From 0342290a898dcfc7c03089e7988036d82daecc27 Mon Sep 17 00:00:00 2001 From: chan000518 Date: Sat, 4 Oct 2025 11:32:50 +0900 Subject: [PATCH 01/14] =?UTF-8?q?=E2=9C=A8=20LLM=20=EB=AA=A8=EB=93=88?= =?UTF-8?q?=ED=99=94=20=EB=B0=8F=20Gemini=20=ED=86=B5=ED=95=A9?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- TASK.md | 211 +++++++++++++++++++++++--- package.json | 1 + src/config.ts | 5 +- src/controllers/ai.controller.ts | 4 +- src/llm/index.ts | 28 ++++ src/llm/model-registry.ts | 12 ++ src/llm/providers/gemini.ts | 75 +++++++++ src/llm/providers/openai-responses.ts | 158 +++++++++++++++++++ src/llm/types.ts | 33 ++++ src/services/qa.service.ts | 75 ++++----- src/types/ai.types.ts | 13 ++ 11 files changed, 555 insertions(+), 60 deletions(-) create mode 100644 src/llm/index.ts create mode 100644 src/llm/model-registry.ts create mode 100644 src/llm/providers/gemini.ts create mode 100644 src/llm/providers/openai-responses.ts create mode 100644 src/llm/types.ts diff --git a/TASK.md b/TASK.md index 803edc7..3bb0eca 100644 --- a/TASK.md +++ b/TASK.md @@ -1,23 +1,188 @@ -## 리팩토링 계획 - -### Phase 1: Repository 계층 도입 (데이터 로직 분리) - -1. **[Repo] `src/repositories` 디렉토리 생성**: 데이터베이스 쿼리 로직을 모아둘 디렉토리를 생성합니다. -2. **[Repo] `post.repository.ts` 생성 및 이전**: - * `blog_post`, `post_chunks`, `post_title_embeddings` 테이블 관련 쿼리를 이 파일로 옮깁니다. - * `qa.service.ts`의 `findPostById`, `findSimilarChunks` 로직을 이전합니다. - * `embedding.service.ts`의 `storeTitleEmbedding`, `storeContentEmbeddings` 로직을 이전합니다. -3. **[Repo] `persona.repository.ts` 생성 및 이전**: - * `persona` 테이블 관련 쿼리를 이 파일로 옮깁니다. - * `qa.service.ts`의 `getSpeechTonePrompt` 내부 DB 조회 로직을 `findPersonaById`와 같은 함수로 분리하여 이전합니다. -4. **[Service] 서비스 계층 수정**: - * `qa.service.ts`와 `embedding.service.ts`가 DB에 직접 접근하는 대신, 새로 만든 Repository의 함수를 호출하도록 코드를 수정합니다. - -### Phase 2: 프롬프트 관리 분리 - -5. **[Prompt] `src/prompts` 디렉토리 생성**: 프롬프트 템플릿을 관리할 디렉토리를 생성합니다. -6. **[Prompt] `qa.prompts.ts` 파일 생성**: - * `qa.service.ts`에 하드코딩된 시스템 프롬프트와 사용자 메시지 생성 로직을 이 파일로 옮깁니다. - * `createRagPrompt`, `createPostContextPrompt`와 같이 동적으로 프롬프트를 생성하는 함수를 만듭니다. -7. **[Service] `qa.service.ts` 수정**: - * `qa.prompts.ts`에서 프롬프트 생성 함수를 가져와(import) 사용하도록 수정합니다. \ No newline at end of file +## 작업 계획 + +작업 순서 +- 1) LLM 모듈화(퍼사드/프로바이더/모델 레지스트리) + GPT-5 mini 기본 적용 +- 2) Gemini 도입: 병행 사용(전용) + 대체 사용(퍼사드) +- 3) 토큰 카운트 및 비용 로깅 추가(양 프로바이더 공통) + +참고: 아래 문서의 섹션 순서와 무관하게 실제 구현 순서는 위의 "작업 순서"를 따릅니다. + + +### 1) LLM 모듈화(퍼사드/프로바이더/모델 레지스트리) + GPT-5 mini 기본 적용 + +목적: LLM 호출을 모듈화하여 옵션 기반으로 모델/프로바이더를 교체 가능하게 만들고, 기본 모델을 `gpt-5-mini`로 전환합니다. 