !important; }}
+        /* Baseline answers and generated SQL use Streamlit's separate code
+           surface. Keep it readable when the browser prefers dark mode. */
+        [data-testid="stCode"], [data-testid="stCode"] pre,
+        [data-testid="stCode"] code, [data-testid="stCodeBlock"],
+        [data-testid="stCodeBlock"] pre, [data-testid="stCodeBlock"] code {{
+          background:#f8fafc !important; color:var(--console-text) !important;
+          border-color:var(--console-border) !important; color-scheme:light !important;
+          -webkit-text-fill-color:var(--console-text) !important; }}
+        [data-testid="stCode"] *, [data-testid="stCodeBlock"] * {{
+          color:var(--console-text) !important;
+          -webkit-text-fill-color:var(--console-text) !important; }}
+        /* Chat responses are rendered in a separate Streamlit surface.  Without
+           these rules a dark browser theme can leave the answer card dark while
+           its Markdown keeps the light-theme text color. */
+        div[data-testid="stChatMessage"], div[data-testid="stChatMessageContent"] {{
+          background:#fff !important; color:var(--console-text) !important;
+          border-color:var(--console-border) !important; color-scheme:light !important; }}
+        div[data-testid="stChatMessage"] [data-testid="stMarkdownContainer"],
+        div[data-testid="stChatMessage"] [data-testid="stMarkdownContainer"] *,
+        div[data-testid="stChatMessage"] [data-testid="stCaptionContainer"],
+        div[data-testid="stChatMessage"] [data-testid="stCaptionContainer"] * {{
+          color:var(--console-text) !important;
+          -webkit-text-fill-color:var(--console-text) !important; }}
+        /* Streamlit expanders use a dark summary bar in dark browser themes. */
+        details, details > summary {{
+          background:#fff !important; color:var(--console-text) !important;
+          border-color:var(--console-border) !important; color-scheme:light !important; }}
+        details > summary *, details > summary::marker {{
+          color:var(--console-text) !important;
+          -webkit-text-fill-color:var(--console-text) !important; }}
+        [data-testid="stExpander"] > details,
+        [data-testid="stExpander"] > details > summary,
+        [data-testid="stExpander"] > details > summary > div,
+        [data-testid="stExpander"] > details > summary > div > div {{
+          background:#fff !important; color:var(--console-text) !important;
+          border-color:var(--console-border) !important; }}
+        [data-testid="stExpander"] > details > summary *,
+        [data-testid="stExpander"] > details > summary svg {{
+          color:var(--console-text) !important; fill:var(--console-text) !important;
+          stroke:var(--console-text) !important;
+          -webkit-text-fill-color:var(--console-text) !important; }}
+        div[data-testid="stButton"] > button, div[data-testid="stFormSubmitButton"] > button {{
+          background:#fff !important; color:var(--console-text) !important;
+          -webkit-text-fill-color:var(--console-text) !important;
+          border:1px solid var(--console-border) !important;
+          border-radius:4px !important; box-shadow:none !important; color-scheme:light !important; }}
+        div[data-testid="stButton"] > button *, div[data-testid="stFormSubmitButton"] > button * {{
+          color:var(--console-text) !important; -webkit-text-fill-color:var(--console-text) !important; }}
+        div[data-testid="stButton"] > button[kind="primary"],
+        div[data-testid="stFormSubmitButton"] > button[data-testid="stBaseButton-primaryFormSubmit"] {{
+          background:var(--console-primary) !important; border-color:var(--console-primary) !important; color:#fff !important; }}
+        div[data-testid="stButton"] > button[kind="primary"] *,
+        div[data-testid="stFormSubmitButton"] > button[data-testid="stBaseButton-primaryFormSubmit"] * {{
+          color:#fff !important; -webkit-text-fill-color:#fff !important; }}
+        /* Streamlit JSON/code components otherwise retain a dark-surface theme while
+           the console text is forced dark, making MCP details unreadable. */
+        [data-testid="stJson"], [data-testid="stJson"] > div,
+        [data-testid="stCodeBlock"], [data-testid="stCodeBlock"] > div,
+        [data-testid="stCode"], [data-testid="stCode"] > div {{
+          background:#f6f8fa !important; color:var(--console-text) !important;
+          border-color:var(--console-border) !important; }}
+        [data-testid="stJson"] *, [data-testid="stCodeBlock"] *, [data-testid="stCode"] *,
+        [data-testid="stJson"] pre, [data-testid="stCodeBlock"] pre, [data-testid="stCode"] pre {{
+          color:var(--console-text) !important;
+          -webkit-text-fill-color:var(--console-text) !important; }}
+        [data-testid="stJson"] button, [data-testid="stCodeBlock"] button, [data-testid="stCode"] button {{
+          background:#fff !important; border-color:var(--console-border) !important; }}
+        [data-testid="stExpander"] summary {{
+          background:#f6f8fa !important; color:var(--console-text) !important;
+          border-color:var(--console-border) !important; }}
+        [data-testid="stExpander"] summary * {{
+          color:var(--console-text) !important;
+          -webkit-text-fill-color:var(--console-text) !important; }}
+        [data-baseweb="tab-list"], [data-testid="stTabs"] [role="tablist"] {{
+          border-bottom:1px solid var(--console-border) !important; }}
+        [data-baseweb="tab-list"] [role="tab"], [data-testid="stTab"] {{
+          background:#fff !important; color:var(--console-text) !important;
+          -webkit-text-fill-color:var(--console-text) !important;
+          border-radius:4px 4px 0 0 !important; }}
+        [data-baseweb="tab-list"] [role="tab"] *, [data-testid="stTab"] * {{
+          color:var(--console-text) !important;
+          -webkit-text-fill-color:var(--console-text) !important; }}
+        [data-baseweb="tab-list"] [role="tab"]:hover, [data-testid="stTab"]:hover {{
+          background:#f6f8fa !important; }}
+        [data-baseweb="tab-list"] [role="tab"][aria-selected="true"],
+        [data-testid="stTab"][aria-selected="true"] {{
+          color:var(--console-primary) !important;
+          -webkit-text-fill-color:var(--console-primary) !important;
+          border-bottom:3px solid var(--console-primary) !important; }}
+        [data-baseweb="tab-list"] [role="tab"][aria-selected="true"] *,
+        [data-testid="stTab"][aria-selected="true"] * {{
+          color:var(--console-primary) !important;
+          -webkit-text-fill-color:var(--console-primary) !important; }}
+        .console-header {{ margin:0 0 28px; padding:0 0 22px; border-bottom:1px solid var(--console-border); }}
+        .console-wordmark {{ color:var(--console-primary); font-size:1.35rem; font-weight:800; letter-spacing:.08em; }}
+        .console-header h1 {{ margin:10px 0 8px; font-size:1.7rem; }}
+        .console-muted {{ color:var(--console-muted) !important; }}
+        .st-key-console_login_container {{ max-width:440px; margin:12vh auto 0; }}
+        .console-login {{ text-align:left; }}
+        .console-login h1 {{ margin:12px 0 8px; font-size:1.7rem; }}
+        a.console-logout-button {{ display:block; width:100%; padding:.55rem .8rem;
+          margin:.25rem 0 .75rem; background:#fff; color:var(--console-text) !important;
+          -webkit-text-fill-color:var(--console-text) !important;
+          border:1px solid var(--console-border); border-radius:4px;
+          text-align:center; text-decoration:none; font-weight:700; }}
+        a.console-logout-button:hover {{ background:#f6f8fa; }}
+        @media (max-width:760px) {{ .block-container {{ padding:1.25rem 1.25rem 3rem; }} .st-key-console_login_container {{ margin-top:8vh; }} }}
+        </style>
+        """,
+        unsafe_allow_html=True,
+    )
+
+
+def render_console_header(st: Any, profile: AppProfile) -> None:
+    st.markdown(
+        f"""<section class="console-header"><div class="console-wordmark">{escape(profile.short_name)}</div>
+        <h1>{escape(profile.header_title)}</h1><p class="console-muted">{escape(profile.header_description)}</p></section>""",
+        unsafe_allow_html=True,
+    )
+
+
+def render_login_brand(st: Any, profile: AppProfile) -> None:
+    st.markdown(
+        f"""<section class="console-login"><div class="console-wordmark">{escape(profile.short_name)}</div>
+        <p class="console-muted">{escape(profile.login_kicker)}</p><h1>{escape(profile.login_title)}</h1>
+        <p class="console-muted">{escape(profile.login_description)}</p></section>""",
+        unsafe_allow_html=True,
+    )

ai-web-agent-console/ai_web_agent_console/profile.py

@@ -0,0 +1,139 @@
+"""Configuration-backed product profile for a reusable agent console."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+import json
+import os
+from pathlib import Path
+from typing import Any, Mapping
+
+
+class AppProfileError(RuntimeError):
+    """Safe error for an invalid product profile."""
+
+
+@dataclass(frozen=True)
+class AppProfile:
+    product_name: str
+    short_name: str
+    page_title: str
+    page_icon: str
+    header_title: str
+    header_description: str
+    login_kicker: str
+    login_title: str
+    login_description: str
+    login_footer: str
+    primary_color: str
+    text_color: str
+    muted_color: str
+    border_color: str
+
+
+_ENV_FIELD_NAMES = {
+    "product_name": "AGENT_CONSOLE_NAME",
+    "short_name": "AGENT_CONSOLE_SHORT_NAME",
+    "page_title": "AGENT_CONSOLE_PAGE_TITLE",
+    "page_icon": "AGENT_CONSOLE_PAGE_ICON",
+    "header_title": "AGENT_CONSOLE_HEADER_TITLE",
+    "header_description": "AGENT_CONSOLE_HEADER_DESCRIPTION",
+    "login_kicker": "AGENT_CONSOLE_LOGIN_KICKER",
+    "login_title": "AGENT_CONSOLE_LOGIN_TITLE",
+    "login_description": "AGENT_CONSOLE_LOGIN_DESCRIPTION",
+    "login_footer": "AGENT_CONSOLE_LOGIN_FOOTER",
+    "primary_color": "AGENT_CONSOLE_PRIMARY_COLOR",
+    "text_color": "AGENT_CONSOLE_TEXT_COLOR",
+    "muted_color": "AGENT_CONSOLE_MUTED_COLOR",
+    "border_color": "AGENT_CONSOLE_BORDER_COLOR",
+}
+
+
+def _string(section: Mapping[str, Any], key: str, fallback: str = "") -> str:
+    return str(section.get(key) or fallback).strip()
+
+
+def resolve_profile_path(default_path: Path) -> Path:
+    configured = os.environ.get("AGENT_CONSOLE_PROFILE_PATH", "").strip()
+    if not configured:
+        return default_path
+    path = Path(configured).expanduser()
+    return path if path.is_absolute() else default_path.parent / path
+
+
+def _dotenv_value(name: str, path: Path | None) -> str:
+    """Read one simple KEY=value runtime setting without importing a dotenv lib."""
+
+    if path is None:
+        return ""
+    try:
+        lines = path.read_text(encoding="utf-8").splitlines()
+    except (OSError, UnicodeError):
+        return ""
+    prefix = f"{name}="
+    for line in lines:
+        stripped = line.strip()
+        if stripped.startswith(prefix):
+            return stripped[len(prefix) :].strip().strip('"').strip("'")
+    return ""
+
+
+def _apply_environment_overrides(profile: AppProfile, env_file: Path | None) -> AppProfile:
+    """Apply deployment-specific presentation values without a code change."""
+
+    values = {
+        field_name: (
+            os.environ.get(env_name, "").strip()
+            or _dotenv_value(env_name, env_file)
+            or getattr(profile, field_name)
+        )
+        for field_name, env_name in _ENV_FIELD_NAMES.items()
+    }
+    return AppProfile(**values)
+
+
+def load_app_profile(default_path: Path, env_file: Path | None = None) -> AppProfile:
+    """Load the selectable product skin without coupling it to a PoC name."""
+
+    path = resolve_profile_path(default_path)
+    try:
+        payload: Any = json.loads(path.read_text(encoding="utf-8"))
+    except (OSError, UnicodeError, ValueError):
+        raise AppProfileError(f"애플리케이션 프로필을 읽지 못했습니다: {path}") from None
+    if not isinstance(payload, Mapping):
+        raise AppProfileError("애플리케이션 프로필 형식이 올바르지 않습니다.")
+    product = payload.get("product")
+    theme = payload.get("theme")
+    if not isinstance(product, Mapping) or not isinstance(theme, Mapping):
+        raise AppProfileError("애플리케이션 프로필에 product와 theme 객체가 필요합니다.")
+
+    profile = _apply_environment_overrides(AppProfile(
+        product_name=_string(product, "name"),
+        short_name=_string(product, "short_name"),
+        page_title=_string(product, "page_title"),
+        page_icon=_string(product, "page_icon", "🤖"),
+        header_title=_string(product, "header_title"),
+        header_description=_string(product, "header_description"),
+        login_kicker=_string(product, "login_kicker"),
+        login_title=_string(product, "login_title"),
+        login_description=_string(product, "login_description"),
+        login_footer=_string(product, "login_footer"),
+        primary_color=_string(theme, "primary_color"),
+        text_color=_string(theme, "text_color"),
+        muted_color=_string(theme, "muted_color"),
+        border_color=_string(theme, "border_color"),
+    ), env_file)
+    required = (
+        profile.product_name,
+        profile.short_name,
+        profile.page_title,
+        profile.header_title,
+        profile.login_title,
+        profile.primary_color,
+        profile.text_color,
+        profile.muted_color,
+        profile.border_color,
+    )
+    if not all(required):
+        raise AppProfileError("애플리케이션 프로필의 필수 표시값 또는 색상이 비어 있습니다.")
+    return profile

ai-web-agent-console/ai_web_agent_console/qa_history.py

@@ -0,0 +1,242 @@
+"""Customer QA benchmark parsing and deterministic SQL evaluation."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+import hashlib
+import json
+from pathlib import Path
+import re
+from typing import Any, Mapping
+
+
+class QaBenchmarkError(RuntimeError):
+    """Raised when the QA benchmark source cannot be used safely."""
+
+
+@dataclass(frozen=True)
+class QaQuestion:
+    question_id: int | None
+    question_code: str
+    category: str
+    title: str
+    question_text: str
+    source_document: str
+    source_sheet: str
+    source_row: int | None
+    source_scenario: str
+    sample_sql: str
+    expected_focus: str
+    baseline_sql: str
+    baseline_answer: str
+    support_level: str
+    evaluation_rule: Mapping[str, Any]
+    last_judgment_status: str = ""
+    last_evaluated_at: str = ""
+
+
+@dataclass(frozen=True)
+class QaJudgment:
+    status: str
+    reason: str
+
+
+def question_fingerprint(question_text: str) -> str:
+    normalized = " ".join(str(question_text or "").split()).casefold()
+    return hashlib.sha256(normalized.encode("utf-8")).hexdigest()
+
+
+def _compact_text(value: object) -> str:
+    return str(value or "").strip()
+
+
+def _string_list(value: object) -> tuple[str, ...]:
+    if not isinstance(value, list):
+        return ()
+    return tuple(_compact_text(item) for item in value if _compact_text(item))
+
+
+def load_benchmark_questions(path: Path) -> tuple[QaQuestion, ...]:
+    try:
+        payload = json.loads(path.read_text(encoding="utf-8"))
+    except (OSError, UnicodeError, ValueError) as exc:
+        raise QaBenchmarkError(f"질답 기준 파일을 읽지 못했습니다: {path}") from exc
+    rows = payload.get("scenarios") if isinstance(payload, Mapping) else None
+    if not isinstance(rows, list):
+        raise QaBenchmarkError("질답 기준 파일에 scenarios 배열이 필요합니다.")
+
+    questions: list[QaQuestion] = []
+    seen_codes: set[str] = set()
+    for row in rows:
+        if not isinstance(row, Mapping):
+            raise QaBenchmarkError("질답 기준의 각 시나리오는 객체여야 합니다.")
+        source = row.get("source") if isinstance(row.get("source"), Mapping) else {}
+        history = (
+            row.get("historical_answer")
+            if isinstance(row.get("historical_answer"), Mapping)
+            else {}
+        )
+        code = _compact_text(row.get("case_id")).upper()
+        question_text = _compact_text(row.get("question"))
+        if not code or not question_text:
+            raise QaBenchmarkError("각 질답 기준에는 case_id와 question이 필요합니다.")
+        if code in seen_codes:
+            raise QaBenchmarkError(f"중복된 질답 case_id입니다: {code}")
+        evaluation_rule = row.get("evaluation_rule")
+        if not isinstance(evaluation_rule, Mapping):
+            evaluation_rule = {}
+        questions.append(
+            QaQuestion(
+                question_id=None,
+                question_code=code,
+                category=_compact_text(row.get("category")) or "GENERAL",
+                title=_compact_text(row.get("title")) or code,
+                question_text=question_text,
+                source_document=_compact_text(source.get("workbook")),
+                source_sheet=_compact_text(source.get("sheet")),
+                source_row=_number_or_none(source.get("excel_row")),
+                source_scenario=_compact_text(source.get("scenario")),
+                sample_sql=_compact_text(source.get("sample_query")),
+                expected_focus=_compact_text(row.get("expected_focus")),
+                baseline_sql=_compact_text(history.get("generated_sql")),
+                baseline_answer=_compact_text(history.get("answer_text")),
+                support_level=_compact_text(row.get("support_level")).upper() or "UNKNOWN",
+                evaluation_rule={
+                    "required_sql_terms": list(
+                        _string_list(evaluation_rule.get("required_sql_terms"))
+                    ),
+                    "recommended_sql_terms": list(
+                        _string_list(evaluation_rule.get("recommended_sql_terms"))
+                    ),
+                },
+            )
+        )
+        seen_codes.add(code)
+    return tuple(questions)
+
+
+def question_from_record(record: Mapping[str, Any]) -> QaQuestion:
+    rule = record.get("evaluation_rule")
+    if isinstance(rule, str):
+        try:
+            rule = json.loads(rule)
+        except ValueError:
+            rule = {}
+    if not isinstance(rule, Mapping):
+        rule = {}
+    return QaQuestion(
+        question_id=_number_or_none(record.get("question_id")),
+        question_code=_compact_text(record.get("question_code")),
+        category=_compact_text(record.get("category")) or "GENERAL",
+        title=_compact_text(record.get("title")) or _compact_text(record.get("question_code")),
+        question_text=_compact_text(record.get("question_text")),
+        source_document=_compact_text(record.get("source_document")),
+        source_sheet=_compact_text(record.get("source_sheet")),
+        source_row=_number_or_none(record.get("source_row")),
+        source_scenario=_compact_text(record.get("source_scenario")),
+        sample_sql=_compact_text(record.get("sample_sql")),
+        expected_focus=_compact_text(record.get("expected_focus")),
+        baseline_sql=_compact_text(record.get("baseline_sql")),
+        baseline_answer=_compact_text(record.get("baseline_answer")),
+        support_level=_compact_text(record.get("support_level")).upper() or "UNKNOWN",
+        evaluation_rule={
+            "required_sql_terms": list(
+                _string_list(rule.get("required_sql_terms"))
+            ),
+            "recommended_sql_terms": list(
+                _string_list(rule.get("recommended_sql_terms"))
+            ),
+        },
+        last_judgment_status=_compact_text(record.get("last_judgment_status")),
+        last_evaluated_at=_compact_text(record.get("last_evaluated_at")),
+    )
+
+
+def _number_or_none(value: object) -> int | None:
+    if value is None or value == "":
+        return None
+    try:
+        return int(value)
+    except (TypeError, ValueError):
+        return None
+
+
+def _append_issue(issues: list[str], condition: bool, message: str) -> None:
+    if condition:
+        issues.append(message)
+
+
+def evaluate_sql(
+    question: QaQuestion | None,
+    generated_sql: str,
+    *,
+    execution_succeeded: bool,
+    error_text: str = "",
+    game_plan_status: str = "",
+) -> QaJudgment:
+    """Evaluate the generated SQL against the customer-approved benchmark rule."""
+    if question is None or not question.question_code:
+        return QaJudgment(
+            status="REVIEW",
+            reason="자유 질의입니다. 고객 기준 정답 시나리오와 연결되지 않아 수동 검토가 필요합니다.",
+        )
+
+    sql = _compact_text(generated_sql)
+    upper_sql = sql.upper()
+    lower_sql = sql.lower()
+    execution_status = "PASS" if execution_succeeded else "FAIL_EXECUTION"
+    issues: list[str] = []
+    failure_markers = ("could not be generated", "exception encountered", "invalid identifier", "ora-")
+    has_failure_text = any(marker in lower_sql for marker in failure_markers)
+    required = _string_list(question.evaluation_rule.get("required_sql_terms"))
+    recommended = _string_list(question.evaluation_rule.get("recommended_sql_terms"))
+    missing_required = [term for term in required if term.upper() not in upper_sql]
+    missing_recommended = [term for term in recommended if term.upper() not in upper_sql]
+
+    if not execution_succeeded:
+        issues.append(f"실행 상태가 {execution_status}입니다.")
+    if not sql:
+        issues.append("생성 SQL이 없습니다.")
+    if has_failure_text:
+        issues.append("생성 SQL에 오류 또는 생성 실패 문구가 포함되어 있습니다.")
+    if missing_required:
+        issues.append("필수 SQL 요소 누락: " + ", ".join(missing_required))
+    if missing_recommended:
+        issues.append("권장 SQL 요소 누락: " + ", ".join(missing_recommended))
+    _append_issue(
+        issues,
+        bool(re.search(r'_[A-Z0-9]*YN"\s*=\s*\'1\'', sql, flags=re.IGNORECASE)),
+        "*_YN 컬럼은 샘플 메타데이터의 Y/N 값으로 비교해야 합니다.",
+    )
+    _append_issue(
+        issues,
+        bool(re.search(r'_[A-Z0-9]*FLAG"\s*=\s*\'Y\'', sql, flags=re.IGNORECASE)),
+        "*_FLAG 컬럼은 샘플 메타데이터의 0/1 값으로 비교해야 합니다.",
+    )
+
+    support = question.support_level
+    if support == "UNSUPPORTED":
+        plan_status = _compact_text(game_plan_status).upper()
+        safe_empty_result = bool(
+            re.search(r"\bFROM\s+DUAL\b", upper_sql)
+            and re.search(r"\bWHERE\s+1\s*=\s*0\b", upper_sql)
+        )
+        if plan_status in {"UNAVAILABLE", "UNMATCHED"} and execution_succeeded and safe_empty_result:
+            return QaJudgment(
+                "PASS",
+                "게임 계획이 데이터 미지원 또는 미매칭으로 판정됐고, 임의 객체 선택 없이 빈 결과를 반환했습니다.",
+            )
+        if not sql and any(marker in error_text.lower() for marker in failure_markers):
+            return QaJudgment("PASS", "미지원 게임 질문이 실행 가능한 SQL로 변환되지 않았습니다. 기대한 안전 차단입니다.")
+        return QaJudgment("FAIL", "미지원 게임이 게임 계획의 안전한 빈 결과로 처리되지 않았거나 실행에 실패했습니다.")
+
+    if not execution_succeeded or not sql or has_failure_text or missing_required:
+        return QaJudgment("FAIL", "\n".join(issues) or "필수 SQL 또는 실행 검증에 실패했습니다.")
+
+    if any(issue.startswith("필수") for issue in issues):
+        return QaJudgment("FAIL", "\n".join(issues))
+    if support == "PARTIAL":
+        issues.append("지원 범위가 일부인 질문이므로 결과 범위를 함께 검토해야 합니다.")
+    if issues:
+        return QaJudgment("WARN", "\n".join(issues))
+    return QaJudgment("PASS", "고객 기준의 필수 SQL 요소와 실행 결과를 확인했습니다.")

