commit 209a38109103cf15dd98d9bb7e6dc50f7e217f38 Author: hbyang Date: Wed Sep 9 11:56:34 2026 +0900 SEO/AEO 키워드 온톨로지 서비스 초기 구현 발행된 사이트에 업체별 SEO/AEO 키워드를 제공하는 서비스. - PostgreSQL 16 + pgvector/ltree/pg_trgm 단일 스토어 (정확·의미·계층 조회를 한 엔진에서 처리) - 키워드는 전역 사전 + merchant_keyword 연결 테이블 구조 - 4단계 계단식 중복제거: 금칙어 → normalized 완전일치 → pg_trgm → 코사인 유사도, 걸린 표기는 aliases[] 로 흡수 - BullMQ 생성 큐 (발행 즉시 / 일 1회 크론 / 성과 기반) - OpenAI Structured Outputs + mock provider (API 키 없이 로컬 전 구간 동작) - 서빙 API: /v1/sites/:id/seo, /aeo, /performance, /keywords/search - docs/architecture.html 설계 도식 JSON-LD 조립과 o2o-site-AEO 연동은 후속 작업. Co-Authored-By: Claude Opus 5 (1M context) diff --git a/.env.example b/.env.example new file mode 100644 index 0000000..e3559db --- /dev/null +++ b/.env.example @@ -0,0 +1,29 @@ +# --- server --- +PORT=3100 + +# --- postgres (docker-compose 기본값) --- +DATABASE_URL=postgres://ontology:ontology@localhost:55432/ontology + +# --- redis (BullMQ) --- +REDIS_HOST=localhost +REDIS_PORT=56379 + +# --- LLM --- +# mock : API 키 없이 로컬에서 전체 파이프라인 동작 (기본값) +# openai : 실제 OpenAI 호출 +LLM_PROVIDER=mock +OPENAI_API_KEY= +OPENAI_MODEL=gpt-4.1-mini +OPENAI_EMBEDDING_MODEL=text-embedding-3-small + +# --- 생성/중복제거 튜닝 --- +# 코사인 유사도가 이 값 이상이면 기존 키워드에 alias 로 흡수 +DEDUP_COSINE_THRESHOLD=0.92 +# trigram 유사도 사전 필터 +DEDUP_TRIGRAM_THRESHOLD=0.6 +# 벡터 비교 대상 상위 후보 수 +DEDUP_CANDIDATE_LIMIT=20 +# 1회 생성 요청당 키워드 목표 개수 +GENERATION_TARGET_KEYWORDS=15 +# 주기 리프레시 간격(일) +REFRESH_INTERVAL_DAYS=30 diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..d369c55 --- /dev/null +++ b/.gitignore @@ -0,0 +1,5 @@ +node_modules/ +dist/ +.env +*.tsbuildinfo +.DS_Store diff --git a/README.md b/README.md new file mode 100644 index 0000000..322cd6f --- /dev/null +++ b/README.md @@ -0,0 +1,133 @@ +# o2o-site-ontology + +o2o-site-AEO 가 발행한 사이트에 **업체별 SEO/AEO 키워드**를 제공하는 온톨로지 서비스. + +- 주기적으로 LLM 에게 업체 정보를 주고 키워드·태그·Q&A 를 생성 +- **4단계 계단식 중복제거**로 전역 키워드 사전을 오염 없이 유지 +- 발행 사이트는 REST 로 SEO/AEO payload 만 받아 쓴다 (JSON-LD 조립은 후속 단계) + +## 빠른 시작 (로컬) + +```bash +npm install +cp .env.example .env # 기본값은 LLM_PROVIDER=mock — API 키 불필요 +npm run db:up # postgres(pgvector) + redis +npm run db:migrate +npm run db:seed # 업종/지역 계층 + 데모 업체 3곳 +npm start # http://localhost:3100 +npm run smoke # (다른 터미널) 엔드투엔드 점검 +``` + +`npm run db:reset` 은 볼륨까지 지우고 migrate + seed 를 다시 돌린다. + +### 실제 OpenAI 로 전환 + +```bash +# .env +LLM_PROVIDER=openai +OPENAI_API_KEY=sk-... +OPENAI_MODEL=gpt-4.1-mini +OPENAI_EMBEDDING_MODEL=text-embedding-3-small +``` + +`LLM_PROVIDER=mock` 은 문자 bigram 해싱 임베딩을 쓴다. 랜덤이 아니라 **비슷한 문자열이면 +비슷한 벡터**가 나오므로 중복제거 파이프라인 검증에는 충분하지만, 의미 기반 중복 +(`강남 미용실` ↔ `강남 헤어샵`) 은 실제 임베딩 모델에서만 잡힌다. + +## 데이터 모델 + +| 테이블 | 역할 | +|---|---| +| `industry` / `region` | `ltree` 업종·지역 계층. 상위 노드 키워드 상속의 기반 | +| `merchant` | 업체. `external_id` 가 o2o-site-AEO 의 사이트 ID | +| `keyword` | **전역** 키워드 사전. `normalized` 유니크, `aliases[]`, `embedding vector(1536)` | +| `merchant_keyword` | 업체 ↔ 키워드 연결. `relevance` / `status` / `impressions` / `ctr` | +| `qa_pair` | AEO 용 질문-답변 쌍 | +| `generation_run` | 생성 감사 로그 (프롬프트 버전·토큰·통계) | + +키워드는 업체에 복제하지 않고 전역 사전 + 연결 테이블로 둔다. 그래야 임베딩이 하나만 +저장되고, `강남 미용실` 을 쓰는 업체가 100곳이어도 중복제거가 성립한다. + +## 중복제거 4단계 + +값비싼 벡터 비교를 마지막에 두고, 후보 집합 안에서만 수행한다. + +| 단계 | 방법 | 걸러내는 것 | +|---|---|---| +| 0 | 금칙어 필터 | `최고`, `1위`, `100%` 등 과장광고 | +| 1 | `normalized` 완전 일치 (공백·구두점 제거) | `강남 뿌리 염색` = `강남 뿌리염색` | +| 2 | `pg_trgm` 유사도 ≥ 0.6 | `강남 뿌리염색약` → `강남 뿌리염색` | +| 3 | 코사인 유사도 ≥ 0.92 | `강남 미용실` ↔ `강남 헤어샵` (의미 중복) | +| 4 | 신규 등록 | 위에 안 걸리면 새 키워드 | + +1~3 단계에서 매칭되면 원래 표기는 버리지 않고 기존 키워드의 `aliases[]` 로 흡수한다 +(롱테일 검색어 보존 + 성과 피드백 매칭에 사용). + +임계값은 `.env` 의 `DEDUP_COSINE_THRESHOLD` / `DEDUP_TRIGRAM_THRESHOLD` 로 조정. + +## API + +| 메서드 | 경로 | 용도 | +|---|---|---| +| `GET` | `/health` | 헬스체크 | +| `POST` | `/v1/merchants/publish` | **사이트 발행 웹훅** — 업체 upsert + 생성 예약 (`sync:true` 면 동기 실행) | +| `POST` | `/v1/merchants/:id/generate?sync=true` | 수동 재생성 | +| `GET` | `/v1/merchants` `/v1/merchants/:id` | 조회 | +| `GET` | `/v1/sites/:id/seo?limit=20` | **발행 사이트가 렌더링 시 호출** — title/description/keywords/tags | +| `GET` | `/v1/sites/:id/aeo?limit=10` | 답변엔진용 topics/FAQ/structuredDataHints | +| `POST` | `/v1/keywords/search` | 의미 기반 키워드 검색 (어드민) | +| `POST` | `/v1/sites/:id/performance` | Search Console·유입 로그 피드백 → 저성과 키워드 강등 | + +`:id` 는 `external_id` 또는 내부 UUID 둘 다 받는다. + +### 발행 웹훅 예시 + +```bash +curl -X POST http://localhost:3100/v1/merchants/publish \ + -H 'content-type: application/json' \ + -d '{ + "externalId": "site-1003", + "name": "강남 뷰티랩", + "industryId": "beauty.hair", + "regionId": "kr.seoul.gangnam", + "description": "강남 미용실. 염색 전문.", + "profile": { "services": ["뿌리염색", "여성펌"], "features": ["주차가능"] } + }' +``` + +### 서빙 예시 + +```bash +curl 'http://localhost:3100/v1/sites/site-1001/seo?limit=8' +``` + +```json +{ + "title": "레브살롱 | 강남 미용실", + "description": "강남역 3번 출구 앞 프라이빗 헤어살롱. ... 정보를 확인하세요.", + "keywords": ["레브살롱", "강남 미용실", "강남 남자 커트", "..."], + "tags": [{ "keyword": "강남 미용실", "intent": "local", "relevance": 0.95, "aliases": ["강남미용실"] }] +} +``` + +## 생성 주기 + +- **발행 즉시** — `/v1/merchants/publish` 가 BullMQ 에 적재 (60초 dedupe 창) +- **주기 리프레시** — 매일 03:00 크론이 `REFRESH_INTERVAL_DAYS`(기본 30일) 지난 업체를 적재 +- **성과 기반** — 노출 100회 이상 & CTR < 0.2% 인 키워드는 `demoted` 로 강등, 다음 사이클에서 대체 + +프롬프트에는 해당 업체와 같은 업종의 기존 키워드 목록을 넣어 **중복 후보 생성 자체를 줄인다.** +그래도 남는 중복만 위 4단계가 처리한다. + +## 남은 작업 + +- [ ] JSON-LD (`LocalBusiness` / `FAQPage` / `Service`) 조립 — `structuredDataHints` 를 그대로 매핑 +- [ ] `/llms.txt` 서빙 +- [ ] 업종 `ltree` 상위 노드 키워드 상속 (`source: 'inherited'`) +- [ ] Redis 응답 캐시 (서빙은 읽기 99%) +- [ ] Search Console API 연동 (현재는 `/performance` 수동 주입) +- [ ] 어드민 UI + +## 아키텍처 도식 + +`docs/architecture.html` 을 브라우저로 열면 전체 흐름·중복제거 단계·데이터 모델을 볼 수 있다. diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 0000000..4f85276 --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,33 @@ +services: + postgres: + image: pgvector/pgvector:pg16 + container_name: ontology-postgres + restart: unless-stopped + environment: + POSTGRES_USER: ontology + POSTGRES_PASSWORD: ontology + POSTGRES_DB: ontology + ports: + - '55432:5432' + volumes: + - pgdata:/var/lib/postgresql/data + healthcheck: + test: ['CMD-SHELL', 'pg_isready -U ontology -d ontology'] + interval: 5s + timeout: 3s + retries: 20 + + redis: + image: redis:7-alpine + container_name: ontology-redis + restart: unless-stopped + ports: + - '56379:6379' + healthcheck: + test: ['CMD', 'redis-cli', 'ping'] + interval: 5s + timeout: 3s + retries: 20 + +volumes: + pgdata: diff --git a/docs/architecture.html b/docs/architecture.html new file mode 100644 index 0000000..4b4392a --- /dev/null +++ b/docs/architecture.html @@ -0,0 +1,714 @@ +키워드 온톨로지 설계 + + + + + +
+ +
+

o2o-site-ontology

+

발행 사이트에 붙는
SEO/AEO 키워드 온톨로지

+

+ 업체 사이트를 발행하면 그 업체에 맞는 검색 키워드·태그·질문답변이 따라붙어야 한다. + LLM 이 주기적으로 후보를 만들고, 4단계 중복제거가 전역 키워드 사전을 깨끗하게 유지하고, + 발행된 사이트는 REST 로 완성된 payload 만 받아 쓴다. +

+
+ PostgreSQL 16 + pgvector + NestJS + BullMQ + OpenAI Structured Outputs +
+
+ + +
+
+

일반 DB 냐 벡터 DB 냐

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둘 중 하나를 고르는 문제가 아니다. 이 서비스는 성격이 다른 세 종류의 조회를 동시에 요구한다.