토크나이저/가격표는 3단계에서 처리합니다. + +1. [구조] 파일/모듈 구성 + - 디렉토리: `src/llm/` + - `src/llm/types.ts` — 공통 인터페이스 정의 + - `GenerateRequest`: `{ provider?: 'openai'|'gemini', model?: string, messages?: OpenAIStyleMessages, contents?: GeminiStyleContents, stream?: boolean, tools?, options?: { temperature?, top_p?, max_output_tokens?, reasoning?, text? }, meta?: { userId?, categoryId?, postId? } }` + - `GenerateStream`: `onToken(text)`, `onToolCall(json)`, `onEnd()`, `onError(err)`(또는 AsyncIterable) + - `src/llm/modelRegistry.ts` — 모델 레지스트리/기본값 + - 논리 모델 키 → `{ provider, modelId, defaults, tokenizerKey?, pricingKey? }` + - 기본값: `defaultChat = { provider: 'openai', modelId: 'gpt-5-mini' }` + - `src/llm/providers/openaiResponses.ts` — OpenAI Responses API 구현 + - `src/llm/providers/gemini.ts` — @google/gemini 구현 + - `src/llm/index.ts` — 퍼사드: `generate(req: GenerateRequest): GenerateStream` 선택 라우팅 + - 기존 서비스(`qa.service.ts`)는 퍼사드만 사용하도록 변경 + +2. [기본 모델] GPT-5 mini 적용(Responses API) + - `src/config.ts`의 `CHAT_MODEL` 기본값을 `gpt-5-mini`로 변경 + - OpenAI 경로: `openai.responses.create/stream`로 마이그레이션(SSE 어댑터 포함) + - 기존 Chat Completions 경로는 임시 백업/옵션으로 유지 가능(필요 시) + +3. [옵션 기반 모델/프로바이더 선택] + - 요청 바디에 `llm?: { provider?: 'openai'|'gemini', model?: string, options?: {...} }` 허용 + - 미지정 시 레지스트리의 기본값 사용(`gpt-5-mini` on OpenAI) + - 향후 기능(Reasoning/Text 옵션, tool calls, timeout 등) 확장 용이 + +4. [검증/수용 기준] + - `/ai/ask` SSE 정상 동작(중단/지연 없음) + - 기존 프롬프트/툴 호출이 동일하게 동작(필요 시 어댑터) + - 로그/오류 처리 기존 수준 유지 + +### 2) Gemini 도입: 병행 사용(전용) + 대체 사용(퍼사드) + +목적: Gemini를 독립 엔드포인트로 직접 쓰는 경로와, 기존 GPT 경로의 대체 제공자로 모두 사용할 수 있게 합니다(퍼사드 경유). 이후 3단계에서 토큰/비용 로깅을 공통 적용합니다. + +1. [Config] Gemini 키/모델 설정 + - `.env` + - `GEMINI_API_KEY=...` + - `GEMINI_CHAT_MODEL=gemini-2.5-flash` (예: 변경 가능) + - `src/config.ts`에 항목 반영 및 기본값/검증 추가(Provider 고정 ENV는 사용하지 않음) + +2. [Provider] 퍼사드에 Gemini 구현 추가 + - 1단계에서 만든 LLM 퍼사드(`src/llm/index.ts`)에 Gemini 프로바이더를 추가 + - 구현 위치: `src/llm/providers/gemini.ts` (OpenAI 구현은 `src/llm/providers/openaiResponses.ts`) + - 퍼사드 인터페이스로 라우팅되어 기존 `qa.service.ts`는 퍼사드만 사용(교체 투명) + +3. [Gemini 호출] @google/genai SDK 적용 및 스트리밍 + - 의존성: `@google/genai` 추가 (설치 커맨드: `npm i @google/genai`) + - 클라이언트: `import { GoogleGenAI } from "@google/genai"; const ai = new GoogleGenAI({});` (`GEMINI_API_KEY`는 환경변수에서 자동 주입) + - 비스트리밍(우선 적용): + - `ai.models.generateContent({ model: GEMINI_CHAT_MODEL, contents, config: { thinkingConfig: { thinkingBudget }}})` + - 기본값으로 `thinkingBudget=0`(생각 비활성화) 적용, `.env`에서 오버라이드 가능 + - 응답 텍스트를 한번에 수신한 뒤 SSE로 순차 chunk 분할하여 `answer` 이벤트로 전송(간단 구현) + - 스트리밍(선택 적용): + - SDK 제공 시 스트리밍 API 사용(예: `generateContentStream` 유사 기능)으로 델타를 받아 즉시 SSE로 전달 + - SDK에서 미지원일 경우, 비스트리밍으로 우선 릴리즈 후 스트리밍 전환 + - (옵션) Safety 설정, generationConfig(temperature/topP/maxOutputTokens) 파라미터는 설정값으로 노출 + +5. [토큰 카운팅] Gemini 대응 + - 사전 카운트(가능 시): SDK의 토큰 카운트 API(`tokens:count`/`countTokens`)가 제공되면 이를 사용해 프롬프트 토큰 계산 → 비용 선로깅 + - 네트워크 요청이므로 로깅 토글이 켜져 있을 때만 수행하도록 옵션화 + - 사후 카운트: 응답 텍스트 기준 동일 API로 출력 토큰 계산(또는 비가용 시 근사치) + - 폴백 전략: 카운트 API가 불가한 환경에서는 근사치 사용(문자수/4), 추후 정확도 개선 시 교체 + +6. [가격 정책] Gemini 추가 + - `src/config/pricing.ts`의 `PRICING_TABLE`에 Gemini 모델(`gemini-2.5-flash`, 임베딩 모델 등) 단가 추가 + - 동일한 `calcCost`, `formatCost` 로직 재사용 + +7. [생각(Thinking) 설정] 기본 비활성화 + - Gemini 2.5 Flash의 생각 기능은 응답 품질 대신 비용/지연이 증가하므로 기본 `thinkingBudget=0`으로 비활성화 + - `.env`에 `GEMINI_THINKING_BUDGET`를 두어 필요 시 활성화(정수값) + +8. [도구/함수 호출] 호환성 계획(선택) + - 현재 OpenAI `tool_calls`를 사용 중. Gemini는 `functionDeclarations`/`toolConfig` 형태로 유사 기능 제공 + - 1단계: Gemini 경로에서는 도구 호출 비활성화(빠른 도입) + - 2단계: 필요 시 `report_content_insufficient`를 Gemini `functionDeclarations`로 매핑하여 동일 동작 구현 + +9. [Wiring] 사용 패턴 + - 독립 사용(A): `POST /ai/gemini/ask`로 직접 호출(옵션: thinkingBudget 등) + - 대체 사용(B): 기존 `POST /ai/ask`에 `llm.provider?: 'openai'|'gemini'`, `llm.model?` 허용 → 퍼사드가 라우팅 + - 로깅 시 `provider` 필드를 포함(3단계에서 적용) + +10. [검증/수용 기준] + - OpenAI/Gemini 각각에서 동일한 SSE 응답 형식으로 동작 + - 요청 전/후 토큰·비용 로그가 두 프로바이더 모두에서 출력 + - 로깅 토글이 정상 작동, 스트리밍 성능 저하 없음 + +설정 확정 +- `GEMINI_CHAT_MODEL=gemini-2.5-flash` +- `GEMINI_THINKING_BUDGET=0` (기본값으로 비활성화) + +### 토큰 카운트 및 비용 로깅 추가 + +목적: LLM에 요청을 보내기 직전에 프롬프트(메시지) 토큰 수를 계산해 예상 입력 비용을 콘솔로 로깅하고, 스트리밍 응답 완료 후 실제 출력 토큰 수 기반 최종 비용을 추가 로깅합니다. 초기에는 `console.log`만 사용합니다. + +1. [Utils] 토크나이저 유틸 추가 + - 파일: `src/utils/tokenizer.ts` + - 내용: + - `getTokenizerForModel(model: string)` → 모델명에 따라 적절한 인코딩을 선택 + - `gpt-5*` → 자료 제공 전까지 임시로 `o200k_base` 사용(TBD, 전환 시 교체) + - `gpt-4o`, `gpt-4o-mini`, 기타 `o`계열 → `o200k_base` + - `countTextTokens(text: string, model: string): number` + - `countChatMessagesTokens(messages: OpenAI.Chat.Completions.ChatCompletionMessageParam[], model: string): number` + - 메시지 `content`들을 토크나이즈하여 합산하고, 채팅 포맷 오버헤드(메시지당 소량, 모델별 상수)를 보정치로 가산 + - 주의: 보정치는 근사치이며, 정확한 정산은 응답 토큰 합산으로 후처리 + - 비고: 이미 프로젝트에 `@dqbd/tiktoken`이 포함되어 있으므로 이를 사용합니다. + +2. [Config] 가격 정책 맵 구조 설계 (임시 하드코딩 + ENV 오버라이드) + - 파일: `src/config/pricing.ts` + - 내용: + - `export type Pricing = { input_per_1k: number; output_per_1k: number; cached_input_per_1k?: number; currency: 'USD' | 'KRW' }` + - `PRICING_TABLE: Record`: 모델명 키에 따른 단가 설정 + - 선택: `LLM_PRICING_OVERRIDES`(JSON) 환경변수로 런타임 오버라이드 허용 + - 초기값은 사용자 제공 정책으로 채울 예정. 