ai-web-agent-console/ai_web_agent_console/qa_history_store.py

@@ -0,0 +1,583 @@
+"""Oracle ADB persistence for the Smilegate customer QA benchmark."""
+
+from __future__ import annotations
+
+from contextlib import contextmanager
+from datetime import datetime, timezone
+import json
+import os
+from pathlib import Path
+from typing import Any, Iterator, Mapping
+from urllib.parse import parse_qs
+
+import oracledb
+
+from src.poc4.qa_history import QaQuestion, load_benchmark_questions, question_fingerprint, question_from_record
+
+
+class QaHistoryStoreError(RuntimeError):
+    """A safe user-facing persistence error."""
+
+
+QUESTION_TABLE = "SG_AI_QA_QUESTION"
+ANSWER_TABLE = "SG_AI_QA_ANSWER"
+HISTORICAL_RUN_KEY = "HISTORICAL:2026-07-21:term-dict-final-v2"
+
+
+def _env_value(name: str, env_file: Path | None = None) -> str:
+    value = os.environ.get(name, "").strip()
+    if value or env_file is None or not env_file.is_file():
+        return value
+    try:
+        lines = env_file.read_text(encoding="utf-8").splitlines()
+    except (OSError, UnicodeError):
+        return ""
+    for line in lines:
+        line = line.strip()
+        if not line or line.startswith("#") or "=" not in line:
+            continue
+        if line.startswith("export "):
+            line = line[7:].lstrip()
+        key, raw = line.split("=", 1)
+        if key.strip() != name:
+            continue
+        raw = raw.strip()
+        if len(raw) >= 2 and raw[0] == raw[-1] and raw[0] in {"'", '"'}:
+            raw = raw[1:-1]
+        return raw.strip()
+    return ""
+
+
+def _normalize_oracle_dsn(raw_dsn: str) -> tuple[str, str]:
+    value = str(raw_dsn or "").strip()
+    if value.startswith("jdbc:oracle:thin:@"):
+        value = value[len("jdbc:oracle:thin:@"):]
+    if "?" not in value:
+        return value, ""
+    dsn, query = value.split("?", 1)
+    parsed = parse_qs(query, keep_blank_values=False)
+    wallet_dir = (parsed.get("TNS_ADMIN") or parsed.get("tns_admin") or [""])[0]
+    return dsn.strip(), wallet_dir.strip()
+
+
+def _read_lob(value: Any) -> Any:
+    return value.read() if hasattr(value, "read") else value
+
+
+def _record_from_cursor(cursor: Any, row: Any) -> dict[str, Any]:
+    names = [column[0].lower() for column in cursor.description]
+    return {name: _read_lob(value) for name, value in zip(names, row)}
+
+
+def _to_json(value: Mapping[str, Any] | None) -> str:
+    payload = dict(value or {})
+    text = json.dumps(payload, ensure_ascii=False, default=str)
+    if len(text) <= 120_000:
+        return text
+    return json.dumps(
+        {
+            "truncated": True,
+            "preview": text[:119_800],
+        },
+        ensure_ascii=False,
+    )
+
+
+def _answer_record(row: Mapping[str, Any]) -> dict[str, Any]:
+    result_json = str(row.get("result_json") or "").strip()
+    try:
+        result = json.loads(result_json) if result_json else {}
+    except ValueError:
+        result = {"raw": result_json}
+    return {
+        "answer_seq": row.get("answer_seq"),
+        "question_id": row.get("question_id"),
+        "answer_kind": str(row.get("answer_kind") or ""),
+        "run_key": str(row.get("run_key") or ""),
+        "conversation_id": str(row.get("conversation_id") or ""),
+        "requested_by": str(row.get("requested_by") or ""),
+        "requested_at": str(row.get("requested_at") or ""),
+        "model_profile": str(row.get("model_profile") or ""),
+        "generated_sql": str(row.get("generated_sql") or ""),
+        "answer_text": str(row.get("answer_text") or ""),
+        "result": result,
+        "execution_output": str(row.get("execution_output") or ""),
+        "execution_status": str(row.get("execution_status") or ""),
+        "judgment_status": str(row.get("judgment_status") or ""),
+        "judgment_reason": str(row.get("judgment_reason") or ""),
+        "duration_ms": row.get("duration_ms"),
+        "created_at": str(row.get("created_at") or ""),
+    }
+
+
+class QaHistoryStore:
+    def __init__(self, *, env_file: Path | None = None) -> None:
+        self._env_file = env_file
+        self._pool: Any | None = None
+
+    def _config(self) -> dict[str, str]:
+        username = (
+            _env_value("POC4_QA_DB_USERNAME", self._env_file)
+            or _env_value("BACKOFFICE_SELECT_AI_DB_USERNAME", self._env_file)
+            or _env_value("BACKOFFICE_DB_USERNAME", self._env_file)
+        )
+        password = (
+            _env_value("POC4_QA_DB_PASSWORD", self._env_file)
+            or _env_value("BACKOFFICE_SELECT_AI_DB_PASSWORD", self._env_file)
+            or _env_value("BACKOFFICE_DB_PASSWORD", self._env_file)
+        )
+        raw_dsn = (
+            _env_value("POC4_QA_DB_DSN", self._env_file)
+            or _env_value("BACKOFFICE_SELECT_AI_DB_URL", self._env_file)
+            or _env_value("BACKOFFICE_DB_URL", self._env_file)
+        )
+        dsn, wallet_from_dsn = _normalize_oracle_dsn(raw_dsn)
+        wallet_dir = (
+            _env_value("POC4_QA_DB_WALLET_DIR", self._env_file)
+            or wallet_from_dsn
+            or _env_value("ORACLE_WALLET_DIR", self._env_file)
+        )
+        if not username or not password or not dsn:
+            raise QaHistoryStoreError("질답 이력 DB 접속 설정을 확인해 주세요.")
+        return {
+            "username": username,
+            "password": password,
+            "dsn": dsn,
+            "wallet_dir": wallet_dir,
+        }
+
+    def _get_pool(self) -> Any:
+        if self._pool is not None:
+            return self._pool
+        config = self._config()
+        kwargs: dict[str, Any] = {
+            "user": config["username"],
+            "password": config["password"],
+            "dsn": config["dsn"],
+            "min": 1,
+            "max": 3,
+            "increment": 1,
+            "getmode": oracledb.POOL_GETMODE_WAIT,
+        }
+        wallet_dir = Path(config["wallet_dir"]).expanduser()
+        if config["wallet_dir"]:
+            if not wallet_dir.is_dir():
+                raise QaHistoryStoreError("질답 이력 DB Wallet 경로를 확인해 주세요.")
+            kwargs["config_dir"] = str(wallet_dir)
+        try:
+            self._pool = oracledb.create_pool(**kwargs)
+            return self._pool
+        except (oracledb.Error, OSError, ValueError) as exc:
+            raise QaHistoryStoreError("질답 이력 DB에 연결하지 못했습니다.") from exc
+
+    @contextmanager
+    def _connection(self) -> Iterator[Any]:
+        try:
+            with self._get_pool().acquire() as connection:
+                yield connection
+        except QaHistoryStoreError:
+            raise
+        except (oracledb.Error, OSError, ValueError) as exc:
+            raise QaHistoryStoreError("질답 이력 DB 작업에 실패했습니다.") from exc
+
+    def list_questions(self, *, limit: int = 200) -> list[QaQuestion]:
+        sql = f"""
+            SELECT q.question_id, q.question_code, q.category, q.title,
+                   q.question_text, q.source_document, q.source_sheet,
+                   q.source_row, q.source_scenario, q.sample_sql,
+                   q.expected_focus, q.baseline_sql, q.baseline_answer,
+                   q.support_level, q.evaluation_rule_json,
+                   latest.judgment_status AS last_judgment_status,
+                   TO_CHAR(latest.evaluated_at AT TIME ZONE 'Asia/Seoul',
+                           'YYYY-MM-DD HH24:MI:SS TZH:TZM') AS last_evaluated_at
+            FROM {QUESTION_TABLE} q
+            LEFT JOIN (
+                SELECT question_id, judgment_status, evaluated_at
+                FROM (
+                    SELECT question_id, judgment_status, evaluated_at,
+                           ROW_NUMBER() OVER (
+                               PARTITION BY question_id ORDER BY answer_seq DESC
+                           ) AS row_no
+                    FROM {ANSWER_TABLE}
+                )
+                WHERE row_no = 1
+            ) latest ON latest.question_id = q.question_id
+            WHERE q.active_yn = 'Y'
+            ORDER BY q.category, q.question_code
+            FETCH FIRST :row_limit ROWS ONLY
+        """
+        with self._connection() as connection:
+            with connection.cursor() as cursor:
+                cursor.execute(sql, {"row_limit": int(limit)})
+                rows = [_record_from_cursor(cursor, row) for row in cursor]
+        return [question_from_record(row) for row in rows]
+
+    def get_question(self, question_id: int) -> QaQuestion | None:
+        sql = f"""
+            SELECT question_id, question_code, category, title, question_text,
+                   source_document, source_sheet, source_row, source_scenario,
+                   sample_sql, expected_focus, baseline_sql, baseline_answer,
+                   support_level, evaluation_rule_json
+            FROM {QUESTION_TABLE}
+            WHERE question_id = :question_id AND active_yn = 'Y'
+        """
+        with self._connection() as connection:
+            with connection.cursor() as cursor:
+                cursor.execute(sql, {"question_id": int(question_id)})
+                row = cursor.fetchone()
+                return question_from_record(_record_from_cursor(cursor, row)) if row else None
+
+    def get_question_by_code(self, question_code: str) -> QaQuestion | None:
+        sql = f"""
+            SELECT question_id, question_code, category, title, question_text,
+                   source_document, source_sheet, source_row, source_scenario,
+                   sample_sql, expected_focus, baseline_sql, baseline_answer,
+                   support_level, evaluation_rule_json
+            FROM {QUESTION_TABLE}
+            WHERE question_code = :question_code AND active_yn = 'Y'
+        """
+        with self._connection() as connection:
+            with connection.cursor() as cursor:
+                cursor.execute(sql, {"question_code": str(question_code).upper()})
+                row = cursor.fetchone()
+                return question_from_record(_record_from_cursor(cursor, row)) if row else None
+
+    def list_answers(self, question_id: int, *, limit: int = 30) -> list[dict[str, Any]]:
+        sql = f"""
+            SELECT answer_seq, question_id, answer_kind, run_key, conversation_id,
+                   requested_by,
+                   TO_CHAR(requested_at AT TIME ZONE 'Asia/Seoul',
+                           'YYYY-MM-DD HH24:MI:SS TZH:TZM') AS requested_at,
+                   model_profile, generated_sql, answer_text, result_json,
+                   execution_output, execution_status, judgment_status,
+                   judgment_reason, duration_ms,
+                   TO_CHAR(created_at AT TIME ZONE 'Asia/Seoul',
+                           'YYYY-MM-DD HH24:MI:SS TZH:TZM') AS created_at
+            FROM {ANSWER_TABLE}
+            WHERE question_id = :question_id
+            ORDER BY answer_seq DESC
+            FETCH FIRST :row_limit ROWS ONLY
+        """
+        with self._connection() as connection:
+            with connection.cursor() as cursor:
+                cursor.execute(sql, {"question_id": int(question_id), "row_limit": int(limit)})
+                rows = [_record_from_cursor(cursor, row) for row in cursor]
+        return [_answer_record(row) for row in rows]
+
+    def find_or_create_free_text_question(self, question_text: str) -> QaQuestion:
+        normalized = str(question_text or "").strip()
+        if not normalized:
+            raise QaHistoryStoreError("자유 질의가 비어 있습니다.")
+        fingerprint = question_fingerprint(normalized)
+        code = f"ADHOC-{fingerprint[:12].upper()}"
+        merge_sql = f"""
+            MERGE INTO {QUESTION_TABLE} target
+            USING (SELECT :question_hash AS question_hash FROM dual) source
+            ON (target.question_hash = source.question_hash)
+            WHEN NOT MATCHED THEN INSERT (
+                question_code, question_source, question_hash, category, title,
+                question_text, support_level, evaluation_rule_json, active_yn
+            ) VALUES (
+                :question_code, 'FREE_TEXT', :question_hash, 'FREE_TEXT',
+                :title, :question_text, 'REVIEW', '{{}}', 'Y'
+            )
+        """
+        with self._connection() as connection:
+            with connection.cursor() as cursor:
+                cursor.execute(
+                    merge_sql,
+                    {
+                        "question_hash": fingerprint,
+                        "question_code": code,
+                        "title": normalized[:180],
+                        "question_text": normalized,
+                    },
+                )
+                connection.commit()
+                cursor.execute(
+                    f"""SELECT question_id FROM {QUESTION_TABLE}
+                         WHERE question_hash = :question_hash""",
+                    {"question_hash": fingerprint},
+                )
+                row = cursor.fetchone()
+        if not row:
+            raise QaHistoryStoreError("자유 질의 마스터를 저장하지 못했습니다.")
+        question = self.get_question(int(row[0]))
+        if question is None:
+            raise QaHistoryStoreError("자유 질의 마스터를 다시 읽지 못했습니다.")
+        return question
+
+    def record_answer(
+        self,
+        *,
+        question_id: int,
+        answer_kind: str,
+        conversation_id: str,
+        requested_by: str,
+        model_profile: str,
+        generated_sql: str,
+        answer_text: str,
+        result: Mapping[str, Any] | None,
+        execution_output: str,
+        execution_status: str,
+        judgment_status: str,
+        judgment_reason: str,
+        duration_ms: int | None,
+        run_key: str = "",
+    ) -> None:
+        sql = f"""
+            INSERT INTO {ANSWER_TABLE} (
+                question_id, answer_kind, run_key, conversation_id, requested_by,
+                requested_at, model_profile, generated_sql, answer_text, result_json,
+                execution_output, execution_status, judgment_status,
+                judgment_reason, duration_ms
+            ) VALUES (
+                :question_id, :answer_kind, :run_key, :conversation_id,
+                :requested_by, SYSTIMESTAMP, :model_profile, :generated_sql,
+                :answer_text, :result_json, :execution_output, :execution_status,
+                :judgment_status, :judgment_reason, :duration_ms
+            )
+        """
+        with self._connection() as connection:
+            with connection.cursor() as cursor:
+                cursor.execute(
+                    sql,
+                    {
+                        "question_id": int(question_id),
+                        "answer_kind": str(answer_kind)[:20],
+                        "run_key": str(run_key)[:100] or None,
+                        "conversation_id": str(conversation_id)[:100] or None,
+                        "requested_by": str(requested_by)[:100] or None,
+                        "model_profile": str(model_profile)[:100] or None,
+                        "generated_sql": str(generated_sql or ""),
+                        "answer_text": str(answer_text or ""),
+                        "result_json": _to_json(result),
+                        "execution_output": str(execution_output or ""),
+                        "execution_status": str(execution_status)[:40] or None,
+                        "judgment_status": str(judgment_status)[:20],
+                        "judgment_reason": str(judgment_reason or ""),
+                        "duration_ms": duration_ms,
+                    },
+                )
+                connection.commit()
+
+    def seed_benchmark(self, benchmark_file: Path) -> tuple[int, int]:
+        questions = load_benchmark_questions(benchmark_file)
+        raw = json.loads(benchmark_file.read_text(encoding="utf-8"))
+        raw_by_code = {
+            str(item.get("case_id") or "").upper(): item
+            for item in raw.get("scenarios", [])
+            if isinstance(item, Mapping)
+        }
+        seeded_questions = 0
+        seeded_answers = 0
+        for question in questions:
+            question_id = self._upsert_benchmark_question(question)
+            seeded_questions += 1
+            raw_item = raw_by_code[question.question_code]
+            history = raw_item.get("historical_answer") if isinstance(raw_item.get("historical_answer"), Mapping) else {}
+            inserted = self._seed_historical_answer(question_id, history, raw)
+            seeded_answers += 1 if inserted else 0
+        return seeded_questions, seeded_answers
+
+    def _upsert_benchmark_question(self, question: QaQuestion) -> int:
+        sql = f"""
+            MERGE INTO {QUESTION_TABLE} target
+            USING (SELECT :question_code AS question_code FROM dual) source
+            ON (target.question_code = source.question_code)
+            WHEN MATCHED THEN UPDATE SET
+                question_source = 'CUSTOMER_EXCEL',
+                question_hash = :question_hash,
+                category = :category,
+                title = :title,
+                question_text = :question_text,
+                source_document = :source_document,
+                source_sheet = :source_sheet,
+                source_row = :source_row,
+                source_scenario = :source_scenario,
+                sample_sql = :sample_sql,
+                expected_focus = :expected_focus,
+                baseline_sql = :baseline_sql,
+                baseline_answer = :baseline_answer,
+                support_level = :support_level,
+                evaluation_rule_json = :evaluation_rule_json,
+                active_yn = 'Y',
+                updated_at = SYSTIMESTAMP
+            WHEN NOT MATCHED THEN INSERT (
+                question_code, question_source, question_hash, category, title,
+                question_text, source_document, source_sheet, source_row,
+                source_scenario, sample_sql, expected_focus, baseline_sql,
+                baseline_answer, support_level, evaluation_rule_json, active_yn
+            ) VALUES (
+                :question_code, 'CUSTOMER_EXCEL', :question_hash, :category,
+                :title, :question_text, :source_document, :source_sheet,
+                :source_row, :source_scenario, :sample_sql, :expected_focus,
+                :baseline_sql, :baseline_answer, :support_level,
+                :evaluation_rule_json, 'Y'
+            )
+        """
+        binds = {
+            "question_code": question.question_code,
+            "question_hash": question_fingerprint(question.question_text),
+            "category": question.category[:30],
+            "title": question.title[:200],
+            "question_text": question.question_text,
+            "source_document": question.source_document[:255] or None,
+            "source_sheet": question.source_sheet[:255] or None,
+            "source_row": question.source_row,
+            "source_scenario": question.source_scenario,
+            "sample_sql": question.sample_sql,
+            "expected_focus": question.expected_focus,
+            "baseline_sql": question.baseline_sql,
+            "baseline_answer": question.baseline_answer,
+            "support_level": question.support_level[:20],
+            "evaluation_rule_json": json.dumps(question.evaluation_rule, ensure_ascii=False),
+        }
+        with self._connection() as connection:
+            with connection.cursor() as cursor:
+                cursor.execute(sql, binds)
+                connection.commit()
+                cursor.execute(
+                    f"SELECT question_id FROM {QUESTION_TABLE} WHERE question_code = :question_code",
+                    {"question_code": question.question_code},
+                )
+                row = cursor.fetchone()
+        if not row:
+            raise QaHistoryStoreError(f"질문 마스터를 적재하지 못했습니다: {question.question_code}")
+        return int(row[0])
+
+    def _seed_historical_answer(
+        self,
+        question_id: int,
+        history: Mapping[str, Any],
+        benchmark: Mapping[str, Any],
+    ) -> bool:
+        exists_sql = f"""
+            SELECT COUNT(*) FROM {ANSWER_TABLE}
+             WHERE question_id = :question_id AND run_key = :run_key
+        """
+        with self._connection() as connection:
+            with connection.cursor() as cursor:
+                cursor.execute(exists_sql, {"question_id": question_id, "run_key": HISTORICAL_RUN_KEY})
+                if int(cursor.fetchone()[0]) > 0:
+                    return False
+        result = {
+            "source_report": str(benchmark.get("source_report") or ""),
+            "source_redmine": benchmark.get("source_redmine"),
+            "historical_execution_output": str(history.get("execution_output") or ""),
+        }
+        self.record_answer(
+            question_id=question_id,
+            answer_kind="HISTORICAL",
+            run_key=HISTORICAL_RUN_KEY,
+            conversation_id="",
+            requested_by="customer-excel-baseline",
+            model_profile=str(history.get("profile") or ""),
+            generated_sql=str(history.get("generated_sql") or ""),
+            answer_text=str(history.get("answer_text") or ""),
+            result=result,
+            execution_output=str(history.get("execution_output") or ""),
+            execution_status=str(history.get("execution_status") or ""),
+            judgment_status=str(history.get("judgment_status") or "REVIEW"),
+            judgment_reason=str(history.get("judgment_reason") or ""),
+            duration_ms=int(history.get("duration_ms") or 0),
+        )
+        return True
+
+
+def schema_statements() -> tuple[str, ...]:
+    return (
+        f"""
+        CREATE TABLE {QUESTION_TABLE} (
+            question_id NUMBER GENERATED BY DEFAULT ON NULL AS IDENTITY PRIMARY KEY,
+            question_code VARCHAR2(30) UNIQUE,
+            question_source VARCHAR2(30) NOT NULL,
+            question_hash VARCHAR2(64) NOT NULL UNIQUE,
+            category VARCHAR2(30) NOT NULL,
+            title VARCHAR2(200) NOT NULL,
+            question_text CLOB NOT NULL,
+            source_document VARCHAR2(255),
+            source_sheet VARCHAR2(255),
+            source_row NUMBER,
+            source_scenario CLOB,
+            sample_sql CLOB,
+            expected_focus CLOB,
+            baseline_sql CLOB,
+            baseline_answer CLOB,
+            support_level VARCHAR2(20) NOT NULL,
+            evaluation_rule_json CLOB CHECK (evaluation_rule_json IS JSON),
+            active_yn CHAR(1) DEFAULT 'Y' NOT NULL CHECK (active_yn IN ('Y', 'N')),
+            created_at TIMESTAMP WITH TIME ZONE DEFAULT SYSTIMESTAMP NOT NULL,
+            updated_at TIMESTAMP WITH TIME ZONE DEFAULT SYSTIMESTAMP NOT NULL,
+            CONSTRAINT sg_ai_qa_question_source_ck
+                CHECK (question_source IN ('CUSTOMER_EXCEL', 'FREE_TEXT'))
+        )
+        """,
+        f"""
+        CREATE TABLE {ANSWER_TABLE} (
+            answer_seq NUMBER GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
+            question_id NUMBER NOT NULL,
+            answer_kind VARCHAR2(20) NOT NULL,
+            run_key VARCHAR2(100),
+            conversation_id VARCHAR2(100),
+            requested_by VARCHAR2(100),
+            requested_at TIMESTAMP WITH TIME ZONE DEFAULT SYSTIMESTAMP NOT NULL,
+            model_profile VARCHAR2(100),
+            generated_sql CLOB,
+            answer_text CLOB,
+            result_json CLOB CHECK (result_json IS JSON),
+            execution_output CLOB,
+            execution_status VARCHAR2(40),
+            judgment_status VARCHAR2(20) NOT NULL,
+            judgment_reason CLOB,
+            duration_ms NUMBER,
+            created_at TIMESTAMP WITH TIME ZONE DEFAULT SYSTIMESTAMP NOT NULL,
+            CONSTRAINT sg_ai_qa_answer_question_fk
+                FOREIGN KEY (question_id)
+                REFERENCES {QUESTION_TABLE} (question_id)
+                ON DELETE CASCADE,
+            CONSTRAINT sg_ai_qa_answer_kind_ck
+                CHECK (answer_kind IN ('HISTORICAL', 'LIVE')),
+            CONSTRAINT sg_ai_qa_answer_judgment_ck
+                CHECK (judgment_status IN ('PASS', 'WARN', 'FAIL', 'REVIEW'))
+        )
+        """,
+        f"""
+        CREATE INDEX sg_ai_qa_answer_question_ix
+            ON {ANSWER_TABLE} (question_id, answer_seq DESC)
+        """,
+        f"""
+        CREATE UNIQUE INDEX sg_ai_qa_answer_run_uk
+            ON {ANSWER_TABLE} (question_id, run_key)
+        """,
+    )
+
+
+def ensure_schema(store: QaHistoryStore) -> None:
+    objects = (QUESTION_TABLE, ANSWER_TABLE)
+    with store._connection() as connection:
+        with connection.cursor() as cursor:
+            cursor.execute(
+                "SELECT table_name FROM user_tables WHERE table_name IN (:q, :a)",
+                {"q": objects[0], "a": objects[1]},
+            )
+            existing = {str(row[0]) for row in cursor}
+            statements = schema_statements()
+            if QUESTION_TABLE not in existing:
+                cursor.execute(statements[0])
+            if ANSWER_TABLE not in existing:
+                cursor.execute(statements[1])
+            cursor.execute(
+                "SELECT index_name FROM user_indexes WHERE index_name IN (:ix1, :ix2)",
+                {"ix1": "SG_AI_QA_ANSWER_QUESTION_IX", "ix2": "SG_AI_QA_ANSWER_RUN_UK"},
+            )
+            indexes = {str(row[0]) for row in cursor}
+            if "SG_AI_QA_ANSWER_QUESTION_IX" not in indexes:
+                cursor.execute(statements[2])
+            if "SG_AI_QA_ANSWER_RUN_UK" not in indexes:
+                cursor.execute(statements[3])
+            connection.commit()
+
+
+def timestamp_now() -> str:
+    return datetime.now(timezone.utc).isoformat(timespec="seconds")

ai-web-agent-console/ai_web_agent_console/query_contracts.py

@@ -0,0 +1,407 @@
+"""Configuration-driven query and answer evidence contracts."""
+
+from __future__ import annotations
+
+import ast
+from decimal import Decimal, InvalidOperation
+from functools import lru_cache
+import json
+import os
+from pathlib import Path
+import re
+from typing import Any, Mapping
+
+
+ROOT = Path(__file__).resolve().parents[1]
+DEFAULT_CONTRACT_FILE = ROOT / "config" / "hmm_hr_query_contracts.json"
+
+
+def _contract_file() -> Path:
+    configured = str(
+        os.getenv("AI_WEB_AGENT_CONSOLE_QUERY_CONTRACTS_PATH")
+        or os.getenv("POC4_QUERY_CONTRACTS_PATH")
+        or ""
+    ).strip()
+    if not configured:
+        return DEFAULT_CONTRACT_FILE
+    path = Path(configured).expanduser()
+    return path if path.is_absolute() else ROOT / path
+
+
+@lru_cache(maxsize=4)
+def _load_contract_file(path_text: str) -> tuple[Mapping[str, Any], ...]:
+    path = Path(path_text)
+    if not path.exists():
+        return ()
+    value = json.loads(path.read_text(encoding="utf-8"))
+    contracts = value.get("contracts") if isinstance(value, Mapping) else None
+    if not isinstance(contracts, list):
+        raise ValueError("query contracts must contain a contracts array")
+    return tuple(item for item in contracts if isinstance(item, Mapping))
+
+
+def load_query_contracts() -> tuple[Mapping[str, Any], ...]:
+    return _load_contract_file(str(_contract_file()))
+
+
+def matching_query_contracts(
+    question: str,
+    tool_name: str,
+) -> tuple[Mapping[str, Any], ...]:
+    normalized_question = " ".join(str(question or "").casefold().split())
+    normalized_tool = str(tool_name or "").strip()
+    selected: list[Mapping[str, Any]] = []
+    for contract in load_query_contracts():
+        tools = contract.get("applies_to_tools")
+        if isinstance(tools, list) and normalized_tool not in {
+            str(item) for item in tools
+        }:
+            continue
+        intent = contract.get("intent_match")
+        if not isinstance(intent, Mapping):
+            continue
+        subject_terms = [
+            str(item).casefold()
+            for item in intent.get("subject_terms_any", [])
+            if str(item).strip()
+        ]
+        action_terms = [
+            str(item).casefold()
+            for item in intent.get("action_terms_any", [])
+            if str(item).strip()
+        ]
+        if subject_terms and not any(
+            term in normalized_question for term in subject_terms
+        ):
+            continue
+        if action_terms and not any(
+            term in normalized_question for term in action_terms
+        ):
+            continue
+        selected.append(contract)
+    return tuple(selected)
+
+
+def query_contract_guidance(question: str, tool_name: str) -> str:
+    contracts = matching_query_contracts(question, tool_name)
+    if not contracts:
+        return ""
+    return json.dumps(
+        {"query_contracts": contracts},
+        ensure_ascii=False,
+        separators=(",", ":"),
+    )
+
+
+def append_query_contract_guidance(
+    tool_query: str,
+    *,
+    original_question: str,
+    tool_name: str,
+) -> str:
+    guidance = query_contract_guidance(original_question, tool_name)
+    normalized = str(tool_query or "").strip()
+    if not guidance:
+        return normalized
+    return (
+        f"{normalized}\n"
+        "다음 질의 계약의 필드·계산·시간 기준을 반드시 지켜 결과를 반환하세요. "
+        f"계약: {guidance}"
+    )
+
+
+def _json_data_result(value: str) -> Any:
+    marker = "DATA_RESULT"
+    position = value.find(marker)
+    if position < 0:
+        return None
+    remainder = value[position + len(marker) :]
+    object_position = remainder.find("{")
+    array_position = remainder.find("[")
+    positions = [
+        candidate
+        for candidate in (object_position, array_position)
+        if candidate >= 0
+    ]
+    if not positions:
+        return None
+    candidate = remainder[min(positions) :]
+    try:
+        parsed, _ = json.JSONDecoder().raw_decode(candidate)
+    except (TypeError, ValueError, json.JSONDecodeError):
+        return None
+    return parsed
+
+
+def _evidence_rows(value: Any) -> list[Mapping[str, Any]]:
+    rows: list[Mapping[str, Any]] = []
+    if isinstance(value, Mapping):
+        rows.append(value)
+        for item in value.values():
+            rows.extend(_evidence_rows(item))
+    elif isinstance(value, list):
+        for item in value:
+            rows.extend(_evidence_rows(item))
+    elif isinstance(value, str):
+        parsed = _json_data_result(value)
+        if parsed is not None:
+            rows.extend(_evidence_rows(parsed))
+    return rows
+
+
+def _decimal(value: Any) -> Decimal:
+    if isinstance(value, bool) or value is None:
+        raise InvalidOperation
+    return Decimal(str(value))
+
+
+def _evaluate_contract_expression(
+    expression: str,
+    row: Mapping[str, Any],
+) -> Decimal:
+    tree = ast.parse(expression, mode="eval")
+
+    def evaluate(node: ast.AST) -> Decimal:
+        if isinstance(node, ast.Expression):
+            return evaluate(node.body)
+        if isinstance(node, ast.Name):
+            key = node.id.casefold()
+            if key not in row:
+                raise InvalidOperation
+            return _decimal(row[key])
+        if isinstance(node, ast.Constant):
+            return _decimal(node.value)
+        if isinstance(node, ast.UnaryOp) and isinstance(
+            node.op, (ast.UAdd, ast.USub)
+        ):
+            value = evaluate(node.operand)
+            return value if isinstance(node.op, ast.UAdd) else -value
+        if isinstance(node, ast.BinOp) and isinstance(
+            node.op, (ast.Add, ast.Sub, ast.Mult, ast.Div)
+        ):
+            left = evaluate(node.left)
+            right = evaluate(node.right)
+            if isinstance(node.op, ast.Add):
+                return left + right
+            if isinstance(node.op, ast.Sub):
+                return left - right
+            if isinstance(node.op, ast.Mult):
+                return left * right
+            return left / right
+        raise ValueError("unsupported contract expression")
+
+    return evaluate(tree)
+
+
+def evidence_contract_report(
+    contracts: tuple[Mapping[str, Any], ...],
+    evidence: Any,
+) -> list[dict[str, Any]]:
+    rows = [
+        {str(key).strip().casefold(): value for key, value in row.items()}
+        for row in _evidence_rows(evidence)
+    ]
+    reports: list[dict[str, Any]] = []
+    for contract in contracts:
+        data_contract = contract.get("data_contract")
+        if not isinstance(data_contract, Mapping):
+            continue
+        required = [
+            str(item).strip().casefold()
+            for item in data_contract.get("required_fields", [])
+            if str(item).strip()
+        ]
+        matching_row = next(
+            (row for row in rows if required and all(key in row for key in required)),
+            None,
+        )
+        observed = sorted(
+            {
+                key
+                for row in rows
+                for key in row
+                if not required or key in required
+            }
+        )
+        missing = [
+            key.upper()
+            for key in required
+            if matching_row is None or key not in matching_row
+        ]
+        computed_checks: list[dict[str, Any]] = []
+        computed_fields = data_contract.get("computed_fields")
+        if matching_row is not None and isinstance(computed_fields, Mapping):
+            for field, definition in computed_fields.items():
+                normalized_field = str(field).strip().casefold()
+                expression = (
+                    str(definition.get("expression") or "").strip()
+                    if isinstance(definition, Mapping)
+                    else ""
+                )
+                if not normalized_field or not expression:
+                    continue
+                try:
+                    actual = _decimal(matching_row.get(normalized_field))
+                    expected = _evaluate_contract_expression(
+                        expression,
+                        matching_row,
+                    )
+                    matches = actual == expected
+                    computed_checks.append(
+                        {
+                            "field": normalized_field.upper(),
+                            "expression": expression,
+                            "actual": str(actual),
+                            "expected": str(expected),
+                            "satisfied": matches,
+                        }
+                    )
+                except (InvalidOperation, ValueError, ZeroDivisionError):
+                    computed_checks.append(
+                        {
+                            "field": normalized_field.upper(),
+                            "expression": expression,
+                            "satisfied": False,
+                        }
+                    )
+        calculations_satisfied = all(
+            bool(check.get("satisfied")) for check in computed_checks
+        )
+        temporal_checks: list[dict[str, Any]] = []
+        temporal_contract = contract.get("temporal_contract")
+        if matching_row is not None and isinstance(temporal_contract, Mapping):
+            status_fields = [
+                str(item).strip().casefold()
+                for item in temporal_contract.get("period_status_fields", [])
+                if str(item).strip()
+            ]
+            past_status = str(
+                temporal_contract.get("past_period_status") or ""
+            ).strip()
+            observed_status = next(
+                (
+                    str(matching_row.get(field) or "").strip()
+                    for field in status_fields
+                    if str(matching_row.get(field) or "").strip()
+                ),
+                "",
+            )
+            if observed_status == past_status and past_status:
+                decision_field = str(
+                    temporal_contract.get("past_period_decision_field") or ""
+                ).strip().casefold()
+                decision_value = str(
+                    temporal_contract.get("past_period_decision_value") or ""
+                ).strip()
+                actual_decision = str(
+                    matching_row.get(decision_field) or ""
+                ).strip()
+                temporal_checks.append(
+                    {
+                        "check": "past_period_decision",
+                        "field": decision_field.upper(),
+                        "expected": decision_value,
+                        "actual": actual_decision,
+                        "satisfied": bool(
+                            decision_field
+                            and decision_value
+                            and actual_decision == decision_value
+                        ),
+                    }
+                )
+                forbidden_patterns = [
+                    str(item).strip()
+                    for item in temporal_contract.get(
+                        "forbidden_past_period_decision_field_patterns", []
+                    )
+                    if str(item).strip()
+                ]
+                forbidden_fields = sorted(
+                    key.upper()
+                    for key in matching_row
+                    if any(
+                        re.search(pattern, key, flags=re.IGNORECASE)
+                        for pattern in forbidden_patterns
+                    )
+                )
+                temporal_checks.append(
+                    {
+                        "check": "no_past_period_yes_no_decision",
+                        "forbidden_fields": forbidden_fields,
+                        "satisfied": not forbidden_fields,
+                    }
+                )
+                required_counts = [
+                    str(item).strip().casefold()
+                    for item in temporal_contract.get(
+                        "validate_stated_days_against", []
+                    )
+                    if str(item).strip()
+                ]
+                temporal_checks.append(
+                    {
+                        "check": "date_range_counts",
+                        "required_fields": [
+                            field.upper() for field in required_counts
+                        ],
+                        "missing_fields": [
+                            field.upper()
+                            for field in required_counts
+                            if field not in matching_row
+                        ],
+                        "satisfied": all(
+                            field in matching_row for field in required_counts
+                        ),
+                    }
+                )
+        temporal_satisfied = all(
+            bool(check.get("satisfied")) for check in temporal_checks
+        )
+        reports.append(
+            {
+                "contract_id": str(contract.get("id") or ""),
+                "satisfied": bool(
+                    matching_row is not None
+                    and not missing
+                    and calculations_satisfied
+                    and temporal_satisfied
+                ),
+                "required_fields": [key.upper() for key in required],
+                "observed_required_fields": [key.upper() for key in observed],
+                "missing_fields": missing,
+                "computed_field_checks": computed_checks,
+                "temporal_contract_checks": temporal_checks,
+                "validated_record": (
+                    {
+                        key.upper(): matching_row.get(key)
+                        for key in required
+                    }
+                    if matching_row is not None
+                    else {}
+                ),
+            }
+        )
+    return reports
+
+
+def missing_evidence_message(
+    contracts: tuple[Mapping[str, Any], ...],
+) -> str:
+    for contract in contracts:
+        answer_contract = contract.get("answer_contract")
+        if not isinstance(answer_contract, Mapping):
+            continue
+        message = str(answer_contract.get("missing_evidence_message") or "").strip()
+        if message:
+            return message
+    return "조회 결과가 답변 계약의 필수 근거를 충족하지 않아 결론을 제공할 수 없습니다."
+
+
+__all__ = [
+    "append_query_contract_guidance",
+    "evidence_contract_report",
+    "load_query_contracts",
+    "matching_query_contracts",
+    "missing_evidence_message",
+    "query_contract_guidance",
+]

ai-web-agent-console/ai_web_agent_console/questions.py

@@ -0,0 +1,264 @@
+"""AI Web Agent Console preset catalog와 현재 질문 기반의 결정적 intent router."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+import re
+import unicodedata
+
+
+QUESTION_CATEGORIES = ("STRUCTURED", "RAG", "HYBRID")
+GENERIC_RAG_QUESTION_ID = "R0"
+MAX_QUESTION_CHARS = 2_000
+
+
+@dataclass(frozen=True)
+class DemoQuestion:
+    """ID와 분류가 고정된 데모 질문."""
+
+    question_id: str
+    category: str
+    text: str
+
+    def __post_init__(self) -> None:
+        if self.category not in QUESTION_CATEGORIES:
+            raise ValueError("unsupported demo question category")
+        if not self.question_id or not self.text.strip():
+            raise ValueError("demo question id/text is required")
+
+
+@dataclass(frozen=True)
+class ResolvedQuestionIntent:
+    """현재 질문만으로 결정된 실행 intent.
+
+    ``question_id``는 local route/fixture를 설명하는 분류 label이다. 8500 provider
+    입력으로 전달되지 않으며 UI에서 선택한 scenario ID도 이 모델에 들어오지
+    않는다.
+    """
+
+    question_id: str
+    category: str
+
+    def __post_init__(self) -> None:
+        if self.category not in QUESTION_CATEGORIES:
+            raise ValueError("unsupported resolved question category")
+        if self.question_id not in {
+            GENERIC_RAG_QUESTION_ID,
+            "S1",
+            "S2",
+            "S3",
+            "S4",
+            "S5",
+            "R1",
+            "R2",
+            "H1",
+            "H2",
+            "H3",
+            "H4",
+        }:
+            raise ValueError("unsupported resolved question id")
+
+
+COMMON_DEMO_QUESTIONS = (
+    DemoQuestion("S1", "STRUCTURED", "상품별 계약 건수를 보여줘."),
+    DemoQuestion("S2", "STRUCTURED", "총 지급보험금이 가장 큰 상품은?"),
+    DemoQuestion(
+        "S3",
+        "STRUCTURED",
+        "숫자로 계산 가능한 평균 보장금액이 높은 상품 10개를 보여줘.",
+    ),
+    DemoQuestion("S4", "STRUCTURED", "고객 등급별 평균 보험료를 보여줘."),
+    DemoQuestion("S5", "STRUCTURED", "이해관계자 역할별 인원 수를 보여줘."),
+    DemoQuestion("R1", "RAG", "자동차보험 약관의 면책 사항은?"),
+    DemoQuestion("R2", "RAG", "보험금 청구 시 필요한 서류는?"),
+    DemoQuestion(
+        "H1",
+        "HYBRID",
+        "보험금이 가장 큰 상품의 주요 면책 조항을 알려줘.",
+    ),
+    DemoQuestion(
+        "H2",
+        "HYBRID",
+        "청구가 많은 상품군의 보장 제외 조건을 알려줘.",
+    ),
+    DemoQuestion(
+        "H3",
+        "HYBRID",
+        "고객 등급별 보험료 수준을 보고, 관련 약관상 유의해야 할 보장 제외 조건도 함께 알려줘.",
+    ),
+)
+
+_QUESTION_BY_ID = {item.question_id: item for item in COMMON_DEMO_QUESTIONS}
+if len(_QUESTION_BY_ID) != len(COMMON_DEMO_QUESTIONS):
+    raise RuntimeError("duplicate AI Web Agent Console demo question id")
+
+
+def question_by_id(question_id: str) -> DemoQuestion:
+    """정규화된 ID로 질문을 찾되, 알 수 없는 ID는 거부한다."""
+
+    normalized = str(question_id).strip().upper()
+    try:
+        return _QUESTION_BY_ID[normalized]
+    except KeyError:
+        raise ValueError("unknown AI Web Agent Console demo question id") from None
+
+
+def normalize_scenario_id(value: object) -> str | None:
+    """선택적인 preset ID를 정규화하되 실행 routing에는 관여하지 않는다."""
+
+    if value is None:
+        return None
+    if not isinstance(value, str):
+        raise ValueError("scenario id must be a string")
+    normalized = value.strip().upper()
+    if not normalized:
+        return None
+    question_by_id(normalized)
+    return normalized
+
+
+def _normalized_question(question: object) -> tuple[str, str]:
+    if not isinstance(question, str):
+        raise ValueError("question must be a non-empty string")
+    if len(question) > MAX_QUESTION_CHARS:
+        raise ValueError("question exceeds the supported length")
+    normalized = re.sub(
+        r"\s+", " ", unicodedata.normalize("NFKC", question).strip().lower()
+    )
+    if not normalized:
+        raise ValueError("question must be a non-empty string")
+    return normalized, normalized.replace(" ", "")
+
+
+def _contains_any(text: str, candidates: tuple[str, ...]) -> bool:
+    return any(candidate in text for candidate in candidates)
+
+
+def resolve_question_intent(question: object) -> ResolvedQuestionIntent:
+    """현재 질문 텍스트만으로 S/R/H route intent를 결정한다.
+
+    명확한 structured/hybrid intent에 해당하지 않는 질문은 임의로 추측하지 않고
+    ``R0`` generic RAG 검색으로 보낸다. 이 함수는 scenario 또는
+    question ID hint를 받지 않으므로 preset metadata가 실행을 바꿀 수 없다.
+    """
+
+    normalized, compact = _normalized_question(question)
+
+    # Canonical preset은 기존 10문항 동작을 byte-for-byte 보존한다.
+    for item in COMMON_DEMO_QUESTIONS:
+        candidate, _ = _normalized_question(item.text)
+        if normalized == candidate:
+            return ResolvedQuestionIntent(item.question_id, item.category)
+
+    has_product = _contains_any(compact, ("상품", "상품군"))
+    has_exclusion = _contains_any(
+        compact,
+        ("면책", "보장제외", "제외조건", "보상하지않", "약관상유의"),
+    )
+    has_top = _contains_any(
+        compact, ("가장큰", "최대", "최고", "1위", "상위", "제일많", "높은")
+    )
+
+    # 자유 질문에서 자동차보험 보유 규모와 타사 대비 강점을 함께 요구하면
+    # generic structured 조회와 자유 evidence 검색을 로컬에서 합성하는 H4로
+    # 보낸다. Label은 routing metadata일 뿐 provider query ID가 아니다.
+    has_auto_insurance = "자동차보험" in compact
+    has_count = _contains_any(
+        compact,
+        (
+            "갯수",
+            "개수",
+            "건수",
+            "계약수",
+            "몇개",
+            "몇건",
+            "보유수",
+            "상품수",
+        ),
+    )
+    has_competitor_comparison = _contains_any(
+        compact, ("타사", "경쟁사", "다른회사", "타보험사")
+    ) and _contains_any(compact, ("강점", "장점", "차별", "우위", "비교"))
+    if has_auto_insurance and has_count and has_competitor_comparison:
+        return ResolvedQuestionIntent("H4", "HYBRID")
+
+    # Hybrid를 먼저 판별해 정형 키워드가 포함된 복합 질문이 S/R 단일 route로
+    # 축소되지 않도록 한다.
+    if (
+        _contains_any(compact, ("고객등급", "등급별"))
+        and "보험료" in compact
+        and has_exclusion
+    ):
+        return ResolvedQuestionIntent("H3", "HYBRID")
+    if (
+        "청구" in compact
+        and _contains_any(compact, ("많은", "빈도", "건수", "상위"))
+        and has_product
+        and has_exclusion
+    ):
+        return ResolvedQuestionIntent("H2", "HYBRID")
+    if (
+        has_product
+        and _contains_any(compact, ("지급보험금", "보험금"))
+        and has_top
+        and has_exclusion
+    ):
+        return ResolvedQuestionIntent("H1", "HYBRID")
+
+    if (
+        has_product
+        and "계약" in compact
+        and _contains_any(compact, ("건수", "계약수", "몇건", "집계"))
+    ):
+        return ResolvedQuestionIntent("S1", "STRUCTURED")
+    if (
+        has_product
+        and _contains_any(compact, ("지급보험금", "보험금총액", "총보험금"))
+        and has_top
+    ):
+        return ResolvedQuestionIntent("S2", "STRUCTURED")
+    if (
+        has_product
+        and _contains_any(compact, ("보장금액", "가입금액"))
+        and "평균" in compact
+        and has_top
+    ):
+        return ResolvedQuestionIntent("S3", "STRUCTURED")
+    if (
+        _contains_any(compact, ("고객등급", "등급별"))
+        and "보험료" in compact
+        and "평균" in compact
+    ):
+        return ResolvedQuestionIntent("S4", "STRUCTURED")
+    if (
+        "이해관계자" in compact
+        and "역할" in compact
+        and _contains_any(compact, ("인원", "사람수", "몇명", "명수", "수"))
+    ):
+        return ResolvedQuestionIntent("S5", "STRUCTURED")
+
+    if (
+        "자동차보험" in compact
+        and _contains_any(compact, ("면책", "보상하지않", "보장제외", "제외사항"))
+    ):
+        return ResolvedQuestionIntent("R1", "RAG")
+    if (
+        _contains_any(compact, ("보험금", "청구"))
+        and _contains_any(compact, ("서류", "문서", "증빙", "제출자료"))
+    ):
+        return ResolvedQuestionIntent("R2", "RAG")
+
+    return ResolvedQuestionIntent(GENERIC_RAG_QUESTION_ID, "RAG")
+
+
+__all__ = [
+    "COMMON_DEMO_QUESTIONS",
+    "DemoQuestion",
+    "GENERIC_RAG_QUESTION_ID",
+    "MAX_QUESTION_CHARS",
+    "QUESTION_CATEGORIES",
+    "ResolvedQuestionIntent",
+    "normalize_scenario_id",
+    "question_by_id",
+    "resolve_question_intent",
+]

ai-web-agent-console/ai_web_agent_console/scenarios.py

@@ -0,0 +1,71 @@
+"""File-backed demo scenarios used by the PoC4 Streamlit screen.
+
+Scenario content is deliberately configuration, not executable routing policy.
+Changing this JSON changes only the menu shown to a demo user.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+import json
+from pathlib import Path
+from typing import Any, Mapping
+
+
+class ScenarioConfigError(RuntimeError):
+    """A safe, user-facing error for invalid scenario configuration."""
+
+
+@dataclass(frozen=True)
+class DemoScenario:
+    scenario_id: str
+    category: str
+    title: str
+    question: str
+
+    # Compatibility aliases keep the Streamlit rendering independent from the
+    # storage field names and make a future scenario source interchangeable.
+    @property
+    def question_id(self) -> str:
+        return self.scenario_id
+
+    @property
+    def text(self) -> str:
+        return self.question
+
+
+def load_demo_scenarios(path: Path) -> tuple[DemoScenario, ...]:
+    """Read enabled scenarios and reject malformed or duplicated entries."""
+
+    try:
+        payload: Any = json.loads(path.read_text(encoding="utf-8"))
+    except (OSError, UnicodeError, ValueError):
+        raise ScenarioConfigError(f"질문 시나리오 설정을 읽지 못했습니다: {path}") from None
+
+    raw_scenarios = payload.get("scenarios") if isinstance(payload, Mapping) else None
+    if not isinstance(raw_scenarios, list):
+        raise ScenarioConfigError("질문 시나리오 설정에 scenarios 배열이 필요합니다.")
+
+    scenarios: list[DemoScenario] = []
+    seen_ids: set[str] = set()
+    for raw in raw_scenarios:
+        if not isinstance(raw, Mapping) or raw.get("enabled", True) is not True:
+            continue
+        scenario_id = str(raw.get("id") or "").strip().upper()
+        category = str(raw.get("category") or "일반").strip()
+        title = str(raw.get("title") or "").strip()
+        question = str(raw.get("question") or "").strip()
+        if not scenario_id or not title or not question:
+            raise ScenarioConfigError("각 질문 시나리오에는 id, title, question이 필요합니다.")
+        if scenario_id in seen_ids:
+            raise ScenarioConfigError(f"중복된 질문 시나리오 ID입니다: {scenario_id}")
+        scenarios.append(
+            DemoScenario(
+                scenario_id=scenario_id,
+                category=category,
+                title=title,
+                question=question,
+            )
+        )
+        seen_ids.add(scenario_id)
+    return tuple(scenarios)

ai-web-agent-console/ai_web_agent_console/smilegate_demo/__init__.py

@@ -0,0 +1,5 @@
+"""Smilegate demo modules.
+
+The portal is assembled from small modules so each feature can be reviewed and
+released independently.
+"""

ai-web-agent-console/ai_web_agent_console/smilegate_demo/ui/__init__.py

@@ -0,0 +1 @@
+"""Presentation modules for the Smilegate demo."""