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+ +
+ + + + + + + + + + + + + + + + + + + + + +
조회 유형실제 질의필요한 것
정확 조회업체 A 의 활성 키워드 20개B-tree / 관계형 조인
의미 조회이 후보가 기존 키워드와 의미상 겹치는가vector (HNSW)
관계 탐색업종 트리 상위에서 물려받을 공통 키워드ltree 계층 / recursive CTE
+
+ +
+

결론 — PostgreSQL 하나로 시작한다. + pgvector + ltree + pg_trgm + JSONB 로 세 가지가 모두 한 엔진 안에서 해결되고, + 무엇보다 키워드 조회에는 항상 "어느 업체의"라는 조인이 따라붙는다.

+

전용 벡터 DB 를 지금 분리하면 매 요청이 2-hop 이 되고 정합성을 따로 관리해야 한다. + 벡터 행이 1천만 건을 넘거나 ANN 지연이 실제로 문제가 되는 시점에 Qdrant 로 떼어내도 늦지 않다. + Neo4j 도 같은 논리 — 고정 깊이 상속이면 ltree 로 충분하다.

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+ + +
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+

전체 흐름

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생성은 큐 뒤에서 비동기로, 서빙은 DB 읽기만으로. 두 경로가 만나는 지점은 Postgres 한 곳뿐이다.

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+ + + + + + + + + + + + + + + + + + + + + + + + + 트리거 + 사이트 발행 — 즉시 + 크론 03:00 — 30일 경과 + 성과 저조 — 재생성 + + BullMQ 큐 + 60초 dedupe 창 + 재시도 3회 · 지수 백오프 + 동시성 2 + + 생성 워커 + OpenAI · gpt-4.1-mini + Structured Outputs + 임베딩 배치 1회 + + 중복제거 4단계 + 해시 → trigram → 벡터 + 미일치만 신규 등록 + 나머지는 alias 흡수 + + PostgreSQL 16 + pgvector · ltree · pg_trgm + keyword (전역 사전) + merchant_keyword + qa_pair · generation_run + + Serving API + GET /v1/sites/:id/seo + GET /v1/sites/:id/aeo + 읽기 99% · 캐시 대상 + + 발행된 사이트 + o2o-site-AEO + 렌더링 시 호출 + + 성과 수집 + Search Console · 네이버 서치어드바이저 · 유입 로그 + + + + + + + + + + + + + + + + + + + + + + 적재 + job + 후보 + write + 읽기 + SEO / AEO payload + 노출 · 클릭 + CTR < 0.2% → 강등 + + 기존 키워드 주입 — 중복 후보 생성 자체를 억제 + +
+
+ 점선 화살표가 이 설계의 핵심이다. 프롬프트에 해당 업종의 기존 키워드를 넣어 중복 후보가 만들어지기 전에 줄이고, + 그래도 남는 것만 중복제거 단계가 처리한다. 생성 경로(위)와 서빙 경로(아래)는 Postgres 에서만 만나므로 + OpenAI 가 느리거나 죽어도 발행된 사이트의 응답에는 영향이 없다. +
+
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+ + +
+
+

중복제거 4단계

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+ 값싼 판정을 먼저, 비싼 판정을 나중에. 벡터 비교는 후보 20건 안에서만 일어나므로 전수 비교가 발생하지 않는다. +

+
+ +
+
+ + + + + + + + + + + + + + + + + LLM 후보 키워드 + + + + + + + + + + + + + 0 · 금칙어 필터 + 최고 · 1위 · 100% · 완치 + + 1 · normalized 완전 일치 + NFKC · 소문자 · 구두점/공백 제거 + + 2 · pg_trgm 유사도 ≥ 0.6 + 표기 변형 · 오타 + + 3 · 코사인 유사도 ≥ 0.92 + 의미 중복 — 후보 20건 안에서만 + + 4 · 새 키워드로 INSERT + embedding 저장 · usage_count 1 + + + + + + + + + + + 미일치 + 미일치 + 미일치 + 미일치 + + + + + 비용 0 + B-tree 1회 + GIN trgm + HNSW top-20 + INSERT + + + + + + 차단 — 저장하지 않음 + rejected_banned + + + + + + + + + + + + + 기존 키워드에 alias 흡수 + 강남 뿌리 염색 → 강남 뿌리염색 + + 기존 키워드에 alias 흡수 + 강남 뿌리염색약 → 강남 뿌리염색 (0.67) + + 기존 키워드에 alias 흡수 + 강남 헤어샵 → 강남 미용실 (0.94) + + + + + + + 어느 경로든 업체에는 연결된다 + merchant_keyword · relevance · status + +
+
+ 1~3 단계에서 걸린 표기는 버리지 않고 기존 키워드의 aliases[] 에 흡수한다. + 롱테일 검색어를 잃지 않으면서 사전은 한 행으로 유지되고, 나중에 Search Console 이 + 강남 뿌리염색약 으로 성과를 보고해도 같은 키워드에 매칭된다. +
+
+ +
+

실제 로컬 실행 결과

+

같은 지역·업종 업체를 순서대로 발행했을 때 npm run smoke 출력이다.

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+ 
1. 레브살롱 (첫 업체)      후보 19 → 신규 19 / 중복 0
+2. 헤어랩 강남점            후보 19 → 신규 4  / 중복(정확 15, 표기 0, 의미 0)
+3. 강남 뷰티랩              후보 16 → 신규 3  / 중복(정확 12, 표기 1, 의미 0)
+
+   matched_exact    강남 뿌리 염색      (sim=1.000 → '강남 뿌리염색')
+   matched_trigram  강남 뿌리염색약     (sim=0.667 → '강남 뿌리염색')
+   matched_exact    강남미용실추천      (sim=1.000 → '강남 미용실 추천')
+
+

+ 참고 + 위 수치는 LLM_PROVIDER=mock 기준이다. mock 임베딩은 문자 bigram 해싱이라 표기 유사도만 잡는다. + 의미 중복(강남 미용실 ↔ 강남 헤어샵)은 실제 text-embedding-3-small 로 전환해야 3단계가 발동한다. +

+
+
+ + +
+
+

데이터 모델

+

+ 키워드를 업체에 복제하지 않는 것이 이 스키마의 전부다. 복제하는 순간 중복제거 자체가 성립하지 않는다. +

+
+ +
+
+ + + + + + + + + + + + + + + + + + industry + path ltree · beauty.hair + + region + path ltree · kr.seoul.gangnam + + merchant + external_id ← 사이트 ID + description · profile jsonb + last_generated_at + + merchant_keyword + relevance · status · source + impressions · clicks · ctr + PK (merchant_id, keyword_id) + + keyword — 전역 사전 + canonical · 표시용 + normalized UNIQUE · 판정용 + aliases text[] · 흡수된 표기 + embedding vector(1536) HNSW + intent · locale + usage_count + + qa_pair + question · answer + normalized_question UNIQUE + embedding vector(1536) + + + + + + + + + + + + 업종 분류 + 지역 분류 + 1 : N + N : 1 + 1 : N + + +
+
+ 강남 미용실 을 100개 업체가 쓰더라도 keyword 에는 행이 하나, 임베딩도 하나뿐이다. + 업체별 관련도·성과는 전부 merchant_keyword 가 들고 있으므로 사전을 오염시키지 않고 + 업체마다 다른 순위를 낼 수 있다. +
+
+
+ + +
+
+

API

+

+ :id 는 o2o-site-AEO 의 external_id 와 내부 UUID 를 모두 받는다. + 연동 쪽에서 ID 매핑 테이블을 따로 들 필요가 없다. +

+
+ +
+ + + + + + + + + + + + + +
메서드경로용도
GET/health헬스체크 · 현재 LLM provider 확인
POST/v1/merchants/publish사이트 발행 웹훅. 업체 upsert 후 생성 작업 적재. sync:true 면 동기 실행
POST/v1/merchants/:id/generate수동 재생성. ?sync=true 로 결과를 즉시 확인
GET/v1/sites/:id/seo발행 사이트가 렌더링 시 호출. title · description · keywords · tags(alias 포함)
GET/v1/sites/:id/aeo답변엔진용 topics · FAQ · structuredDataHints
POST/v1/keywords/search어드민 — 자연어 질의로 키워드 사전 벡터 검색
POST/v1/sites/:id/performance노출·클릭 주입 → CTR 갱신 → 저성과 키워드 강등
+
+ +
+

SEO 응답

+
+ 
$ curl 'http://localhost:3100/v1/sites/site-1001/seo?limit=8'
+
+{
+  "title": "레브살롱 | 강남 미용실",
+  "description": "강남역 3번 출구 앞 프라이빗 헤어살롱. … 정보를 확인하세요.",
+  "keywords": ["레브살롱", "강남 미용실", "강남 남자 커트", "강남 두피 클리닉", …],
+  "tags": [
+    { "keyword": "강남 미용실", "intent": "local", "relevance": 0.95,
+      "aliases": ["강남미용실"] }
+  ]
+}
+ +
+

AEO 응답

+

+ SEO 가 키워드라면 AEO 는 질문-답변 쌍과 구조화 데이터다. AI 검색 크롤러가 인용하는 것은 이쪽이다. + structuredDataHints 는 후속 단계에서 LocalBusiness / FAQPage JSON-LD 로 그대로 매핑되도록 + 필드를 미리 맞춰 두었다. +

+
+ 
{
+  "topics": ["강남 미용실", "강남 남자 커트", "강남 여성 펌"],
+  "faqs": [
+    { "question": "레브살롱은(는) 어디에 있나요?",
+      "answer": "레브살롱은(는) 강남에 위치한 미용실입니다." }
+  ],
+  "structuredDataHints": {
+    "type": "LocalBusiness", "name": "레브살롱",
+    "areaServed": "강남", "category": "미용실"
+  }
+}
+
+ + +
+
+

기술 선택

+
+
+ + + + + + + + + + +
레이어선택이유
런타임NestJS · TypeScripto2o-site-AEO 와 payload 타입을 공유할 수 있다
DBPostgreSQL 16 + pgvector
+ ltree + pg_trgm		정확 · 의미 · 계층 조회 3-in-1
DB 접근postgres.js (raw SQL)벡터 연산자 <=> 와 ltree 는 어차피 raw SQL. ORM 을 얹으면 우회 코드가 더 는다
큐 · 스케줄BullMQ + Redis60초 dedupe 창, 지수 백오프 재시도, 크론이 전부 내장
LLMOpenAI Structured Outputs
text-embedding-3-small		JSON Schema 강제 — 자유 텍스트 파싱은 반드시 깨진다
관측generation_run 테이블프롬프트 버전 · 토큰 · 단계별 통계를 행으로 남긴다
+
+ +
+

로컬 실행

+
+ 
npm install
+cp .env.example .env      # 기본 LLM_PROVIDER=mock — API 키 불필요
+npm run db:up             # postgres(pgvector) + redis
+npm run db:migrate && npm run db:seed
+npm start                 # http://localhost:3100
+npm run smoke             # 다른 터미널 — 엔드투엔드 점검
+
+ + +
+
+

남은 작업

+

연동에 필요한 API 표면은 이미 고정되어 있다. 아래는 그 뒤에서 채워 넣는 것들이다.