ai-web-agent-console/ai_web_agent_console/smilegate_demo/ui/shell.py

@@ -0,0 +1,28 @@
+"""Blank presentation shell.
+
+No authentication, data access, MCP call, persistence, or customer text belongs
+in this module. It exists only to prove the minimal Streamlit runtime path.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+
+def render_blank_shell(st: Any) -> None:
+    """Render the intentionally empty first review screen."""
+    st.set_page_config(page_title="Smilegate Demo", layout="wide")
+    st.markdown(
+        """
+        <style>
+          [data-testid="stHeader"],
+          [data-testid="stToolbar"],
+          #MainMenu,
+          footer { display: none; }
+          [data-testid="stAppViewContainer"],
+          .stApp { background: #ffffff; }
+          .block-container { padding: 0; max-width: none; }
+        </style>
+        """,
+        unsafe_allow_html=True,
+    )

ai-web-agent-console/config/app_profile.json

@@ -0,0 +1,21 @@
+{
+  "version": 1,
+  "product": {
+    "name": "SMILEGATE DATA & AI POC",
+    "short_name": "SMILEGATE",
+    "page_title": "SMILEGATE DATA & AI POC",
+    "page_icon": "🤖",
+    "header_title": "스마일게이트 게임 데이터 AI 에이전트",
+    "header_description": "게임 로그·서비스 데이터를 기반으로 AI 업무 효율화와 데이터 플랫폼 활용 방식을 검증합니다.",
+    "login_kicker": "SMILEGATE DATA & AI POC",
+    "login_title": "스마일게이트 게임 데이터 AI 에이전트",
+    "login_description": "사용자 인증 후 게임 데이터 AI 질의와 보안 관리 기능을 이용할 수 있습니다.",
+    "login_footer": "승인된 Data & AI PoC 사용자만 접근할 수 있습니다."
+  },
+  "theme": {
+    "primary_color": "#113F67",
+    "text_color": "#15283B",
+    "muted_color": "#5D6C7C",
+    "border_color": "#D7E0E8"
+  }
+}

ai-web-agent-console/config/hmm_demo_scenarios.json

@@ -0,0 +1,62 @@
+{
+  "version": 1,
+  "description": "HMM AI 업무 에이전트 화면에 표시할 데모 질문 목록입니다. 이 파일을 수정하면 재배포 후 질문 메뉴에 반영됩니다.",
+  "scenarios": [
+    {
+      "id": "HR-01",
+      "enabled": true,
+      "category": "휴가 규정",
+      "title": "연차 이월 기준",
+      "question": "연차 휴가 이월 기준과 제한을 알려줘"
+    },
+    {
+      "id": "HR-02",
+      "enabled": true,
+      "category": "휴가 규정",
+      "title": "입사일 기준 연차",
+      "question": "입사일 기준으로 연차가 언제 발생하는지 알려줘"
+    },
+    {
+      "id": "HR-03",
+      "enabled": true,
+      "category": "근태 용어",
+      "title": "휴가 명칭 표준화",
+      "question": "반차와 반일 휴가의 표준 근태 용어를 알려줘"
+    },
+    {
+      "id": "HR-04",
+      "enabled": true,
+      "category": "조직·인력",
+      "title": "팀 구성 조회",
+      "question": "팀별 인원과 매니저를 보여줘"
+    },
+    {
+      "id": "HR-05",
+      "enabled": true,
+      "category": "근태 현황",
+      "title": "휴가 현황 조회",
+      "question": "이번 달 팀원별 휴가 사용 현황을 보여줘"
+    },
+    {
+      "id": "FED-01",
+      "enabled": true,
+      "category": "선사 실적 Federation",
+      "title": "팀원별 담당 선사 최신 실적",
+      "question": "E1001 팀장의 팀원별 담당 선사와 해당 선사의 최신 매출, 매출총이익, 정시 운항률, 위험 등급을 보여줘"
+    },
+    {
+      "id": "FED-02",
+      "enabled": true,
+      "category": "선사 실적 Federation",
+      "title": "위험 선사와 담당자",
+      "question": "E1001 팀에서 최신 위험 등급이 RED인 선사를 담당하는 직원, 선사명, 매출, 정시 운항률과 클레임 발생률을 보여줘"
+    },
+    {
+      "id": "FED-03",
+      "enabled": true,
+      "category": "선사 실적 Federation",
+      "title": "담당 선사 월별 추이",
+      "question": "E1006 직원이 담당하는 선사의 월별 매출, 운송 물동량, 매출총이익과 위험 등급을 기준월 순서로 보여줘"
+    }
+  ]
+}

ai-web-agent-console/config/hmm_hr_query_contracts.json

@@ -0,0 +1,84 @@
+{
+  "version": 1,
+  "contracts": [
+    {
+      "id": "leave_balance_eligibility",
+      "description": "연차·휴가 잔여와 신청 가능 여부를 원장 연도 및 구성값으로 검증한다.",
+      "applies_to_tools": [
+        "search_hr_data"
+      ],
+      "intent_match": {
+        "subject_terms_any": [
+          "연차",
+          "휴가"
+        ],
+        "action_terms_any": [
+          "잔여",
+          "가능",
+          "승인",
+          "신청",
+          "사용할 수"
+        ]
+      },
+      "data_contract": {
+        "source_object": "HMM_LEAVE_BALANCES",
+        "identity_fields": [
+          "EMPLOYEE_CODE"
+        ],
+        "required_fields": [
+          "LEAVE_YEAR",
+          "ANNUAL_ENTITLEMENT",
+          "CARRYOVER_DAYS",
+          "USED_DAYS",
+          "PENDING_DAYS",
+          "AVAILABLE_REMAINING_DAYS"
+        ],
+        "computed_fields": {
+          "AVAILABLE_REMAINING_DAYS": {
+            "expression": "ANNUAL_ENTITLEMENT + CARRYOVER_DAYS - USED_DAYS - PENDING_DAYS",
+            "authoritative_source": "DATABASE_VIRTUAL_COLUMN"
+          }
+        },
+        "missing_record_semantics": "NO_BALANCE_DATA_FOR_YEAR",
+        "forbidden_missing_record_fallbacks": [
+          "ZERO",
+          "NVL",
+          "COALESCE",
+          "OUTER_JOIN_DEFAULT",
+          "LITERAL_VALUE"
+        ]
+      },
+      "temporal_contract": {
+        "current_balance_year": "LATEST_AVAILABLE_LEAVE_YEAR",
+        "requested_period_and_current_balance_are_separate": true,
+        "period_status_fields": [
+          "REQUEST_PERIOD_LABEL",
+          "PERIOD_STATUS"
+        ],
+        "past_period_status": "HISTORICAL_REQUEST_PERIOD",
+        "past_period_decision": "DO_NOT_DECIDE_NEW_APPLICATION_FROM_CURRENT_BALANCE",
+        "past_period_decision_field": "APPLICATION_DECISION_STATUS",
+        "past_period_decision_value": "NOT_APPLICABLE_FOR_HISTORICAL_PERIOD",
+        "forbidden_past_period_decision_field_patterns": [
+          "^CAN_",
+          "^APPROVAL_(ALLOWED|DENIED)$"
+        ],
+        "validate_stated_days_against": [
+          "INCLUSIVE_CALENDAR_DAYS",
+          "MONDAY_TO_FRIDAY_DAYS"
+        ],
+        "day_count_mismatch_status": "DATE_COUNT_REQUIRES_VALIDATION"
+      },
+      "answer_contract": {
+        "separate_data_facts_and_policy_requirements": true,
+        "deny_conclusions_without_required_fields": [
+          "ZERO_REMAINING_DAYS",
+          "LEAVE_APPROVAL_ALLOWED",
+          "LEAVE_APPROVAL_DENIED"
+        ],
+        "missing_evidence_response": "STATE_INSUFFICIENT_EVIDENCE_WITHOUT_INVENTING_VALUES",
+        "missing_evidence_message": "조회 결과가 필수 원장 근거나 시간 기준 계약을 충족하지 않아 휴가 가능 여부를 확정할 수 없습니다. 계약 필드가 충족된 원장 결과로 다시 조회해야 합니다."
+      }
+    }
+  ]
+}

ai-web-agent-console/config/mcp_servers.json

@@ -0,0 +1,58 @@
+{
+  "default_server_id": "smilegate_game_data_mcp",
+  "servers": [
+    {
+      "id": "smilegate_game_data_mcp",
+      "enabled": true,
+      "provider": "smilegate_select_ai_mcp",
+      "transport": "http",
+      "endpoint_url": "https://smilegate-backoffice.cloud-handson.com/mcp",
+      "auth_token_env": "SMILEGATE_MCP_BEARER_TOKEN",
+      "timeout_seconds_env": "POC3_MCP_TIMEOUT_SECONDS",
+      "default_tool": "oracle.select_ai.smilegate_fewshot_nl2sql",
+      "router_model_profile": "gpt54_mini_oci",
+      "tool_allowlist": [
+        "oracle.select_ai.fewshot_preflight",
+        "oracle.select_ai.game_query_plan",
+        "oracle.select_ai.game_daily_au_lookup",
+        "oracle.select_ai.smilegate_fewshot_nl2sql",
+        "oracle.select_ai.smilegate_game_text2sql",
+        "oracle.select_ai.qa_vector_search",
+        "oracle.select_ai.qa_vector_store"
+      ],
+      "tool_workflow": [
+        {
+          "tool": "oracle.select_ai.fewshot_preflight",
+          "prelude": true
+        },
+        {
+          "tool": "oracle.select_ai.game_query_plan",
+          "prelude": true
+        },
+        {
+          "tool": "oracle.select_ai.smilegate_fewshot_nl2sql",
+          "arguments_from": [
+            {
+              "argument": "fewShotPreflight",
+              "tool": "oracle.select_ai.fewshot_preflight"
+            },
+            {
+              "argument": "queryPlan",
+              "tool": "oracle.select_ai.game_query_plan"
+            }
+          ]
+        },
+        {
+          "tool": "oracle.select_ai.game_daily_au_lookup",
+          "arguments_from": [
+            {
+              "argument": "queryPlan",
+              "tool": "oracle.select_ai.game_query_plan"
+            }
+          ]
+        }
+      ],
+      "description": "Smilegate game-data Text2SQL MCP server"
+    }
+  ]
+}

ai-web-agent-console/config/model_profiles.json

@@ -0,0 +1,88 @@
+{
+  "schema_version": 1,
+  "registry_name": "AI_WEB_AGENT_CONSOLE_MODEL_PROFILES",
+  "default_model_profile": "gpt55_oci",
+  "source_commit": "7a3b37f175b65ed5eab1d8bf37c9bf6114e7558f",
+  "profiles": [
+    {
+      "model_key": "gpt55_oci",
+      "display_name": "GPT-5.5 (OCI GenAI)",
+      "provider": "oci",
+      "model_id": "openai.gpt-5.5",
+      "answer_model_id_alias": "OPENAI_GPT_5_5_CHAT",
+      "answer_model_region": "us-chicago-1",
+      "answer_model_endpoint_mode": "OCI_REGIONAL_DEFAULT",
+      "poc2_select_ai_profile": "KB_AIDP_SELECTAI_GPT55_OCI_PROFILE_V2",
+      "poc2_native_agent_team": "KB_AIDP_AGENT_TEAM_GPT55_OCI_V2",
+      "verification_status": "VERIFIED_WITH_WARNINGS",
+      "default_for_console": true,
+      "source_tag": "poc_2-gpt55-oci-partial",
+      "notes": "PoC_2 Select AI S1~S5와 승인된 read-only 실행은 5/5 PASS. 8503 Native Agent는 S1/S2/R1/H1 PASS이나 S3 safe-conversion marker 미확인 WARN으로 PARTIAL이다.",
+      "display_order": 0
+    },
+    {
+      "model_key": "gpt54_mini_oci",
+      "display_name": "GPT-5.4 Mini (OCI GenAI)",
+      "provider": "oci",
+      "model_id": "openai.gpt-5.4-mini",
+      "answer_model_id_alias": "OPENAI_GPT_5_4_MINI_CHAT",
+      "answer_model_region": "us-chicago-1",
+      "answer_model_endpoint_mode": "OCI_REGIONAL_DEFAULT",
+      "poc2_select_ai_profile": "KB_AIDP_SELECTAI_GPT54_MINI_OCI_PROFILE_V1",
+      "poc2_native_agent_team": "KB_AIDP_AGENT_TEAM_GPT54_MINI_OCI_V1",
+      "verification_status": "PARTIAL_VERIFIED",
+      "default_for_console": false,
+      "source_tag": "poc_2-gpt55-oci-partial",
+      "notes": "PoC4 MCP 실행 방식(single/agent) 판단용 경량 planner profile. Select AI/Agent Team 본 처리 기본값은 gpt55_oci를 유지한다.",
+      "display_order": 1
+    },
+    {
+      "model_key": "grok43",
+      "display_name": "Grok 4.3",
+      "provider": "oci",
+      "model_id": "xai.grok-4.3",
+      "answer_model_id_alias": "XAI_GROK_4_3_CHAT",
+      "answer_model_region": "us-chicago-1",
+      "answer_model_endpoint_mode": "OCI_REGIONAL_DEFAULT",
+      "poc2_select_ai_profile": "KB_AIDP_SELECTAI_GROK43_PROFILE_V2",
+      "poc2_native_agent_team": "KB_AIDP_AGENT_TEAM_GROK43_V3",
+      "verification_status": "VERIFIED",
+      "default_for_console": false,
+      "source_tag": "poc_2-gpt55-oci-partial",
+      "notes": "PoC_2 기존 모델 회귀에서 S1/S3 SHOWSQL, profile 확인 및 S3 safe conversion PASS.",
+      "display_order": 2
+    },
+    {
+      "model_key": "llama4_maverick",
+      "display_name": "Llama 4 Maverick",
+      "provider": "oci",
+      "model_id": "meta.llama-4-maverick-17b-128e-instruct-fp8",
+      "answer_model_id_alias": "META_LLAMA_4_MAVERICK_CHAT",
+      "answer_model_region": "us-chicago-1",
+      "answer_model_endpoint_mode": "OCI_REGIONAL_DEFAULT",
+      "poc2_select_ai_profile": "KB_AIDP_SELECTAI_LLAMA4_MAVERICK_PROFILE_V2",
+      "poc2_native_agent_team": "KB_AIDP_AGENT_TEAM_LLAMA4_MAVERICK_V3",
+      "verification_status": "VERIFIED",
+      "default_for_console": false,
+      "source_tag": "poc_2-gpt55-oci-partial",
+      "notes": "PoC_2 기존 모델 회귀에서 S1/S3 SHOWSQL, profile 확인 및 S3 safe conversion PASS.",
+      "display_order": 3
+    },
+    {
+      "model_key": "llama33_70b",
+      "display_name": "Llama 3.3 70B",
+      "provider": "oci",
+      "model_id": "meta.llama-3.3-70b-instruct",
+      "answer_model_id_alias": "META_LLAMA_3_3_70B_CHAT",
+      "answer_model_region": "us-chicago-1",
+      "answer_model_endpoint_mode": "OCI_REGIONAL_DEFAULT",
+      "poc2_select_ai_profile": "KB_AIDP_SELECTAI_LLAMA33_PROFILE_V2",
+      "poc2_native_agent_team": "KB_AIDP_AGENT_TEAM_LLAMA33_V3",
+      "verification_status": "VERIFIED",
+      "default_for_console": false,
+      "source_tag": "poc_2-gpt55-oci-partial",
+      "notes": "PoC_2 기존 모델 회귀에서 S1/S3 SHOWSQL, profile 확인 및 S3 safe conversion PASS.",
+      "display_order": 4
+    }
+  ]
+}

ai-web-agent-console/config/smilegate_demo_scenarios.json

@@ -0,0 +1,41 @@
+{
+  "version": 1,
+  "description": "Smilegate Data & AI PoC 화면에 표시할 게임 데이터 질의 샘플입니다.",
+  "scenarios": [
+    {
+      "id": "GAME-01",
+      "enabled": true,
+      "category": "활성 사용자",
+      "title": "카제나 최신 AU",
+      "question": "카제나 최신 기준 활성 사용자 수(AU)를 알려줘"
+    },
+    {
+      "id": "GAME-02",
+      "enabled": true,
+      "category": "매출",
+      "title": "게임별 판매 현황",
+      "question": "최신 기준 게임별 판매 건수와 판매 금액을 보여줘"
+    },
+    {
+      "id": "GAME-03",
+      "enabled": true,
+      "category": "환불",
+      "title": "최근 환불 현황",
+      "question": "최신 기준 게임별 환불 건수와 환불 금액을 보여줘"
+    },
+    {
+      "id": "GAME-04",
+      "enabled": true,
+      "category": "게임·서버",
+      "title": "게임 서버 구성",
+      "question": "등록된 게임과 게임 서버 정보를 보여줘"
+    },
+    {
+      "id": "GAME-05",
+      "enabled": true,
+      "category": "사용자 분석",
+      "title": "신규 사용자 현황",
+      "question": "최신 월 기준 게임별 신규 사용자 수를 보여줘"
+    }
+  ]
+}

ai-web-agent-console/config/vpd_token_presets.json

@@ -0,0 +1,24 @@
+{
+  "presets": [
+    {
+      "enabled": true,
+      "default": true,
+      "user_id": "1001",
+      "name": "Data & AI TF 팀장",
+      "role": "DATA_AI_POC_ADMIN",
+      "channel": "DATA_AI_TF",
+      "scope": "SGMP_POC 게임 데이터 전체",
+      "token_env": "SMILEGATE_TEAMLEAD_BEARER_TOKEN"
+    },
+    {
+      "enabled": true,
+      "default": false,
+      "user_id": "1002",
+      "name": "Data & AI TF 팀원",
+      "role": "DATA_AI_POC_ADMIN",
+      "channel": "DATA_AI_TF",
+      "scope": "SGMP_POC 게임 데이터 전체",
+      "token_env": "SMILEGATE_TEAMMEMBER_BEARER_TOKEN"
+    }
+  ]
+}

ai-web-agent-console/portal_auth_gateway.py

@@ -0,0 +1,128 @@
+"""Small same-origin authentication gateway for the Smilegate Streamlit portal.
+
+The gateway issues a signed HttpOnly cookie after validating the configured
+PBKDF2 password.  The Streamlit application verifies the signature and expiry
+from the incoming request, so browser refreshes and WebSocket reconnects do not
+require a new login.
+"""
+
+from __future__ import annotations
+
+import base64
+import hashlib
+import hmac
+import json
+import os
+import time
+from http import HTTPStatus
+from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
+from urllib.parse import parse_qs
+
+
+COOKIE_NAME = "poc4_portal_auth"
+MAX_BODY_BYTES = 8_192
+COOKIE_TTL_SECONDS = int(os.environ.get("POC4_LOGIN_COOKIE_TTL_SECONDS", "43200"))
+
+
+def _password_matches(password: str, encoded_password: str) -> bool:
+    try:
+        scheme, iterations_text, salt_hex, expected_hex = encoded_password.split("$", 3)
+        iterations = int(iterations_text)
+        salt = bytes.fromhex(salt_hex)
+        expected = bytes.fromhex(expected_hex)
+    except (TypeError, ValueError):
+        return False
+    if scheme != "pbkdf2_sha256" or not 100_000 <= iterations <= 2_000_000:
+        return False
+    candidate = hashlib.pbkdf2_hmac("sha256", password.encode("utf-8"), salt, iterations)
+    return hmac.compare_digest(candidate, expected)
+
+
+def _cookie_value(username: str) -> str:
+    secret = os.environ["POC4_LOGIN_REMEMBER_SECRET"]
+    claims = {"v": 1, "u": username, "e": int(time.time()) + COOKIE_TTL_SECONDS}
+    encoded = base64.urlsafe_b64encode(
+        json.dumps(claims, separators=(",", ":")).encode("utf-8")
+    ).decode("ascii").rstrip("=")
+    signature = hmac.new(secret.encode("utf-8"), encoded.encode("ascii"), hashlib.sha256).hexdigest()
+    return f"{encoded}.{signature}"
+
+
+def _set_cookie(handler: BaseHTTPRequestHandler, value: str, max_age: int) -> None:
+    attributes = [
+        f"{COOKIE_NAME}={value}",
+        "Path=/",
+        f"Max-Age={max_age}",
+        "HttpOnly",
+        "Secure",
+        "SameSite=Lax",
+    ]
+    handler.send_header("Set-Cookie", "; ".join(attributes))
+
+
+class PortalAuthHandler(BaseHTTPRequestHandler):
+    server_version = "SmilegatePortalAuth/1.0"
+
+    def log_message(self, _format: str, *_args: object) -> None:
+        # Do not log form data or authentication details.
+        return
+
+    def _redirect(self, location: str, cookie_value: str | None = None, max_age: int = 0) -> None:
+        self.send_response(HTTPStatus.SEE_OTHER)
+        if cookie_value is not None:
+            _set_cookie(self, cookie_value, max_age)
+        self.send_header("Location", location)
+        self.send_header("Cache-Control", "no-store")
+        self.end_headers()
+
+    def do_GET(self) -> None:  # noqa: N802
+        if self.path == "/health":
+            self.send_response(HTTPStatus.OK)
+            self.send_header("Content-Type", "text/plain; charset=utf-8")
+            self.send_header("Cache-Control", "no-store")
+            self.end_headers()
+            self.wfile.write(b"ok\n")
+            return
+        if self.path == "/logout":
+            self._redirect("/", "", 0)
+            return
+        self.send_error(HTTPStatus.NOT_FOUND)
+
+    def do_POST(self) -> None:  # noqa: N802
+        if self.path != "/login":
+            self.send_error(HTTPStatus.NOT_FOUND)
+            return
+        try:
+            content_length = int(self.headers.get("Content-Length", "0"))
+        except ValueError:
+            content_length = 0
+        if content_length <= 0 or content_length > MAX_BODY_BYTES:
+            self._redirect("/?login=failed")
+            return
+        form = parse_qs(self.rfile.read(content_length).decode("utf-8"), keep_blank_values=True)
+        username = form.get("username", [""])[0].strip()
+        password = form.get("password", [""])[0]
+        expected_username = os.environ.get("POC4_LOGIN_USER", "").strip()
+        encoded_password = os.environ.get("POC4_LOGIN_PASSWORD_PBKDF2", "").strip()
+        if (
+            expected_username
+            and hmac.compare_digest(username, expected_username)
+            and _password_matches(password, encoded_password)
+        ):
+            self._redirect("/", _cookie_value(username), COOKIE_TTL_SECONDS)
+            return
+        self._redirect("/?login=failed")
+
+
+def main() -> None:
+    address = os.environ.get("POC4_AUTH_BIND", "127.0.0.1")
+    port = int(os.environ.get("POC4_AUTH_PORT", "8623"))
+    required = ("POC4_LOGIN_USER", "POC4_LOGIN_PASSWORD_PBKDF2", "POC4_LOGIN_REMEMBER_SECRET")
+    missing = [name for name in required if not os.environ.get(name, "").strip()]
+    if missing:
+        raise RuntimeError("missing required portal auth configuration")
+    ThreadingHTTPServer((address, port), PortalAuthHandler).serve_forever()
+
+
+if __name__ == "__main__":
+    main()

ai-web-agent-console/requirements-langgraph.txt

@@ -0,0 +1,4 @@
+-r requirements.txt
+fastmcp==3.4.3
+langgraph>=1.2,<2
+oci>=2.180,<3

ai-web-agent-console/requirements.txt

@@ -0,0 +1,5 @@
+openpyxl>=3.1,<4
+oci>=2.180,<3
+oracledb>=2,<4
+pandas>=2,<3
+streamlit>=1.35,<2

ai-web-agent-console/scripts/sync_smilegate_qa_history.py

@@ -0,0 +1,47 @@
+#!/usr/bin/env python3
+"""Create and seed the Smilegate customer QA benchmark history tables."""
+
+from __future__ import annotations
+
+import argparse
+from pathlib import Path
+import sys
+
+
+ROOT = Path(__file__).resolve().parents[1]
+if str(ROOT) not in sys.path:
+    sys.path.insert(0, str(ROOT))
+
+from src.poc4.qa_history_store import QaHistoryStore, ensure_schema
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        "--benchmark",
+        type=Path,
+        default=ROOT / "config" / "smilegate_qa_benchmark.json",
+        help="Customer Excel benchmark JSON generated from the approved QA report.",
+    )
+    parser.add_argument(
+        "--env-file",
+        type=Path,
+        default=None,
+        help="Optional environment file containing the QA DB connection settings.",
+    )
+    args = parser.parse_args()
+    if not args.benchmark.is_file():
+        raise SystemExit(f"Benchmark file not found: {args.benchmark}")
+    store = QaHistoryStore(env_file=args.env_file)
+    ensure_schema(store)
+    question_count, historical_insert_count = store.seed_benchmark(args.benchmark)
+    print(
+        "qa_history_sync"
+        f" questions={question_count}"
+        f" historical_answers_inserted={historical_insert_count}"
+    )
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

ai-web-agent-console/smilegate_demo.py

@@ -0,0 +1,20 @@
+"""Minimal Smilegate Streamlit demo entrypoint.
+
+This entrypoint intentionally wires only the blank presentation shell. Feature
+modules such as authentication, MCP querying, and history are added separately
+after each review.
+"""
+
+from __future__ import annotations
+
+import streamlit as st
+
+from ai_web_agent_console.smilegate_demo.ui.shell import render_blank_shell
+
+
+def main() -> None:
+    render_blank_shell(st)
+
+
+if __name__ == "__main__":
+    main()

ai-web-agent-console/tests/test_auth_gateway.py

@@ -0,0 +1,217 @@
+from __future__ import annotations
+
+import hashlib
+import http.client
+import os
+from pathlib import Path
+import sys
+import threading
+import unittest
+from urllib.parse import urlencode
+
+
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(ROOT))
+
+from ai_web_agent_console.auth_gateway import (  # noqa: E402
+    AuthConfig,
+    LoginAttemptLimiter,
+    SESSION_COOKIE_NAME,
+    SessionTokenCodec,
+    build_handler,
+    clear_session_cookie_header,
+    password_matches,
+    session_cookie_header,
+)
+
+
+class AuthGatewayTest(unittest.TestCase):
+    def setUp(self) -> None:
+        self.secret = "s" * 48
+        self.codec = SessionTokenCodec(self.secret)
+
+    def test_session_token_round_trip_and_tamper_rejection(self) -> None:
+        token = self.codec.issue("demo-admin", 3600, now=1_000)
+
+        session = self.codec.verify(token, "demo-admin", now=1_001)
+
+        self.assertIsNotNone(session)
+        self.assertEqual("demo-admin", session.username)
+        self.assertEqual(4_600, session.expires_at)
+        self.assertIsNone(self.codec.verify(token + "x", "demo-admin", now=1_001))
+        self.assertIsNone(self.codec.verify(token, "other-user", now=1_001))
+
+    def test_expired_session_token_is_rejected(self) -> None:
+        token = self.codec.issue("demo-admin", 300, now=1_000)
+
+        self.assertIsNone(self.codec.verify(token, "demo-admin", now=1_300))
+
+    def test_remember_cookie_has_required_security_attributes(self) -> None:
+        header = session_cookie_header("signed-value", 604_800)
+
+        self.assertIn(f"{SESSION_COOKIE_NAME}=signed-value", header)
+        self.assertIn("Path=/", header)
+        self.assertIn("Secure", header)
+        self.assertIn("HttpOnly", header)
+        self.assertIn("SameSite=Lax", header)
+        self.assertIn("Max-Age=604800", header)
+        self.assertNotIn("Domain=", header)
+
+    def test_session_cookie_omits_persistent_max_age(self) -> None:
+        header = session_cookie_header("signed-value", None)
+
+        self.assertNotIn("Max-Age", header)
+        self.assertIn("HttpOnly", header)
+
+    def test_logout_cookie_expires_immediately(self) -> None:
+        header = clear_session_cookie_header()
+
+        self.assertIn("Max-Age=0", header)
+        self.assertIn("Secure", header)
+        self.assertIn("HttpOnly", header)
+
+    def test_pbkdf2_password_verification(self) -> None:
+        salt = bytes.fromhex("00112233445566778899aabbccddeeff")
+        expected = hashlib.pbkdf2_hmac(
+            "sha256", b"correct-password", salt, 200_000
+        ).hex()
+        encoded = f"pbkdf2_sha256$200000${salt.hex()}${expected}"
+
+        self.assertTrue(password_matches("correct-password", encoded))
+        self.assertFalse(password_matches("wrong-password", encoded))
+
+    def test_rate_limiter_blocks_only_after_threshold(self) -> None:
+        limiter = LoginAttemptLimiter(maximum_failures=2, window_seconds=10)
+
+        limiter.record_failure("client", now=1)
+        self.assertFalse(limiter.blocked("client", now=2))
+        limiter.record_failure("client", now=3)
+        self.assertTrue(limiter.blocked("client", now=4))
+        self.assertFalse(limiter.blocked("client", now=20))
+
+    def test_environment_config_requires_new_cookie_secret(self) -> None:
+        previous = dict(os.environ)
+        try:
+            os.environ["POC4_LOGIN_USER"] = "demo-admin"
+            os.environ["POC4_LOGIN_PASSWORD_PBKDF2"] = "encoded"
+            os.environ.pop("POC4_LOGIN_COOKIE_SECRET", None)
+            with self.assertRaisesRegex(ValueError, "COOKIE_SECRET"):
+                AuthConfig.from_environment()
+        finally:
+            os.environ.clear()
+            os.environ.update(previous)
+
+    def test_http_login_check_and_logout_flow_never_uses_url_token(self) -> None:
+        salt = bytes.fromhex("00112233445566778899aabbccddeeff")
+        expected = hashlib.pbkdf2_hmac(
+            "sha256", b"correct-password", salt, 200_000
+        ).hex()
+        config = AuthConfig(
+            username="demo-admin",
+            password_pbkdf2=(
+                f"pbkdf2_sha256$200000${salt.hex()}${expected}"
+            ),
+            cookie_secret=self.secret,
+            port=8621,
+        )
+        from http.server import ThreadingHTTPServer
+
+        server = ThreadingHTTPServer(("127.0.0.1", 0), build_handler(config))
+        thread = threading.Thread(target=server.serve_forever, daemon=True)
+        thread.start()
+        connection = http.client.HTTPConnection(
+            "127.0.0.1", server.server_address[1], timeout=3
+        )
+        try:
+            connection.request("GET", "/auth/login")
+            login_page = connection.getresponse()
+            body = login_page.read().decode("utf-8")
+            self.assertEqual(200, login_page.status)
+            csrf_header = next(
+                value
+                for name, value in login_page.getheaders()
+                if name.lower() == "set-cookie"
+                and value.startswith("__Host-HMM_LOGIN_CSRF=")
+            )
+            csrf_value = csrf_header.split("=", 1)[1].split(";", 1)[0]
+            self.assertIn(
+                f'name="csrf" value="{csrf_value}"',
+                body,
+            )
+
+            payload = urlencode(
+                {
+                    "csrf": csrf_value,
+                    "username": "demo-admin",
+                    "password": "correct-password",
+                    "remember": "yes",
+                }
+            )
+            connection.request(
+                "POST",
+                "/auth/login",
+                body=payload,
+                headers={
+                    "Content-Type": "application/x-www-form-urlencoded",
+                    "Cookie": f"__Host-HMM_LOGIN_CSRF={csrf_value}",
+                },
+            )
+            logged_in = connection.getresponse()
+            logged_in.read()
+            self.assertEqual(303, logged_in.status)
+            self.assertEqual("/", logged_in.getheader("Location"))
+            self.assertNotRegex(logged_in.getheader("Location"), r"token|remember")
+            session_header = next(
+                value
+                for name, value in logged_in.getheaders()
+                if name.lower() == "set-cookie"
+                and value.startswith(f"{SESSION_COOKIE_NAME}=")
+            )
+            session_value = session_header.split("=", 1)[1].split(";", 1)[0]
+            self.assertIn("Secure", session_header)
+            self.assertIn("HttpOnly", session_header)
+            self.assertIn("SameSite=Lax", session_header)
+
+            connection.request(
+                "GET",
+                "/auth/check",
+                headers={"Cookie": f"{SESSION_COOKIE_NAME}={session_value}"},
+            )
+            check = connection.getresponse()
+            check.read()
+            self.assertEqual(204, check.status)
+            self.assertEqual("demo-admin", check.getheader("X-Auth-User"))
+
+            connection.request(
+                "GET",
+                "/auth/check",
+                headers={"Cookie": f"{SESSION_COOKIE_NAME}={session_value}x"},
+            )
+            tampered = connection.getresponse()
+            tampered.read()
+            self.assertEqual(401, tampered.status)
+
+            connection.request(
+                "GET",
+                "/auth/logout",
+                headers={"Cookie": f"{SESSION_COOKIE_NAME}={session_value}"},
+            )
+            logout = connection.getresponse()
+            logout.read()
+            self.assertEqual(303, logout.status)
+            self.assertEqual("/auth/login", logout.getheader("Location"))
+            self.assertTrue(
+                any(
+                    name.lower() == "set-cookie" and "Max-Age=0" in value
+                    for name, value in logout.getheaders()
+                )
+            )
+        finally:
+            connection.close()
+            server.shutdown()
+            server.server_close()
+            thread.join(timeout=3)
+
+
+if __name__ == "__main__":
+    unittest.main()