+
    +
  • next JSON-LD 조립 — structuredDataHints → LocalBusiness / FAQPage / Service
    • +
  • next /llms.txt 서빙 — AI 검색 크롤러 진입점
    • +
  • later 업종 ltree 상위 노드 키워드 상속 (source: 'inherited')
    • +
  • later Redis 응답 캐시 — 서빙은 읽기 99%, TTL 1시간 + 발행 이벤트 무효화
    • +
  • later Search Console API 직접 연동 (지금은 /performance 수동 주입)
    • +
  • later 키워드 승인 · 차단 어드민 UI
    • +
+
+
+ +
+ o2o-site-ontology · 설계 문서 · 코드와 함께 docs/architecture.html 에 보관 +
+
diff --git a/drizzle/0000_init.sql b/drizzle/0000_init.sql new file mode 100644 index 0000000..3de6ede --- /dev/null +++ b/drizzle/0000_init.sql @@ -0,0 +1,124 @@ +-- o2o-site-ontology : initial schema +-- pgvector(유사도) + ltree(업종/지역 계층) + pg_trgm(표기 변형) 3-in-1 + +CREATE EXTENSION IF NOT EXISTS vector; +CREATE EXTENSION IF NOT EXISTS ltree; +CREATE EXTENSION IF NOT EXISTS pg_trgm; + +-- ---------------------------------------------------------------- 분류 계층 +CREATE TABLE IF NOT EXISTS industry ( + id text PRIMARY KEY, + path ltree NOT NULL UNIQUE, + name text NOT NULL, + created_at timestamptz NOT NULL DEFAULT now() +); +CREATE INDEX IF NOT EXISTS industry_path_gist ON industry USING gist (path); + +CREATE TABLE IF NOT EXISTS region ( + id text PRIMARY KEY, + path ltree NOT NULL UNIQUE, + name text NOT NULL, + created_at timestamptz NOT NULL DEFAULT now() +); +CREATE INDEX IF NOT EXISTS region_path_gist ON region USING gist (path); + +-- ---------------------------------------------------------------- 업체 +CREATE TABLE IF NOT EXISTS merchant ( + id uuid PRIMARY KEY DEFAULT gen_random_uuid(), + external_id text NOT NULL UNIQUE, -- o2o-site-AEO 의 사이트/업체 ID + name text NOT NULL, + industry_id text REFERENCES industry(id), + region_id text REFERENCES region(id), + description text NOT NULL DEFAULT '', + profile jsonb NOT NULL DEFAULT '{}'::jsonb, + site_url text, + last_generated_at timestamptz, + created_at timestamptz NOT NULL DEFAULT now(), + updated_at timestamptz NOT NULL DEFAULT now() +); +CREATE INDEX IF NOT EXISTS merchant_industry_idx ON merchant (industry_id); +CREATE INDEX IF NOT EXISTS merchant_stale_idx ON merchant (last_generated_at NULLS FIRST); + +-- ---------------------------------------------------------------- 전역 키워드 사전 +DO $$ BEGIN + CREATE TYPE keyword_intent AS ENUM + ('informational','navigational','transactional','local','brand'); +EXCEPTION WHEN duplicate_object THEN NULL; END $$; + +CREATE TABLE IF NOT EXISTS keyword ( + id uuid PRIMARY KEY DEFAULT gen_random_uuid(), + canonical text NOT NULL, -- 화면 노출용 대표 표기 + normalized text NOT NULL, -- 중복 판정용 정규화 표기 + locale text NOT NULL DEFAULT 'ko-KR', + aliases text[] NOT NULL DEFAULT '{}', -- 흡수된 표기 변형 (롱테일 확보) + intent keyword_intent NOT NULL DEFAULT 'informational', + industry_id text REFERENCES industry(id), + region_id text REFERENCES region(id), + embedding vector(1536), + usage_count integer NOT NULL DEFAULT 0, -- 몇 개 업체가 쓰는가 + created_at timestamptz NOT NULL DEFAULT now(), + updated_at timestamptz NOT NULL DEFAULT now(), + CONSTRAINT keyword_normalized_locale_uq UNIQUE (normalized, locale) +); +CREATE INDEX IF NOT EXISTS keyword_embedding_hnsw + ON keyword USING hnsw (embedding vector_cosine_ops); +CREATE INDEX IF NOT EXISTS keyword_normalized_trgm + ON keyword USING gin (normalized gin_trgm_ops); +CREATE INDEX IF NOT EXISTS keyword_industry_idx ON keyword (industry_id); + +-- ---------------------------------------------------------------- 업체 <-> 키워드 +DO $$ BEGIN + CREATE TYPE merchant_keyword_status AS ENUM + ('candidate','active','demoted','blocked'); +EXCEPTION WHEN duplicate_object THEN NULL; END $$; + +CREATE TABLE IF NOT EXISTS merchant_keyword ( + merchant_id uuid NOT NULL REFERENCES merchant(id) ON DELETE CASCADE, + keyword_id uuid NOT NULL REFERENCES keyword(id) ON DELETE CASCADE, + relevance real NOT NULL DEFAULT 0, + source text NOT NULL DEFAULT 'llm', -- llm | manual | inherited + status merchant_keyword_status NOT NULL DEFAULT 'candidate', + rationale text, + impressions bigint NOT NULL DEFAULT 0, + clicks bigint NOT NULL DEFAULT 0, + ctr real NOT NULL DEFAULT 0, + created_at timestamptz NOT NULL DEFAULT now(), + updated_at timestamptz NOT NULL DEFAULT now(), + PRIMARY KEY (merchant_id, keyword_id) +); +CREATE INDEX IF NOT EXISTS merchant_keyword_serving_idx + ON merchant_keyword (merchant_id, status, relevance DESC); + +-- ---------------------------------------------------------------- AEO 질문-답변 +CREATE TABLE IF NOT EXISTS qa_pair ( + id uuid PRIMARY KEY DEFAULT gen_random_uuid(), + merchant_id uuid NOT NULL REFERENCES merchant(id) ON DELETE CASCADE, + question text NOT NULL, + answer text NOT NULL, + normalized_question text NOT NULL, + embedding vector(1536), + status merchant_keyword_status NOT NULL DEFAULT 'active', + created_at timestamptz NOT NULL DEFAULT now(), + updated_at timestamptz NOT NULL DEFAULT now(), + CONSTRAINT qa_pair_merchant_question_uq UNIQUE (merchant_id, normalized_question) +); +CREATE INDEX IF NOT EXISTS qa_pair_merchant_idx ON qa_pair (merchant_id, status); + +-- ---------------------------------------------------------------- 생성 감사 로그 +CREATE TABLE IF NOT EXISTS generation_run ( + id uuid PRIMARY KEY DEFAULT gen_random_uuid(), + merchant_id uuid REFERENCES merchant(id) ON DELETE CASCADE, + provider text NOT NULL, + model text NOT NULL, + prompt_version text NOT NULL, + trigger text NOT NULL, -- published | scheduled | manual + status text NOT NULL DEFAULT 'running', -- running | succeeded | failed + input jsonb NOT NULL DEFAULT '{}'::jsonb, + output jsonb NOT NULL DEFAULT '{}'::jsonb, + stats jsonb NOT NULL DEFAULT '{}'::jsonb, + error text, + started_at timestamptz NOT NULL DEFAULT now(), + finished_at timestamptz +); +CREATE INDEX IF NOT EXISTS generation_run_merchant_idx + ON generation_run (merchant_id, started_at DESC); diff --git a/nest-cli.json b/nest-cli.json new file mode 100644 index 0000000..f9aa683 --- /dev/null +++ b/nest-cli.json @@ -0,0 +1,8 @@ +{ + "$schema": "https://json.schemastore.org/nest-cli", + "collection": "@nestjs/schematics", + "sourceRoot": "src", + "compilerOptions": { + "deleteOutDir": true + } +} diff --git a/package-lock.json b/package-lock.json new file mode 100644 index 0000000..efb3eb9 --- /dev/null +++ b/package-lock.json @@ -0,0 +1,5449 @@ +{ + "name": "o2o-site-ontology", + "version": "0.1.0", + 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"@nestjs/bullmq": "^11.0.2", + "@nestjs/common": "^11.0.12", + "@nestjs/core": "^11.0.12", + "@nestjs/platform-express": "^11.0.12", + "@nestjs/schedule": "^5.0.1", + "bullmq": "^5.44.0", + "class-transformer": "^0.5.1", + "class-validator": "^0.14.1", + "dotenv": "^16.4.7", + "openai": "^4.89.0", + "postgres": "^3.4.5", + "reflect-metadata": "^0.2.2", + "rxjs": "^7.8.2" + }, + "devDependencies": { + "@nestjs/cli": "^11.0.5", + "@nestjs/schematics": "^11.0.2", + "@types/express": "^5.0.1", + "@types/node": "^22.13.14", + "tsx": "^4.19.3", + "typescript": "^5.8.2" + } +} diff --git a/scripts/smoke.ts b/scripts/smoke.ts new file mode 100644 index 0000000..367afe7 --- /dev/null +++ b/scripts/smoke.ts @@ -0,0 +1,103 @@ +/** + * 로컬 엔드투엔드 점검 스크립트. + * npm run db:reset && npm start (다른 터미널) + * npm run smoke + */ +const BASE = process.env.BASE_URL ?? 'http://localhost:3100'; + +const j = async (method: string, path: string, body?: unknown) => { + const res = await fetch(`${BASE}${path}`, { + method, + headers: body ? { 'content-type': 'application/json' } : undefined, + body: body ? JSON.stringify(body) : undefined, + }); + const text = await res.text(); + if (!res.ok) throw new Error(`${method} ${path} → ${res.status} ${text}`); + return text ? JSON.parse(text) : null; +}; + +const h = (t: string) => console.log(`\n\x1b[1m${t}\x1b[0m`); + +async function main() { + h('0. health'); + console.log(' ', await j('GET', '/health')); + + h('1. site-1001 생성 (첫 업체 — 전부 신규)'); + const a = await j('POST', '/v1/merchants/site-1001/generate?sync=true'); + printStats(a); + + h('2. site-1002 생성 (같은 강남/미용실 — 중복제거 발동)'); + const b = await j('POST', '/v1/merchants/site-1002/generate?sync=true'); + printStats(b); + printDetails(b); + + h('3. publish 웹훅 + 표기 변형 (trigram 단계)'); + const c = await j('POST', '/v1/merchants/publish', { + externalId: 'site-1003', + name: '강남 뷰티랩', + industryId: 'beauty.hair', + regionId: 'kr.seoul.gangnam', + description: '강남 미용실. 염색 전문.', + profile: { services: ['뿌리염색약', '여성펌'], features: ['주차가능'] }, + sync: true, + }); + printStats(c.generation); + printDetails(c.generation); + + h('4. SEO payload'); + const seo = await j('GET', '/v1/sites/site-1001/seo?limit=8'); + console.log(' title :', seo.title); + console.log(' description:', seo.description); + console.log(' keywords :', seo.keywords.join(', ')); + + h('5. AEO payload'); + const aeo = await j('GET', '/v1/sites/site-1001/aeo?limit=3'); + for (const f of aeo.faqs) console.log(` Q. ${f.question}\n A. ${f.answer}`); + + h('6. 의미 기반 키워드 검색'); + const found = await j('POST', '/v1/keywords/search', { query: '강남 미용실 예약하고 싶어요', limit: 5 }); + for (const r of found) console.log(` ${r.score.toFixed(3)} ${r.canonical} (${r.intent}, ${r.usage_count}개 업체)`); + + h('7. 성과 피드백 → 저성과 강등'); + console.log( + ' ', + await j('POST', '/v1/sites/site-1001/performance', { + items: [ + { keyword: '강남 미용실 후기', impressions: 500, clicks: 0 }, + { keyword: '강남 미용실', impressions: 300, clicks: 40 }, + ], + }), + ); + + h('8. 