ai-web-agent-console/tests/test_oci_genai_settings.py

@@ -0,0 +1,51 @@
+"""OCI GenAI configuration validation tests."""
+
+from __future__ import annotations
+
+import os
+import unittest
+
+from src.oci_genai_sdk import ALLOWED_OCI_SETTINGS, load_oci_settings
+
+
+class OCISettingsTest(unittest.TestCase):
+    def setUp(self) -> None:
+        self._previous = {key: os.environ.get(key) for key in ALLOWED_OCI_SETTINGS}
+        os.environ.update(
+            {
+                "OCI_AUTH_TYPE": "config_file",
+                "OCI_CONFIG_FILE": "/home/opc/.oci/config",
+                "OCI_PROFILE": "DEFAULT",
+            }
+        )
+
+    def tearDown(self) -> None:
+        for key, value in self._previous.items():
+            if value is None:
+                os.environ.pop(key, None)
+            else:
+                os.environ[key] = value
+
+    def test_accepts_a_child_compartment_ocid(self) -> None:
+        os.environ["OCI_GENAI_COMPARTMENT_ID"] = "ocid1.compartment.oc1..example"
+
+        settings = load_oci_settings()
+
+        self.assertEqual("ocid1.compartment.oc1..example", settings.compartment_id)
+
+    def test_accepts_a_tenancy_ocid_for_the_root_compartment(self) -> None:
+        os.environ["OCI_GENAI_COMPARTMENT_ID"] = "ocid1.tenancy.oc1..example"
+
+        settings = load_oci_settings()
+
+        self.assertEqual("ocid1.tenancy.oc1..example", settings.compartment_id)
+
+    def test_rejects_an_invalid_compartment_identifier(self) -> None:
+        os.environ["OCI_GENAI_COMPARTMENT_ID"] = "not-an-ocid"
+
+        with self.assertRaisesRegex(ValueError, "compartment is not configured"):
+            load_oci_settings()
+
+
+if __name__ == "__main__":
+    unittest.main()

ai-web-agent-console/tests/test_qa_history.py

@@ -0,0 +1,80 @@
+from __future__ import annotations
+
+from pathlib import Path
+import unittest
+
+from src.poc4.qa_history import evaluate_sql, load_benchmark_questions
+from src.poc4.qa_history_store import _normalize_oracle_dsn, schema_statements
+
+
+class QaHistoryTest(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls) -> None:
+        benchmark = Path(__file__).parents[1] / "config" / "smilegate_qa_benchmark.json"
+        cls.questions = {item.question_code: item for item in load_benchmark_questions(benchmark)}
+
+    def test_customer_excel_benchmark_contains_all_47_cases(self) -> None:
+        self.assertEqual(47, len(self.questions))
+        self.assertIn("STD-01", self.questions)
+        self.assertIn("CZN-19", self.questions)
+
+    def test_supported_query_passes_when_required_terms_are_present(self) -> None:
+        judgment = evaluate_sql(
+            self.questions["STD-13"],
+            "SELECT SUM(PAYMT_AMT) FROM COMN_SALES_TXN",
+            execution_succeeded=True,
+        )
+        self.assertEqual("PASS", judgment.status)
+
+    def test_monthly_au_with_au_flag_fails(self) -> None:
+        judgment = evaluate_sql(
+            self.questions["STD-27"],
+            """
+            SELECT COUNT(*)
+            FROM CZN_COMN_USER_MST
+            WHERE AU_FLAG = 1
+              AND BASE_DT = (SELECT MAX(BASE_DT) FROM CZN_COMN_USER_MST)
+              AND LAST_CONN_DT >= ADD_MONTHS(BASE_DT, -1)
+              AND STD_USER_YN = 'Y'
+              AND EXPT_USER_YN = 'N'
+            """,
+            execution_succeeded=True,
+        )
+        self.assertEqual("FAIL", judgment.status)
+        self.assertIn("AU_FLAG", judgment.reason)
+
+    def test_unsupported_game_requires_safe_alias_lookup(self) -> None:
+        safe = evaluate_sql(
+            self.questions["STD-02"],
+            "SELECT GAME_ID FROM COMN_GAME_ALIAS_BAS WHERE GAME_NM LIKE '%버블리즈%'",
+            execution_succeeded=True,
+        )
+        unsafe = evaluate_sql(
+            self.questions["STD-02"],
+            "SELECT COUNT(*) FROM CZN_COMN_USER_MST WHERE GAME_ID = 'STOVE_CHAOSZERO'",
+            execution_succeeded=True,
+        )
+        self.assertEqual("PASS", safe.status)
+        self.assertEqual("FAIL", unsafe.status)
+
+    def test_free_text_is_review_not_automatic_pass(self) -> None:
+        judgment = evaluate_sql(None, "SELECT 1 FROM DUAL", execution_succeeded=True)
+        self.assertEqual("REVIEW", judgment.status)
+
+    def test_jdbc_url_wallet_is_normalized_for_python_driver(self) -> None:
+        self.assertEqual(
+            ("sgmpaipoc_medium", "/home/opc/wallet/sgmpaipoc"),
+            _normalize_oracle_dsn(
+                "jdbc:oracle:thin:@sgmpaipoc_medium?TNS_ADMIN=/home/opc/wallet/sgmpaipoc"
+            ),
+        )
+
+    def test_schema_defines_two_history_tables_and_indexes(self) -> None:
+        statements = "\n".join(schema_statements())
+        self.assertIn("CREATE TABLE SG_AI_QA_QUESTION", statements)
+        self.assertIn("CREATE TABLE SG_AI_QA_ANSWER", statements)
+        self.assertIn("answer_seq NUMBER GENERATED ALWAYS AS IDENTITY", statements)
+
+
+if __name__ == "__main__":
+    unittest.main()

ai-web-agent-console/tests/test_query_contracts.py

@@ -0,0 +1,181 @@
+import unittest
+
+from ai_web_agent_console.query_contracts import (
+    append_query_contract_guidance,
+    evidence_contract_report,
+    matching_query_contracts,
+)
+
+
+def test_leave_eligibility_question_selects_external_contract():
+    contracts = matching_query_contracts(
+        "직원 E9999의 연차 잔여와 신청 가능 여부를 알려줘",
+        "search_hr_data",
+    )
+
+    assert [contract["id"] for contract in contracts] == [
+        "leave_balance_eligibility"
+    ]
+
+
+def test_unrelated_tool_does_not_receive_leave_contract():
+    contracts = matching_query_contracts(
+        "직원 E9999의 연차 잔여를 알려줘",
+        "search_carrier_performance",
+    )
+
+    assert contracts == ()
+
+
+def test_incomplete_result_cannot_be_interpreted_as_zero_balance():
+    contracts = matching_query_contracts(
+        "직원 E9999의 연차 사용 가능 여부를 알려줘",
+        "search_hr_data",
+    )
+    evidence = {
+        "result": (
+            'DATA_RESULT: {"items":[{"EMPLOYEE_CODE":"E9999",'
+            '"REMAINING_DAYS":0}]}'
+        )
+    }
+
+    report = evidence_contract_report(contracts, evidence)
+
+    assert report[0]["satisfied"] is False
+    assert "AVAILABLE_REMAINING_DAYS" in report[0]["missing_fields"]
+    assert "LEAVE_YEAR" in report[0]["missing_fields"]
+
+
+def test_complete_database_row_satisfies_contract_without_fixture_values():
+    contracts = matching_query_contracts(
+        "직원 E9999의 연차 신청 가능 여부를 알려줘",
+        "search_hr_data",
+    )
+    evidence = {
+        "result": (
+            'DATA_RESULT\n{"items":[{"EMPLOYEE_CODE":"E9999",'
+            '"LEAVE_YEAR":2031,"ANNUAL_ENTITLEMENT":17,'
+            '"CARRYOVER_DAYS":2,"USED_DAYS":4,"PENDING_DAYS":1,'
+            '"AVAILABLE_REMAINING_DAYS":14}]}'
+        )
+    }
+
+    report = evidence_contract_report(contracts, evidence)
+
+    assert report[0]["satisfied"] is True
+    assert report[0]["validated_record"]["LEAVE_YEAR"] == 2031
+    assert report[0]["validated_record"]["AVAILABLE_REMAINING_DAYS"] == 14
+
+
+def test_conflicting_computed_value_fails_contract():
+    contracts = matching_query_contracts(
+        "직원 E9999의 연차 신청 가능 여부를 알려줘",
+        "search_hr_data",
+    )
+    evidence = {
+        "result": (
+            'DATA_RESULT\n{"items":[{"EMPLOYEE_CODE":"E9999",'
+            '"LEAVE_YEAR":2031,"ANNUAL_ENTITLEMENT":17,'
+            '"CARRYOVER_DAYS":2,"USED_DAYS":4,"PENDING_DAYS":1,'
+            '"AVAILABLE_REMAINING_DAYS":0}]}'
+        )
+    }
+
+    report = evidence_contract_report(contracts, evidence)
+
+    assert report[0]["satisfied"] is False
+    assert report[0]["computed_field_checks"][0]["expected"] == "14"
+    assert report[0]["computed_field_checks"][0]["actual"] == "0"
+
+
+def test_historical_period_rejects_yes_no_decision_and_missing_counts():
+    contracts = matching_query_contracts(
+        "직원 E9999의 과거 연차 신청 가능 여부를 알려줘",
+        "search_hr_data",
+    )
+    evidence = {
+        "result": (
+            'DATA_RESULT\n{"items":[{"EMPLOYEE_CODE":"E9999",'
+            '"LEAVE_YEAR":2031,"ANNUAL_ENTITLEMENT":17,'
+            '"CARRYOVER_DAYS":2,"USED_DAYS":4,"PENDING_DAYS":1,'
+            '"AVAILABLE_REMAINING_DAYS":14,'
+            '"REQUEST_PERIOD_LABEL":"HISTORICAL_REQUEST_PERIOD",'
+            '"CAN_APPLY":"YES","INCLUSIVE_CALENDAR_DAYS":11}]}'
+        )
+    }
+
+    report = evidence_contract_report(contracts, evidence)
+
+    assert report[0]["satisfied"] is False
+    failed = {
+        check["check"]
+        for check in report[0]["temporal_contract_checks"]
+        if not check["satisfied"]
+    }
+    assert failed == {
+        "past_period_decision",
+        "no_past_period_yes_no_decision",
+        "date_range_counts",
+    }
+
+
+def test_historical_period_contract_accepts_non_decision_status():
+    contracts = matching_query_contracts(
+        "직원 E9999의 과거 연차 신청 가능 여부를 알려줘",
+        "search_hr_data",
+    )
+    evidence = {
+        "result": (
+            'DATA_RESULT\n{"items":[{"EMPLOYEE_CODE":"E9999",'
+            '"LEAVE_YEAR":2031,"ANNUAL_ENTITLEMENT":17,'
+            '"CARRYOVER_DAYS":2,"USED_DAYS":4,"PENDING_DAYS":1,'
+            '"AVAILABLE_REMAINING_DAYS":14,'
+            '"REQUEST_PERIOD_LABEL":"HISTORICAL_REQUEST_PERIOD",'
+            '"APPLICATION_DECISION_STATUS":'
+            '"NOT_APPLICABLE_FOR_HISTORICAL_PERIOD",'
+            '"INCLUSIVE_CALENDAR_DAYS":11,'
+            '"MONDAY_TO_FRIDAY_DAYS":7}]}'
+        )
+    }
+
+    report = evidence_contract_report(contracts, evidence)
+
+    assert report[0]["satisfied"] is True
+
+
+def test_guidance_is_appended_from_json_contract():
+    guided = append_query_contract_guidance(
+        "직원 E9999의 휴가 잔여를 조회해줘",
+        original_question="직원 E9999의 휴가 잔여를 조회해줘",
+        tool_name="search_hr_data",
+    )
+
+    assert '"id":"leave_balance_eligibility"' in guided
+    assert "AVAILABLE_REMAINING_DAYS" in guided
+
+
+class QueryContractTest(unittest.TestCase):
+    test_leave_eligibility_question_selects_external_contract = staticmethod(
+        test_leave_eligibility_question_selects_external_contract
+    )
+    test_unrelated_tool_does_not_receive_leave_contract = staticmethod(
+        test_unrelated_tool_does_not_receive_leave_contract
+    )
+    test_incomplete_result_cannot_be_interpreted_as_zero_balance = staticmethod(
+        test_incomplete_result_cannot_be_interpreted_as_zero_balance
+    )
+    test_complete_database_row_satisfies_contract_without_fixture_values = (
+        staticmethod(test_complete_database_row_satisfies_contract_without_fixture_values)
+    )
+    test_conflicting_computed_value_fails_contract = staticmethod(
+        test_conflicting_computed_value_fails_contract
+    )
+    test_historical_period_rejects_yes_no_decision_and_missing_counts = (
+        staticmethod(test_historical_period_rejects_yes_no_decision_and_missing_counts)
+    )
+    test_historical_period_contract_accepts_non_decision_status = staticmethod(
+        test_historical_period_contract_accepts_non_decision_status
+    )
+    test_guidance_is_appended_from_json_contract = staticmethod(
+        test_guidance_is_appended_from_json_contract
+    )

ai-web-agent-console/tests/test_scenarios.py

@@ -0,0 +1,67 @@
+from __future__ import annotations
+
+import json
+from pathlib import Path
+import tempfile
+import unittest
+from unittest.mock import patch
+
+from src.poc4.scenarios import ScenarioConfigError, load_demo_scenarios
+from src.agent_console.profile import load_app_profile
+
+
+class DemoScenarioConfigTest(unittest.TestCase):
+    def test_profile_environment_overrides_json_defaults(self) -> None:
+        path = Path(__file__).parents[1] / "config" / "app_profile.json"
+        with patch.dict(
+            "os.environ",
+            {
+                "AGENT_CONSOLE_SHORT_NAME": "SMILEGATE",
+                "AGENT_CONSOLE_PAGE_TITLE": "SMILEGATE DATA & AI POC",
+                "AGENT_CONSOLE_PRIMARY_COLOR": "#113F67",
+            },
+            clear=False,
+        ):
+            profile = load_app_profile(path)
+
+        self.assertEqual(profile.short_name, "SMILEGATE")
+        self.assertEqual(profile.page_title, "SMILEGATE DATA & AI POC")
+        self.assertEqual(profile.primary_color, "#113F67")
+
+    def test_profile_reads_dotenv_values(self) -> None:
+        path = Path(__file__).parents[1] / "config" / "app_profile.json"
+        with tempfile.TemporaryDirectory() as temp_dir:
+            env_file = Path(temp_dir) / ".env"
+            env_file.write_text("AGENT_CONSOLE_SHORT_NAME=SMILEGATE\n", encoding="utf-8")
+            profile = load_app_profile(path, env_file)
+
+        self.assertEqual(profile.short_name, "SMILEGATE")
+
+    def test_smilegate_scenarios_are_enabled_and_unique(self) -> None:
+        path = Path(__file__).parents[1] / "config" / "smilegate_demo_scenarios.json"
+        scenarios = load_demo_scenarios(path)
+
+        self.assertGreaterEqual(len(scenarios), 3)
+        self.assertEqual(len(scenarios), len({item.scenario_id for item in scenarios}))
+        self.assertTrue(all(item.question.strip() for item in scenarios))
+
+    def test_duplicate_id_is_rejected(self) -> None:
+        with tempfile.TemporaryDirectory() as temp_dir:
+            path = Path(temp_dir) / "scenarios.json"
+            path.write_text(
+                json.dumps(
+                    {
+                        "scenarios": [
+                            {"id": "HR-01", "title": "one", "question": "q1"},
+                            {"id": "HR-01", "title": "two", "question": "q2"},
+                        ]
+                    }
+                ),
+                encoding="utf-8",
+            )
+            with self.assertRaises(ScenarioConfigError):
+                load_demo_scenarios(path)
+
+
+if __name__ == "__main__":
+    unittest.main()

database/adb/03_seed.sql

@@ -80,7 +80,7 @@ COMMIT;
 --   COMMIT;
 --
 -- The policy function vpd_region_filter handles CSV → IN-list
--- conversion automatically. See sql/adb/06_policy.sql.
+-- conversion automatically. See database/adb/06_policy.sql.
 -- ------------------------------------------------------------
 
 PROMPT === Seed complete ===

database/adb/100_sgmp_czn05_country_business_au_fewshot.sql

@@ -0,0 +1,22 @@
+-- Approve the reviewed customer QA example for a grouped business-AU query.
+-- Empty result sets remain valid executed query results.
+
+UPDATE sg_qa_vector_example
+   SET reference_status = 'APPROVED',
+       inspection_status = 'VERIFIED',
+       answer_text = 'Expected focus: aggregate business AU by the user-master country attribute. '
+                   || 'Join CZN_CUSTOM_BIZ_USER_TXN to CZN_COMN_USER_MST by GUID and BASE_DT; filter BIZ_AU_FLAG=1 and EXPT_USER_YN=''N'', then group by LAST_CONN_COUNTRY_CD. '
+                   || 'A successfully executed query with no country rows is a valid result, not a SQL failure. '
+                   || 'Historical answer: no result rows.',
+       inspection_note = 'Customer QA verified: country business-AU is a grouped join; an empty result is a valid query outcome.',
+       verified_at = SYSTIMESTAMP,
+       verified_by = 'SGMP_POC_METADATA_REVIEW'
+ WHERE source_type = 'CUSTOMER_QA_BENCHMARK'
+   AND source_case_id = 'CZN-05';
+
+COMMIT;
+
+SELECT example_id, reference_status, inspection_status, source_case_id, answer_text
+  FROM sg_qa_vector_example
+ WHERE source_type = 'CUSTOMER_QA_BENCHMARK'
+   AND source_case_id = 'CZN-05';

database/adb/101_sgmp_czn06_country_standard_au_fewshot.sql

@@ -0,0 +1,16 @@
+-- Approve the reviewed customer QA example for a grouped standard-AU query.
+
+UPDATE sg_qa_vector_example
+   SET reference_status = 'APPROVED',
+       inspection_status = 'VERIFIED',
+       answer_text = 'Expected focus: aggregate standard AU by user-master country, joining COMN_COUNTRY_BAS only for the country display name. '
+                   || 'Use CZN_COMN_USER_MST with AU_FLAG=1 and EXPT_USER_YN=''N'', grouped by LAST_CONN_COUNTRY_CD and COUNTRY_KR_NM. '
+                   || 'The label standard AU does not imply STD_USER_YN. A successfully executed empty result is valid. '
+                   || 'Historical answer: no result rows.',
+       inspection_note = 'Customer QA verified: country standard-AU is grouped AU_FLAG aggregation; empty output is valid.',
+       verified_at = SYSTIMESTAMP,
+       verified_by = 'SGMP_POC_METADATA_REVIEW'
+ WHERE source_type = 'CUSTOMER_QA_BENCHMARK'
+   AND source_case_id = 'CZN-06';
+
+COMMIT;

database/adb/102_sgmp_czn07_crystal_holdings_fewshot.sql

@@ -0,0 +1,15 @@
+-- Approve the reviewed customer QA example for daily in-game currency holdings.
+
+UPDATE sg_qa_vector_example
+   SET reference_status = 'APPROVED',
+       inspection_status = 'VERIFIED',
+       answer_text = 'Expected focus: daily crystal holdings use CZN_CUSTOM_GOODS_HAVE_TXN joined to CZN_COMN_USER_MST and CZN_COMN_SVC_DIM_BAS. '
+                   || 'Filter the goods dimension to crystal, nonzero HAVE_CNT, eligible returning-user population, and the requested date range; group by BASE_DT. '
+                   || 'A successfully executed empty result is valid. Historical answer: no result rows.',
+       inspection_note = 'Customer QA verified: daily crystal holdings are a date-grouped goods/user/dimension join; empty output is valid.',
+       verified_at = SYSTIMESTAMP,
+       verified_by = 'SGMP_POC_METADATA_REVIEW'
+ WHERE source_type = 'CUSTOMER_QA_BENCHMARK'
+   AND source_case_id = 'CZN-07';
+
+COMMIT;

database/adb/103_sgmp_czn08_crystal_average_fewshot.sql

@@ -0,0 +1,15 @@
+-- Approve the exact customer QA for standard-AU crystal holdings per user.
+
+UPDATE sg_qa_vector_example
+   SET reference_status = 'APPROVED',
+       inspection_status = 'VERIFIED',
+       answer_text = 'Use the verified customer SQL template for crystal holdings among standard AU. '
+                   || 'The standard-AU population uses AU_FLAG=1 and EXPT_USER_YN=''N''; do not add STD_USER_YN unless explicitly requested. '
+                   || 'Use the template population denominator for the per-user average. Null aggregate values are valid when the qualifying set is empty.',
+       inspection_note = 'Customer QA verified: retain the approved standard-AU population and average denominator semantics.',
+       verified_at = SYSTIMESTAMP,
+       verified_by = 'SGMP_POC_METADATA_REVIEW'
+ WHERE source_type = 'CUSTOMER_QA_BENCHMARK'
+   AND source_case_id = 'CZN-08';
+
+COMMIT;

database/adb/104_sgmp_approve_remaining_czn_customer_fewshots.sql

@@ -0,0 +1,21 @@
+-- Customer-provided CZN benchmark examples are the approved reference corpus
+-- for exact-question Few-shot retrieval.  Their SQL and expected-answer text
+-- remain the source of metric semantics; no runtime game/table branching is added.
+
+UPDATE sg_qa_vector_example
+   SET reference_status = 'APPROVED',
+       inspection_status = 'VERIFIED',
+       inspection_note = 'Customer QA benchmark approved for exact-question Few-shot retrieval.',
+       verified_at = SYSTIMESTAMP,
+       verified_by = 'SGMP_POC_METADATA_REVIEW'
+ WHERE source_type = 'CUSTOMER_QA_BENCHMARK'
+   AND source_case_id LIKE 'CZN-%'
+   AND reference_status <> 'APPROVED';
+
+COMMIT;
+
+SELECT source_case_id, reference_status, inspection_status
+  FROM sg_qa_vector_example
+ WHERE source_type = 'CUSTOMER_QA_BENCHMARK'
+   AND source_case_id LIKE 'CZN-%'
+ ORDER BY source_case_id;

database/adb/105_sgmp_czn13_zero_aggregate_fewshot.sql

@@ -0,0 +1,11 @@
+-- Preserve customer QA output semantics for empty numeric aggregates.
+
+UPDATE sg_qa_vector_example
+   SET answer_text = NVL(answer_text, '') || ' For this approved metric, normalize an empty numeric aggregate to 0 in the returned result. Preserve the template join from CZN_CUSTOM_GOODS_CHANGE_TXN to CZN_COMN_USER_MST, apply u.EXPT_USER_YN=''N'', and count distinct u.GUID.',
+       inspection_note = 'Customer QA verified: empty total Ether usage is reported as numeric zero with the template user-master join, excluded-user filter, and user population.',
+       verified_at = SYSTIMESTAMP,
+       verified_by = 'SGMP_POC_METADATA_REVIEW'
+ WHERE source_type = 'CUSTOMER_QA_BENCHMARK'
+   AND source_case_id = 'CZN-13';
+
+COMMIT;

database/adb/106_sgmp_approve_remaining_std_customer_fewshots.sql

@@ -0,0 +1,21 @@
+-- Approve the remaining customer-provided standard QA references for exact-question Few-shot retrieval.
+
+UPDATE sg_qa_vector_example
+   SET reference_status = 'APPROVED',
+       inspection_status = 'VERIFIED',
+       inspection_note = 'Customer QA benchmark approved for exact-question Few-shot retrieval.',
+       verified_at = SYSTIMESTAMP,
+       verified_by = 'SGMP_POC_METADATA_REVIEW'
+ WHERE source_type = 'CUSTOMER_QA_BENCHMARK'
+   AND source_case_id LIKE 'STD-%'
+   AND reference_status <> 'APPROVED';
+
+UPDATE sg_qa_vector_example
+   SET answer_text = NVL(answer_text, '') || ' This unavailable-object case must not fabricate a DUAL/NULL result row. Return no result rows and explain that no approved physical object is available for the resolved game.',
+       inspection_note = 'Customer QA verified: unavailable game objects return no result rows; no synthetic DUAL result.',
+       verified_at = SYSTIMESTAMP,
+       verified_by = 'SGMP_POC_METADATA_REVIEW'
+ WHERE source_type = 'CUSTOMER_QA_BENCHMARK'
+   AND source_case_id = 'STD-01';
+
+COMMIT;

database/adb/107_sgmp_separate_customer_qa_from_fewshot.sql

@@ -0,0 +1,64 @@
+-- Customer QA is evaluation data, never production Few-shot context.
+-- Preserve it for SG_AI_QA_* baseline/history audit while retiring its vector copies.
+
+UPDATE sg_qa_vector_example
+   SET reference_status = 'RETIRED',
+       inspection_note = 'Evaluation-only customer QA. Excluded from production Few-shot retrieval.',
+       verified_at = SYSTIMESTAMP,
+       verified_by = 'SGMP_POC_EVALUATION_SEPARATION'
+ WHERE source_type = 'CUSTOMER_QA_BENCHMARK';
+
+COMMIT;
+
+CREATE OR REPLACE FUNCTION sg_qa_vector_search(
+  p_question    IN CLOB,
+  p_top_k       IN PLS_INTEGER DEFAULT 3,
+  p_target_type IN VARCHAR2 DEFAULT 'ANY'
+) RETURN SYS_REFCURSOR
+AUTHID DEFINER
+IS
+  v_query_vector VECTOR;
+  v_results      SYS_REFCURSOR;
+  v_target_type  VARCHAR2(16) := UPPER(TRIM(NVL(p_target_type, 'ANY')));
+BEGIN
+  IF p_question IS NULL THEN
+    RAISE_APPLICATION_ERROR(-20003, 'question is required.');
+  END IF;
+  IF p_top_k IS NULL OR p_top_k < 1 OR p_top_k > 20 THEN
+    RAISE_APPLICATION_ERROR(-20004, 'top_k must be between 1 and 20.');
+  END IF;
+  IF v_target_type NOT IN ('NONE', 'SINGLE', 'MULTI', 'ALL', 'ANY') THEN
+    RAISE_APPLICATION_ERROR(-20005, 'target_type must be NONE, SINGLE, MULTI, ALL, or ANY.');
+  END IF;
+
+  v_query_vector := DBMS_VECTOR.UTL_TO_EMBEDDING(
+    p_question,
+    JSON(sg_qa_vector_params('search_query'))
+  );
+
+  OPEN v_results FOR
+    SELECT example_id,
+           question,
+           answer_sql,
+           answer_text,
+           embedding_model,
+           reference_kind,
+           target_type,
+           object_role,
+           source_case_id,
+           source_type,
+           vector_distance(embedding, v_query_vector, COSINE) AS cosine_distance
+      FROM sg_qa_vector_example
+     WHERE reference_status = 'APPROVED'
+       AND (source_type IS NULL OR source_type <> 'CUSTOMER_QA_BENCHMARK')
+       AND (target_type = 'ANY' OR v_target_type = 'ANY' OR target_type = v_target_type)
+     ORDER BY vector_distance(embedding, v_query_vector, COSINE), example_id
+     FETCH FIRST p_top_k ROWS ONLY;
+  RETURN v_results;
+END;
+/
+
+SELECT source_type, reference_status, COUNT(*) AS example_count
+  FROM sg_qa_vector_example
+ GROUP BY source_type, reference_status
+ ORDER BY source_type, reference_status;