비동기 큐 (BullMQ)'); + console.log(' ', await j('POST', '/v1/merchants/site-2001/generate')); + for (let i = 0; i < 30; i++) { + const s = await j('GET', '/v1/sites/site-2001/seo?limit=5'); + if (s.keywords.length) { + console.log(' 워커 처리 완료 →', s.keywords.join(', ')); + break; + } + await new Promise((r) => setTimeout(r, 500)); + } + + console.log('\n✅ smoke 완료'); +} + +function printStats(s: any) { + console.log( + ` 후보 ${s.candidates} → 신규 ${s.created} / 중복(정확 ${s.matchedExact}, 표기 ${s.matchedTrigram}, 의미 ${s.matchedVector})` + + ` / 차단 ${s.rejected} / 연결 ${s.linked} / QA ${s.qaCreated} (${s.durationMs}ms)`, + ); +} + +function printDetails(s: any) { + for (const d of s.details ?? []) { + const sim = d.similarity != null ? ` (sim=${d.similarity.toFixed(3)} → '${d.matchedTo}')` : ''; + console.log(` ${d.action.padEnd(16)} ${d.candidate}${sim}`); + } +} + +main().catch((e) => { + console.error('\n❌', e.message); + process.exit(1); +}); diff --git a/src/app.module.ts b/src/app.module.ts new file mode 100644 index 0000000..c48aa35 --- /dev/null +++ b/src/app.module.ts @@ -0,0 +1,29 @@ +import { BullModule } from '@nestjs/bullmq'; +import { Controller, Get, Module } from '@nestjs/common'; +import { ScheduleModule } from '@nestjs/schedule'; +import { env } from './config/env'; +import { DbModule } from './db/db.module'; +import { GenerationModule } from './generation/generation.module'; +import { MerchantsHttpModule } from './merchants/merchants.controller.module'; +import { ServingModule } from './serving/serving.module'; + +@Controller() +class HealthController { + @Get('health') + health() { + return { status: 'ok', llmProvider: env.llm.provider, ts: new Date().toISOString() }; + } +} + +@Module({ + imports: [ + DbModule, + ScheduleModule.forRoot(), + BullModule.forRoot({ connection: { host: env.redis.host, port: env.redis.port } }), + GenerationModule, + MerchantsHttpModule, + ServingModule, + ], + controllers: [HealthController], +}) +export class AppModule {} diff --git a/src/config/env.ts b/src/config/env.ts new file mode 100644 index 0000000..5a8f39b --- /dev/null +++ b/src/config/env.ts @@ -0,0 +1,30 @@ +import 'dotenv/config'; + +const num = (v: string | undefined, d: number) => (v === undefined || v === '' ? d : Number(v)); + +export const env = { + port: num(process.env.PORT, 3100), + databaseUrl: process.env.DATABASE_URL ?? 'postgres://ontology:ontology@localhost:55432/ontology', + redis: { + host: process.env.REDIS_HOST ?? 'localhost', + port: num(process.env.REDIS_PORT, 56379), + }, + llm: { + provider: (process.env.LLM_PROVIDER ?? 'mock') as 'mock' | 'openai', + apiKey: process.env.OPENAI_API_KEY ?? '', + model: process.env.OPENAI_MODEL ?? 'gpt-4.1-mini', + embeddingModel: process.env.OPENAI_EMBEDDING_MODEL ?? 'text-embedding-3-small', + }, + dedup: { + cosineThreshold: num(process.env.DEDUP_COSINE_THRESHOLD, 0.92), + trigramThreshold: num(process.env.DEDUP_TRIGRAM_THRESHOLD, 0.6), + candidateLimit: num(process.env.DEDUP_CANDIDATE_LIMIT, 20), + }, + generation: { + targetKeywords: num(process.env.GENERATION_TARGET_KEYWORDS, 15), + refreshIntervalDays: num(process.env.REFRESH_INTERVAL_DAYS, 30), + }, +} as const; + +export const EMBEDDING_DIM = 1536; +export const PROMPT_VERSION = 'kw-v1'; diff --git a/src/db/db.module.ts b/src/db/db.module.ts new file mode 100644 index 0000000..5e3018d --- /dev/null +++ b/src/db/db.module.ts @@ -0,0 +1,16 @@ +import { Global, Module, OnModuleDestroy } from '@nestjs/common'; +import { createSql, Sql } from './db'; + +export const PG = Symbol('PG'); + +@Global() +@Module({ + providers: [{ provide: PG, useFactory: () => createSql() }], + exports: [PG], +}) +export class DbModule implements OnModuleDestroy { + constructor() {} + async onModuleDestroy() {} +} + +export type { Sql }; diff --git a/src/db/db.ts b/src/db/db.ts new file mode 100644 index 0000000..e16275d --- /dev/null +++ b/src/db/db.ts @@ -0,0 +1,17 @@ +import postgres from 'postgres'; +import { env } from '../config/env'; + +export type Sql = postgres.Sql<{}>; + +export const createSql = (): Sql => + postgres(env.databaseUrl, { + max: 10, + // pgvector 컬럼은 텍스트로 주고받는다 ('[0.1,0.2,...]') + transform: { undefined: null }, + }); + +/** number[] -> pgvector 리터럴 */ +export const toVector = (v: number[]): string => `[${v.join(',')}]`; + +/** postgres.js 의 JSONValue 타입 제약 우회용 캐스트 */ +export const asJson = (v: unknown) => v as Parameters[0]; diff --git a/src/db/migrate.ts b/src/db/migrate.ts new file mode 100644 index 0000000..a699b21 --- /dev/null +++ b/src/db/migrate.ts @@ -0,0 +1,24 @@ +import { readFileSync, readdirSync } from 'node:fs'; +import { join } from 'node:path'; +import { createSql } from './db'; + +async function main() { + const sql = createSql(); + const dir = join(process.cwd(), 'drizzle'); + const files = readdirSync(dir).filter((f) => f.endsWith('.sql')).sort(); + + for (const file of files) { + const ddl = readFileSync(join(dir, file), 'utf8'); + process.stdout.write(`▶ applying ${file} ... `); + await sql.unsafe(ddl); + process.stdout.write('done\n'); + } + + await sql.end(); + console.log('✅ migration complete'); +} + +main().catch((e) => { + console.error('❌ migration failed:', e); + process.exit(1); +}); diff --git a/src/db/seed.ts b/src/db/seed.ts new file mode 100644 index 0000000..f15c350 --- /dev/null +++ b/src/db/seed.ts @@ -0,0 +1,92 @@ +import { asJson, createSql } from './db'; + +const industries = [ + ['beauty', 'beauty', '뷰티'], + ['beauty.hair', 'beauty.hair', '미용실'], + ['beauty.nail', 'beauty.nail', '네일샵'], + ['food', 'food', '음식점'], + ['food.korean', 'food.korean', '한식당'], + ['health', 'health', '의료'], + ['health.dental', 'health.dental', '치과'], +]; + +const regions = [ + ['kr', 'kr', '대한민국'], + ['kr.seoul', 'kr.seoul', '서울'], + ['kr.seoul.gangnam', 'kr.seoul.gangnam', '강남'], + ['kr.seoul.mapo', 'kr.seoul.mapo', '마포'], + ['kr.busan', 'kr.busan', '부산'], + ['kr.busan.haeundae', 'kr.busan.haeundae', '해운대'], +]; + +const merchants = [ + { + externalId: 'site-1001', + name: '레브살롱', + industryId: 'beauty.hair', + regionId: 'kr.seoul.gangnam', + description: '강남역 3번 출구 앞 프라이빗 헤어살롱. 1:1 디자이너 전담 시스템.', + siteUrl: 'https://rev-salon.example.com', + profile: { + services: ['남자 커트', '여성 펌', '뿌리염색', '두피 클리닉'], + features: ['주차 가능', '심야 영업', '예약제'], + priceRange: '30,000~120,000원', + }, + }, + { + externalId: 'site-1002', + name: '헤어랩 강남점', + industryId: 'beauty.hair', + regionId: 'kr.seoul.gangnam', + description: '강남 대형 헤어샵. 염색과 클리닉 전문.', + siteUrl: 'https://hairlab.example.com', + profile: { + services: ['뿌리 염색', '여성 펌', '두피클리닉'], + features: ['주차가능', '단체 예약'], + priceRange: '25,000~150,000원', + }, + }, + { + externalId: 'site-2001', + name: '해운대 소담한상', + industryId: 'food.korean', + regionId: 'kr.busan.haeundae', + description: '해운대 해변 인근 한정식집. 제철 해산물 코스 제공.', + siteUrl: 'https://sodam.example.com', + profile: { + services: ['한정식 코스', '점심 특선', '단체 예약'], + features: ['오션뷰', '룸 완비', '발렛파킹'], + priceRange: '25,000~80,000원', + }, + }, +]; + +async function main() { + const sql = createSql(); + + for (const [id, path, name] of industries) { + await sql`INSERT INTO industry (id, path, name) VALUES (${id}, ${path}::ltree, ${name}) + ON CONFLICT (id) DO UPDATE SET name = EXCLUDED.name`; + } + for (const [id, path, name] of regions) { + await sql`INSERT INTO region (id, path, name) VALUES (${id}, ${path}::ltree, ${name}) + ON CONFLICT (id) DO UPDATE SET name = EXCLUDED.name`; + } + for (const m of merchants) { + await sql` + INSERT INTO merchant (external_id, name, industry_id, region_id, description, profile, site_url) + VALUES (${m.externalId}, ${m.name}, ${m.industryId}, ${m.regionId}, + ${m.description}, ${sql.json(asJson(m.profile))}, ${m.siteUrl}) + ON CONFLICT (external_id) DO UPDATE SET + name = EXCLUDED.name, description = EXCLUDED.description, + profile = EXCLUDED.profile, updated_at = now()`; + } + + await sql.end(); + console.log(`✅ seed: industry=${industries.length} region=${regions.length} merchant=${merchants.length}`); +} + +main().catch((e) => { + console.error('❌ seed failed:', e); + process.exit(1); +}); diff --git a/src/generation/generation.module.ts b/src/generation/generation.module.ts new file mode 100644 index 0000000..7ff8dce --- /dev/null +++ b/src/generation/generation.module.ts @@ -0,0 +1,20 @@ +import { BullModule } from '@nestjs/bullmq'; +import { Module } from '@nestjs/common'; +import { KeywordsModule } from '../keywords/keywords.module'; +import { LlmModule } from '../llm/llm.module'; +import { MerchantsModule } from '../merchants/merchants.module'; +import { GenerationProcessor } from './generation.processor'; +import { GENERATION_QUEUE, GenerationQueue } from './generation.queue'; +import { GenerationService } from './generation.service'; + +@Module({ + imports: [ + BullModule.registerQueue({ name: GENERATION_QUEUE }), + LlmModule, + KeywordsModule, + MerchantsModule, + ], + providers: [GenerationService, GenerationQueue, GenerationProcessor], + exports: [GenerationService, GenerationQueue], +}) +export class GenerationModule {} diff --git a/src/generation/generation.processor.ts b/src/generation/generation.processor.ts new file mode 100644 index 0000000..10dba6a --- /dev/null +++ b/src/generation/generation.processor.ts @@ -0,0 +1,21 @@ +import { Processor, WorkerHost } from '@nestjs/bullmq'; +import { Logger } from '@nestjs/common'; +import { Job } from 'bullmq'; +import { GenerationService } from './generation.service'; +import { GENERATION_QUEUE, GenerationJob } from './generation.queue'; + +@Processor(GENERATION_QUEUE, { concurrency: 2 }) +export class GenerationProcessor extends WorkerHost { + private readonly logger = new Logger(GenerationProcessor.name); + + constructor(private readonly generation: GenerationService) { + super(); + } + + async process(job: Job) { + const { merchantId, trigger } = job.data; + this.logger.log(`processing ${job.id} (${trigger})`); + const stats = await this.generation.runForMerchant(merchantId, trigger); + return { ...stats, details: undefined }; + } +} diff --git a/src/generation/generation.queue.ts b/src/generation/generation.queue.ts new file mode 100644 index 0000000..bd90239 --- /dev/null +++ b/src/generation/generation.queue.ts @@ -0,0 +1,50 @@ +import { InjectQueue } from '@nestjs/bullmq'; +import { Injectable, Logger } from '@nestjs/common'; +import { Cron, CronExpression } from '@nestjs/schedule'; +import { Queue } from 'bullmq'; +import { env } from '../config/env'; +import { MerchantsService } from '../merchants/merchants.service'; +import { GenerationTrigger } from './generation.service'; + +export const GENERATION_QUEUE = 'keyword-generation'; + +export interface GenerationJob { + merchantId: string; + trigger: GenerationTrigger; +} + +@Injectable() +export class GenerationQueue { + private readonly logger = new Logger(GenerationQueue.name); + + constructor( + @InjectQueue(GENERATION_QUEUE) private readonly queue: Queue, + private readonly merchants: MerchantsService, + ) {} + + async enqueue(merchantId: string, trigger: GenerationTrigger): Promise { + // 짧은 시간 내 같은 업체가 여러 번 발행돼도 한 번만 처리 (60초 dedupe 창) + const job = await this.queue.add( + 'generate', + { merchantId, trigger }, + { + deduplication: { id: `${merchantId}-${trigger}`, ttl: 60_000 }, + removeOnComplete: 100, + removeOnFail: 500, + attempts: 3, + backoff: { type: 'exponential', delay: 5_000 }, + }, + ); + return String(job.id); + } + + /** 주기 리프레시: 매일 03:00, N일 지난 업체를 큐에 적재 */ + @Cron(CronExpression.EVERY_DAY_AT_3AM) + async scheduleRefresh() { + const stale = await this.merchants.findStale(env.generation.refreshIntervalDays, 200); + for (const m of stale) { + await this.enqueue(m.id, 'scheduled'); + } + if (stale.length) this.logger.log(`scheduled refresh queued: ${stale.length} merchants`); + } +} diff --git a/src/generation/generation.service.ts b/src/generation/generation.service.ts new file mode 100644 index 0000000..840a054 --- /dev/null +++ b/src/generation/generation.service.ts @@ -0,0 +1,213 @@ +import { Inject, Injectable, Logger } from '@nestjs/common'; +import { PG } from '../db/db.module'; +import { asJson, Sql, toVector } from '../db/db'; +import { env, PROMPT_VERSION } from '../config/env'; +import { DedupAction, DedupService } from '../keywords/dedup.service'; +import { canonicalizeKeyword, normalizeKeyword } from '../keywords/normalize'; +import { LlmProvider, MerchantContext } from '../llm/types'; +import { MerchantsService } from '../merchants/merchants.service'; + +export type GenerationTrigger = 'published' | 'scheduled' | 'manual'; + +export interface GenerationStats { + runId: string; + merchantId: string; + merchantName: string; + provider: string; + model: string; + candidates: number; + created: number; + matchedExact: number; + matchedTrigram: number; + matchedVector: number; + rejected: number; + linked: number; + qaCreated: number; + durationMs: number; + details: Array<{ candidate: string; action: DedupAction; matchedTo?: string; similarity?: number }>; +} + +@Injectable() +export class GenerationService { + private readonly logger = new Logger(GenerationService.name); + + constructor( + @Inject(PG) private readonly sql: Sql, + private readonly merchants: MerchantsService, + private readonly llm: LlmProvider, + private readonly dedup: DedupService, + ) {} + + async runForMerchant( + idOrExternalId: string, + trigger: GenerationTrigger = 'manual', + ): Promise { + const startedAt = Date.now(); + const merchant = await this.merchants.findWithTaxonomy(idOrExternalId); + + const runRows = await this.sql>` + INSERT INTO generation_run (merchant_id, provider, model, prompt_version, trigger, status, input) + VALUES (${merchant.id}, ${this.llm.name}, ${this.llm.model}, ${PROMPT_VERSION}, + ${trigger}, 'running', ${this.sql.json(asJson({ externalId: merchant.external_id }))}) + RETURNING id`; + const runId = runRows[0].id; + + try { + const existing = await this.existingKeywordsFor(merchant.id, merchant.industry_id); + const ctx: MerchantContext = { + externalId: merchant.external_id, + name: merchant.name, + description: merchant.description, + industryName: merchant.industry_name, + industryPath: merchant.industry_path, + regionName: merchant.region_name, + regionPath: merchant.region_path, + profile: merchant.profile ?? {}, + existingKeywords: existing, + targetCount: env.generation.targetKeywords, + }; + + const output = await this.llm.generate(ctx); + + // 임베딩은 한 번에 배치 호출 (후보 수만큼 왕복하지 않는다) + const texts = output.keywords.map((k) => canonicalizeKeyword(k.keyword)); + const embeddings = texts.length ? await this.llm.embed(texts) : []; + + const stats: GenerationStats = { + runId, + merchantId: merchant.id, + merchantName: merchant.name, + provider: this.llm.name, + model: output.model, + candidates: output.keywords.length, + created: 0, + matchedExact: 0, + matchedTrigram: 0, + matchedVector: 0, + rejected: 0, + linked: 0, + qaCreated: 0, + durationMs: 0, + details: [], + }; + + for (let i = 0; i < output.keywords.length; i++) { + const cand = output.keywords[i]; + const result = await this.dedup.resolve({ + raw: cand.keyword, + intent: cand.intent, + embedding: embeddings[i], + locale: 'ko-KR', + industryId: merchant.industry_id, + regionId: merchant.region_id, + }); + + stats.details.push({ + candidate: canonicalizeKeyword(cand.keyword), + action: result.action, + matchedTo: result.matchedTo, + similarity: result.similarity, + }); + + switch (result.action) { + case 'created': stats.created++; break; + case 'matched_exact': stats.matchedExact++; break; + case 'matched_trigram': stats.matchedTrigram++; break; + case 'matched_vector': stats.matchedVector++; break; + case 'rejected_banned': stats.rejected++; break; + } + + if (result.keywordId) { + const linked = await this.linkKeyword(merchant.id, result.keywordId, cand.relevance, cand.rationale); + if (linked) stats.linked++; + } + } + + stats.qaCreated = await this.upsertQaPairs(merchant.id, output.qaPairs); + await this.merchants.markGenerated(merchant.id); + stats.durationMs = Date.now() - startedAt; + + await this.sql` + UPDATE generation_run + SET status = 'succeeded', + output = ${this.sql.json(asJson(output))}, + stats = ${this.sql.json(asJson({ ...stats, details: undefined }))}, + finished_at = now() + WHERE id = ${runId}`; + + this.logger.log( + `[${merchant.name}] cand=${stats.candidates} new=${stats.created} ` + + `dup(exact/trg/vec)=${stats.matchedExact}/${stats.matchedTrigram}/${stats.matchedVector} ` + + `rejected=${stats.rejected} qa=${stats.qaCreated} ${stats.durationMs}ms`, + ); + return stats; + } catch (err) { + const message = err instanceof Error ? err.message : String(err); + await this.sql` + UPDATE generation_run + SET status = 'failed', error = ${message}, finished_at = now() + WHERE id = ${runId}`; + throw err; + } + } + + /** 프롬프트에 넣을 "이미 보유한 키워드": 자기 것 + 같은 업종에서 많이 쓰는 것 */ + private async existingKeywordsFor(merchantId: string, industryId: string | null): Promise { + const rows = await this.sql>` + SELECT DISTINCT k.canonical + FROM keyword k + LEFT JOIN merchant_keyword mk ON mk.keyword_id = k.id AND mk.merchant_id = ${merchantId} + WHERE mk.merchant_id IS NOT NULL + OR (${industryId}::text IS NOT NULL AND k.industry_id = ${industryId} AND k.usage_count > 0) + ORDER BY k.canonical + LIMIT 100`; + return rows.map((r) => r.canonical); + } + + private async linkKeyword( + merchantId: string, + keywordId: string, + relevance: number, + rationale: string, + ): Promise { + const status = relevance >= 0.5 ? 'active' : 'candidate'; + const rows = await this.sql>` + INSERT INTO merchant_keyword (merchant_id, keyword_id, relevance, source, status, rationale) + VALUES (${merchantId}, ${keywordId}, ${relevance}, 'llm', ${status}, ${rationale}) + ON CONFLICT (merchant_id, keyword_id) DO UPDATE SET + relevance = GREATEST(merchant_keyword.relevance, EXCLUDED.relevance), + rationale = COALESCE(EXCLUDED.rationale, merchant_keyword.rationale), + updated_at = now() + RETURNING (xmax = 0) AS inserted`; + + if (rows[0]?.inserted) { + await this.sql`UPDATE keyword SET usage_count = usage_count + 1 WHERE id = ${keywordId}`; + return true; + } + return false; + } + + private async upsertQaPairs( + merchantId: string, + pairs: Array<{ question: string; answer: string }>, + ): Promise { + if (pairs.length === 0) return 0; + const embeddings = await this.llm.embed(pairs.map((p) => p.question)); + let created = 0; + + for (let i = 0; i < pairs.length; i++) { + const p = pairs[i]; + const nq = normalizeKeyword(p.question); + if (!nq) continue; + const rows = await this.sql>` + INSERT INTO qa_pair (merchant_id, question, answer, normalized_question, embedding) + VALUES (${merchantId}, ${canonicalizeKeyword(p.question)}, ${p.answer.trim()}, + ${nq}, ${toVector(embeddings[i])}::vector) + ON CONFLICT (merchant_id, normalized_question) DO UPDATE SET + answer = EXCLUDED.answer, updated_at = now() + RETURNING (xmax = 0) AS inserted`; + if (rows[0]?.inserted) created++; + } + return created; + } +} diff --git a/src/keywords/dedup.service.ts b/src/keywords/dedup.service.ts new file mode 100644 index 0000000..b6bc8ee --- /dev/null +++ b/src/keywords/dedup.service.ts @@ -0,0 +1,107 @@ +import { Injectable, Logger } from '@nestjs/common'; +import { env } from '../config/env'; +import { KeywordIntent } from '../llm/types'; +import { KeywordRepository } from './keyword.repository'; +import { canonicalizeKeyword, isBanned, normalizeKeyword } from './normalize'; + +export type DedupAction = + | 'created' // 새 키워드 + | 'matched_exact' // 1단계: 정규화 해시 일치 + | 'matched_trigram' // 2단계: 표기 변형/오타 + | 'matched_vector' // 3단계: 의미 중복 → alias 흡수 + | 'rejected_banned'; // 금칙어 + +export interface DedupResult { + action: DedupAction; + keywordId: string | null; + canonical: string; + matchedTo?: string; + similarity?: number; +} + +export interface ResolveInput { + raw: string; + intent: KeywordIntent; + embedding: number[]; + locale: string; + industryId: string | null; + regionId: string | null; +} + +/** + * 4단계 계단식 중복제거. + * 값비싼 벡터 비교는 마지막에, 후보 집합 안에서만 수행한다. + */ +@Injectable() +export class DedupService { + private readonly logger = new Logger(DedupService.name); + + constructor(private readonly repo: KeywordRepository) {} + + async resolve(input: ResolveInput): Promise { + const canonical = canonicalizeKeyword(input.raw); + const normalized = normalizeKeyword(input.raw); + + // 0단계 — 금칙어/과장광고 차단 + if (!normalized || isBanned(canonical)) { + return { action: 'rejected_banned', keywordId: null, canonical }; + } + + // 1단계 — 정규화 완전 일치 (공백/구두점 차이 흡수) + const exact = await this.repo.findByNormalized(normalized, input.locale); + if (exact) { + await this.repo.absorbAlias(exact.id, canonical); + return { + action: 'matched_exact', + keywordId: exact.id, + canonical: exact.canonical, + matchedTo: exact.canonical, + similarity: 