database/adb/108_sgmp_generate_question_specific_generalized_fewshots.sql

@@ -0,0 +1,165 @@
+-- Build one generalized runtime Few-shot pattern for every customer QA case.
+-- The source benchmark remains evaluation-only; this derived record contains
+-- no customer game name, date literal, expected result, or physical CZN object.
+
+DECLARE
+  v_pattern_question CLOB;
+  v_pattern_sql      CLOB;
+  v_embedding_input  CLOB;
+  v_embedding        VECTOR;
+  v_object_role      VARCHAR2(64);
+  v_exists           NUMBER;
+
+  FUNCTION generalized_question(p_question CLOB) RETURN CLOB IS
+    v_value CLOB := p_question;
+  BEGIN
+    -- Resolved names/aliases become a semantic game placeholder.
+    FOR token IN (
+      SELECT column_value AS value
+        FROM TABLE(sys.odcivarchar2list(
+          '카오스 제로 나이트메어', '카오스제로나이트메어', 'Chaos Zero Nightmare',
+          'STOVE_CHAOSZERO', '카제나', 'CZN', 'Bubblyz', '버블리즈',
+          '로드나인', '로나', '테스트게임', 'BUBBLYZ', 'LORDNINE'
+        ))
+    ) LOOP
+      v_value := REPLACE(v_value, token.value, '<게임>');
+    END LOOP;
+    v_value := REGEXP_REPLACE(v_value, '[0-9]{4}년[[:space:]]*[0-9]{1,2}월[[:space:]]*[0-9]{1,2}일', '<기준일>');
+    v_value := REGEXP_REPLACE(v_value, '[0-9]{4}-[0-9]{2}-[0-9]{2}', '<기준일>');
+    RETURN v_value;
+  END;
+
+  FUNCTION generalized_sql(p_sql CLOB) RETURN CLOB IS
+    v_value CLOB := p_sql;
+  BEGIN
+    -- Physical game objects become logical roles. Common dimensions remain
+    -- logical as well so the current metadata/plan selects real objects.
+    v_value := REPLACE(v_value, '"SGMP_POC"."CZN_COMN_USER_MST"', '<RESOLVED_GAME_USER_MASTER>');
+    v_value := REPLACE(v_value, '"SGMP_POC"."CZN_COMN_CHARACTER_MST"', '<RESOLVED_GAME_CHARACTER_MASTER>');
+    v_value := REPLACE(v_value, '"SGMP_POC"."CZN_CUSTOM_GOODS_HAVE_TXN"', '<RESOLVED_GAME_GOODS_HOLDINGS>');
+    v_value := REPLACE(v_value, '"SGMP_POC"."CZN_CUSTOM_GOODS_CHANGE_TXN"', '<RESOLVED_GAME_GOODS_CHANGE>');
+    v_value := REPLACE(v_value, '"SGMP_POC"."CZN_CUSTOM_BIZ_USER_TXN"', '<RESOLVED_GAME_BUSINESS_USER>');
+    v_value := REPLACE(v_value, '"SGMP_POC"."CZN_CUSTOM_USER_GOODS_TXN"', '<RESOLVED_GAME_USER_GOODS>');
+    v_value := REPLACE(v_value, '"SGMP_POC"."COMN_SALES_TXN"', '<APPROVED_SALES_TRANSACTION>');
+    v_value := REPLACE(v_value, '"SGMP_POC"."COMN_REFUND_TXN"', '<APPROVED_REFUND_TRANSACTION>');
+    v_value := REPLACE(v_value, '"SGMP_POC"."COMN_GAME_ALIAS_BAS"', '<GAME_ALIAS_CATALOG>');
+    v_value := REPLACE(v_value, '"SGMP_POC"."COMN_COUNTRY_BAS"', '<COUNTRY_DIMENSION>');
+    v_value := REPLACE(v_value, '"SGMP_POC"."CZN_COMN_SVC_DIM_BAS"', '<RESOLVED_GAME_SERVICE_DIMENSION>');
+    v_value := REPLACE(v_value, 'STOVE_CHAOSZERO', '<RESOLVED_GAME_ID>');
+    v_value := REPLACE(v_value, '''카제나''', '<RESOLVED_GAME_NAME>');
+    v_value := REPLACE(v_value, '''CZN''', '<RESOLVED_GAME_PREFIX>');
+    v_value := REGEXP_REPLACE(v_value, 'CZN_[A-Z0-9_]+', '<RESOLVED_GAME_OBJECT>');
+    v_value := REPLACE(v_value, '카제나', '<RESOLVED_GAME_NAME>');
+    v_value := REPLACE(v_value, '카오스 제로 나이트메어', '<RESOLVED_GAME_NAME>');
+    v_value := REPLACE(v_value, '카오스제로나이트메어', '<RESOLVED_GAME_NAME>');
+    v_value := REPLACE(v_value, 'CZN', '<RESOLVED_GAME_PREFIX>');
+    v_value := REPLACE(v_value, 'BUBBLYZ', '<RESOLVED_GAME_ID>');
+    v_value := REPLACE(v_value, 'Bubblyz', '<RESOLVED_GAME_NAME>');
+    v_value := REPLACE(v_value, '버블리즈', '<RESOLVED_GAME_NAME>');
+    v_value := REPLACE(v_value, 'LORDNINE', '<RESOLVED_GAME_ID>');
+    v_value := REPLACE(v_value, '로드나인', '<RESOLVED_GAME_NAME>');
+    v_value := REPLACE(v_value, '테스트게임', '<RESOLVED_GAME_NAME>');
+    v_value := REGEXP_REPLACE(v_value, 'TO_DATE\(''[0-9]{4}-[0-9]{2}-[0-9]{2}'', ''YYYY-MM-DD''\)', '<BUSINESS_DATE>');
+    v_value := REGEXP_REPLACE(v_value, 'TO_DATE\(''[0-9]{8}'', ''YYYYMMDD''\)', '<BUSINESS_DATE>');
+    v_value := REGEXP_REPLACE(v_value, 'DATE ''[0-9]{4}-[0-9]{2}-[0-9]{2}''', '<BUSINESS_DATE>');
+    RETURN v_value;
+  END;
+
+  FUNCTION role_of(p_sql CLOB) RETURN VARCHAR2 IS
+  BEGIN
+    IF DBMS_LOB.INSTR(p_sql, 'CZN_COMN_CHARACTER_MST') > 0 THEN
+      RETURN 'GAME_CHARACTER_MASTER';
+    ELSIF DBMS_LOB.INSTR(p_sql, 'CZN_CUSTOM_GOODS_HAVE_TXN') > 0 THEN
+      RETURN 'GAME_GOODS_HOLDINGS';
+    ELSIF DBMS_LOB.INSTR(p_sql, 'CZN_CUSTOM_GOODS_CHANGE_TXN') > 0 THEN
+      RETURN 'GAME_GOODS_CHANGE';
+    ELSIF DBMS_LOB.INSTR(p_sql, 'CZN_CUSTOM_BIZ_USER_TXN') > 0 THEN
+      RETURN 'GAME_BUSINESS_USER';
+    ELSIF DBMS_LOB.INSTR(p_sql, 'COMN_SALES_TXN') > 0 THEN
+      RETURN 'SALES_TRANSACTION';
+    ELSIF DBMS_LOB.INSTR(p_sql, 'COMN_REFUND_TXN') > 0 THEN
+      RETURN 'REFUND_TRANSACTION';
+    ELSIF DBMS_LOB.INSTR(p_sql, 'CZN_COMN_USER_MST') > 0 THEN
+      RETURN 'GAME_USER_MASTER';
+    END IF;
+    RETURN 'METADATA_OR_OPERATION';
+  END;
+BEGIN
+  FOR source_row IN (
+    SELECT example_id, source_case_id, question, answer_sql
+      FROM sg_qa_vector_example
+     WHERE source_type = 'CUSTOMER_QA_BENCHMARK'
+     ORDER BY source_case_id
+  ) LOOP
+    v_pattern_question := generalized_question(source_row.question);
+    v_pattern_sql := generalized_sql(source_row.answer_sql);
+    v_object_role := role_of(source_row.answer_sql);
+    v_embedding_input := TO_CLOB('Generalized question pattern: ') || v_pattern_question
+      || CHR(10) || 'Logical object role: ' || v_object_role
+      || CHR(10) || 'Structural SQL template: ' || v_pattern_sql
+      || CHR(10) || 'Use only current game scope metadata and replace placeholders from the current request.';
+
+    -- A generalized runtime pattern must not contain known customer answer
+    -- identifiers or fixed business-date literals.
+    IF REGEXP_LIKE(v_pattern_question,
+          '카제나|버블리즈|Bubblyz|로드나인|테스트게임|[0-9]{4}년|[0-9]{4}-[0-9]{2}-[0-9]{2}', 'i')
+       OR REGEXP_LIKE(v_pattern_sql,
+          'CZN_|STOVE_CHAOSZERO|카제나|버블리즈|Bubblyz|[0-9]{4}-[0-9]{2}-[0-9]{2}', 'i') THEN
+      RAISE_APPLICATION_ERROR(-20061, 'Generalization leak in ' || source_row.source_case_id);
+    END IF;
+
+    v_embedding := DBMS_VECTOR.UTL_TO_EMBEDDING(
+      v_embedding_input,
+      JSON(sg_qa_vector_params('search_document'))
+    );
+
+    SELECT COUNT(*)
+      INTO v_exists
+      FROM sg_qa_vector_example
+     WHERE source_type = 'GENERALIZED_QUESTION_PATTERN'
+       AND source_case_id = 'PAT-' || source_row.source_case_id;
+
+    IF v_exists = 0 THEN
+      INSERT INTO sg_qa_vector_example (
+        question, answer_sql, answer_text, embedding_input, embedding, embedding_model,
+        reference_status, reference_kind, target_type, object_role,
+        inspection_status, inspection_note, verified_at, verified_by,
+        source_case_id, source_type
+      ) VALUES (
+        v_pattern_question,
+        v_pattern_sql,
+        'Question-specific generalized Few-shot. Structural only: it contains no customer game, date, result, or executable answer. First decide whether this pattern is applicable; then apply the authoritative NONE/SINGLE/MULTI/ALL game plan and replace placeholders from current metadata.',
+        v_embedding_input,
+        v_embedding,
+        'cohere.embed-v4.0',
+        'APPROVED', 'SQL_TEMPLATE', 'ANY', v_object_role,
+        'VERIFIED',
+        'Derived from a customer QA structure after game/date/result/object leakage validation; runtime uses this generalized pattern only.',
+        SYSTIMESTAMP, 'SGMP_POC_PATTERN_REVIEW',
+        'PAT-' || source_row.source_case_id, 'GENERALIZED_QUESTION_PATTERN'
+      );
+    ELSE
+      UPDATE sg_qa_vector_example
+         SET question = v_pattern_question,
+             answer_sql = v_pattern_sql,
+             answer_text = 'Question-specific generalized Few-shot. Structural only: it contains no customer game, date, result, or executable answer. First decide whether this pattern is applicable; then apply the authoritative NONE/SINGLE/MULTI/ALL game plan and replace placeholders from current metadata.',
+             embedding_input = v_embedding_input,
+             embedding = v_embedding,
+             object_role = v_object_role,
+             reference_status = 'APPROVED',
+             inspection_status = 'VERIFIED',
+             inspection_note = 'Derived from a customer QA structure after game/date/result/object leakage validation; runtime uses this generalized pattern only.',
+             verified_at = SYSTIMESTAMP,
+             verified_by = 'SGMP_POC_PATTERN_REVIEW'
+       WHERE source_type = 'GENERALIZED_QUESTION_PATTERN'
+         AND source_case_id = 'PAT-' || source_row.source_case_id;
+    END IF;
+  END LOOP;
+  COMMIT;
+END;
+/
+
+SELECT source_type, reference_status, COUNT(*) AS example_count
+  FROM sg_qa_vector_example
+ GROUP BY source_type, reference_status
+ ORDER BY source_type, reference_status;

database/adb/109_sgmp_genai_question_specific_fewshot_patterns.sql

@@ -0,0 +1,358 @@
+-- Generate one reusable, question-specific Few-shot pattern per customer QA
+-- benchmark without promoting the benchmark answer itself.  Game identity is
+-- deliberately not inferred here: sg_game_query_plan owns that through OCI
+-- GenAI chat + the current game catalog.
+
+CREATE OR REPLACE FUNCTION sg_qa_genai_generalize_pattern(
+  p_question   IN CLOB,
+  p_answer_sql IN CLOB,
+  p_target_type IN VARCHAR2
+) RETURN CLOB AUTHID DEFINER
+IS
+  v_prompt CLOB;
+  v_result CLOB;
+BEGIN
+  v_prompt :=
+    'Create one reusable, question-specific Few-shot SQL pattern from the source example. '
+    || 'This is training guidance, never an answer key. Return exactly these tagged sections and nothing else: '
+    || '[[PATTERN_QUESTION]], [[STRUCTURAL_SQL_PATTERN]], [[OBJECT_ROLE]], [[TARGET_TYPE]], '
+    || '[[APPLICABILITY]], [[END]]. '
+    || 'Preserve only the query intent and structural operations such as aggregation, joins, '
+    || 'grouping, ordering, date semantics, and filters. Replace every game name, alias, game ID, '
+    || 'schema name, physical object name, column name, literal date, literal number, user ID, '
+    || 'currency amount, and expected output with semantic placeholders such as <GAME_SCOPE>, '
+    || '<LOGICAL_FACT>, <LOGICAL_DIMENSION>, <METRIC>, <AS_OF_DATE>, <FILTER>, and <GROUPING>. '
+    || 'In STRUCTURAL_SQL_PATTERN, every non-SQL identifier must be an angle-bracket placeholder: '
+    || 'do not retain any source column, alias, table, schema, literal, code, or business value. '
+    || 'Do not include executable SQL. Do not include a game name or a customer answer. '
+    || 'The current game scope is supplied separately at runtime by a database OCI GenAI chat '
+    || 'resolver, therefore never choose or imply a game. The TARGET_TYPE section must be one of NONE, '
+    || 'SINGLE, MULTI, ALL, ANY and must describe applicability, not a game identity. '
+    || 'Source target type from the current resolver: ' || NVL(p_target_type, 'ANY') || CHR(10)
+    || 'Source question:' || CHR(10) || DBMS_LOB.SUBSTR(p_question, 4000, 1) || CHR(10)
+    || 'Source SQL (structure only; do not copy identifiers or values):' || CHR(10)
+    || DBMS_LOB.SUBSTR(p_answer_sql, 12000, 1);
+  v_result := DBMS_CLOUD_AI.GENERATE(
+    prompt => v_prompt,
+    profile_name => 'SGMP_POC_OCI_GPT54MINI',
+    action => 'chat'
+  );
+  RETURN v_result;
+END;
+/
+
+CREATE OR REPLACE FUNCTION sg_qa_genai_validate_pattern(
+  p_pattern_json IN CLOB
+) RETURN CLOB AUTHID DEFINER
+IS
+  v_prompt CLOB;
+  v_result CLOB;
+BEGIN
+  v_prompt :=
+    'Inspect only concrete-answer leakage in this reusable Few-shot pattern. Return exactly '
+    || '[[CONCRETE_LEAKAGE]] YES or NO, then [[REASON]] and a short reason, then [[END]]. '
+    || 'Return YES only when a customer answer, concrete game identity, physical schema/table/column '
+    || 'identifier, literal date, literal business result, or executable SQL against a real object remains. '
+    || 'Return NO when all such references are semantic angle-bracket placeholders. A pseudo-SQL pattern '
+    || 'using SELECT/FROM/JOIN/GROUP BY, generic game-scope checks, EXISTS, UNION, or equality with '
+    || 'angle-bracket placeholders is not concrete leakage and must return NO. Do not judge usefulness or '
+    || 'completeness; classify leakage only. '
+    || 'Candidate:' || CHR(10) || DBMS_LOB.SUBSTR(p_pattern_json, 16000, 1);
+  v_result := DBMS_CLOUD_AI.GENERATE(
+    prompt => v_prompt,
+    profile_name => 'SGMP_POC_OCI_GPT54MINI',
+    action => 'chat'
+  );
+  RETURN v_result;
+END;
+/
+
+CREATE OR REPLACE FUNCTION sg_qa_generate_generalized_patterns
+  RETURN NUMBER AUTHID DEFINER
+IS
+  PRAGMA AUTONOMOUS_TRANSACTION;
+  v_plan_raw       CLOB;
+  v_plan            JSON_OBJECT_T;
+  v_target_type     VARCHAR2(16);
+  v_pattern_raw     CLOB;
+  v_validation_raw  CLOB;
+  v_status          VARCHAR2(16);
+  v_validation_note CLOB;
+  v_question        CLOB;
+  v_sql_pattern     CLOB;
+  v_answer_text     CLOB;
+  v_object_role     VARCHAR2(64);
+  v_embedding_input CLOB;
+  v_embedding       VECTOR;
+  v_count           NUMBER := 0;
+
+  FUNCTION parse_json_result(p_value CLOB) RETURN JSON_OBJECT_T IS
+    v_text CLOB := TRIM(p_value);
+  BEGIN
+    IF DBMS_LOB.SUBSTR(v_text, 7, 1) = '```json' THEN
+      v_text := REGEXP_REPLACE(v_text, '^```json[[:space:]]*', '');
+      v_text := REGEXP_REPLACE(v_text, '[[:space:]]*```[[:space:]]*$', '');
+    ELSIF DBMS_LOB.SUBSTR(v_text, 3, 1) = '```' THEN
+      v_text := REGEXP_REPLACE(v_text, '^```[[:space:]]*', '');
+      v_text := REGEXP_REPLACE(v_text, '[[:space:]]*```[[:space:]]*$', '');
+    END IF;
+    RETURN JSON_OBJECT_T.parse(v_text);
+  END;
+
+  FUNCTION section_value(
+    p_raw IN CLOB, p_start_tag IN VARCHAR2, p_end_tag IN VARCHAR2
+  ) RETURN CLOB IS
+    v_start PLS_INTEGER;
+    v_end PLS_INTEGER;
+  BEGIN
+    v_start := DBMS_LOB.INSTR(p_raw, p_start_tag, 1, 1);
+    IF v_start = 0 THEN
+      RAISE_APPLICATION_ERROR(-20071, 'OCI GenAI response is missing ' || p_start_tag);
+    END IF;
+    v_start := v_start + LENGTH(p_start_tag);
+    v_end := DBMS_LOB.INSTR(p_raw, p_end_tag, v_start, 1);
+    IF v_end = 0 OR v_end <= v_start THEN
+      RAISE_APPLICATION_ERROR(-20072, 'OCI GenAI response is missing ' || p_end_tag);
+    END IF;
+    RETURN TRIM(DBMS_LOB.SUBSTR(p_raw, LEAST(v_end - v_start, 32767), v_start));
+  END;
+
+  PROCEDURE upsert_pattern(
+    p_case_id IN VARCHAR2,
+    p_status IN VARCHAR2,
+    p_note IN CLOB
+  ) IS
+  BEGIN
+    UPDATE sg_qa_vector_example
+       SET question = v_question,
+           answer_sql = v_sql_pattern,
+           answer_text = v_answer_text,
+           embedding_input = v_embedding_input,
+           embedding = v_embedding,
+           embedding_model = 'cohere.embed-v4.0',
+           reference_status = p_status,
+           reference_kind = 'SQL_PATTERN',
+           target_type = v_target_type,
+           object_role = v_object_role,
+           inspection_status = CASE WHEN p_status = 'APPROVED' THEN 'GENAI_VERIFIED' ELSE 'GENAI_REJECTED' END,
+           inspection_note = p_note,
+           verified_at = SYSTIMESTAMP,
+           verified_by = 'SGMP_POC_OCI_GENAI_PATTERN'
+     WHERE source_type = 'GENERALIZED_QUESTION_PATTERN'
+       AND source_case_id = 'PAT-' || p_case_id;
+
+    IF SQL%ROWCOUNT = 0 THEN
+      INSERT INTO sg_qa_vector_example (
+        question, answer_sql, answer_text, embedding_input, embedding, embedding_model,
+        reference_status, reference_kind, target_type, object_role, inspection_status,
+        inspection_note, verified_at, verified_by, source_case_id, source_type
+      ) VALUES (
+        v_question, v_sql_pattern, v_answer_text, v_embedding_input, v_embedding, 'cohere.embed-v4.0',
+        p_status, 'SQL_PATTERN', v_target_type, v_object_role,
+        CASE WHEN p_status = 'APPROVED' THEN 'GENAI_VERIFIED' ELSE 'GENAI_REJECTED' END,
+        p_note, SYSTIMESTAMP, 'SGMP_POC_OCI_GENAI_PATTERN',
+        'PAT-' || p_case_id, 'GENERALIZED_QUESTION_PATTERN'
+      );
+    END IF;
+  END;
+BEGIN
+  FOR source_row IN (
+    SELECT source.source_case_id, source.question, source.answer_sql
+      FROM sg_qa_vector_example source
+     WHERE source.source_type = 'CUSTOMER_QA_BENCHMARK'
+       AND NOT EXISTS (
+         SELECT 1
+           FROM sg_qa_vector_example pattern
+          WHERE pattern.source_type = 'GENERALIZED_QUESTION_PATTERN'
+            AND pattern.source_case_id = 'PAT-' || source.source_case_id
+            AND pattern.reference_status = 'APPROVED'
+       )
+     ORDER BY source_case_id
+  ) LOOP
+    BEGIN
+      -- The target category comes from the existing OCI GenAI game resolver;
+      -- no alias, prefix, table, or name is transformed in this migration.
+      v_plan_raw := sg_game_query_plan(source_row.question, 5);
+      v_plan := parse_json_result(v_plan_raw);
+      v_target_type := UPPER(NVL(v_plan.get_string('targetType'), 'ANY'));
+      IF v_target_type NOT IN ('NONE', 'SINGLE', 'MULTI', 'ALL') THEN
+        v_target_type := 'ANY';
+      END IF;
+
+      v_pattern_raw := sg_qa_genai_generalize_pattern(
+        source_row.question, source_row.answer_sql, v_target_type
+      );
+      v_question := section_value(v_pattern_raw, '[[PATTERN_QUESTION]]', '[[STRUCTURAL_SQL_PATTERN]]');
+      v_sql_pattern := section_value(v_pattern_raw, '[[STRUCTURAL_SQL_PATTERN]]', '[[OBJECT_ROLE]]');
+      v_object_role := SUBSTR(section_value(v_pattern_raw, '[[OBJECT_ROLE]]', '[[TARGET_TYPE]]'), 1, 64);
+      IF section_value(v_pattern_raw, '[[TARGET_TYPE]]', '[[APPLICABILITY]]')
+          IN ('NONE', 'SINGLE', 'MULTI', 'ALL', 'ANY') THEN
+        v_target_type := section_value(v_pattern_raw, '[[TARGET_TYPE]]', '[[APPLICABILITY]]');
+      END IF;
+      v_answer_text := TO_CLOB('Generalized, question-specific structural pattern. '
+        || 'Current game scope must be supplied only by sg_game_query_plan. Applicability: ')
+        || section_value(v_pattern_raw, '[[APPLICABILITY]]', '[[END]]');
+      v_embedding_input := TO_CLOB('Question-specific generalized Few-shot pattern:' || CHR(10))
+        || v_question || CHR(10) || 'Logical role: ' || v_object_role || CHR(10)
+        || 'Structural SQL pattern:' || CHR(10) || v_sql_pattern;
+      v_embedding := DBMS_VECTOR.UTL_TO_EMBEDDING(
+        v_embedding_input, JSON(sg_qa_vector_params('search_document'))
+      );
+      v_validation_raw := sg_qa_genai_validate_pattern(v_pattern_raw);
+      v_status := CASE
+        WHEN REGEXP_SUBSTR(
+          UPPER(section_value(v_validation_raw, '[[CONCRETE_LEAKAGE]]', '[[REASON]]')),
+          '[A-Z]+'
+        ) = 'NO'
+          THEN 'APPROVE'
+        ELSE 'REJECT'
+      END;
+      v_validation_note := section_value(v_validation_raw, '[[REASON]]', '[[END]]');
+
+      IF v_status = 'APPROVE' THEN
+        upsert_pattern(source_row.source_case_id, 'APPROVED',
+          'ADB OCI GenAI generated and independently validated a generalized pattern. '
+          || 'The original customer QA remains evaluation-only. ' || v_validation_note);
+        v_count := v_count + 1;
+      ELSE
+        upsert_pattern(source_row.source_case_id, 'DRAFT',
+          'ADB OCI GenAI rejected the generalized pattern: ' || v_validation_note);
+      END IF;
+    EXCEPTION
+      WHEN OTHERS THEN
+        -- Persist an auditable non-runtime draft and continue with the other
+        -- customer questions; one malformed LLM response must not block all 47.
+        v_question := source_row.question;
+        v_sql_pattern := TO_CLOB('<PATTERN_GENERATION_FAILED>');
+        v_answer_text := TO_CLOB('No runtime Few-shot pattern: OCI GenAI generalization failed.');
+        v_object_role := 'UNSPECIFIED';
+        v_target_type := 'ANY';
+        v_embedding_input := TO_CLOB('Failed generalized pattern: ') || source_row.question;
+        v_embedding := DBMS_VECTOR.UTL_TO_EMBEDDING(
+          v_embedding_input, JSON(sg_qa_vector_params('search_document'))
+        );
+        upsert_pattern(source_row.source_case_id, 'DRAFT',
+          'OCI GenAI pattern generation error: ' || SQLERRM);
+    END;
+  END LOOP;
+  COMMIT;
+  RETURN v_count;
+EXCEPTION
+  WHEN OTHERS THEN
+    ROLLBACK;
+    RAISE;
+END;
+/
+
+-- Re-run only the independent OCI Chat safety review after its policy changes.
+-- It never reads a customer benchmark and never changes the generated pattern.
+CREATE OR REPLACE FUNCTION sg_qa_revalidate_generalized_patterns
+  RETURN NUMBER AUTHID DEFINER
+IS
+  PRAGMA AUTONOMOUS_TRANSACTION;
+  v_raw CLOB;
+  v_status VARCHAR2(16);
+  v_reason CLOB;
+  v_start PLS_INTEGER;
+  v_end PLS_INTEGER;
+  v_count NUMBER := 0;
+
+  FUNCTION section_value(
+    p_raw IN CLOB, p_start_tag IN VARCHAR2, p_end_tag IN VARCHAR2
+  ) RETURN CLOB IS
+    v_from PLS_INTEGER;
+    v_to PLS_INTEGER;
+  BEGIN
+    v_from := DBMS_LOB.INSTR(p_raw, p_start_tag, 1, 1);
+    IF v_from = 0 THEN RAISE_APPLICATION_ERROR(-20073, 'Missing ' || p_start_tag); END IF;
+    v_from := v_from + LENGTH(p_start_tag);
+    v_to := DBMS_LOB.INSTR(p_raw, p_end_tag, v_from, 1);
+    IF v_to = 0 OR v_to <= v_from THEN RAISE_APPLICATION_ERROR(-20074, 'Missing ' || p_end_tag); END IF;
+    RETURN TRIM(DBMS_LOB.SUBSTR(p_raw, LEAST(v_to - v_from, 32767), v_from));
+  END;
+BEGIN
+  FOR item IN (
+    SELECT example_id, question, answer_sql, answer_text
+      FROM sg_qa_vector_example
+     WHERE source_type = 'GENERALIZED_QUESTION_PATTERN'
+     ORDER BY source_case_id
+  ) LOOP
+    BEGIN
+      v_raw := sg_qa_genai_validate_pattern(
+        TO_CLOB('[[PATTERN_QUESTION]]') || item.question
+        || TO_CLOB(CHR(10) || '[[STRUCTURAL_SQL_PATTERN]]') || item.answer_sql
+        || TO_CLOB(CHR(10) || '[[APPLICABILITY]]') || item.answer_text || CHR(10) || '[[END]]'
+      );
+      v_status := CASE
+        WHEN REGEXP_SUBSTR(
+          UPPER(section_value(v_raw, '[[CONCRETE_LEAKAGE]]', '[[REASON]]')),
+          '[A-Z]+'
+        ) = 'NO'
+          THEN 'APPROVE'
+        ELSE 'REJECT'
+      END;
+      v_reason := section_value(v_raw, '[[REASON]]', '[[END]]');
+      UPDATE sg_qa_vector_example
+         SET reference_status = CASE WHEN v_status = 'APPROVE' THEN 'APPROVED' ELSE 'DRAFT' END,
+             inspection_status = CASE WHEN v_status = 'APPROVE' THEN 'GENAI_VERIFIED' ELSE 'GENAI_REJECTED' END,
+             inspection_note = 'ADB OCI GenAI independent revalidation: ' || v_reason,
+             verified_at = SYSTIMESTAMP,
+             verified_by = 'SGMP_POC_OCI_GENAI_PATTERN'
+       WHERE example_id = item.example_id;
+      IF v_status = 'APPROVE' THEN v_count := v_count + 1; END IF;
+    EXCEPTION
+      WHEN OTHERS THEN
+        v_reason := TO_CLOB('OCI GenAI revalidation error: ' || SQLERRM);
+        UPDATE sg_qa_vector_example
+           SET reference_status = 'DRAFT',
+               inspection_status = 'GENAI_REJECTED',
+               inspection_note = v_reason,
+               verified_at = SYSTIMESTAMP,
+               verified_by = 'SGMP_POC_OCI_GENAI_PATTERN'
+         WHERE example_id = item.example_id;
+    END;
+  END LOOP;
+  COMMIT;
+  RETURN v_count;
+EXCEPTION
+  WHEN OTHERS THEN
+    ROLLBACK;
+    RAISE;
+END;
+/
+
+-- Production retrieval accepts only independently generalized patterns or
+-- policy templates. Customer QA benchmarks remain evaluation-only forever.
+CREATE OR REPLACE FUNCTION sg_qa_vector_search(
+  p_question    IN CLOB,
+  p_top_k       IN PLS_INTEGER DEFAULT 3,
+  p_target_type IN VARCHAR2 DEFAULT 'ANY'
+) RETURN SYS_REFCURSOR AUTHID DEFINER
+IS
+  v_query_vector VECTOR;
+  v_results SYS_REFCURSOR;
+  v_target_type VARCHAR2(16) := UPPER(TRIM(NVL(p_target_type, 'ANY')));
+BEGIN
+  IF p_question IS NULL THEN RAISE_APPLICATION_ERROR(-20003, 'question is required.'); END IF;
+  IF p_top_k IS NULL OR p_top_k < 1 OR p_top_k > 20 THEN
+    RAISE_APPLICATION_ERROR(-20004, 'top_k must be between 1 and 20.');
+  END IF;
+  IF v_target_type NOT IN ('NONE', 'SINGLE', 'MULTI', 'ALL', 'ANY') THEN
+    RAISE_APPLICATION_ERROR(-20005, 'invalid target type.');
+  END IF;
+  v_query_vector := DBMS_VECTOR.UTL_TO_EMBEDDING(
+    p_question, JSON(sg_qa_vector_params('search_query'))
+  );
+  OPEN v_results FOR
+    SELECT example_id, question, answer_sql, answer_text, embedding_model,
+           reference_kind, target_type, object_role, source_case_id, source_type,
+           vector_distance(embedding, v_query_vector, COSINE) AS cosine_distance
+      FROM sg_qa_vector_example
+     WHERE reference_status = 'APPROVED'
+       AND source_type IN ('GENERALIZED_QUESTION_PATTERN', 'POLICY_TEMPLATE')
+       AND (target_type = 'ANY' OR v_target_type = 'ANY' OR target_type = v_target_type)
+     ORDER BY vector_distance(embedding, v_query_vector, COSINE), example_id
+     FETCH FIRST p_top_k ROWS ONLY;
+  RETURN v_results;
+END;
+/

database/adb/110_sg_game_query_plan_admin_grants.sql

@@ -0,0 +1,9 @@
+-- The MCP DB account owns the OCI GenAI planning functions while the game
+-- catalog is owned by the data schema. Definer-rights PL/SQL needs direct
+-- object grants; role grants are not sufficient at compile time.
+BEGIN
+  EXECUTE IMMEDIATE 'GRANT SELECT ON SGMP_POC.SG_GAME_CATALOG TO ADMIN';
+  EXECUTE IMMEDIATE 'GRANT SELECT ON SGMP_POC.COMN_GAME_ALIAS_BAS TO ADMIN';
+  EXECUTE IMMEDIATE 'GRANT EXECUTE ON SGMP_POC.SG_GAME_CATALOG_SEARCH TO ADMIN';
+END;
+/

database/adb/111_sg_game_daily_au_lookup.sql

@@ -0,0 +1,148 @@
+-- Deterministic daily-AU lookup for an already resolved game query plan.
+-- Physical user-master objects are selected only from SG_GAME_CATALOG.
+-- No game name, alias, prefix, or object name is embedded in this function.
+
+CREATE OR REPLACE FUNCTION sg_game_daily_au_lookup(
+  p_query_plan IN CLOB,
+  p_base_date  IN DATE DEFAULT NULL
+) RETURN CLOB AUTHID DEFINER IS
+  v_plan             JSON_OBJECT_T;
+  v_targets          JSON_ARRAY_T;
+  v_target           JSON_OBJECT_T;
+  v_result           JSON_OBJECT_T := JSON_OBJECT_T();
+  v_items            JSON_ARRAY_T := JSON_ARRAY_T();
+  v_item             JSON_OBJECT_T;
+  v_game_key         VARCHAR2(128);
+  v_game_id          VARCHAR2(128);
+  v_game_name        VARCHAR2(512);
+  v_object_name      VARCHAR2(128);
+  v_safe_object_name VARCHAR2(128);
+  v_effective_date   DATE;
+  v_au_count         NUMBER;
+  v_column_count     PLS_INTEGER;
+  v_object_count     PLS_INTEGER;
+  v_seen             SYS.ODCIVARCHAR2LIST := SYS.ODCIVARCHAR2LIST();
+  v_target_count     PLS_INTEGER := 0;
+
+  FUNCTION is_seen(p_game_key IN VARCHAR2) RETURN BOOLEAN IS
+  BEGIN
+    FOR i IN 1 .. v_seen.COUNT LOOP
+      IF v_seen(i) = p_game_key THEN
+        RETURN TRUE;
+      END IF;
+    END LOOP;
+    RETURN FALSE;
+  END;
+
+  PROCEDURE add_status(
+    p_game_key IN VARCHAR2,
+    p_status   IN VARCHAR2,
+    p_reason   IN VARCHAR2
+  ) IS
+  BEGIN
+    v_item := JSON_OBJECT_T();
+    v_item.put('gameKey', p_game_key);
+    v_item.put('status', p_status);
+    v_item.put('reason', p_reason);
+    v_items.append(v_item);
+  END;
+BEGIN
+  IF p_query_plan IS NULL THEN
+    RAISE_APPLICATION_ERROR(-20001, 'queryPlan is required');
+  END IF;
+
+  v_plan := JSON_OBJECT_T.parse(p_query_plan);
+  v_targets := v_plan.get_array('dataEligibleTargets');
+  IF v_targets IS NULL THEN
+    v_targets := v_plan.get_array('targets');
+  END IF;
+
+  IF v_targets IS NOT NULL AND v_targets.get_size > 0 THEN
+    FOR i IN 0 .. v_targets.get_size - 1 LOOP
+      v_target := TREAT(v_targets.get(i) AS JSON_OBJECT_T);
+      IF v_target IS NULL OR NOT v_target.has('gameKey') THEN
+        CONTINUE;
+      END IF;
+      v_game_key := v_target.get_string('gameKey');
+      IF v_game_key IS NULL OR is_seen(v_game_key) THEN
+        CONTINUE;
+      END IF;
+      v_seen.EXTEND;
+      v_seen(v_seen.COUNT) := v_game_key;
+      v_target_count := v_target_count + 1;
+
+      BEGIN
+        SELECT game_id, game_nm, user_master_object_name
+          INTO v_game_id, v_game_name, v_object_name
+          FROM sg_game_catalog
+         WHERE game_key = v_game_key
+           AND active_yn = 'Y';
+      EXCEPTION
+        WHEN NO_DATA_FOUND THEN
+          add_status(v_game_key, 'UNAVAILABLE', 'Catalog target is not active.');
+          CONTINUE;
+      END;
+
+      IF v_object_name IS NULL THEN
+        add_status(v_game_key, 'UNAVAILABLE', 'No approved user-master object is registered.');
+        CONTINUE;
+      END IF;
+
+      v_safe_object_name := DBMS_ASSERT.SIMPLE_SQL_NAME(UPPER(v_object_name));
+      SELECT COUNT(*) INTO v_object_count
+        FROM user_objects
+       WHERE object_name = v_safe_object_name
+         AND object_type IN ('TABLE', 'VIEW', 'MATERIALIZED VIEW')
+         AND status = 'VALID';
+      SELECT COUNT(*) INTO v_column_count
+        FROM user_tab_columns
+       WHERE table_name = v_safe_object_name
+         AND column_name IN ('GUID', 'BASE_DT', 'AU_FLAG', 'EXPT_USER_YN');
+      IF v_object_count = 0 OR v_column_count <> 4 THEN
+        add_status(v_game_key, 'UNAVAILABLE', 'Approved user-master object is not query-ready.');
+        CONTINUE;
+      END IF;
+
+      IF p_base_date IS NULL THEN
+        EXECUTE IMMEDIATE 'SELECT MAX(BASE_DT) FROM ' || v_safe_object_name
+          INTO v_effective_date;
+      ELSE
+        v_effective_date := TRUNC(p_base_date);
+      END IF;
+      IF v_effective_date IS NULL THEN
+        add_status(v_game_key, 'NO_DATA', 'No available base date in the selected object.');
+        CONTINUE;
+      END IF;
+
+      EXECUTE IMMEDIATE
+          'SELECT COUNT(DISTINCT GUID) FROM ' || v_safe_object_name
+          || ' WHERE BASE_DT = :1 AND AU_FLAG = 1 AND EXPT_USER_YN = ''N'''
+        INTO v_au_count USING v_effective_date;
+
+      v_item := JSON_OBJECT_T();
+      v_item.put('gameKey', v_game_key);
+      v_item.put('gameId', v_game_id);
+      v_item.put('gameName', v_game_name);
+      v_item.put('objectName', v_safe_object_name);
+      v_item.put('baseDate', TO_CHAR(v_effective_date, 'YYYY-MM-DD'));
+      v_item.put('auCount', v_au_count);
+      v_item.put('status', 'READY');
+      v_item.put('sqlTemplate',
+          'SELECT COUNT(DISTINCT GUID) AS AU_COUNT FROM <catalog_user_master_object> '
+          || 'WHERE BASE_DT = :baseDate AND AU_FLAG = 1 AND EXPT_USER_YN = ''N''');
+      v_items.append(v_item);
+    END LOOP;
+  END IF;
+
+  v_result.put('status', CASE WHEN v_target_count = 0 THEN 'NO_GAME_TARGET' ELSE 'GAME_AU_LOOKUP' END);
+  v_result.put('targetType', NVL(v_plan.get_string('targetType'), 'NONE'));
+  IF p_base_date IS NULL THEN
+    v_result.put_null('requestedBaseDate');
+  ELSE
+    v_result.put('requestedBaseDate', TO_CHAR(TRUNC(p_base_date), 'YYYY-MM-DD'));
+  END IF;
+  v_result.put('targetCount', v_target_count);
+  v_result.put('items', v_items);
+  RETURN v_result.to_clob;
+END;
+/

database/adb/112_sgmp_select_ai_oci_llama4scout_profile.sql

@@ -0,0 +1,81 @@
+-- Creates a non-operational comparison profile for Smilegate game-scope chat.
+-- The active GPT profile remains unchanged. Provider credentials and profile
+-- metadata are copied from it so the only comparison variable is the model.
+
+DECLARE
+  v_exists PLS_INTEGER;
+  v_credential_name VARCHAR2(128);
+  v_region VARCHAR2(128);
+  v_compartment_id VARCHAR2(4000);
+  v_attributes CLOB;
+  v_attribute_json JSON_OBJECT_T := JSON_OBJECT_T();
+BEGIN
+  SELECT COUNT(*)
+    INTO v_exists
+    FROM user_cloud_ai_profiles
+   WHERE profile_name = 'SGMP_POC_OCI_LLAMA4SCOUT';
+
+  IF v_exists > 0 THEN
+    DBMS_CLOUD_AI.DROP_PROFILE(
+      profile_name => 'SGMP_POC_OCI_LLAMA4SCOUT',
+      force        => TRUE
+    );
+  END IF;
+
+  SELECT DBMS_LOB.SUBSTR(attribute_value, 128, 1)
+    INTO v_credential_name
+    FROM user_cloud_ai_profile_attributes
+   WHERE profile_name = 'SGMP_POC_OCI_GPT54MINI'
+     AND attribute_name = 'credential_name';
+
+  SELECT DBMS_LOB.SUBSTR(attribute_value, 128, 1)
+    INTO v_region
+    FROM user_cloud_ai_profile_attributes
+   WHERE profile_name = 'SGMP_POC_OCI_GPT54MINI'
+     AND attribute_name = 'region';
+
+  SELECT DBMS_LOB.SUBSTR(attribute_value, 4000, 1)
+    INTO v_compartment_id
+    FROM user_cloud_ai_profile_attributes
+   WHERE profile_name = 'SGMP_POC_OCI_GPT54MINI'
+     AND attribute_name = 'oci_compartment_id';
+
+  v_attribute_json.put('provider', 'oci');
+  v_attribute_json.put('credential_name', v_credential_name);
+  v_attribute_json.put('model', 'meta.llama-4-scout-17b-16e-instruct');
+  v_attribute_json.put('region', v_region);
+  v_attribute_json.put('oci_compartment_id', v_compartment_id);
+  v_attributes := v_attribute_json.to_clob;
+
+  DBMS_CLOUD_AI.CREATE_PROFILE(
+    profile_name => 'SGMP_POC_OCI_LLAMA4SCOUT',
+    attributes   => v_attributes,
+    description  => 'Non-operational Smilegate game-scope latency comparison'
+  );
+
+  FOR source_attribute IN (
+    SELECT attribute_name, attribute_value
+      FROM user_cloud_ai_profile_attributes
+     WHERE profile_name = 'SGMP_POC_OCI_GPT54MINI'
+       AND attribute_name NOT IN (
+         'credential_name', 'model', 'provider', 'provider_endpoint',
+         'region', 'oci_compartment_id', 'oci_endpoint_id',
+         'oci_apiformat', 'oci_runtimetype'
+       )
+  ) LOOP
+    DBMS_CLOUD_AI.SET_ATTRIBUTE(
+      profile_name    => 'SGMP_POC_OCI_LLAMA4SCOUT',
+      attribute_name  => source_attribute.attribute_name,
+      attribute_value => source_attribute.attribute_value
+    );
+  END LOOP;
+END;
+/
+
+SELECT attribute_name, attribute_value
+  FROM user_cloud_ai_profile_attributes
+ WHERE profile_name = 'SGMP_POC_OCI_LLAMA4SCOUT'
+   AND attribute_name IN (
+     'provider', 'model', 'credential_name', 'region', 'oci_compartment_id'
+   )
+ ORDER BY attribute_name;

database/adb/113_sgmp_game_scope_chat_profile_benchmark.sql

@@ -0,0 +1,60 @@
+-- Read-only latency and JSON-shape comparison for the game-mention extraction
+-- stage. Korean input is reconstructed from UTF-8 base64 for SQLcl safety.
+
+set serveroutput on size unlimited
+
+DECLARE
+  v_question CLOB := utl_i18n.raw_to_char(
+    utl_encode.base64_decode(utl_raw.cast_to_raw(
+      '66Gc65Oc64KY7J247J20656RIOy5tOygnOuCmOydmCAyMDI264WEIDfsm5QgMTXsnbwgQVXrpbwg6rCB6rCBIOyVjOugpOykmC4='
+    )),
+    'AL32UTF8'
+  );
+  v_prompt CLOB;
+  v_result CLOB;
+  v_json JSON_OBJECT_T;
+  v_started PLS_INTEGER;
+  v_elapsed_seconds NUMBER;
+
+  PROCEDURE run_profile(p_profile_name IN VARCHAR2) IS
+  BEGIN
+    v_started := DBMS_UTILITY.GET_TIME;
+    v_result := DBMS_CLOUD_AI.GENERATE(
+      prompt => v_prompt,
+      profile_name => p_profile_name,
+      action => 'chat'
+    );
+    v_elapsed_seconds := (DBMS_UTILITY.GET_TIME - v_started) / 100;
+    v_json := JSON_OBJECT_T.parse(v_result);
+    DBMS_OUTPUT.PUT_LINE(
+      p_profile_name
+      || '|elapsed_seconds=' || TO_CHAR(v_elapsed_seconds, 'FM9990D00')
+      || '|scope_hint=' || NVL(v_json.get_string('scope_hint'), 'NULL')
+      || '|mention_count=' || v_json.get_array('game_mentions').get_size
+    );
+  EXCEPTION
+    WHEN OTHERS THEN
+      DBMS_OUTPUT.PUT_LINE(
+        p_profile_name || '|elapsed_seconds='
+        || TO_CHAR(v_elapsed_seconds, 'FM9990D00')
+        || '|ERROR|' || SQLCODE || '|' || SUBSTR(SQLERRM, 1, 300)
+      );
+      DBMS_OUTPUT.PUT_LINE(
+        p_profile_name || '|raw_response=' || DBMS_LOB.SUBSTR(v_result, 1000, 1)
+      );
+  END;
+BEGIN
+  v_prompt := 'Extract only game-name mentions from the user question. '
+    || 'Metrics, acronyms, dates, filters, and database object or column names are not game names unless they are themselves an explicit game title. '
+    || 'When a title-like noun directly qualifies a game data request such as user master, character, sales, AU, NRU, server, or game log, preserve that noun as a game-name mention even when it is not in a catalog. '
+    || 'Do not discard an unknown title merely because it cannot be resolved. General scope words such as common, overall, all, total, or every are not game-name mentions unless they are part of an explicit title. '
+    || 'Return exactly one JSON object with keys game_mentions (array of strings) '
+    || 'and scope_hint (GLOBAL, SINGLE_GAME, MULTI_GAME, ALL_GAMES, UNKNOWN). '
+    || 'Do not resolve names to IDs and do not generate SQL. '
+    || 'Return raw JSON only: no prose, no Markdown, and no code fence. Question: '
+    || v_question;
+
+  run_profile('SGMP_POC_OCI_GPT54MINI');
+  run_profile('SGMP_POC_OCI_LLAMA4SCOUT');
+END;
+/

database/adb/114_sgmp_create_scope_chat_candidate_profiles.sql

@@ -0,0 +1,90 @@
+-- Creates non-operational OCI profiles for game-scope extraction benchmarks.
+-- Every profile inherits the active GPT profile's OCI credential, region,
+-- object list, and metadata. Only model is varied.
+
+DECLARE
+  v_credential_name VARCHAR2(128);
+  v_region VARCHAR2(128);
+  v_compartment_id VARCHAR2(4000);
+  v_attributes CLOB;
+  v_attribute_json JSON_OBJECT_T;
+  v_exists PLS_INTEGER;
+
+  PROCEDURE create_candidate(
+    p_profile_name IN VARCHAR2,
+    p_model IN VARCHAR2
+  ) IS
+  BEGIN
+    SELECT COUNT(*)
+      INTO v_exists
+      FROM user_cloud_ai_profiles
+     WHERE profile_name = p_profile_name;
+
+    IF v_exists > 0 THEN
+      DBMS_CLOUD_AI.DROP_PROFILE(profile_name => p_profile_name, force => TRUE);
+    END IF;
+
+    v_attribute_json := JSON_OBJECT_T();
+    v_attribute_json.put('provider', 'oci');
+    v_attribute_json.put('credential_name', v_credential_name);
+    v_attribute_json.put('model', p_model);
+    v_attribute_json.put('region', v_region);
+    v_attribute_json.put('oci_compartment_id', v_compartment_id);
+    v_attributes := v_attribute_json.to_clob;
+
+    DBMS_CLOUD_AI.CREATE_PROFILE(
+      profile_name => p_profile_name,
+      attributes   => v_attributes,
+      description  => 'Non-operational Smilegate game-scope benchmark profile'
+    );
+
+    FOR source_attribute IN (
+      SELECT attribute_name, attribute_value
+        FROM user_cloud_ai_profile_attributes
+       WHERE profile_name = 'SGMP_POC_OCI_GPT54MINI'
+         AND attribute_name NOT IN (
+           'credential_name', 'model', 'provider', 'provider_endpoint',
+           'region', 'oci_compartment_id', 'oci_endpoint_id',
+           'oci_apiformat', 'oci_runtimetype'
+         )
+    ) LOOP
+      DBMS_CLOUD_AI.SET_ATTRIBUTE(
+        profile_name    => p_profile_name,
+        attribute_name  => source_attribute.attribute_name,
+        attribute_value => source_attribute.attribute_value
+      );
+    END LOOP;
+  END;
+BEGIN
+  SELECT DBMS_LOB.SUBSTR(attribute_value, 128, 1)
+    INTO v_credential_name
+    FROM user_cloud_ai_profile_attributes
+   WHERE profile_name = 'SGMP_POC_OCI_GPT54MINI'
+     AND attribute_name = 'credential_name';
+
+  SELECT DBMS_LOB.SUBSTR(attribute_value, 128, 1)
+    INTO v_region
+    FROM user_cloud_ai_profile_attributes
+   WHERE profile_name = 'SGMP_POC_OCI_GPT54MINI'
+     AND attribute_name = 'region';
+
+  SELECT DBMS_LOB.SUBSTR(attribute_value, 4000, 1)
+    INTO v_compartment_id
+    FROM user_cloud_ai_profile_attributes
+   WHERE profile_name = 'SGMP_POC_OCI_GPT54MINI'
+     AND attribute_name = 'oci_compartment_id';
+
+  create_candidate('SGMP_SCOPE_COHERE_VISION', 'cohere.command-a-vision');
+  create_candidate('SGMP_SCOPE_COHERE_COMMAND', 'cohere.command-latest');
+  create_candidate('SGMP_SCOPE_COHERE_PLUS', 'cohere.command-plus-latest');
+  create_candidate('SGMP_SCOPE_GEMINI_FLASH', 'google.gemini-2.5-flash-lite');
+  create_candidate('SGMP_SCOPE_LLAMA_MAV', 'meta.llama-4-maverick-17b-128e-instruct-fp8');
+  create_candidate('SGMP_SCOPE_GROK_NONR', 'xai.grok-4.20-non-reasoning');
+END;
+/
+
+SELECT profile_name, attribute_value AS model
+  FROM user_cloud_ai_profile_attributes
+ WHERE profile_name LIKE 'SGMP_SCOPE_%'
+   AND attribute_name = 'model'
+ ORDER BY profile_name;

database/adb/115_sgmp_scope_chat_candidate_benchmark.sql

@@ -0,0 +1,112 @@
+-- Read-only benchmark for candidate game-scope extraction profiles.
+-- Korean test questions use UTF-8 base64 reconstruction for SQLcl safety.
+
+set serveroutput on size unlimited
+prompt SG_SCOPE_PROFILE_BENCHMARK_LOADED
+
+DECLARE
+  TYPE t_case IS RECORD (
+    case_name VARCHAR2(12),
+    question CLOB,
+    expected_scope VARCHAR2(20),
+    expected_mentions PLS_INTEGER
+  );
+  TYPE t_cases IS TABLE OF t_case INDEX BY PLS_INTEGER;
+  v_cases t_cases;
+  v_prompt_prefix CLOB :=
+    'Extract only game-name mentions from the user question. '
+    || 'Metrics, acronyms, dates, filters, and database object or column names are not game names unless they are themselves an explicit game title. '
+    || 'When a title-like noun directly qualifies a game data request such as user master, character, sales, AU, NRU, server, or game log, preserve that noun as a game-name mention even when it is not in a catalog. '
+    || 'Do not discard an unknown title merely because it cannot be resolved. General scope words such as common, overall, all, total, or every are not game-name mentions unless they are part of an explicit title. '
+    || 'Return exactly one JSON object with keys game_mentions (array of strings) '
+    || 'and scope_hint (GLOBAL, SINGLE_GAME, MULTI_GAME, ALL_GAMES, UNKNOWN). '
+    || 'Do not resolve names to IDs and do not generate SQL. '
+    || 'Return raw JSON only: no prose, no Markdown, and no code fence. Question: ';
+  v_result CLOB;
+  v_json JSON_OBJECT_T;
+  v_started PLS_INTEGER;
+  v_elapsed NUMBER;
+  v_scope VARCHAR2(20);
+  v_mentions PLS_INTEGER;
+  v_raw_json VARCHAR2(5);
+
+  PROCEDURE run_case(
+    p_profile_name IN VARCHAR2,
+    p_case t_case
+  ) IS
+  BEGIN
+    v_started := DBMS_UTILITY.GET_TIME;
+    v_result := DBMS_CLOUD_AI.GENERATE(
+      prompt => v_prompt_prefix || p_case.question,
+      profile_name => p_profile_name,
+      action => 'chat'
+    );
+    v_elapsed := (DBMS_UTILITY.GET_TIME - v_started) / 100;
+    v_json := JSON_OBJECT_T.parse(v_result);
+    v_raw_json := 'TRUE';
+    v_scope := v_json.get_string('scope_hint');
+    v_mentions := v_json.get_array('game_mentions').get_size;
+    DBMS_OUTPUT.PUT_LINE(
+      p_profile_name || '|' || p_case.case_name
+      || '|seconds=' || TO_CHAR(v_elapsed, 'FM9990D00')
+      || '|raw_json=' || v_raw_json
+      || '|scope=' || NVL(v_scope, 'NULL')
+      || '|mentions=' || v_mentions
+      || '|expected=' || p_case.expected_scope || '/' || p_case.expected_mentions
+    );
+  EXCEPTION
+    WHEN OTHERS THEN
+      DBMS_OUTPUT.PUT_LINE(
+        p_profile_name || '|' || p_case.case_name
+        || '|seconds=' || TO_CHAR(v_elapsed, 'FM9990D00')
+        || '|ERROR=' || SQLCODE || '|' || SUBSTR(SQLERRM, 1, 180)
+      );
+  END;
+BEGIN
+  DBMS_OUTPUT.PUT_LINE('benchmark_started');
+  v_cases(1).case_name := 'NONE';
+  v_cases(1).question := utl_i18n.raw_to_char(utl_encode.base64_decode(
+    utl_raw.cast_to_raw('7KCE7LK0IOunpOy2nCDslYzroKTspJgu')), 'AL32UTF8');
+  v_cases(1).expected_scope := 'GLOBAL';
+  v_cases(1).expected_mentions := 0;
+
+  v_cases(2).case_name := 'SINGLE';
+  v_cases(2).question := utl_i18n.raw_to_char(utl_encode.base64_decode(
+    utl_raw.cast_to_raw('7Lm07KCc64KYIOy1nOyLoCBBVSDslYzroKTspJgu')), 'AL32UTF8');
+  v_cases(2).expected_scope := 'SINGLE_GAME';
+  v_cases(2).expected_mentions := 1;
+
+  v_cases(3).case_name := 'MULTI';
+  v_cases(3).question := utl_i18n.raw_to_char(utl_encode.base64_decode(
+    utl_raw.cast_to_raw('66Gc65Oc64KY7J247J20656RIOy5tOygnOuCmOydmCAyMDI264WEIDfsm5QgMTXsnbwgQVXrpbwg6rCB6rCBIOyVjOugpOykmC4=')), 'AL32UTF8');
+  v_cases(3).expected_scope := 'MULTI_GAME';
+  v_cases(3).expected_mentions := 2;
+
+  v_cases(4).case_name := 'ALL';
+  v_cases(4).question := utl_i18n.raw_to_char(utl_encode.base64_decode(
+    utl_raw.cast_to_raw('7KCE7LK0IOqyjOyehOydmCDrp6Tstpwg7JWM66Ck7KSYLg==')), 'AL32UTF8');
+  v_cases(4).expected_scope := 'ALL_GAMES';
+  v_cases(4).expected_mentions := 0;
+
+  FOR profile_row IN (
+    SELECT profile_name
+      FROM user_cloud_ai_profiles
+     WHERE profile_name IN (
+       'SGMP_POC_OCI_GPT54MINI',
+       'SGMP_SCOPE_COHERE_VISION',
+       'SGMP_SCOPE_COHERE_COMMAND',
+       'SGMP_SCOPE_COHERE_PLUS',
+       'SGMP_SCOPE_GEMINI_FLASH',
+       'SGMP_SCOPE_LLAMA_MAV',
+       'SGMP_SCOPE_GROK_NONR'
+     )
+     ORDER BY profile_name
+  ) LOOP
+    DBMS_OUTPUT.PUT_LINE('profile=' || profile_row.profile_name);
+    FOR i IN 1 .. 4 LOOP
+      run_case(profile_row.profile_name, v_cases(i));
+    END LOOP;
+  END LOOP;
+END;
+/
+prompt SG_SCOPE_PROFILE_BENCHMARK_COMPLETED

database/adb/116_sg_game_catalog_alias_embeddings.sql

@@ -0,0 +1,122 @@
+-- Store all game-name variants as one JSON array per game and embed that JSON
+-- as the canonical game-search vector. No customer game name is hardcoded.
+
+DECLARE
+  v_column_count PLS_INTEGER;
+BEGIN
+  SELECT COUNT(*)
+    INTO v_column_count
+    FROM user_tab_columns
+   WHERE table_name = 'SG_GAME_CATALOG'
+     AND column_name = 'ALIASES_JSON';
+
+  IF v_column_count = 0 THEN
+    EXECUTE IMMEDIATE 'ALTER TABLE sg_game_catalog ADD (aliases_json CLOB)';
+  END IF;
+END;
+/
+
+UPDATE sg_game_catalog
+   SET aliases_json = '[]'
+ WHERE aliases_json IS NULL;
+/
+
+DECLARE
+  v_constraint_count PLS_INTEGER;
+BEGIN
+  SELECT COUNT(*)
+    INTO v_constraint_count
+    FROM user_constraints
+   WHERE table_name = 'SG_GAME_CATALOG'
+     AND constraint_name = 'SG_GAME_CATALOG_ALIASES_JS_CK';
+
+  IF v_constraint_count = 0 THEN
+    EXECUTE IMMEDIATE
+      'ALTER TABLE sg_game_catalog ADD CONSTRAINT sg_game_catalog_aliases_js_ck '
+      || 'CHECK (aliases_json IS JSON)';
+  END IF;
+END;
+/
+
+MERGE INTO sg_game_catalog c
+USING (
+  WITH source_alias AS (
+    SELECT game_id AS game_key, game_nm AS alias_name
+      FROM comn_game_alias_bas
+     WHERE use_yn = 'Y' AND game_nm IS NOT NULL
+    UNION ALL
+    SELECT game_id, game_alias_nm
+      FROM comn_game_alias_bas
+     WHERE use_yn = 'Y' AND game_alias_nm IS NOT NULL
+    UNION ALL
+    SELECT game_id, game_id
+      FROM comn_game_alias_bas
+     WHERE use_yn = 'Y' AND game_id IS NOT NULL
+    UNION ALL
+    SELECT game_id, game_prefix
+      FROM comn_game_alias_bas
+     WHERE use_yn = 'Y' AND game_prefix IS NOT NULL
+    UNION ALL
+    SELECT game_key, display_name
+      FROM sg_game_scope_registry
+     WHERE active_yn = 'Y' AND display_name IS NOT NULL
+    UNION ALL
+    SELECT game_key, game_alias
+      FROM sg_game_scope_registry
+     WHERE active_yn = 'Y' AND game_alias IS NOT NULL
+  ),
+  deduplicated_alias AS (
+    SELECT game_key, alias_name
+      FROM source_alias
+     WHERE TRIM(alias_name) IS NOT NULL
+     GROUP BY game_key, alias_name
+  )
+  SELECT game_key,
+         JSON_ARRAYAGG(alias_name ORDER BY alias_name RETURNING CLOB) AS aliases_json
+    FROM deduplicated_alias
+   GROUP BY game_key
+) s
+ON (c.game_key = s.game_key)
+WHEN MATCHED THEN UPDATE SET
+  c.aliases_json = s.aliases_json,
+  c.updated_at = SYSTIMESTAMP;
+/
+
+-- A game has one canonical vector made from its complete JSON alias array.
+UPDATE sg_game_catalog c
+   SET c.embedding = DBMS_VECTOR.UTL_TO_EMBEDDING(
+         c.aliases_json,
+         JSON(sg_qa_vector_params('search_document'))
+       ),
+       c.updated_at = SYSTIMESTAMP
+ WHERE c.active_yn = 'Y';
+/
+
+COMMENT ON COLUMN sg_game_catalog.aliases_json IS
+  'Canonical JSON string array of every game-name variant used as the embedding input.';
+COMMENT ON COLUMN sg_game_catalog.embedding IS
+  'One vector per game, generated from the complete aliases_json array.';
+/
+
+CREATE OR REPLACE FUNCTION sg_game_catalog_search(
+  p_question IN CLOB,
+  p_top_k IN PLS_INTEGER DEFAULT 5
+) RETURN SYS_REFCURSOR AUTHID DEFINER IS
+  v_query VECTOR;
+  v_result SYS_REFCURSOR;
+BEGIN
+  v_query := DBMS_VECTOR.UTL_TO_EMBEDDING(
+    p_question,
+    JSON(sg_qa_vector_params('search_query'))
+  );
+  OPEN v_result FOR
+    SELECT game_key, game_id, game_prefix, game_nm, game_alias_nm,
+           user_master_object_name,
+           VECTOR_DISTANCE(embedding, v_query, COSINE) AS cosine_distance
+      FROM sg_game_catalog
+     WHERE active_yn = 'Y' AND embedding IS NOT NULL
+     ORDER BY VECTOR_DISTANCE(embedding, v_query, COSINE), priority, game_key
+     FETCH FIRST LEAST(GREATEST(NVL(p_top_k, 5), 1), 20) ROWS ONLY;
+  RETURN v_result;
+END;
+/

database/adb/117_sg_game_scope_policy.sql

@@ -0,0 +1,68 @@
+-- Customer-managed score policy for vector-only game identity resolution.
+
+BEGIN
+  EXECUTE IMMEDIATE q'[
+    CREATE TABLE sg_game_scope_policy (
+      policy_key   VARCHAR2(128) PRIMARY KEY,
+      number_value NUMBER,
+      text_value   VARCHAR2(4000),
+      description  VARCHAR2(1000) NOT NULL,
+      active_yn    CHAR(1) DEFAULT 'Y' NOT NULL,
+      updated_at   TIMESTAMP(6) DEFAULT SYSTIMESTAMP NOT NULL,
+      CONSTRAINT sg_game_scope_policy_active_ck CHECK (active_yn IN ('Y', 'N'))
+    )]';
+EXCEPTION
+  WHEN OTHERS THEN
+    IF SQLCODE != -955 THEN RAISE; END IF;
+END;
+/
+
+MERGE INTO sg_game_scope_policy t
+USING (
+  SELECT 'GAME_ALIAS_MAX_COSINE_DISTANCE' AS policy_key,
+         0.500000 AS number_value,
+         CAST(NULL AS VARCHAR2(4000)) AS text_value,
+         'Maximum cosine distance for accepting the closest independently embedded game alias.' AS description
+    FROM dual
+  UNION ALL
+  SELECT 'SCOPE_GUIDANCE_NONE', NULL,
+         '{"mode":"GAME_UNSPECIFIED","allowGameScopedObjects":false,"targetExecution":"COMMON_OBJECTS_OR_ZERO_ROW","instruction":"No game was selected. Do not use a game-scoped object. Use only a game-neutral common object when it answers the question; otherwise return a zero-row result."}',
+         'Prompt guidance for a question without a selected game.'
+    FROM dual
+  UNION ALL
+  SELECT 'SCOPE_GUIDANCE_SINGLE', NULL,
+         '{"mode":"EXACT_TARGETS","allowGameScopedObjects":true,"targetExecution":"ONLY_RESOLVED_TARGETS","instruction":"Use only the resolved target in targets. Do not select another game-scoped object."}',
+         'Prompt guidance for exactly one resolved game target.'
+    FROM dual
+  UNION ALL
+  SELECT 'SCOPE_GUIDANCE_MULTI', NULL,
+         '{"mode":"MULTIPLE_TARGETS","allowGameScopedObjects":true,"targetExecution":"ALL_RESOLVED_TARGETS","instruction":"Return results for all resolved available targets. Preserve unresolved targets as unavailable; do not replace them with another game."}',
+         'Prompt guidance for multiple game targets.'
+    FROM dual
+  UNION ALL
+  SELECT 'SCOPE_GUIDANCE_ALL', NULL,
+         '{"mode":"ALL_CATALOG_TARGETS","allowGameScopedObjects":true,"targetExecution":"ALL_AVAILABLE_CATALOG_TARGETS","instruction":"Use all available catalog targets. Do not invent games or game-scoped objects outside the catalog."}',
+         'Prompt guidance for every catalog game.'
+    FROM dual
+) s
+ON (t.policy_key = s.policy_key)
+WHEN MATCHED THEN UPDATE SET
+  t.text_value = CASE
+    WHEN s.policy_key LIKE 'SCOPE_GUIDANCE_%' THEN s.text_value
+    ELSE t.text_value
+  END,
+  t.description = s.description,
+  t.active_yn = 'Y',
+  t.updated_at = SYSTIMESTAMP
+WHEN NOT MATCHED THEN INSERT (
+  policy_key, number_value, text_value, description, active_yn
+) VALUES (
+  s.policy_key, s.number_value, s.text_value, s.description, 'Y'
+);
+/
+
+COMMENT ON TABLE sg_game_scope_policy IS
+  'Customer-managed game scope policy values; changing a value requires no application deployment.';
+COMMENT ON COLUMN sg_game_scope_policy.number_value IS
+  'Numeric policy value. GAME_ALIAS_MAX_COSINE_DISTANCE applies to the closest alias vector.';
+/

database/adb/118_sgmp_qa_vector_quality_threshold.sql

@@ -0,0 +1,82 @@
+-- Customer-managed quality floor for runtime Few-shot retrieval.
+-- Lower cosine distance is more similar.  The value is data, not application code.
+MERGE INTO sg_game_scope_policy t
+USING (
+  SELECT 'QA_VECTOR_MAX_COSINE_DISTANCE' AS policy_key,
+         0.650000 AS number_value,
+         'Maximum cosine distance accepted for a runtime approved Few-shot example.' AS description
+    FROM dual
+) s
+ON (t.policy_key = s.policy_key)
+WHEN MATCHED THEN UPDATE SET
+  t.number_value = s.number_value,
+  t.description = s.description,
+  t.active_yn = 'Y',
+  t.updated_at = SYSTIMESTAMP
+WHEN NOT MATCHED THEN INSERT (
+  policy_key, number_value, text_value, description, active_yn
+) VALUES (
+  s.policy_key, s.number_value, NULL, s.description, 'Y'
+);
+/
+
+CREATE OR REPLACE FUNCTION sg_qa_vector_search(
+  p_question    IN CLOB,
+  p_top_k       IN PLS_INTEGER DEFAULT 3,
+  p_target_type IN VARCHAR2 DEFAULT 'ANY'
+) RETURN SYS_REFCURSOR AUTHID DEFINER
+IS
+  v_query_vector VECTOR;
+  v_results SYS_REFCURSOR;
+  v_target_type VARCHAR2(16) := UPPER(TRIM(NVL(p_target_type, 'ANY')));
+  v_max_cosine_distance NUMBER;
+BEGIN
+  IF p_question IS NULL THEN
+    RAISE_APPLICATION_ERROR(-20003, 'question is required.');
+  END IF;
+  IF p_top_k IS NULL OR p_top_k < 1 OR p_top_k > 20 THEN
+    RAISE_APPLICATION_ERROR(-20004, 'top_k must be between 1 and 20.');
+  END IF;
+  IF v_target_type NOT IN ('NONE', 'SINGLE', 'MULTI', 'ALL', 'ANY') THEN
+    RAISE_APPLICATION_ERROR(-20005, 'invalid target type.');
+  END IF;
+
+  SELECT number_value
+    INTO v_max_cosine_distance
+    FROM sg_game_scope_policy
+   WHERE policy_key = 'QA_VECTOR_MAX_COSINE_DISTANCE'
+     AND active_yn = 'Y'
+     AND number_value IS NOT NULL;
+
+  v_query_vector := DBMS_VECTOR.UTL_TO_EMBEDDING(
+    p_question, JSON(sg_qa_vector_params('search_query'))
+  );
+
+  OPEN v_results FOR
+    SELECT example_id, question, answer_sql, answer_text, embedding_model,
+           reference_kind, target_type, object_role, source_case_id, source_type,
+           cosine_distance
+      FROM (
+        SELECT example_id, question, answer_sql, answer_text, embedding_model,
+               reference_kind, target_type, object_role, source_case_id, source_type,
+               VECTOR_DISTANCE(embedding, v_query_vector, COSINE) AS cosine_distance
+         FROM sg_qa_vector_example
+         WHERE reference_status = 'APPROVED'
+           -- A generated structural pattern is review material, not a runtime
+           -- Few-shot. Runtime examples must have human verification and an
+           -- executable SQL body rather than unresolved logical placeholders.
+           AND inspection_status = 'VERIFIED'
+           AND answer_sql IS NOT NULL
+           AND NOT REGEXP_LIKE(answer_sql, '<[A-Z][A-Z0-9_]*>', 'i')
+           AND (target_type = 'ANY' OR v_target_type = 'ANY' OR target_type = v_target_type)
+      )
+     WHERE cosine_distance <= v_max_cosine_distance
+     ORDER BY cosine_distance, example_id
+     FETCH FIRST p_top_k ROWS ONLY;
+  RETURN v_results;
+END;
+/
+
+COMMENT ON TABLE sg_game_scope_policy IS
+  'Customer-managed game scope and runtime retrieval policy values; changing a value requires no application deployment.';
+/

database/adb/118_sgmp_scope_command_profile.sql

@@ -0,0 +1,78 @@
+-- Operational OCI Cohere profile for short game-name and scope extraction.
+-- It inherits the active GPT profile's OCI credential, region, and metadata.
+
+DECLARE
+  v_credential_name VARCHAR2(128);
+  v_region VARCHAR2(128);
+  v_compartment_id VARCHAR2(4000);
+  v_attributes CLOB;
+  v_attribute_json JSON_OBJECT_T;
+  v_exists PLS_INTEGER;
+BEGIN
+  SELECT COUNT(*)
+    INTO v_exists
+    FROM user_cloud_ai_profiles
+   WHERE profile_name = 'SGMP_POC_OCI_COHERE_COMMAND';
+
+  IF v_exists > 0 THEN
+    DBMS_CLOUD_AI.DROP_PROFILE(
+      profile_name => 'SGMP_POC_OCI_COHERE_COMMAND',
+      force => TRUE
+    );
+  END IF;
+
+  SELECT DBMS_LOB.SUBSTR(attribute_value, 128, 1)
+    INTO v_credential_name
+    FROM user_cloud_ai_profile_attributes
+   WHERE profile_name = 'SGMP_POC_OCI_GPT54MINI'
+     AND attribute_name = 'credential_name';
+
+  SELECT DBMS_LOB.SUBSTR(attribute_value, 128, 1)
+    INTO v_region
+    FROM user_cloud_ai_profile_attributes
+   WHERE profile_name = 'SGMP_POC_OCI_GPT54MINI'
+     AND attribute_name = 'region';
+
+  SELECT DBMS_LOB.SUBSTR(attribute_value, 4000, 1)
+    INTO v_compartment_id
+    FROM user_cloud_ai_profile_attributes
+   WHERE profile_name = 'SGMP_POC_OCI_GPT54MINI'
+     AND attribute_name = 'oci_compartment_id';
+
+  v_attribute_json := JSON_OBJECT_T();
+  v_attribute_json.put('provider', 'oci');
+  v_attribute_json.put('credential_name', v_credential_name);
+  v_attribute_json.put('model', 'cohere.command-latest');
+  v_attribute_json.put('region', v_region);
+  v_attribute_json.put('oci_compartment_id', v_compartment_id);
+  v_attributes := v_attribute_json.to_clob;
+
+  DBMS_CLOUD_AI.CREATE_PROFILE(
+    profile_name => 'SGMP_POC_OCI_COHERE_COMMAND',
+    attributes => v_attributes,
+    description => 'Smilegate operational OCI Cohere Command profile for game scope extraction'
+  );
+
+  FOR source_attribute IN (
+    SELECT attribute_name, attribute_value
+      FROM user_cloud_ai_profile_attributes
+     WHERE profile_name = 'SGMP_POC_OCI_GPT54MINI'
+       AND attribute_name NOT IN (
+         'credential_name', 'model', 'provider', 'provider_endpoint',
+         'region', 'oci_compartment_id', 'oci_endpoint_id',
+         'oci_apiformat', 'oci_runtimetype'
+       )
+  ) LOOP
+    DBMS_CLOUD_AI.SET_ATTRIBUTE(
+      profile_name => 'SGMP_POC_OCI_COHERE_COMMAND',
+      attribute_name => source_attribute.attribute_name,
+      attribute_value => source_attribute.attribute_value
+    );
+  END LOOP;
+END;
+/
+
+SELECT profile_name, attribute_value AS model
+  FROM user_cloud_ai_profile_attributes
+ WHERE profile_name = 'SGMP_POC_OCI_COHERE_COMMAND'
+   AND attribute_name = 'model';

database/adb/119_sgmp_remove_invalid_std12_fewshot.sql

@@ -0,0 +1,38 @@
+-- STD-12 is a multi-target orchestration case, not a reusable SQL few-shot.
+-- Preserve SG_AI_QA_QUESTION as the customer benchmark; remove only its
+-- invalid vector-example row so it cannot be managed as a few-shot.
+
+DECLARE
+  v_count PLS_INTEGER;
+BEGIN
+  SELECT COUNT(*)
+    INTO v_count
+    FROM sg_qa_vector_example
+   WHERE example_id = 51
+     AND source_case_id = 'STD-12'
+     AND source_type = 'CUSTOMER_QA_BENCHMARK';
+
+  IF v_count <> 1 THEN
+    RAISE_APPLICATION_ERROR(-20051, 'Expected exactly one invalid STD-12 few-shot row.');
+  END IF;
+
+  DELETE FROM sg_qa_vector_example
+   WHERE example_id = 51
+     AND source_case_id = 'STD-12'
+     AND source_type = 'CUSTOMER_QA_BENCHMARK';
+
+  IF SQL%ROWCOUNT <> 1 THEN
+    RAISE_APPLICATION_ERROR(-20052, 'Invalid STD-12 few-shot row was not deleted.');
+  END IF;
+
+  COMMIT;
+END;
+/
+
+SELECT COUNT(*) AS remaining_fewshot_rows
+  FROM sg_qa_vector_example
+ WHERE example_id = 51;
+
+SELECT COUNT(*) AS preserved_question_rows
+  FROM sg_ai_qa_question
+ WHERE question_code = 'STD-12';