1, + }; + } + + // 2~3단계 — trigram 후보 + 벡터 ANN 후보를 모아 최고 유사도 판정 + const candidates = await this.repo.findDedupCandidates( + input.embedding, + normalized, + input.locale, + env.dedup.candidateLimit, + ); + + const trigramHit = candidates.find((c) => c.trg >= env.dedup.trigramThreshold); + if (trigramHit) { + await this.repo.absorbAlias(trigramHit.id, canonical); + return { + action: 'matched_trigram', + keywordId: trigramHit.id, + canonical: trigramHit.canonical, + matchedTo: trigramHit.canonical, + similarity: trigramHit.trg, + }; + } + + const best = candidates[0]; + if (best && best.cosine >= env.dedup.cosineThreshold) { + await this.repo.absorbAlias(best.id, canonical); + return { + action: 'matched_vector', + keywordId: best.id, + canonical: best.canonical, + matchedTo: best.canonical, + similarity: best.cosine, + }; + } + + // 4단계 — 신규 등록 + const created = await this.repo.insert({ + canonical, + normalized, + locale: input.locale, + intent: input.intent, + embedding: input.embedding, + industryId: input.industryId, + regionId: input.regionId, + }); + return { action: 'created', keywordId: created.id, canonical: created.canonical }; + } +} diff --git a/src/keywords/keyword.repository.ts b/src/keywords/keyword.repository.ts new file mode 100644 index 0000000..56a5104 --- /dev/null +++ b/src/keywords/keyword.repository.ts @@ -0,0 +1,124 @@ +import { Inject, Injectable } from '@nestjs/common'; +import { PG } from '../db/db.module'; +import { Sql, toVector } from '../db/db'; +import { KeywordIntent } from '../llm/types'; + +export interface KeywordRow { + id: string; + canonical: string; + normalized: string; + aliases: string[]; + intent: KeywordIntent; + usage_count: number; +} + +export interface CandidateRow { + id: string; + canonical: string; + normalized: string; + cosine: number; + trg: number; +} + +@Injectable() +export class KeywordRepository { + constructor(@Inject(PG) private readonly sql: Sql) {} + + async findByNormalized(normalized: string, locale: string): Promise { + const rows = await this.sql` + SELECT id, canonical, normalized, aliases, intent, usage_count + FROM keyword + WHERE normalized = ${normalized} AND locale = ${locale} + LIMIT 1`; + return rows[0] ?? null; + } + + /** + * 중복 후보 수집: trigram 인덱스 히트 + 벡터 ANN 상위 N 을 합집합으로 가져온다. + * 벡터 비교는 이 후보 집합 안에서만 하므로 전수 비교가 일어나지 않는다. + */ + async findDedupCandidates( + embedding: number[], + normalized: string, + locale: string, + limit: number, + ): Promise { + const vec = toVector(embedding); + const rows = await this.sql` + ( + SELECT id, canonical, normalized, + 1 - (embedding <=> ${vec}::vector) AS cosine, + similarity(normalized, ${normalized}) AS trg + FROM keyword + WHERE locale = ${locale} + AND embedding IS NOT NULL + AND normalized % ${normalized} + ORDER BY trg DESC + LIMIT ${limit} + ) + UNION ALL + ( + SELECT id, canonical, normalized, + 1 - (embedding <=> ${vec}::vector) AS cosine, + 0::real AS trg + FROM keyword + WHERE locale = ${locale} + AND embedding IS NOT NULL + ORDER BY embedding <=> ${vec}::vector + LIMIT ${limit} + )`; + + const best = new Map(); + for (const r of rows) { + const prev = best.get(r.id); + if (!prev || r.trg > prev.trg) best.set(r.id, { ...r, cosine: Number(r.cosine), trg: Number(r.trg) }); + } + return [...best.values()].sort((a, b) => b.cosine - a.cosine); + } + + async insert(input: { + canonical: string; + normalized: string; + locale: string; + intent: KeywordIntent; + embedding: number[]; + industryId: string | null; + regionId: string | null; + }): Promise { + const rows = await this.sql` + INSERT INTO keyword (canonical, normalized, locale, intent, embedding, industry_id, region_id, usage_count) + VALUES (${input.canonical}, ${input.normalized}, ${input.locale}, ${input.intent}, + ${toVector(input.embedding)}::vector, ${input.industryId}, ${input.regionId}, 0) + ON CONFLICT (normalized, locale) DO UPDATE SET updated_at = now() + RETURNING id, canonical, normalized, aliases, intent, usage_count`; + return rows[0]; + } + + /** 표기 변형을 기존 키워드에 흡수 (롱테일 검색어 보존) */ + async absorbAlias(keywordId: string, alias: string): Promise { + await this.sql` + UPDATE keyword + SET aliases = ( + SELECT ARRAY(SELECT DISTINCT unnest(aliases || ARRAY[${alias}]::text[])) + ), + updated_at = now() + WHERE id = ${keywordId} + AND NOT (${alias} = ANY(aliases)) + AND canonical <> ${alias}`; + } + + async bumpUsage(keywordId: string): Promise { + await this.sql` + UPDATE keyword SET usage_count = usage_count + 1, updated_at = now() WHERE id = ${keywordId}`; + } + + async searchByVector(embedding: number[], locale: string, limit: number) { + const vec = toVector(embedding); + return this.sql>` + SELECT id, canonical, intent, usage_count, 1 - (embedding <=> ${vec}::vector) AS score + FROM keyword + WHERE locale = ${locale} AND embedding IS NOT NULL + ORDER BY embedding <=> ${vec}::vector + LIMIT ${limit}`; + } +} diff --git a/src/keywords/keywords.module.ts b/src/keywords/keywords.module.ts new file mode 100644 index 0000000..7962c0e --- /dev/null +++ b/src/keywords/keywords.module.ts @@ -0,0 +1,9 @@ +import { Module } from '@nestjs/common'; +import { DedupService } from './dedup.service'; +import { KeywordRepository } from './keyword.repository'; + +@Module({ + providers: [KeywordRepository, DedupService], + exports: [KeywordRepository, DedupService], +}) +export class KeywordsModule {} diff --git a/src/keywords/normalize.ts b/src/keywords/normalize.ts new file mode 100644 index 0000000..c6ad2fe --- /dev/null +++ b/src/keywords/normalize.ts @@ -0,0 +1,33 @@ +/** + * 중복 판정용 정규화. + * NFKC → 소문자 → 제로폭 문자 제거 → 구두점 제거 → 공백 전부 제거. + * "강남 미용실" 과 "강남미용실" 을 같은 키로 취급하기 위해 공백을 없앤다. + */ +const ZERO_WIDTH = /[\u200B-\u200D\uFEFF]/g; +const PUNCT = /[!-\/:-@\[-`{-~·ㆍ、。「-』]/g; + +export function normalizeKeyword(raw: string): string { + return raw + .normalize('NFKC') + .toLowerCase() + .replace(ZERO_WIDTH, '') + .replace(PUNCT, '') + .replace(/\s+/g, ''); +} + +/** 표시용 정리: 앞뒤/중복 공백만 정리하고 원문 표기는 보존 */ +export function canonicalizeKeyword(raw: string): string { + return raw.normalize('NFKC').replace(ZERO_WIDTH, '').replace(/\s+/g, ' ').trim(); +} + +/** 과장광고·금칙 표현 필터 (광고심의 리스크 차단) */ +const BANNED = [ + '최고', '1위', '일등', '넘버원', 'no.1', '100%', '무조건', '완치', '부작용없', + '영구', '평생보장', '유일한', '최저가보장', '전국최대', +]; +const BANNED_NORMALIZED = BANNED.map(normalizeKeyword); + +export function isBanned(text: string): boolean { + const n = normalizeKeyword(text); + return BANNED_NORMALIZED.some((b) => b.length > 0 && n.includes(b)); +} diff --git a/src/llm/llm.module.ts b/src/llm/llm.module.ts new file mode 100644 index 0000000..a8daea4 --- /dev/null +++ b/src/llm/llm.module.ts @@ -0,0 +1,16 @@ +import { Module } from '@nestjs/common'; +import { env } from '../config/env'; +import { MockLlmProvider } from './mock.provider'; +import { OpenAiLlmProvider } from './openai.provider'; +import { LlmProvider } from './types'; + +@Module({ + providers: [ + { + provide: LlmProvider, + useClass: env.llm.provider === 'openai' ? OpenAiLlmProvider : MockLlmProvider, + }, + ], + exports: [LlmProvider], +}) +export class LlmModule {} diff --git a/src/llm/mock.provider.ts b/src/llm/mock.provider.ts new file mode 100644 index 0000000..0bb88df --- /dev/null +++ b/src/llm/mock.provider.ts @@ -0,0 +1,115 @@ +import { Injectable } from '@nestjs/common'; +import { EMBEDDING_DIM } from '../config/env'; +import { normalizeKeyword } from '../keywords/normalize'; +import { + GenerationOutput, + KeywordCandidate, + KeywordIntent, + LlmProvider, + MerchantContext, + QaCandidate, +} from './types'; + +/** + * API 키 없이 로컬에서 전체 파이프라인(생성 → 중복제거 → 서빙)을 돌리기 위한 대체 구현. + * + * embed(): 문자 bigram 해싱 + L2 정규화. + * 랜덤이 아니라 "비슷한 문자열이면 비슷한 벡터"가 나오므로 + * 코사인 임계값 기반 중복제거 동작을 실제와 유사하게 검증할 수 있다. + */ +@Injectable() +export class MockLlmProvider extends LlmProvider { + readonly name = 'mock'; + readonly model = 'mock-keyword-v1'; + + async embed(texts: string[]): Promise { + return texts.map((t) => hashEmbedding(t)); + } + + async generate(ctx: MerchantContext): Promise { + const region = ctx.regionName ?? ''; + const industry = ctx.industryName ?? '업체'; + const services = toStringArray(ctx.profile['services']); + const features = toStringArray(ctx.profile['features']); + + const modifiers = ['추천', '예약', '가격', '후기', '잘하는곳', '근처']; + const raw: Array<[string, KeywordIntent, number]> = []; + + raw.push([`${region} ${industry}`.trim(), 'local', 0.95]); + raw.push([ctx.name, 'brand', 0.99]); + for (const m of modifiers) { + raw.push([`${region} ${industry} ${m}`.trim(), m === '예약' ? 'transactional' : 'local', 0.8]); + } + for (const s of services) { + raw.push([`${region} ${s}`.trim(), 'local', 0.85]); + raw.push([`${s} 가격`, 'transactional', 0.7]); + raw.push([`${s} 잘하는 곳`, 'informational', 0.65]); + } + for (const f of features) { + raw.push([`${industry} ${f}`.trim(), 'informational', 0.6]); + } + // 표기 변형을 일부러 섞는다 — 중복제거 단계가 실제로 흡수하는지 확인용 + raw.push([`${region}${industry}추천`, 'local', 0.5]); + raw.push([`${region} ${industry} 추천`, 'local', 0.5]); + + const seen = new Set(); + const keywords: KeywordCandidate[] = []; + for (const [keyword, intent, relevance] of raw) { + const k = keyword.replace(/\s+/g, ' ').trim(); + if (!k) continue; + if (seen.has(k)) continue; + seen.add(k); + keywords.push({ keyword: k, intent, relevance, rationale: `mock: ${intent}` }); + if (keywords.length >= ctx.targetCount + 4) break; + } + + const qaPairs: QaCandidate[] = [ + { + question: `${ctx.name}은(는) 어디에 있나요?`, + answer: `${ctx.name}은(는) ${region || '해당 지역'}에 위치한 ${industry}입니다.`, + }, + { + question: `${ctx.name} 예약은 어떻게 하나요?`, + answer: `${ctx.name}은(는) 사이트 예약 페이지 또는 전화로 예약할 수 있습니다.`, + }, + { + question: `${ctx.name}의 주요 서비스는 무엇인가요?`, + answer: services.length + ? `주요 서비스는 ${services.join(', ')} 입니다.` + : `${industry} 관련 서비스를 제공합니다.