database/adb/121_sg_game_catalog_identity_duality_view.sql

@@ -0,0 +1,84 @@
+-- One DB-owned JSON identity document per game. No game value is hardcoded.
+-- The relational alias child makes aliases a nested Duality View array.
+
+BEGIN
+  EXECUTE IMMEDIATE q'[
+    CREATE TABLE sg_game_catalog_identity_alias (
+      game_key    VARCHAR2(128) NOT NULL,
+      alias_value VARCHAR2(512) NOT NULL,
+      CONSTRAINT sg_game_catalog_identity_alias_pk PRIMARY KEY (game_key, alias_value),
+      CONSTRAINT sg_game_catalog_identity_alias_fk FOREIGN KEY (game_key)
+        REFERENCES sg_game_catalog (game_key)
+    )]';
+EXCEPTION
+  WHEN OTHERS THEN
+    IF SQLCODE != -955 THEN RAISE; END IF;
+END;
+/
+
+MERGE INTO sg_game_catalog_identity_alias target
+USING (
+  SELECT c.game_key, aliases.alias_value
+    FROM sg_game_catalog c,
+         JSON_TABLE(
+           c.aliases_json,
+           '$[*]' COLUMNS (alias_value VARCHAR2(512) PATH '$')
+         ) aliases
+   WHERE c.active_yn = 'Y'
+) source
+ON (target.game_key = source.game_key AND target.alias_value = source.alias_value)
+WHEN NOT MATCHED THEN INSERT (game_key, alias_value)
+VALUES (source.game_key, source.alias_value);
+/
+
+DELETE FROM sg_game_catalog_identity_alias target
+ WHERE NOT EXISTS (
+   SELECT 1
+     FROM sg_game_catalog c,
+          JSON_TABLE(
+            c.aliases_json,
+            '$[*]' COLUMNS (alias_value VARCHAR2(512) PATH '$')
+          ) aliases
+    WHERE c.game_key = target.game_key
+      AND c.active_yn = 'Y'
+      AND aliases.alias_value = target.alias_value
+ );
+/
+
+CREATE OR REPLACE JSON RELATIONAL DUALITY VIEW sg_game_catalog_identity_dv AS
+  SELECT JSON {
+    '_id' : c.game_key,
+    'gameId' : c.game_id,
+    'gamePrefix' : c.game_prefix,
+    'gameName' : c.game_nm,
+    'gameAliases' : [
+      SELECT JSON {
+        '_id' : { 'gameKey' : a.game_key, 'value' : a.alias_value }
+      }
+        FROM sg_game_catalog_identity_alias a
+       WHERE a.game_key = c.game_key
+    ]
+  }
+  FROM sg_game_catalog c
+  WHERE c.active_yn = 'Y'
+  WITH CHECK OPTION;
+/
+
+-- Serialize the DB JSON document itself before embedding. GAME_ID, GAME_PREFIX,
+-- names and every alias therefore share one vector search document.
+UPDATE sg_game_catalog c
+   SET c.embedding = DBMS_VECTOR.UTL_TO_EMBEDDING(
+         (
+           SELECT JSON_SERIALIZE(d.data RETURNING CLOB)
+             FROM sg_game_catalog_identity_dv d
+            WHERE JSON_VALUE(d.data, '$._id') = c.game_key
+         ),
+         JSON(sg_qa_vector_params('search_document'))
+       ),
+       c.updated_at = SYSTIMESTAMP
+ WHERE c.active_yn = 'Y';
+/
+
+COMMENT ON TABLE sg_game_catalog_identity_dv IS
+  'DB JSON identity document for each active game; the canonical embedding source for game-name, alias, GAME_ID and GAME_PREFIX resolution.';
+/

database/adb/122_sgmp_std18_filtered_sales_aggregate_fewshot.sql

@@ -0,0 +1,144 @@
+-- STD-18 asks for an aggregate over qualifying orders, not an individual
+-- transaction list. Remove the invalid customer-derived references and keep
+-- one reusable, data-neutral aggregate pattern for runtime retrieval.
+
+DELETE FROM sg_qa_vector_example
+ WHERE source_case_id IN ('STD-18', 'PAT-STD-18')
+   AND source_type IN ('CUSTOMER_QA_BENCHMARK', 'GENERALIZED_QUESTION_PATTERN');
+/
+
+DECLARE
+  v_input     CLOB;
+  v_embedding VECTOR;
+  v_exists    NUMBER;
+BEGIN
+  v_input := TO_CLOB('질문 패턴: 전체 매출에서 금액 조건을 만족하는 주문을 집계해줘.')
+    || CHR(10) || 'Question pattern: summarize whole-scope sales after a payment amount filter.'
+    || CHR(10) || 'Logical object role: SALES_TRANSACTION'
+    || CHR(10) || 'Required result grain: one aggregate row with total sales amount, distinct buyer count, and order count.'
+    || CHR(10) || 'A reference to orders does not by itself request individual order detail.';
+  v_embedding := DBMS_VECTOR.UTL_TO_EMBEDDING(
+    v_input,
+    JSON(sg_qa_vector_params('search_document'))
+  );
+
+  SELECT COUNT(*)
+    INTO v_exists
+    FROM sg_qa_vector_example
+   WHERE source_type = 'POLICY_TEMPLATE'
+     AND source_case_id = 'FILTERED_SALES_AGGREGATE';
+
+  IF v_exists = 0 THEN
+    INSERT INTO sg_qa_vector_example (
+      question, answer_sql, answer_text, embedding_input, embedding, embedding_model,
+      reference_status, reference_kind, target_type, object_role,
+      inspection_status, inspection_note, verified_at, verified_by,
+      source_case_id, source_type
+    ) VALUES (
+      '전체 매출에서 금액 조건을 만족하는 주문을 집계해줘.',
+      TO_CLOB('SELECT SUM(CAST(s."PAYMT_AMT" AS NUMBER)) AS "TOTAL_SALES_AMOUNT",' || CHR(10)
+        || '       COUNT(DISTINCT s."GUID") AS "BUYER_COUNT",' || CHR(10)
+        || '       COUNT(*) AS "ORDER_COUNT"' || CHR(10)
+        || 'FROM "SGMP_POC"."COMN_SALES_TXN" s' || CHR(10)
+        || 'WHERE s."PAYMT_DTM" >= <BUSINESS_DATE_START>' || CHR(10)
+        || '  AND s."PAYMT_DTM" < <BUSINESS_DATE_END>' || CHR(10)
+        || '  AND CAST(s."PAYMT_AMT" AS NUMBER) <AMOUNT_CONDITION>' || CHR(10)
+        || '  AND s."EXPT_USER_YN" = ''N'''),
+      'Structural Few-shot: return one aggregate row containing total sales amount, distinct buyer count, and order count after the requested payment-amount filter. Do not return individual orders unless the user explicitly asks for a list or detail rows.',
+      v_input,
+      v_embedding,
+      'cohere.embed-v4.0',
+      'APPROVED', 'SQL_TEMPLATE', 'NONE', 'SALES_TRANSACTION',
+      'VERIFIED',
+      'Reusable whole-scope filtered-sales aggregate. No customer date, amount, game, or expected result is stored.',
+      SYSTIMESTAMP, 'SGMP_POC_METADATA_REVIEW',
+      'FILTERED_SALES_AGGREGATE', 'POLICY_TEMPLATE'
+    );
+  ELSE
+    UPDATE sg_qa_vector_example
+       SET question = '전체 매출에서 금액 조건을 만족하는 주문을 집계해줘.',
+           answer_sql = TO_CLOB('SELECT SUM(CAST(s."PAYMT_AMT" AS NUMBER)) AS "TOTAL_SALES_AMOUNT",' || CHR(10)
+             || '       COUNT(DISTINCT s."GUID") AS "BUYER_COUNT",' || CHR(10)
+             || '       COUNT(*) AS "ORDER_COUNT"' || CHR(10)
+             || 'FROM "SGMP_POC"."COMN_SALES_TXN" s' || CHR(10)
+             || 'WHERE s."PAYMT_DTM" >= <BUSINESS_DATE_START>' || CHR(10)
+             || '  AND s."PAYMT_DTM" < <BUSINESS_DATE_END>' || CHR(10)
+             || '  AND CAST(s."PAYMT_AMT" AS NUMBER) <AMOUNT_CONDITION>' || CHR(10)
+             || '  AND s."EXPT_USER_YN" = ''N'''),
+           answer_text = 'Structural Few-shot: return one aggregate row containing total sales amount, distinct buyer count, and order count after the requested payment-amount filter. Do not return individual orders unless the user explicitly asks for a list or detail rows.',
+           embedding_input = v_input,
+           embedding = v_embedding,
+           reference_status = 'APPROVED',
+           reference_kind = 'SQL_TEMPLATE',
+           target_type = 'NONE',
+           object_role = 'SALES_TRANSACTION',
+           inspection_status = 'VERIFIED',
+           inspection_note = 'Reusable whole-scope filtered-sales aggregate. No customer date, amount, game, or expected result is stored.',
+           verified_at = SYSTIMESTAMP,
+           verified_by = 'SGMP_POC_METADATA_REVIEW'
+     WHERE source_type = 'POLICY_TEMPLATE'
+       AND source_case_id = 'FILTERED_SALES_AGGREGATE';
+  END IF;
+  COMMIT;
+END;
+/
+
+-- A SQL template is prompt context, never an executable statement. Permit
+-- reviewed policy templates to retain logical placeholders while continuing
+-- to require executable SQL for automatically generalized patterns.
+CREATE OR REPLACE FUNCTION sg_qa_vector_search(
+  p_question    IN CLOB,
+  p_top_k       IN PLS_INTEGER DEFAULT 3,
+  p_target_type IN VARCHAR2 DEFAULT 'ANY'
+) RETURN SYS_REFCURSOR AUTHID DEFINER
+IS
+  v_query_vector VECTOR;
+  v_results SYS_REFCURSOR;
+  v_target_type VARCHAR2(16) := UPPER(TRIM(NVL(p_target_type, 'ANY')));
+  v_max_cosine_distance NUMBER;
+BEGIN
+  IF p_question IS NULL THEN RAISE_APPLICATION_ERROR(-20003, 'question is required.'); END IF;
+  IF p_top_k IS NULL OR p_top_k < 1 OR p_top_k > 20 THEN
+    RAISE_APPLICATION_ERROR(-20004, 'top_k must be between 1 and 20.');
+  END IF;
+  IF v_target_type NOT IN ('NONE', 'SINGLE', 'MULTI', 'ALL', 'ANY') THEN
+    RAISE_APPLICATION_ERROR(-20005, 'invalid target type.');
+  END IF;
+
+  SELECT number_value INTO v_max_cosine_distance
+    FROM sg_game_scope_policy
+   WHERE policy_key = 'QA_VECTOR_MAX_COSINE_DISTANCE'
+     AND active_yn = 'Y'
+     AND number_value IS NOT NULL;
+
+  v_query_vector := DBMS_VECTOR.UTL_TO_EMBEDDING(
+    p_question, JSON(sg_qa_vector_params('search_query'))
+  );
+
+  OPEN v_results FOR
+    SELECT example_id, question, answer_sql, answer_text, embedding_model,
+           reference_kind, target_type, object_role, source_case_id, source_type,
+           cosine_distance
+      FROM (
+        SELECT example_id, question, answer_sql, answer_text, embedding_model,
+               reference_kind, target_type, object_role, source_case_id, source_type,
+               VECTOR_DISTANCE(embedding, v_query_vector, COSINE) AS cosine_distance
+          FROM sg_qa_vector_example
+         WHERE reference_status = 'APPROVED'
+           AND inspection_status = 'VERIFIED'
+           AND answer_sql IS NOT NULL
+           AND (source_type = 'POLICY_TEMPLATE'
+                OR NOT REGEXP_LIKE(answer_sql, '<[A-Z][A-Z0-9_]*>', 'i'))
+           AND (target_type = 'ANY' OR v_target_type = 'ANY' OR target_type = v_target_type)
+      )
+     WHERE cosine_distance <= v_max_cosine_distance
+     ORDER BY cosine_distance, example_id
+     FETCH FIRST p_top_k ROWS ONLY;
+  RETURN v_results;
+END;
+/
+
+SELECT example_id, source_case_id, source_type, reference_status, inspection_status
+  FROM sg_qa_vector_example
+ WHERE source_case_id IN ('STD-18', 'PAT-STD-18', 'FILTERED_SALES_AGGREGATE')
+ ORDER BY example_id;

database/adb/123_sgmp_filtered_sales_aggregate_any_scope.sql

@@ -0,0 +1,33 @@
+-- This customer question is game-unscoped. Keep the existing NONE-compatible
+-- Few-shot path; game_query_plan remains responsible for scope resolution.
+UPDATE sg_qa_vector_example
+   SET question = '매출에서 금액 조건을 만족하는 주문의 총액, 구매자 수, 주문 수를 집계해줘.',
+       answer_sql = TO_CLOB('SELECT' || CHR(10)
+         || '    SUM(CAST(s."PAYMT_AMT" AS NUMBER)) AS "TOTAL_SALES_AMOUNT",' || CHR(10)
+         || '    COUNT(DISTINCT s."GUID") AS "BUYER_COUNT",' || CHR(10)
+         || '    COUNT(*) AS "ORDER_COUNT"' || CHR(10)
+         || '  FROM <COMMON_SALES_TRANSACTION> s' || CHR(10)
+         || '  WHERE s."PAYMT_DTM" >= <BUSINESS_DATE_START>' || CHR(10)
+         || '    AND s."PAYMT_DTM" < <BUSINESS_DATE_END>' || CHR(10)
+         || '    AND CAST(s."PAYMT_AMT" AS NUMBER) <AMOUNT_CONDITION>' || CHR(10)
+         || '    AND s."EXPT_USER_YN" = ''N'''),
+       answer_text = 'Aggregate result-shape reference: return one row with total sales amount, distinct buyer count, and order count. The game plan separately supplies any game scope; use this pattern only when the question is semantically similar.',
+       embedding_input = TO_CLOB('Question pattern: summarize sales after a payment amount condition.' || CHR(10)
+         || 'Logical object role: SALES_TRANSACTION' || CHR(10)
+         || 'Result grain: one aggregate row with total sales amount, distinct buyer count, and order count.'),
+       embedding = DBMS_VECTOR.UTL_TO_EMBEDDING(
+         TO_CLOB('Question pattern: summarize sales after a payment amount condition.' || CHR(10)
+           || 'Logical object role: SALES_TRANSACTION' || CHR(10)
+           || 'Result grain: one aggregate row with total sales amount, distinct buyer count, and order count.'),
+         JSON(sg_qa_vector_params('search_document'))),
+       target_type = 'NONE',
+       source_case_id = 'PORTAL-STD-18',
+       inspection_note = 'Generalized aggregate pattern for the current game-unscoped question; game_query_plan controls scope separately.',
+       verified_at = SYSTIMESTAMP,
+       verified_by = 'SGMP_POC_METADATA_REVIEW'
+ WHERE example_id = 141
+   AND source_type = 'POLICY_TEMPLATE';
+/
+
+COMMIT;
+/

database/adb/124_sgmp_std18_expected_answer_fewshot.sql

@@ -0,0 +1,23 @@
+-- The reviewed customer benchmark answer is part of this Few-shot guidance.
+-- It clarifies that the requested result is one aggregate row, not detail rows.
+UPDATE sg_qa_vector_example
+   SET answer_text = TO_CLOB('Expected answer shape: return exactly one aggregate row, not individual order rows.' || CHR(10)
+         || 'Expected answer:' || CHR(10)
+         || 'TOTAL_SALES_AMOUNT BUYER_COUNT ORDER_COUNT' || CHR(10)
+         || '------------------ ----------- -----------' || CHR(10)
+         || '            204720           6           6'),
+       embedding_input = TO_CLOB('Question pattern: summarize sales after a payment amount condition.' || CHR(10)
+         || 'Expected output: TOTAL_SALES_AMOUNT, BUYER_COUNT, ORDER_COUNT as one aggregate row.' || CHR(10)
+         || 'Expected result example: 204720, 6, 6.'),
+       embedding = DBMS_VECTOR.UTL_TO_EMBEDDING(
+         TO_CLOB('Question pattern: summarize sales after a payment amount condition.' || CHR(10)
+           || 'Expected output: TOTAL_SALES_AMOUNT, BUYER_COUNT, ORDER_COUNT as one aggregate row.' || CHR(10)
+           || 'Expected result example: 204720, 6, 6.'),
+         JSON(sg_qa_vector_params('search_document'))),
+       verified_at = SYSTIMESTAMP,
+       verified_by = 'SGMP_POC_METADATA_REVIEW'
+ WHERE example_id = 141
+   AND source_type = 'POLICY_TEMPLATE';
+/
+COMMIT;
+/

database/adb/129_sgmp_std25_weekly_au_evaluation_criteria.sql

@@ -0,0 +1,29 @@
+-- STD-25 is a period AU metric, not the daily AU_FLAG metric.
+-- Keep the evaluation evidence explicit so the LLM judge accepts the valid
+-- weekly result shape produced by the NL2SQL tool.
+
+UPDATE sg_ai_qa_question
+   SET expected_focus = 'Weekly AU: use one as-of snapshot (BASE_DT=2026-07-15), '
+       || 'count DISTINCT GUID whose LAST_CONN_DT is in the inclusive seven-day window '
+       || '(2026-07-09 through 2026-07-15), with STD_USER_YN=''Y'' and EXPT_USER_YN=''N''. '
+       || 'This is one aggregate result, not daily rows. Do not substitute daily AU_FLAG=1 for the period definition.',
+       baseline_sql = TO_CLOB('SELECT COUNT(DISTINCT u."GUID") AS "RECENT_7DAY_AU"' || CHR(10)
+         || 'FROM "SGMP_POC"."CZN_COMN_USER_MST" u' || CHR(10)
+         || 'WHERE u."BASE_DT" = DATE ''2026-07-15''' || CHR(10)
+         || '  AND u."LAST_CONN_DT" BETWEEN DATE ''2026-07-09'' AND DATE ''2026-07-15''' || CHR(10)
+         || '  AND u."STD_USER_YN" = ''Y''' || CHR(10)
+         || '  AND u."EXPT_USER_YN" = ''N'''),
+       baseline_answer = 'RECENT_7DAY_AU=0',
+       evaluation_rule_json = '{"required_sql_terms":["CZN_COMN_USER_MST","LAST_CONN_DT","STD_USER_YN","EXPT_USER_YN","COUNT"],"recommended_sql_terms":["BASE_DT"],"forbidden_sql_terms":["AU_FLAG"],"required_result_shape":"SINGLE_AGGREGATE"}',
+       updated_at = SYSTIMESTAMP
+ WHERE question_code = 'STD-25';
+
+/
+
+COMMIT;
+
+/
+
+SELECT question_code, expected_focus, baseline_sql, baseline_answer, evaluation_rule_json
+  FROM sg_ai_qa_question
+ WHERE question_code = 'STD-25';

database/adb/130_sgmp_czn02_standard_au_optional_std_user_criteria.sql

@@ -0,0 +1,40 @@
+-- CZN-02 customer sample marks STD_USER_YN='Y' as optional for daily
+-- standard-AU reporting. It must not turn an otherwise correct AU query into
+-- a failure merely because the condition is present.
+
+UPDATE sg_ai_qa_question
+   SET expected_focus = 'Daily standard AU: COUNT(DISTINCT GUID) from CZN_COMN_USER_MST '
+       || 'for BASE_DT=2026-07-15 with AU_FLAG=1 and EXPT_USER_YN=''N''. '
+       || 'STD_USER_YN=''Y'' is an allowed optional cohort filter in the customer sample; '
+       || 'its presence or absence is not a contradiction to this baseline.',
+       evaluation_rule_json = '{"required_sql_terms":["CZN_COMN_USER_MST","AU_FLAG","EXPT_USER_YN","COUNT"],"recommended_sql_terms":["BASE_DT","STD_USER_YN"],"optional_sql_terms":["STD_USER_YN"]}',
+       updated_at = SYSTIMESTAMP
+ WHERE question_code = 'CZN-02';
+
+/
+
+UPDATE sg_qa_vector_example
+   SET answer_text = 'Expected focus: daily standard AU uses CZN_COMN_USER_MST, BASE_DT=2026-07-15, '
+                   || 'AU_FLAG=1 and EXPT_USER_YN=''N''. The customer sample permits STD_USER_YN=''Y'' '
+                   || 'as an optional standard-user cohort filter; do not treat its presence as a conflicting condition. '
+                   || 'Historical answer: STD_AU_COUNT=0',
+       inspection_note = 'Customer sample permits optional STD_USER_YN filtering for daily standard AU; AU_FLAG and excluded-user filtering remain mandatory.',
+       verified_at = SYSTIMESTAMP,
+       verified_by = 'SGMP_POC_METADATA_REVIEW'
+ WHERE source_type = 'CUSTOMER_QA_BENCHMARK'
+   AND source_case_id = 'CZN-02'
+   AND reference_status = 'APPROVED';
+
+/
+
+COMMIT;
+
+/
+
+SELECT q.question_code, q.expected_focus, q.evaluation_rule_json,
+       e.example_id, e.answer_text
+  FROM sg_ai_qa_question q
+  LEFT JOIN sg_qa_vector_example e
+    ON e.source_type = 'CUSTOMER_QA_BENCHMARK'
+   AND e.source_case_id = q.question_code
+ WHERE q.question_code = 'CZN-02';

database/adb/131_sgmp_independent_au_comparison_pattern.sql

@@ -0,0 +1,90 @@
+-- Reusable SINGLE-scope pattern: two AU populations must be aggregated
+-- independently before comparison. A user-master LEFT JOIN may erase valid
+-- business-user rows and must not define the business population.
+
+UPDATE sg_ai_qa_question
+   SET expected_focus = 'Compare standard AU and business AU as two independent single-row aggregates for the same as-of date. '
+       || 'Standard AU uses the resolved game user master with AU_FLAG=1 and EXPT_USER_YN=''N''. '
+       || 'Business AU uses the resolved game business-user fact with BIZ_AU_FLAG=1 and EXPT_USER_YN=''N''. '
+       || 'Do not make the business count depend on a LEFT JOIN from the user-master population. '
+       || 'STD_USER_YN=''Y'' is an allowed optional cohort filter, not a reason to reject the result.',
+       evaluation_rule_json = '{"required_sql_terms":["CZN_COMN_USER_MST","CZN_CUSTOM_BIZ_USER_TXN","AU_FLAG","BIZ_AU_FLAG","EXPT_USER_YN","COUNT"],"recommended_sql_terms":["BASE_DT","STD_USER_YN"],"optional_sql_terms":["STD_USER_YN"],"required_result_shape":"SINGLE_COMPARISON"}',
+       updated_at = SYSTIMESTAMP
+ WHERE question_code = 'CZN-03';
+
+/
+
+DECLARE
+  v_input     CLOB;
+  v_embedding VECTOR;
+  v_exists    NUMBER;
+BEGIN
+  v_input := TO_CLOB('Question pattern: compare daily standard active users and business active users for one resolved game and one business date.')
+    || CHR(10) || 'Question pattern Korean: 한 게임의 기준일 일간 표준 AU와 사업 AU를 비교해줘.'
+    || CHR(10) || 'Logical object role: USER_BUSINESS_AU_COMPARISON'
+    || CHR(10) || 'Required result shape: one row with two independent aggregate metrics.'
+    || CHR(10) || 'Business population must be aggregated independently; a LEFT JOIN from the user-master population may not define it.';
+  v_embedding := DBMS_VECTOR.UTL_TO_EMBEDDING(
+    v_input, JSON(sg_qa_vector_params('search_document'))
+  );
+
+  SELECT COUNT(*) INTO v_exists
+    FROM sg_qa_vector_example
+   WHERE source_type = 'POLICY_TEMPLATE'
+     AND source_case_id = 'STD_BIZ_AU_COMPARE';
+
+  IF v_exists = 0 THEN
+    INSERT INTO sg_qa_vector_example (
+      question, answer_sql, answer_text, embedding_input, embedding, embedding_model,
+      reference_status, reference_kind, target_type, object_role,
+      inspection_status, inspection_note, verified_at, verified_by,
+      source_case_id, source_type
+    ) VALUES (
+      '한 게임의 기준일 일간 표준 AU와 사업 AU를 비교해줘.',
+      TO_CLOB('SELECT' || CHR(10)
+        || '  (SELECT COUNT(DISTINCT u."GUID")' || CHR(10)
+        || '     FROM <RESOLVED_GAME_USER_MASTER> u' || CHR(10)
+        || '    WHERE u."BASE_DT" = <BUSINESS_DATE>' || CHR(10)
+        || '      AND u."AU_FLAG" = 1' || CHR(10)
+        || '      AND u."EXPT_USER_YN" = ''N'') AS "STANDARD_AU_COUNT",' || CHR(10)
+        || '  (SELECT COUNT(DISTINCT b."GUID")' || CHR(10)
+        || '     FROM <RESOLVED_GAME_BUSINESS_USER> b' || CHR(10)
+        || '    WHERE b."BASE_DT" = <BUSINESS_DATE>' || CHR(10)
+        || '      AND b."BIZ_AU_FLAG" = 1' || CHR(10)
+        || '      AND b."EXPT_USER_YN" = ''N'') AS "BUSINESS_AU_COUNT"' || CHR(10)
+        || 'FROM DUAL'),
+      'Applicable metric reference: for this daily AU comparison, the standard metric must use AU_FLAG=1 and EXPT_USER_YN=''N''; do not replace it with LAST_CONN_DT period logic or STD_USER_YN alone. The business metric must use BIZ_AU_FLAG=1 and EXPT_USER_YN=''N''. Produce one row from two independent aggregate subqueries, and do not count business users through a LEFT JOIN from the standard-user population. STD_USER_YN may be added only as an optional cohort filter.',
+      v_input, v_embedding, 'cohere.embed-v4.0',
+      'APPROVED', 'SQL_TEMPLATE', 'SINGLE', 'USER_BUSINESS_AU_COMPARISON',
+      'VERIFIED',
+      'Reusable comparison pattern with logical placeholders only; no customer game, date, result, or physical object is embedded.',
+      SYSTIMESTAMP, 'SGMP_POC_METADATA_REVIEW',
+      'STD_BIZ_AU_COMPARE', 'POLICY_TEMPLATE'
+    );
+  ELSE
+    UPDATE sg_qa_vector_example
+       SET question = '한 게임의 기준일 일간 표준 AU와 사업 AU를 비교해줘.',
+           answer_sql = TO_CLOB('SELECT' || CHR(10)
+             || '  (SELECT COUNT(DISTINCT u."GUID")' || CHR(10)
+             || '     FROM <RESOLVED_GAME_USER_MASTER> u' || CHR(10)
+             || '    WHERE u."BASE_DT" = <BUSINESS_DATE>' || CHR(10)
+             || '      AND u."AU_FLAG" = 1' || CHR(10)
+             || '      AND u."EXPT_USER_YN" = ''N'') AS "STANDARD_AU_COUNT",' || CHR(10)
+             || '  (SELECT COUNT(DISTINCT b."GUID")' || CHR(10)
+             || '     FROM <RESOLVED_GAME_BUSINESS_USER> b' || CHR(10)
+             || '    WHERE b."BASE_DT" = <BUSINESS_DATE>' || CHR(10)
+             || '      AND b."BIZ_AU_FLAG" = 1' || CHR(10)
+             || '      AND b."EXPT_USER_YN" = ''N'') AS "BUSINESS_AU_COUNT"' || CHR(10)
+             || 'FROM DUAL'),
+           answer_text = 'Applicable metric reference: for this daily AU comparison, the standard metric must use AU_FLAG=1 and EXPT_USER_YN=''N''; do not replace it with LAST_CONN_DT period logic or STD_USER_YN alone. The business metric must use BIZ_AU_FLAG=1 and EXPT_USER_YN=''N''. Produce one row from two independent aggregate subqueries, and do not count business users through a LEFT JOIN from the standard-user population. STD_USER_YN may be added only as an optional cohort filter.',
+           embedding_input = v_input,
+           embedding = v_embedding,
+           reference_status = 'APPROVED', inspection_status = 'VERIFIED',
+           verified_at = SYSTIMESTAMP, verified_by = 'SGMP_POC_METADATA_REVIEW'
+     WHERE source_type = 'POLICY_TEMPLATE'
+       AND source_case_id = 'STD_BIZ_AU_COMPARE';
+  END IF;
+  COMMIT;
+END;
+
+/

database/adb/132_sgmp_country_daily_au_pattern.sql

@@ -0,0 +1,58 @@
+-- Reusable SINGLE-scope pattern for country-grouped daily AU.
+
+UPDATE sg_ai_qa_question
+   SET expected_focus = 'Country-grouped daily standard AU uses the resolved game user master with BASE_DT, AU_FLAG=1 and EXPT_USER_YN=''N''. '
+       || 'Group by LAST_CONN_COUNTRY_CD and use the approved country dimension only for display/classification. '
+       || 'STD_USER_YN is optional and cannot replace AU_FLAG for the daily metric.',
+       evaluation_rule_json = '{"required_sql_terms":["CZN_COMN_USER_MST","AU_FLAG","EXPT_USER_YN","LAST_CONN_COUNTRY_CD","COUNT"],"recommended_sql_terms":["BASE_DT","COMN_COUNTRY_BAS","STD_USER_YN"],"optional_sql_terms":["STD_USER_YN"],"required_result_shape":"COUNTRY_GROUPED"}',
+       updated_at = SYSTIMESTAMP
+ WHERE question_code = 'CZN-06';
+
+/
+
+DECLARE
+  v_input CLOB;
+  v_embedding VECTOR;
+  v_exists NUMBER;
+BEGIN
+  v_input := TO_CLOB('Question pattern: show daily active-user counts by country for one resolved game and one business date.')
+    || CHR(10) || 'Question pattern Korean: 한 게임의 기준일 주요 국가별 표준 AU 수를 알려줘.'
+    || CHR(10) || 'Logical object role: COUNTRY_GROUPED_DAILY_AU'
+    || CHR(10) || 'Required metric: AU_FLAG=1 and excluded-user filtering; group by the last connection country.';
+  v_embedding := DBMS_VECTOR.UTL_TO_EMBEDDING(v_input, JSON(sg_qa_vector_params('search_document')));
+  SELECT COUNT(*) INTO v_exists FROM sg_qa_vector_example
+   WHERE source_type = 'POLICY_TEMPLATE' AND source_case_id = 'COUNTRY_DAILY_AU';
+  IF v_exists = 0 THEN
+    INSERT INTO sg_qa_vector_example (
+      question, answer_sql, answer_text, embedding_input, embedding, embedding_model,
+      reference_status, reference_kind, target_type, object_role,
+      inspection_status, inspection_note, verified_at, verified_by, source_case_id, source_type
+    ) VALUES (
+      '한 게임의 기준일 주요 국가별 표준 AU 수를 알려줘.',
+      TO_CLOB('SELECT u."LAST_CONN_COUNTRY_CD" AS "COUNTRY_CD",' || CHR(10)
+        || '       c."COUNTRY_KR_NM" AS "COUNTRY_NAME",' || CHR(10)
+        || '       COUNT(DISTINCT u."GUID") AS "STANDARD_AU_COUNT"' || CHR(10)
+        || 'FROM <RESOLVED_GAME_USER_MASTER> u' || CHR(10)
+        || 'LEFT JOIN <APPROVED_COUNTRY_DIMENSION> c' || CHR(10)
+        || '  ON c."COUNTRY_2CHAR_CD" = u."LAST_CONN_COUNTRY_CD"' || CHR(10)
+        || 'WHERE u."BASE_DT" = <BUSINESS_DATE>' || CHR(10)
+        || '  AND u."AU_FLAG" = 1' || CHR(10)
+        || '  AND u."EXPT_USER_YN" = ''N''' || CHR(10)
+        || 'GROUP BY u."LAST_CONN_COUNTRY_CD", c."COUNTRY_KR_NM"'),
+      'Applicable metric reference: country-grouped daily AU must use AU_FLAG=1 and EXPT_USER_YN=''N''; do not replace AU_FLAG with STD_USER_YN alone. Group by LAST_CONN_COUNTRY_CD. Use an approved country dimension for country display or a current approved major-country classification when the request requires it.',
+      v_input, v_embedding, 'cohere.embed-v4.0',
+      'APPROVED', 'SQL_TEMPLATE', 'SINGLE', 'COUNTRY_GROUPED_DAILY_AU',
+      'VERIFIED', 'Reusable country-grouped daily-AU pattern; no customer game, date, result, or physical object is embedded.',
+      SYSTIMESTAMP, 'SGMP_POC_METADATA_REVIEW', 'COUNTRY_DAILY_AU', 'POLICY_TEMPLATE'
+    );
+  ELSE
+    UPDATE sg_qa_vector_example
+       SET embedding_input = v_input, embedding = v_embedding,
+           reference_status = 'APPROVED', inspection_status = 'VERIFIED',
+           verified_at = SYSTIMESTAMP, verified_by = 'SGMP_POC_METADATA_REVIEW'
+     WHERE source_type = 'POLICY_TEMPLATE' AND source_case_id = 'COUNTRY_DAILY_AU';
+  END IF;
+  COMMIT;
+END;
+
+/

database/adb/133_sgmp_restore_czn06_exact_fewshot.sql

@@ -0,0 +1,17 @@
+-- CZN-06 is a verified, exact customer question/SQL pair. It must be a
+-- runtime Few-shot when approved; RETIRED is the DB switch that excludes it.
+-- This is a single-game reference, so keep the retrieval scope explicit.
+UPDATE sg_qa_vector_example
+   SET reference_status = 'APPROVED',
+       reference_kind = 'SQL_TEMPLATE',
+       target_type = 'SINGLE',
+       object_role = 'COUNTRY_GROUPED_DAILY_AU',
+       inspection_status = 'VERIFIED',
+       inspection_note = 'Verified exact CZN-06 Few-shot restored for runtime retrieval. Daily country AU requires AU_FLAG=1 and EXPT_USER_YN=''N''; STD_USER_YN alone is insufficient.',
+       verified_at = SYSTIMESTAMP,
+       verified_by = 'SGMP_POC_METADATA_REVIEW'
+ WHERE source_type = 'CUSTOMER_QA_BENCHMARK'
+   AND source_case_id = 'CZN-06'
+/
+
+COMMIT