`, + }, + ]; + + return { keywords, qaPairs, model: this.model, provider: this.name }; + } +} + +function toStringArray(v: unknown): string[] { + return Array.isArray(v) ? v.filter((x): x is string => typeof x === 'string') : []; +} + +/** 문자 bigram 해싱 임베딩 (결정적, L2 정규화) */ +export function hashEmbedding(text: string, dim = EMBEDDING_DIM): number[] { + const s = ` ${normalizeKeyword(text)} `; + const vec = new Float64Array(dim); + for (let i = 0; i < s.length - 1; i++) { + const gram = s.slice(i, i + 2); + const h = fnv1a(gram); + vec[h % dim] += 1; + // 부호 해싱으로 충돌 편향 완화 + vec[(h >>> 8) % dim] += h & 1 ? 1 : -1; + } + let norm = 0; + for (let i = 0; i < dim; i++) norm += vec[i] * vec[i]; + norm = Math.sqrt(norm) || 1; + return Array.from(vec, (x) => x / norm); +} + +function fnv1a(str: string): number { + let h = 0x811c9dc5; + for (let i = 0; i < str.length; i++) { + h ^= str.charCodeAt(i); + h = Math.imul(h, 0x01000193) >>> 0; + } + return h >>> 0; +} diff --git a/src/llm/openai.provider.ts b/src/llm/openai.provider.ts new file mode 100644 index 0000000..ba35213 --- /dev/null +++ b/src/llm/openai.provider.ts @@ -0,0 +1,122 @@ +import { Injectable, Logger } from '@nestjs/common'; +import OpenAI from 'openai'; +import { env } from '../config/env'; +import { GenerationOutput, LlmProvider, MerchantContext } from './types'; + +/** Structured Outputs 로 강제하는 응답 스키마 — 자유 텍스트 파싱 금지 */ +const RESPONSE_SCHEMA = { + type: 'object', + additionalProperties: false, + required: ['keywords', 'qa_pairs'], + properties: { + keywords: { + type: 'array', + items: { + type: 'object', + additionalProperties: false, + required: ['keyword', 'intent', 'relevance', 'rationale'], + properties: { + keyword: { type: 'string' }, + intent: { + type: 'string', + enum: ['informational', 'navigational', 'transactional', 'local', 'brand'], + }, + relevance: { type: 'number' }, + rationale: { type: 'string' }, + }, + }, + }, + qa_pairs: { + type: 'array', + items: { + type: 'object', + additionalProperties: false, + required: ['question', 'answer'], + properties: { + question: { type: 'string' }, + answer: { type: 'string' }, + }, + }, + }, + }, +} as const; + +@Injectable() +export class OpenAiLlmProvider extends LlmProvider { + readonly name = 'openai'; + readonly model = env.llm.model; + private readonly logger = new Logger(OpenAiLlmProvider.name); + private readonly client = new OpenAI({ apiKey: env.llm.apiKey }); + + async embed(texts: string[]): Promise { + if (texts.length === 0) return []; + const res = await this.client.embeddings.create({ + model: env.llm.embeddingModel, + input: texts, + }); + return res.data.map((d) => d.embedding as number[]); + } + + async generate(ctx: MerchantContext): Promise { + const res = await this.client.chat.completions.create({ + model: this.model, + temperature: 0.7, + messages: [ + { role: 'system', content: SYSTEM_PROMPT }, + { role: 'user', content: buildUserPrompt(ctx) }, + ], + response_format: { + type: 'json_schema', + json_schema: { name: 'seo_aeo_keywords', strict: true, schema: RESPONSE_SCHEMA as any }, + }, + }); + + const content = res.choices[0]?.message?.content ?? '{}'; + const parsed = JSON.parse(content) as { + keywords?: GenerationOutput['keywords']; + qa_pairs?: GenerationOutput['qaPairs']; + }; + + return { + keywords: parsed.keywords ?? [], + qaPairs: parsed.qa_pairs ?? [], + model: this.model, + provider: this.name, + usage: { + prompt_tokens: res.usage?.prompt_tokens ?? 0, + completion_tokens: res.usage?.completion_tokens ?? 0, + total_tokens: res.usage?.total_tokens ?? 0, + }, + }; + } +} + +const SYSTEM_PROMPT = `당신은 한국 로컬 비즈니스 SEO/AEO 전문가입니다. +주어진 업체 정보를 바탕으로 검색 유입에 실제로 도움이 되는 키워드와, +답변엔진(AI 검색)이 인용하기 좋은 질문-답변 쌍을 생성합니다. + +규칙: +- 실제 사용자가 검색창에 입력할 법한 자연스러운 한국어 표현만 사용합니다. +- 지역명 + 업종 + 의도어 조합을 적극 활용합니다. +- 과장광고 표현(최고, 1위, 100%, 무조건, 완치 등)은 절대 사용하지 않습니다. +- 제공된 "이미 보유한 키워드"와 의미가 겹치는 것은 생성하지 않습니다. +- relevance 는 해당 업체와의 관련도를 0.0~1.0 으로 매깁니다. +- 답변(answer)은 2~3문장, 업체 정보에 근거한 사실만 씁니다.`; + +function buildUserPrompt(ctx: MerchantContext): string { + return [ + `# 업체 정보`, + `- 상호: ${ctx.name}`, + `- 업종: ${ctx.industryName ?? '미상'} (${ctx.industryPath ?? '-'})`, + `- 지역: ${ctx.regionName ?? '미상'} (${ctx.regionPath ?? '-'})`, + `- 소개: ${ctx.description || '없음'}`, + `- 상세: ${JSON.stringify(ctx.profile, null, 2)}`, + ``, + `# 이미 보유한 키워드 (이것들과 겹치지 않는 새 후보만 생성)`, + ctx.existingKeywords.length ? ctx.existingKeywords.map((k) => `- ${k}`).join('\n') : '- (없음)', + ``, + `# 요청`, + `- 키워드 ${ctx.targetCount}개`, + `- 질문-답변 쌍 5개`, + ].join('\n'); +} diff --git a/src/llm/types.ts b/src/llm/types.ts new file mode 100644 index 0000000..3f00f81 --- /dev/null +++ b/src/llm/types.ts @@ -0,0 +1,47 @@ +export type KeywordIntent = + | 'informational' + | 'navigational' + | 'transactional' + | 'local' + | 'brand'; + +export interface MerchantContext { + externalId: string; + name: string; + description: string; + industryName?: string | null; + industryPath?: string | null; + regionName?: string | null; + regionPath?: string | null; + profile: Record; + /** 이미 보유한 키워드 — 프롬프트에 넣어 중복 후보 생성 자체를 줄인다 */ + existingKeywords: string[]; + targetCount: number; +} + +export interface KeywordCandidate { + keyword: string; + intent: KeywordIntent; + relevance: number; // 0..1 + rationale: string; +} + +export interface QaCandidate { + question: string; + answer: string; +} + +export interface GenerationOutput { + keywords: KeywordCandidate[]; + qaPairs: QaCandidate[]; + model: string; + provider: string; + usage?: Record; +} + +export abstract class LlmProvider { + abstract readonly name: string; + abstract readonly model: string; + abstract generate(ctx: MerchantContext): Promise; + abstract embed(texts: string[]): Promise; +} diff --git a/src/main.ts b/src/main.ts new file mode 100644 index 0000000..909189e --- /dev/null +++ b/src/main.ts @@ -0,0 +1,17 @@ +import 'reflect-metadata'; +import { Logger, ValidationPipe } from '@nestjs/common'; +import { NestFactory } from '@nestjs/core'; +import { AppModule } from './app.module'; +import { env } from './config/env'; + +async function bootstrap() { + const app = await NestFactory.create(AppModule); + app.useGlobalPipes(new ValidationPipe({ transform: true, whitelist: true })); + app.enableCors(); + await app.listen(env.port); + new Logger('bootstrap').log( + `o2o-site-ontology listening on http://localhost:${env.port} (llm=${env.llm.provider})`, + ); +} + +bootstrap(); diff --git a/src/merchants/merchants.controller.module.ts b/src/merchants/merchants.controller.module.ts new file mode 100644 index 0000000..85f5fbe --- /dev/null +++ b/src/merchants/merchants.controller.module.ts @@ -0,0 +1,10 @@ +import { Module } from '@nestjs/common'; +import { GenerationModule } from '../generation/generation.module'; +import { MerchantsController } from './merchants.controller'; +import { MerchantsModule } from './merchants.module'; + +@Module({ + imports: [MerchantsModule, GenerationModule], + controllers: [MerchantsController], +}) +export class MerchantsHttpModule {} diff --git a/src/merchants/merchants.controller.ts b/src/merchants/merchants.controller.ts new file mode 100644 index 0000000..8f96950 --- /dev/null +++ b/src/merchants/merchants.controller.ts @@ -0,0 +1,48 @@ +import { Body, Controller, Get, Param, Post, Query } from '@nestjs/common'; +import { GenerationService } from '../generation/generation.service'; +import { GenerationQueue } from '../generation/generation.queue'; +import { MerchantsService, UpsertMerchantDto } from './merchants.service'; + +@Controller('v1/merchants') +export class MerchantsController { + constructor( + private readonly merchants: MerchantsService, + private readonly generation: GenerationService, + private readonly queue: GenerationQueue, + ) {} + + @Get() + list() { + return this.merchants.list(); + } + + @Get(':id') + get(@Param('id') id: string) { + return this.merchants.findWithTaxonomy(id); + } + + /** o2o-site-AEO 사이트 발행 웹훅: 업체 등록 + 키워드 생성 예약 */ + @Post('publish') + async publish(@Body() dto: UpsertMerchantDto & { generate?: boolean; sync?: boolean }) { + const merchant = await this.merchants.upsert(dto); + if (dto.generate === false) return { merchant, generation: 'skipped' }; + + if (dto.sync) { + const stats = await this.generation.runForMerchant(merchant.id, 'published'); + return { merchant, generation: stats }; + } + const jobId = await this.queue.enqueue(merchant.id, 'published'); + return { merchant, generation: { queued: true, jobId } }; + } + + /** 수동 재생성 */ + @Post(':id/generate') + async generate(@Param('id') id: string, @Query('sync') sync?: string) { + if (sync === 'true' || sync === '1') { + return this.generation.runForMerchant(id, 'manual'); + } + const m = await this.merchants.findWithTaxonomy(id); + const jobId = await this.queue.enqueue(m.id, 'manual'); + return { queued: true, jobId }; + } +} diff --git a/src/merchants/merchants.module.ts b/src/merchants/merchants.module.ts new file mode 100644 index 0000000..17bc9bc --- /dev/null +++ b/src/merchants/merchants.module.ts @@ -0,0 +1,8 @@ +import { Module } from '@nestjs/common'; +import { MerchantsService } from './merchants.service'; + +@Module({ + providers: [MerchantsService], + exports: [MerchantsService], +}) +export class MerchantsModule {} diff --git a/src/merchants/merchants.service.ts b/src/merchants/merchants.service.ts new file mode 100644 index 0000000..1ca386f --- /dev/null +++ b/src/merchants/merchants.service.ts @@ -0,0 +1,92 @@ +import { Inject, Injectable, NotFoundException } from '@nestjs/common'; +import { PG } from '../db/db.module'; +import { asJson, Sql } from '../db/db'; + +export interface MerchantRow { + id: string; + external_id: string; + name: string; + industry_id: string | null; + region_id: string | null; + description: string; + profile: Record; + site_url: string | null; + last_generated_at: Date | null; +} + +export interface MerchantWithTaxonomy extends MerchantRow { + industry_name: string | null; + industry_path: string | null; + region_name: string | null; + region_path: string | null; +} + +export interface UpsertMerchantDto { + externalId: string; + name: string; + industryId?: string | null; + regionId?: string | null; + description?: string; + profile?: Record; + siteUrl?: string | null; +} + +@Injectable() +export class MerchantsService { + constructor(@Inject(PG) private readonly sql: Sql) {} + + /** o2o-site-AEO 가 사이트를 발행할 때 호출하는 진입점 */ + async upsert(dto: UpsertMerchantDto): Promise { + const rows = await this.sql` + INSERT INTO merchant (external_id, name, industry_id, region_id, description, profile, site_url) + VALUES (${dto.externalId}, ${dto.name}, ${dto.industryId ?? null}, ${dto.regionId ?? null}, + ${dto.description ?? ''}, ${this.sql.json(asJson(dto.profile ?? {}))}, ${dto.siteUrl ?? null}) + ON CONFLICT (external_id) DO UPDATE SET + name = EXCLUDED.name, + industry_id = EXCLUDED.industry_id, + region_id = EXCLUDED.region_id, + description = EXCLUDED.description, + profile = EXCLUDED.profile, + site_url = EXCLUDED.site_url, + updated_at = now() + RETURNING *`; + return rows[0]; + } + + async findWithTaxonomy(idOrExternalId: string): Promise { + const rows = await this.sql` + SELECT m.*, + i.name AS industry_name, i.path::text AS industry_path, + r.name AS region_name, r.path::text AS region_path + FROM merchant m + LEFT JOIN industry i ON i.id = m.industry_id + LEFT JOIN region r ON r.id = m.region_id + WHERE m.external_id = ${idOrExternalId} + OR (${isUuid(idOrExternalId)} AND m.id::text = ${idOrExternalId}) + LIMIT 1`; + if (!rows[0]) throw new NotFoundException(`merchant not found: ${idOrExternalId}`); + return rows[0]; + } + + async list(limit = 50) { + return this.sql` + SELECT * FROM merchant ORDER BY created_at DESC LIMIT ${limit}`; + } + + /** 주기 리프레시 대상: 한 번도 생성 안 됐거나 N일 지난 업체 */ + async findStale(intervalDays: number, limit: number) { + return this.sql>` + SELECT id, external_id FROM merchant + WHERE last_generated_at IS NULL + OR last_generated_at < now() - (${intervalDays} || ' days')::interval + ORDER BY last_generated_at NULLS FIRST + LIMIT ${limit}`; + } + + async markGenerated(merchantId: string) { + await this.sql`UPDATE merchant SET last_generated_at = now() WHERE id = ${merchantId}`; + } +} + +const UUID_RE = /^[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}$/i; +const isUuid = (v: string) => UUID_RE.test(v); diff --git a/src/serving/serving.controller.ts b/src/serving/serving.controller.ts new file mode 100644 index 0000000..533f748 --- /dev/null +++ b/src/serving/serving.controller.ts @@ -0,0 +1,39 @@ +import { Body, Controller, Get, Param, Post, Query } from '@nestjs/common'; +import { ServingService } from './serving.service'; + +@Controller('v1') +export class ServingController { + constructor(private readonly serving: ServingService) {} + + /** 발행된 사이트가 렌더링 시 호출 — SEO 메타 */ + @Get('sites/:id/seo') + seo(@Param('id') id: string, @Query('limit') limit?: string) { + return this.serving.seo(id, clamp(limit, 20, 50)); + } + + /** 발행된 사이트가 렌더링 시 호출 — AEO(답변엔진) 소스 */ + @Get('sites/:id/aeo') + aeo(@Param('id') id: string, @Query('limit') limit?: string) { + return this.serving.aeo(id, clamp(limit, 10, 30)); + } + + /** 어드민: 의미 기반 키워드 검색 */ + @Post('keywords/search') + search(@Body() body: { query: string; limit?: number }) { + return this.serving.searchKeywords(body.query, Math.min(body.limit ?? 10, 50)); + } + + /** 성과 피드백 주입 (Search Console / 유입 로그) */ + @Post('sites/:id/performance') + performance( + @Param('id') id: string, + @Body() body: { items: Array<{ keyword: string; impressions: number; clicks: number }> }, + ) { + return this.serving.applyPerformance(id, body.items ?? []); + } +} + +function clamp(v: string | undefined, def: number, max: number): number { + const n = v ? Number(v) : def; + return Number.isFinite(n) ? Math.min(Math.max(1, n), max) : def; +} diff --git a/src/serving/serving.module.ts b/src/serving/serving.module.ts new file mode 100644 index 0000000..3d5eb89 --- /dev/null +++ b/src/serving/serving.module.ts @@ -0,0 +1,13 @@ +import { Module } from '@nestjs/common'; +import { KeywordsModule } from '../keywords/keywords.module'; +import { LlmModule } from '../llm/llm.module'; +import { MerchantsModule } from '../merchants/merchants.module'; +import { ServingController } from './serving.controller'; +import { ServingService } from './serving.service'; + +@Module({ + imports: [MerchantsModule, KeywordsModule, LlmModule], + controllers: [ServingController], + providers: [ServingService], +}) +export class ServingModule {} diff --git a/src/serving/serving.service.ts b/src/serving/serving.service.ts new file mode 100644 index 0000000..cedba96 --- /dev/null +++ b/src/serving/serving.service.ts @@ -0,0 +1,144 @@ +import { Inject, Injectable } from '@nestjs/common'; +import { PG } from '../db/db.module'; +import { Sql } from '../db/db'; +import { KeywordRepository } from '../keywords/keyword.repository'; +import { LlmProvider } from '../llm/types'; +import { MerchantsService } from '../merchants/merchants.service'; + +export interface SeoPayload { + merchant: { id: string; externalId: string; name: string; siteUrl: string | null }; + title: string; + description: string; + keywords: string[]; + tags: Array<{ keyword: string; intent: string; relevance: number; aliases: string[] }>; + generatedAt: string | null; +} + +export interface AeoPayload { + merchant: { id: string; externalId: string; name: string }; + topics: string[]; + faqs: Array<{ question: string; answer: string }>; + /** JSON-LD 는 후속 단계에서 이 payload 를 그대로 매핑해 생성한다 */ + structuredDataHints: { + type: 'LocalBusiness'; + name: string; + description: string; + areaServed: string | null; + category: string | null; + }; +} + +@Injectable() +export class ServingService { + constructor( + @Inject(PG) private readonly sql: Sql, + private readonly merchants: MerchantsService, + private readonly keywords: KeywordRepository, + private readonly llm: LlmProvider, + ) {} + + async seo(idOrExternalId: string, limit: number): Promise { + const m = await this.merchants.findWithTaxonomy(idOrExternalId); + const rows = await this.sql< + Array<{ canonical: string; intent: string; relevance: number; aliases: string[] }> + >` + SELECT k.canonical, k.intent, mk.relevance, k.aliases + FROM merchant_keyword mk + JOIN keyword k ON k.id = mk.keyword_id + WHERE mk.merchant_id = ${m.id} AND mk.status = 'active' + ORDER BY mk.relevance DESC, k.usage_count ASC + LIMIT ${limit}`; + + const kws = rows.map((r) => r.canonical); + const locality = [m.region_name, m.industry_name].filter(Boolean).join(' '); + + return { + merchant: { id: m.id, externalId: m.external_id, name: m.name, siteUrl: m.site_url }, + title: locality ? `${m.name} | ${locality}` : m.name, + description: buildDescription(m.name, m.description, kws), + keywords: kws, + tags: rows.map((r) => ({ + keyword: r.canonical, + intent: r.intent, + relevance: Number(r.relevance), + aliases: r.aliases ?? [], + })), + generatedAt: m.last_generated_at ? new Date(m.last_generated_at).toISOString() : null, + }; + } + + async aeo(idOrExternalId: string, limit: number): Promise { + const m = await this.merchants.findWithTaxonomy(idOrExternalId); + const faqs = await this.sql>` + SELECT question, answer FROM qa_pair + WHERE merchant_id = ${m.id} AND status = 'active' + ORDER BY created_at ASC + LIMIT ${limit}`; + const topics = await this.sql>` + SELECT k.canonical FROM merchant_keyword mk + JOIN keyword k ON k.id = mk.keyword_id + WHERE mk.merchant_id = ${m.id} AND mk.status = 'active' + AND k.intent IN ('informational', 'local') + ORDER BY mk.relevance DESC LIMIT ${limit}`; + + return { + merchant: { id: m.id, externalId: m.external_id, name: m.name }, + topics: topics.map((t) => t.canonical), + faqs, + structuredDataHints: { + type: 'LocalBusiness', + name: m.name, + description: m.description, + areaServed: m.region_name, + category: m.industry_name, + }, + }; + } + + async searchKeywords(query: string, limit: number) { + const [embedding] = await this.llm.embed([query]); + return this.keywords.searchByVector(embedding, 'ko-KR', limit); + } + + /** Search Console / 유입 로그 피드백 → 저성과 키워드 강등 */ + async applyPerformance( + idOrExternalId: string, + items: Array<{ keyword: string; impressions: number; clicks: number }>, + ) { + const m = await this.merchants.findWithTaxonomy(idOrExternalId); + let updated = 0; + for (const it of items) { + const rows = await this.sql>` + UPDATE merchant_keyword mk + SET impressions = mk.impressions + ${it.impressions}, + clicks = mk.clicks + ${it.clicks}, + ctr = CASE WHEN (mk.impressions + ${it.impressions}) > 0 + THEN (mk.clicks + ${it.clicks})::real / (mk.impressions + ${it.impressions}) + ELSE 0 END, + updated_at = now() + FROM keyword k + WHERE k.id = mk.keyword_id + AND mk.merchant_id = ${m.id} + AND (k.canonical = ${it.keyword} OR ${it.keyword} = ANY(k.aliases)) + RETURNING mk.keyword_id`; + updated += rows.length; + } + + // 노출은 충분한데 클릭이 없는 키워드는 강등 → 다음 생성 사이클에서 대체 + const demoted = await this.sql>` + UPDATE merchant_keyword + SET status = 'demoted', updated_at = now() + WHERE merchant_id = ${m.id} AND status = 'active' + AND impressions >= 100 AND ctr < 0.002 + RETURNING keyword_id`; + + return { matched: updated, demoted: demoted.length }; + } +} + +function buildDescription(name: string, desc: string, keywords: string[]): string { + const base = desc?.trim() || `${name} 안내`; + const tail = keywords.slice(0, 3).join(', '); + const full = tail ? `${base} ${tail} 정보를 확인하세요.` : base; + return full.length > 155 ? `${full.slice(0, 152)}...` : full; +} diff --git a/tsconfig.json b/tsconfig.json new file mode 100644 index 0000000..09dc08e --- /dev/null +++ b/tsconfig.json @@ -0,0 +1,26 @@ +{ + "compilerOptions": { + "module": "commonjs", + "target": "ES2022", + "lib": ["ES2022"], + "moduleResolution": "node", + "declaration": false, + "removeComments": true, + "emitDecoratorMetadata": true, + "experimentalDecorators": true, + "allowSyntheticDefaultImports": true, + "esModuleInterop": true, + "sourceMap": true, + "outDir": "./dist", + "baseUrl": "./", + "incremental": true, + "skipLibCheck": true, + "strict": true, + "strictNullChecks": true, + "noImplicitAny": true, + "forceConsistentCasingInFileNames": true, + "resolveJsonModule": true + }, + "include": ["src/**/*", "scripts/**/*"], + "exclude": ["node_modules", "dist"] +}