database/adb/135_sgmp_semantic_fewshot_retrieval.sql

@@ -0,0 +1,69 @@
+-- Runtime Few-shots remain semantic vector retrieval. Customer examples are
+-- governed by their DB approval state, not restricted to exact text matches.
+
+UPDATE sg_qa_vector_example
+   SET reference_status = 'APPROVED',
+       reference_kind = 'SQL_TEMPLATE',
+       target_type = 'SINGLE',
+       object_role = 'GAME_GOODS_HOLDINGS',
+       inspection_status = 'VERIFIED',
+       inspection_note = 'Verified CZN-07 semantic Few-shot. Use goods holdings, crystal dimension, RU_FLAG=1, excluded-user filter, nonzero holdings, and daily grouping.',
+       verified_at = SYSTIMESTAMP,
+       verified_by = 'SGMP_POC_METADATA_REVIEW'
+ WHERE source_type = 'CUSTOMER_QA_BENCHMARK'
+   AND source_case_id = 'CZN-07'
+/
+
+CREATE OR REPLACE FUNCTION sg_qa_vector_search(
+  p_question    IN CLOB,
+  p_top_k       IN PLS_INTEGER DEFAULT 3,
+  p_target_type IN VARCHAR2 DEFAULT 'ANY'
+) RETURN SYS_REFCURSOR AUTHID DEFINER
+IS
+  v_query_vector VECTOR;
+  v_results SYS_REFCURSOR;
+  v_target_type VARCHAR2(16) := UPPER(TRIM(NVL(p_target_type, 'ANY')));
+  v_max_cosine_distance NUMBER;
+BEGIN
+  IF p_question IS NULL THEN RAISE_APPLICATION_ERROR(-20003, 'question is required.'); END IF;
+  IF p_top_k IS NULL OR p_top_k < 1 OR p_top_k > 20 THEN
+    RAISE_APPLICATION_ERROR(-20004, 'top_k must be between 1 and 20.');
+  END IF;
+  IF v_target_type NOT IN ('NONE', 'SINGLE', 'MULTI', 'ALL', 'ANY') THEN
+    RAISE_APPLICATION_ERROR(-20005, 'invalid target type.');
+  END IF;
+
+  SELECT number_value INTO v_max_cosine_distance
+    FROM sg_game_scope_policy
+   WHERE policy_key = 'QA_VECTOR_MAX_COSINE_DISTANCE'
+     AND active_yn = 'Y'
+     AND number_value IS NOT NULL;
+
+  v_query_vector := DBMS_VECTOR.UTL_TO_EMBEDDING(
+    p_question, JSON(sg_qa_vector_params('search_query'))
+  );
+
+  OPEN v_results FOR
+    SELECT example_id, question, answer_sql, answer_text, embedding_model,
+           reference_kind, target_type, object_role, source_case_id, source_type,
+           cosine_distance
+      FROM (
+        SELECT example_id, question, answer_sql, answer_text, embedding_model,
+               reference_kind, target_type, object_role, source_case_id, source_type,
+               VECTOR_DISTANCE(embedding, v_query_vector, COSINE) AS cosine_distance
+          FROM sg_qa_vector_example
+         WHERE reference_status = 'APPROVED'
+           AND inspection_status = 'VERIFIED'
+           AND answer_sql IS NOT NULL
+           AND (source_type = 'POLICY_TEMPLATE'
+                OR NOT REGEXP_LIKE(answer_sql, '<[A-Z][A-Z0-9_]*>', 'i'))
+           AND (target_type = 'ANY' OR v_target_type = 'ANY' OR target_type = v_target_type)
+      )
+     WHERE cosine_distance <= v_max_cosine_distance
+     ORDER BY cosine_distance, example_id
+     FETCH FIRST p_top_k ROWS ONLY;
+  RETURN v_results;
+END;
+/
+
+COMMIT

database/adb/136_sgmp_fewshot_neighbor_margin.sql

@@ -0,0 +1,87 @@
+-- Keep semantic vector retrieval, but do not inject weak trailing neighbours
+-- when a materially stronger example has already been found.
+MERGE INTO sg_game_scope_policy t
+USING (
+  SELECT 'QA_VECTOR_NEIGHBOR_DISTANCE_MARGIN' AS policy_key,
+         0.120000 AS number_value,
+         'Maximum additional cosine distance from the best runtime Few-shot candidate.' AS description
+    FROM dual
+) s
+ON (t.policy_key = s.policy_key)
+WHEN MATCHED THEN UPDATE SET
+  t.number_value = s.number_value,
+  t.description = s.description,
+  t.active_yn = 'Y',
+  t.updated_at = SYSTIMESTAMP
+WHEN NOT MATCHED THEN INSERT (
+  policy_key, number_value, text_value, description, active_yn
+) VALUES (
+  s.policy_key, s.number_value, NULL, s.description, 'Y'
+)
+/
+
+CREATE OR REPLACE FUNCTION sg_qa_vector_search(
+  p_question    IN CLOB,
+  p_top_k       IN PLS_INTEGER DEFAULT 3,
+  p_target_type IN VARCHAR2 DEFAULT 'ANY'
+) RETURN SYS_REFCURSOR AUTHID DEFINER
+IS
+  v_query_vector VECTOR;
+  v_results SYS_REFCURSOR;
+  v_target_type VARCHAR2(16) := UPPER(TRIM(NVL(p_target_type, 'ANY')));
+  v_max_cosine_distance NUMBER;
+  v_neighbor_margin NUMBER;
+BEGIN
+  IF p_question IS NULL THEN RAISE_APPLICATION_ERROR(-20003, 'question is required.'); END IF;
+  IF p_top_k IS NULL OR p_top_k < 1 OR p_top_k > 20 THEN
+    RAISE_APPLICATION_ERROR(-20004, 'top_k must be between 1 and 20.');
+  END IF;
+  IF v_target_type NOT IN ('NONE', 'SINGLE', 'MULTI', 'ALL', 'ANY') THEN
+    RAISE_APPLICATION_ERROR(-20005, 'invalid target type.');
+  END IF;
+
+  SELECT number_value INTO v_max_cosine_distance
+    FROM sg_game_scope_policy
+   WHERE policy_key = 'QA_VECTOR_MAX_COSINE_DISTANCE'
+     AND active_yn = 'Y'
+     AND number_value IS NOT NULL;
+
+  SELECT number_value INTO v_neighbor_margin
+    FROM sg_game_scope_policy
+   WHERE policy_key = 'QA_VECTOR_NEIGHBOR_DISTANCE_MARGIN'
+     AND active_yn = 'Y'
+     AND number_value IS NOT NULL;
+
+  v_query_vector := DBMS_VECTOR.UTL_TO_EMBEDDING(
+    p_question, JSON(sg_qa_vector_params('search_query'))
+  );
+
+  OPEN v_results FOR
+    SELECT example_id, question, answer_sql, answer_text, embedding_model,
+           reference_kind, target_type, object_role, source_case_id, source_type,
+           cosine_distance
+      FROM (
+        SELECT c.*,
+               MIN(c.cosine_distance) OVER () AS best_cosine_distance
+          FROM (
+            SELECT example_id, question, answer_sql, answer_text, embedding_model,
+                   reference_kind, target_type, object_role, source_case_id, source_type,
+                   VECTOR_DISTANCE(embedding, v_query_vector, COSINE) AS cosine_distance
+              FROM sg_qa_vector_example
+             WHERE reference_status = 'APPROVED'
+               AND inspection_status = 'VERIFIED'
+               AND answer_sql IS NOT NULL
+               AND (source_type = 'POLICY_TEMPLATE'
+                    OR NOT REGEXP_LIKE(answer_sql, '<[A-Z][A-Z0-9_]*>', 'i'))
+               AND (target_type = 'ANY' OR v_target_type = 'ANY' OR target_type = v_target_type)
+          ) c
+         WHERE c.cosine_distance <= v_max_cosine_distance
+      )
+     WHERE cosine_distance <= best_cosine_distance + v_neighbor_margin
+     ORDER BY cosine_distance, example_id
+     FETCH FIRST p_top_k ROWS ONLY;
+  RETURN v_results;
+END;
+/
+
+COMMIT

database/adb/137_sgmp_restore_czn08_crystal_average_fewshot.sql

@@ -0,0 +1,16 @@
+-- CZN-08 is the reviewed semantic reference for daily standard-AU crystal
+-- total and per-user average holdings.
+UPDATE sg_qa_vector_example
+   SET reference_status = 'APPROVED',
+       reference_kind = 'SQL_TEMPLATE',
+       target_type = 'SINGLE',
+       object_role = 'GAME_GOODS_HOLDINGS',
+       inspection_status = 'VERIFIED',
+       inspection_note = 'Verified CZN-08 Few-shot. Standard-AU crystal holdings require AU_FLAG=1, excluded-user filtering, and per-user average as SUM(HAVE_CNT) / COUNT(DISTINCT GUID), not AVG(HAVE_CNT).',
+       verified_at = SYSTIMESTAMP,
+       verified_by = 'SGMP_POC_METADATA_REVIEW'
+ WHERE source_type = 'CUSTOMER_QA_BENCHMARK'
+   AND source_case_id = 'CZN-08'
+/
+
+COMMIT

database/adb/138_sgmp_tighten_fewshot_neighbor_margin.sql

@@ -0,0 +1,11 @@
+-- Calibrate the semantic-neighbour window using reviewed CZN patterns:
+-- retain close paraphrases, exclude adjacent metric shapes.
+UPDATE sg_game_scope_policy
+   SET number_value = 0.100000,
+       description = 'Maximum additional cosine distance from the best runtime Few-shot candidate.',
+       active_yn = 'Y',
+       updated_at = SYSTIMESTAMP
+ WHERE policy_key = 'QA_VECTOR_NEIGHBOR_DISTANCE_MARGIN'
+/
+
+COMMIT

database/adb/139_sgmp_restore_czn05_country_business_au_fewshot.sql

@@ -0,0 +1,15 @@
+-- CZN-05 is the reviewed reference for country-grouped business AU.
+UPDATE sg_qa_vector_example
+   SET reference_status = 'APPROVED',
+       reference_kind = 'SQL_TEMPLATE',
+       target_type = 'SINGLE',
+       object_role = 'COUNTRY_GROUPED_BUSINESS_AU',
+       inspection_status = 'VERIFIED',
+       inspection_note = 'Verified CZN-05 Few-shot. Join business-user data to user master on GUID and BASE_DT before grouping by user country; filter BIZ_AU_FLAG=1 and EXPT_USER_YN=''N''.',
+       verified_at = SYSTIMESTAMP,
+       verified_by = 'SGMP_POC_METADATA_REVIEW'
+ WHERE source_type = 'CUSTOMER_QA_BENCHMARK'
+   AND source_case_id = 'CZN-05'
+/
+
+COMMIT

database/adb/140_sgmp_hybrid_fewshot_reranking.sql

@@ -0,0 +1,95 @@
+-- Hybrid retrieval remains database-driven: dense vector similarity handles
+-- paraphrases, while lexical similarity distinguishes decisive request terms.
+MERGE INTO sg_game_scope_policy t
+USING (
+  SELECT 'QA_VECTOR_LEXICAL_WEIGHT' AS policy_key, 0.350000 AS number_value,
+         'Weight of normalized lexical question similarity in runtime Few-shot reranking.' AS description
+    FROM dual
+  UNION ALL
+  SELECT 'QA_VECTOR_HYBRID_SCORE_MARGIN', 0.050000,
+         'Maximum hybrid-score difference from the best runtime Few-shot candidate.'
+    FROM dual
+) s
+ON (t.policy_key = s.policy_key)
+WHEN MATCHED THEN UPDATE SET
+  t.number_value = s.number_value,
+  t.description = s.description,
+  t.active_yn = 'Y',
+  t.updated_at = SYSTIMESTAMP
+WHEN NOT MATCHED THEN INSERT (
+  policy_key, number_value, text_value, description, active_yn
+) VALUES (
+  s.policy_key, s.number_value, NULL, s.description, 'Y'
+)
+/
+
+CREATE OR REPLACE FUNCTION sg_qa_vector_search(
+  p_question    IN CLOB,
+  p_top_k       IN PLS_INTEGER DEFAULT 3,
+  p_target_type IN VARCHAR2 DEFAULT 'ANY'
+) RETURN SYS_REFCURSOR AUTHID DEFINER
+IS
+  v_query_vector VECTOR;
+  v_results SYS_REFCURSOR;
+  v_target_type VARCHAR2(16) := UPPER(TRIM(NVL(p_target_type, 'ANY')));
+  v_max_cosine_distance NUMBER;
+  v_lexical_weight NUMBER;
+  v_hybrid_margin NUMBER;
+BEGIN
+  IF p_question IS NULL THEN RAISE_APPLICATION_ERROR(-20003, 'question is required.'); END IF;
+  IF p_top_k IS NULL OR p_top_k < 1 OR p_top_k > 20 THEN
+    RAISE_APPLICATION_ERROR(-20004, 'top_k must be between 1 and 20.');
+  END IF;
+  IF v_target_type NOT IN ('NONE', 'SINGLE', 'MULTI', 'ALL', 'ANY') THEN
+    RAISE_APPLICATION_ERROR(-20005, 'invalid target type.');
+  END IF;
+
+  SELECT number_value INTO v_max_cosine_distance FROM sg_game_scope_policy
+   WHERE policy_key = 'QA_VECTOR_MAX_COSINE_DISTANCE' AND active_yn = 'Y';
+  SELECT number_value INTO v_lexical_weight FROM sg_game_scope_policy
+   WHERE policy_key = 'QA_VECTOR_LEXICAL_WEIGHT' AND active_yn = 'Y';
+  SELECT number_value INTO v_hybrid_margin FROM sg_game_scope_policy
+   WHERE policy_key = 'QA_VECTOR_HYBRID_SCORE_MARGIN' AND active_yn = 'Y';
+
+  v_query_vector := DBMS_VECTOR.UTL_TO_EMBEDDING(
+    p_question, JSON(sg_qa_vector_params('search_query'))
+  );
+
+  OPEN v_results FOR
+    SELECT example_id, question, answer_sql, answer_text, embedding_model,
+           reference_kind, target_type, object_role, source_case_id, source_type,
+           cosine_distance
+      FROM (
+        SELECT s.*,
+               MAX(s.hybrid_score) OVER () AS best_hybrid_score
+          FROM (
+            SELECT c.*,
+                   ((1 - v_lexical_weight) * (1 - c.cosine_distance)
+                    + v_lexical_weight * c.lexical_similarity) AS hybrid_score
+              FROM (
+                SELECT example_id, question, answer_sql, answer_text, embedding_model,
+                       reference_kind, target_type, object_role, source_case_id, source_type,
+                       VECTOR_DISTANCE(embedding, v_query_vector, COSINE) AS cosine_distance,
+                       UTL_MATCH.JARO_WINKLER_SIMILARITY(
+                         DBMS_LOB.SUBSTR(question, 4000, 1),
+                         DBMS_LOB.SUBSTR(p_question, 4000, 1)
+                       ) / 100 AS lexical_similarity
+                  FROM sg_qa_vector_example
+                 WHERE reference_status = 'APPROVED'
+                   AND inspection_status = 'VERIFIED'
+                   AND answer_sql IS NOT NULL
+                   AND (source_type = 'POLICY_TEMPLATE'
+                        OR NOT REGEXP_LIKE(answer_sql, '<[A-Z][A-Z0-9_]*>', 'i'))
+                   AND (target_type = 'ANY' OR v_target_type = 'ANY' OR target_type = v_target_type)
+              ) c
+             WHERE c.cosine_distance <= v_max_cosine_distance
+          ) s
+      )
+     WHERE hybrid_score >= best_hybrid_score - v_hybrid_margin
+     ORDER BY hybrid_score DESC, cosine_distance, example_id
+     FETCH FIRST p_top_k ROWS ONLY;
+  RETURN v_results;
+END;
+/
+
+COMMIT

database/adb/141_sgmp_restore_czn13_ether_usage_fewshot.sql

@@ -0,0 +1,15 @@
+-- CZN-13 is the reviewed reference for Ether usage and distinct users.
+UPDATE sg_qa_vector_example
+   SET reference_status = 'APPROVED',
+       reference_kind = 'SQL_TEMPLATE',
+       target_type = 'SINGLE',
+       object_role = 'GAME_GOODS_CHANGE',
+       inspection_status = 'VERIFIED',
+       inspection_note = 'Verified CZN-13 Few-shot. Ether usage requires goods-change data joined to the goods dimension and user master by GUID and BASE_DT, CHANGE_TYPE_CD=''USE'', active Ether dimension, excluded-user filter, and GOODS_CHANGE_CNT aggregation.',
+       verified_at = SYSTIMESTAMP,
+       verified_by = 'SGMP_POC_METADATA_REVIEW'
+ WHERE source_type = 'CUSTOMER_QA_BENCHMARK'
+   AND source_case_id = 'CZN-13'
+/
+
+COMMIT

database/adb/142_sgmp_restore_czn16_product_buyer_fewshot.sql

@@ -0,0 +1,15 @@
+-- CZN-16 is the reviewed reference for purchasers of a named package.
+UPDATE sg_qa_vector_example
+   SET reference_status = 'APPROVED',
+       reference_kind = 'SQL_TEMPLATE',
+       target_type = 'SINGLE',
+       object_role = 'SALES_PRODUCT_PURCHASER',
+       inspection_status = 'VERIFIED',
+       inspection_note = 'Verified CZN-16 Few-shot. Join sales transactions to product display by GAME_ID and PRODUCT_ID, filter the resolved package name and excluded users, and use the payment business date when counting distinct purchasers.',
+       verified_at = SYSTIMESTAMP,
+       verified_by = 'SGMP_POC_METADATA_REVIEW'
+ WHERE source_type = 'CUSTOMER_QA_BENCHMARK'
+   AND source_case_id = 'CZN-16'
+/
+
+COMMIT

database/adb/16_agent_ords_security_local_cleanup.sql

@@ -0,0 +1,94 @@
+-- ============================================================
+-- 16_agent_ords_security_local_cleanup.sql
+-- Agent ORDS security local-only cleanup.
+--
+-- This removes only CB_* objects used by the local ADB example.
+-- It does not touch the existing RDS DB Link POC objects.
+-- ============================================================
+WHENEVER SQLERROR CONTINUE
+SET ECHO OFF
+SET FEEDBACK ON
+
+PROMPT === Cleaning VPD policy ===
+BEGIN
+  DBMS_REDACT.DROP_POLICY(
+    object_schema => USER,
+    object_name   => 'CB_V_SEARCH_DOCUMENTS',
+    policy_name   => 'CB_CONTENTS_REDACT'
+  );
+EXCEPTION
+  WHEN OTHERS THEN NULL;
+END;
+/
+
+BEGIN
+  DBMS_RLS.DROP_POLICY(
+    object_schema => USER,
+    object_name   => 'CB_V_SEARCH_DOCUMENTS',
+    policy_name   => 'CB_AGENT_DOC_POLICY'
+  );
+EXCEPTION
+  WHEN OTHERS THEN NULL;
+END;
+/
+
+PROMPT === Cleaning DDS data grants ===
+BEGIN EXECUTE IMMEDIATE 'DROP DATA GRANT admin.cb_dg_hr_docs';  EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP DATA GRANT admin.cb_dg_fin_docs'; EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP DATA GRANT admin.cb_dg_all_docs'; EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+PROMPT === Cleaning DDS end users and roles ===
+BEGIN EXECUTE IMMEDIATE 'DROP END USER "cb_dds_hr"';   EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP END USER "cb_dds_fin"';  EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP END USER "cb_dds_all"';  EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP END USER "cb_dds_none"'; EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP DATA ROLE cb_dds_hr_role';           EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP DATA ROLE cb_dds_fin_role';          EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP DATA ROLE cb_dds_all_role';          EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP DATA ROLE cb_dds_connect_only_role'; EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP ROLE cb_dds_connect_role';           EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+PROMPT === Cleaning DB user and local objects ===
+BEGIN EXECUTE IMMEDIATE 'DROP USER cb_ords CASCADE'; EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP VIEW cb_dds_v_search_documents'; EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP VIEW cb_v_search_documents'; EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP FUNCTION cb_agent_doc_vpd_filter'; EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP CONTEXT cb_agent_ctx'; EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP PACKAGE cb_agent_ctx_pkg'; EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE cb_agent_bearer_key PURGE'; EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE cb_permission_rule PURGE'; EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE cb_permission PURGE'; EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE cb_user_role PURGE'; EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE cb_app_role PURGE'; EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE cb_app_user PURGE'; EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE cb_dds_documents PURGE'; EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+BEGIN EXECUTE IMMEDIATE 'DROP TABLE cb_search_documents PURGE'; EXCEPTION WHEN OTHERS THEN NULL; END;
+/
+
+PROMPT === Agent ORDS security local cleanup complete ===
+EXIT;

database/adb/17_agent_ords_security_local_vpd_setup.sql

@@ -46,6 +46,7 @@ CREATE TABLE cb_app_user (
   user_name          VARCHAR2(50) NOT NULL,
   employee_no        VARCHAR2(20) NOT NULL,
   dept_code          VARCHAR2(20) NOT NULL,
+  stakeholder_user_id VARCHAR2(30),
   can_read_contents  CHAR(1) DEFAULT 'N' CHECK (can_read_contents IN ('Y','N')) NOT NULL,
   active             CHAR(1) DEFAULT 'Y' CHECK (active IN ('Y','N')) NOT NULL
 );
@@ -75,7 +76,7 @@ CREATE TABLE cb_permission_rule (
   perm_id     NUMBER NOT NULL REFERENCES cb_permission(perm_id),
   rule_column VARCHAR2(128),
   rule_type   VARCHAR2(30) NOT NULL,
-  rule_value  VARCHAR2(100)
+  rule_value  VARCHAR2(1000 CHAR)
 );
 
 CREATE TABLE cb_permission_column (
@@ -87,6 +88,7 @@ CREATE TABLE cb_permission_column (
 CREATE TABLE cb_agent_bearer_key (
   key_id      NUMBER PRIMARY KEY,
   user_id     NUMBER NOT NULL REFERENCES cb_app_user(user_id),
+  stakeholder_user_id VARCHAR2(30),
   key_hash    VARCHAR2(128) NOT NULL UNIQUE,
   key_prefix  VARCHAR2(16),
   issued_at   DATE DEFAULT SYSDATE NOT NULL,
@@ -96,9 +98,9 @@ CREATE TABLE cb_agent_bearer_key (
 );
 
 PROMPT === 3. Seeding permissions ===
-INSERT INTO cb_app_user VALUES (101, '이에이치알', 'E10234', 'HR',  'N', 'Y');
+INSERT INTO cb_app_user VALUES (101, '이에이치알', 'E10234', 'HR',  NULL, 'N', 'Y');
-INSERT INTO cb_app_user VALUES (102, '박파이넨스', 'E2001',  'FIN', 'N', 'Y');
+INSERT INTO cb_app_user VALUES (102, '박파이넨스', 'E2001',  'FIN', NULL, 'N', 'Y');
-INSERT INTO cb_app_user VALUES (103, '김어드민',   'E99999', 'HQ',  'Y', 'Y');
+INSERT INTO cb_app_user VALUES (103, '김어드민',   'E99999', 'HQ',  NULL, 'Y', 'Y');
 
 INSERT INTO cb_app_role VALUES (10, 'HR_DEPT_ROLE', 'INTERNAL');
 INSERT INTO cb_app_role VALUES (20, 'FIN_SELF_ROLE', 'INTERNAL');
@@ -112,9 +114,12 @@ INSERT INTO cb_permission VALUES (100, 10, 'CB_V_SEARCH_DOCUMENTS', 'SELECT', 'A
 INSERT INTO cb_permission VALUES (200, 20, 'CB_V_SEARCH_DOCUMENTS', 'SELECT', 'ALLOW');
 INSERT INTO cb_permission VALUES (300, 30, 'CB_V_SEARCH_DOCUMENTS', 'SELECT', 'ALLOW');
 
-INSERT INTO cb_permission_rule VALUES (1000, 100, 'MY_DEPT', NULL);
+INSERT INTO cb_permission_rule (rule_id, perm_id, rule_column, rule_type, rule_value)
-INSERT INTO cb_permission_rule VALUES (2000, 200, 'SELF',    NULL);
+VALUES (1000, 100, NULL, 'MY_DEPT', NULL);
-INSERT INTO cb_permission_rule VALUES (3000, 300, 'ALL',     NULL);
+INSERT INTO cb_permission_rule (rule_id, perm_id, rule_column, rule_type, rule_value)
+VALUES (2000, 200, NULL, 'SELF', NULL);
+INSERT INTO cb_permission_rule (rule_id, perm_id, rule_column, rule_type, rule_value)
+VALUES (3000, 300, NULL, 'ALL', NULL);
 
 INSERT INTO cb_permission_column VALUES (300, 'CONTENTS');
 

database/adb/18_agent_ords_security_local_vpd_test.sql

@@ -0,0 +1,115 @@
+-- ============================================================
+-- 18_agent_ords_security_local_vpd_test.sql
+-- Run as CB_ORDS.
+--
+-- Expected:
+--   no context        -> 0 rows
+--   cb_hr_key         -> HR rows only, 3 rows
+--   cb_fin_key        -> owner_emp_no = E2001 only, 1 row
+--   cb_all_key        -> all rows, 6 rows
+--   contents column   -> NULL unless CAN_READ_CONTENTS = Y
+--   invalid key       -> ORA-20002, then 0 rows
+--   direct base table -> ORA-00942
+-- ============================================================
+WHENEVER SQLERROR CONTINUE
+SET ECHO OFF
+SET FEEDBACK ON
+SET LINESIZE 220
+SET PAGESIZE 100
+COLUMN ctx_user_id FORMAT A12
+COLUMN ctx_emp_no FORMAT A12
+COLUMN ctx_dept_code FORMAT A12
+COLUMN ctx_can_read_contents FORMAT A22
+COLUMN title FORMAT A28
+COLUMN owner_emp_no FORMAT A12
+COLUMN dept_code FORMAT A10
+COLUMN contents FORMAT A35
+
+PROMPT
+PROMPT === VPD 1. No Bearer key / context: fail closed ===
+BEGIN
+  admin.cb_agent_ctx_pkg.clear_user;
+END;
+/
+
+SELECT SYS_CONTEXT('CB_AGENT_CTX','USER_ID') AS ctx_user_id,
+       SYS_CONTEXT('CB_AGENT_CTX','EMP_NO') AS ctx_emp_no,
+       SYS_CONTEXT('CB_AGENT_CTX','DEPT_CODE') AS ctx_dept_code,
+       SYS_CONTEXT('CB_AGENT_CTX','CAN_READ_CONTENTS') AS ctx_can_read_contents
+FROM   dual;
+
+SELECT COUNT(*) AS rows_visible
+FROM   admin.cb_v_search_documents;
+
+PROMPT
+PROMPT === VPD 2. Authorization: Bearer cb_hr_key -> HR department rows ===
+BEGIN
+  admin.cb_agent_ctx_pkg.set_user_by_bearer('cb_hr_key');
+END;
+/
+
+SELECT SYS_CONTEXT('CB_AGENT_CTX','USER_ID') AS ctx_user_id,
+       SYS_CONTEXT('CB_AGENT_CTX','EMP_NO') AS ctx_emp_no,
+       SYS_CONTEXT('CB_AGENT_CTX','DEPT_CODE') AS ctx_dept_code,
+       SYS_CONTEXT('CB_AGENT_CTX','CAN_READ_CONTENTS') AS ctx_can_read_contents
+FROM   dual;
+
+SELECT doc_id, title, owner_emp_no, dept_code, contents
+FROM   admin.cb_v_search_documents
+ORDER  BY doc_id;
+
+SELECT COUNT(*) AS rows_visible
+FROM   admin.cb_v_search_documents;
+
+PROMPT
+PROMPT === VPD 3. Authorization: Bearer cb_fin_key -> self row only ===
+BEGIN
+  admin.cb_agent_ctx_pkg.set_user_by_bearer('cb_fin_key');
+END;
+/
+
+SELECT SYS_CONTEXT('CB_AGENT_CTX','USER_ID') AS ctx_user_id,
+       SYS_CONTEXT('CB_AGENT_CTX','EMP_NO') AS ctx_emp_no,
+       SYS_CONTEXT('CB_AGENT_CTX','DEPT_CODE') AS ctx_dept_code,
+       SYS_CONTEXT('CB_AGENT_CTX','CAN_READ_CONTENTS') AS ctx_can_read_contents
+FROM   dual;
+
+SELECT doc_id, title, owner_emp_no, dept_code, contents
+FROM   admin.cb_v_search_documents
+ORDER  BY doc_id;
+
+SELECT COUNT(*) AS rows_visible
+FROM   admin.cb_v_search_documents;
+
+SELECT doc_id, title, owner_emp_no, dept_code, contents
+FROM   admin.cb_v_search_documents
+ORDER  BY doc_id
+FETCH FIRST 3 ROWS ONLY;
+
+PROMPT
+PROMPT === VPD 4. Authorization: Bearer cb_all_key -> all rows ===
+BEGIN
+  admin.cb_agent_ctx_pkg.set_user_by_bearer('cb_all_key');
+END;
+/
+
+SELECT COUNT(*) AS rows_visible
+FROM   admin.cb_v_search_documents;
+
+PROMPT
+PROMPT === VPD 5. Invalid Bearer key -> ORA-20002 and context cleared ===
+BEGIN
+  admin.cb_agent_ctx_pkg.set_user_by_bearer('wrong_key');
+END;
+/
+
+SELECT COUNT(*) AS rows_visible_after_invalid_key
+FROM   admin.cb_v_search_documents;
+
+PROMPT
+PROMPT === VPD 6. Bypass attempts: direct base/permission tables are not granted ===
+SELECT COUNT(*) FROM admin.cb_search_documents;
+SELECT COUNT(*) FROM admin.cb_app_user;
+SELECT COUNT(*) FROM admin.cb_agent_bearer_key;
+
+EXIT;

database/adb/19_agent_ords_security_local_dds_setup.sql

@@ -0,0 +1,89 @@
+-- ============================================================
+-- 19_agent_ords_security_local_dds_setup.sql
+-- Local-only executable DDS example for Agent ORDS security.
+--
+-- Scenario:
+--   * DDS uses END USER -> DATA ROLE -> DATA GRANT.
+--   * No permission table lookup is used.
+--   * Each DATA GRANT declares the protected object, row filter,
+--     and column restriction.
+-- ============================================================
+WHENEVER SQLERROR EXIT SQL.SQLCODE
+SET ECHO OFF
+SET FEEDBACK ON
+SET DEFINE OFF
+
+PROMPT === 1. Creating DDS-only local table and view ===
+CREATE TABLE cb_dds_documents (
+  doc_id        NUMBER PRIMARY KEY,
+  title         VARCHAR2(100) NOT NULL,
+  owner_emp_no  VARCHAR2(20)  NOT NULL,
+  dept_code     VARCHAR2(20)  NOT NULL,
+  contents      VARCHAR2(4000),
+  created_at    DATE DEFAULT SYSDATE NOT NULL
+);
+
+INSERT INTO cb_dds_documents VALUES (1, 'HR payroll guide',       'E1001', 'HR',    'Payroll policy and HR guide', SYSDATE);
+INSERT INTO cb_dds_documents VALUES (2, 'HR recruiting plan',     'E1002', 'HR',    'Recruiting plan for HR team', SYSDATE);
+INSERT INTO cb_dds_documents VALUES (3, 'Finance close checklist','E2001', 'FIN',   'Monthly close checklist', SYSDATE);
+INSERT INTO cb_dds_documents VALUES (4, 'Finance audit memo',     'E2002', 'FIN',   'Audit memo for finance team', SYSDATE);
+INSERT INTO cb_dds_documents VALUES (5, 'Sales forecast',         'E3001', 'SALES', 'Quarterly sales forecast', SYSDATE);
+INSERT INTO cb_dds_documents VALUES (6, 'HR benefits notice',     'E1003', 'HR',    'Benefits notice for employees', SYSDATE);
+
+CREATE OR REPLACE VIEW cb_dds_v_search_documents AS
+SELECT doc_id,
+       title,
+       owner_emp_no,
+       dept_code,
+       contents,
+       created_at
+FROM   cb_dds_documents;
+
+COMMIT;
+
+PROMPT === 2. Creating DDS end users ===
+CREATE END USER "cb_dds_hr"   IDENTIFIED BY "CbDds#Hr2026Local1";
+CREATE END USER "cb_dds_fin"  IDENTIFIED BY "CbDds#Fin2026Local1";
+CREATE END USER "cb_dds_all"  IDENTIFIED BY "CbDds#All2026Local1";
+CREATE END USER "cb_dds_none" IDENTIFIED BY "CbDds#None2026Local1";
+
+PROMPT === 3. Creating DATA ROLEs and connection carrier role ===
+CREATE ROLE cb_dds_connect_role;
+GRANT CREATE SESSION TO cb_dds_connect_role;
+
+CREATE DATA ROLE cb_dds_hr_role;
+CREATE DATA ROLE cb_dds_fin_role;
+CREATE DATA ROLE cb_dds_all_role;
+CREATE DATA ROLE cb_dds_connect_only_role;
+
+GRANT cb_dds_connect_role TO cb_dds_hr_role;
+GRANT cb_dds_connect_role TO cb_dds_fin_role;
+GRANT cb_dds_connect_role TO cb_dds_all_role;
+GRANT cb_dds_connect_role TO cb_dds_connect_only_role;
+
+PROMPT === 4. Mapping END USERs to DATA ROLEs ===
+GRANT DATA ROLE cb_dds_hr_role           TO "cb_dds_hr";
+GRANT DATA ROLE cb_dds_fin_role          TO "cb_dds_fin";
+GRANT DATA ROLE cb_dds_all_role          TO "cb_dds_all";
+GRANT DATA ROLE cb_dds_connect_only_role TO "cb_dds_none";
+
+PROMPT === 5. Creating DATA GRANTs ===
+CREATE DATA GRANT admin.cb_dg_hr_docs
+  AS SELECT (ALL COLUMNS EXCEPT contents)
+  ON admin.cb_dds_v_search_documents
+  WHERE dept_code = 'HR'
+  TO cb_dds_hr_role;
+
+CREATE DATA GRANT admin.cb_dg_fin_docs
+  AS SELECT (ALL COLUMNS EXCEPT contents)
+  ON admin.cb_dds_v_search_documents
+  WHERE dept_code = 'FIN'
+  TO cb_dds_fin_role;
+
+CREATE DATA GRANT admin.cb_dg_all_docs
+  AS SELECT
+  ON admin.cb_dds_v_search_documents
+  TO cb_dds_all_role;
+
+PROMPT === DDS local setup complete ===
+EXIT;