제공 전까지는 로깅에 `N/A` 표기 또는 0 처리. + + - 초기 PRICING_TABLE(제공 정책 반영, 단위: per 1K tokens, 통화: USD) + - `gpt-5`: { input_per_1k: 0.00125, cached_input_per_1k: 0.000125, output_per_1k: 0.01, currency: 'USD' } + - `gpt-5-mini`: { input_per_1k: 0.00025, cached_input_per_1k: 0.000025, output_per_1k: 0.002, currency: 'USD' } + - `gpt-5-nano`: { input_per_1k: 0.00005, cached_input_per_1k: 0.000005, output_per_1k: 0.0004, currency: 'USD' } + +3. [Utils] 비용 계산 유틸 추가 + - 파일: `src/utils/cost.ts` + - 내용: + - `getModelPricing(model: string): Pricing | null` + - `calcCost(tokens: number, per_1k: number): number` → 반올림 1~4자리(옵션) + - 화폐 표기 함수(선택): `formatCost(amount: number, currency: string)` + +4. [Facade] LLM 퍼사드에 비용 로깅 통합 + - 위치: `src/llm/index.ts` 퍼사드 내부에서 공통 로깅 수행 + - 기능 흐름(공통): + 1) 요청 전: 메시지/콘텐츠 토큰 카운트 → `promptTokens` + - OpenAI: `countChatMessagesTokens`(토크나이저) + - Gemini: `countTokens` API 가능 시 사용(불가 시 근사치) + 2) 단가 조회: `getModelPricing(model)` → `estInputCost` + 3) 선로깅: `{type:'llm.request', provider, model, promptTokens, estInputCost, corrId, userId, categoryId, postId}` + 4) 실제 호출: 등록된 프로바이더(OpenAI Responses 또는 Gemini)로 위임, 스트림은 그대로 중계 + 5) 스트림 종료 후: 출력 텍스트/함수인자 토큰 합산 → `completionTokens` + 6) 비용 계산: 입력/출력(+cached 입력이 있으면 분리) → `totalCost` + 7) 후로깅: `{type:'llm.response', provider, model, promptTokens, completionTokens, inputCost, outputCost, totalCost, durationMs, corrId, cachedInputTokens}` + - 주의: 기존 SSE 흐름(이벤트명/포맷) 불변 유지. 퍼사드는 원본 델타를 그대로 전달. + - 상관관계 ID(`corrId`)는 `uuid` 생성(또는 요청별 식별자 전달 시 사용). + +5. [Wiring] `qa.service.ts`에서 퍼사드 사용 + - 기존 직접 호출부를 LLM 퍼사드로 교체(`generate(req)`) + - 요청 바디의 `llm` 옵션을 퍼사드에 그대로 전달(provider/model/options) + - 출력 토큰 카운트/비용 로깅은 퍼사드 내부에서 처리 + +6. [옵션] 임베딩 호출 비용 로깅(확장) + - 파일: `src/services/embedding.service.ts` + - `createEmbeddings` 호출 직전 `input` 텍스트 전체 토큰 수 계산(`countTextTokens` 누적) → 입력 비용 로깅 + - 임베딩 모델 단가(`text-embedding-3-*`)도 `PRICING_TABLE`에 포함 + +7. [환경변수] 로깅 토글 및 라운딩 + - `.env` 키 추가(기본값은 off) + - `LLM_COST_LOG=true|false` (기본: true로 해도 무방) + - `LLM_COST_ROUND=2` (소수점 자리수, 선택) + - 로깅은 토글 꺼져 있으면 수행하지 않음 + +8. [로그 포맷] 예시(JSON 라인) + - 요청 전: `{ "type": "llm.request", "corrId": "...", "provider": "openai", "model": "gpt-5-mini", "promptTokens": 1234, "estInputCost": 0.00031, "userId": "...", "categoryId": 1, "postId": 42 }` + - 응답 후: `{ "type": "llm.response", "corrId": "...", "provider": "openai", "model": "gpt-5-mini", "promptTokens": 1234, "completionTokens": 456, "inputCost": 0.00031, "outputCost": 0.00091, "totalCost": 0.00122, "durationMs": 987, "cachedInputTokens": 0 }` + +9. [검증/수용 기준] + - `POST /ai/ask` 호출 시 콘솔에 요청 전/후 로그 각각 1회 출력 + - 모델/프롬프트/토큰 수/예상 비용/총 비용/시간(ms)이 포함되어야 함 + - 로깅 on/off 토글 동작, 라운딩 반영 확인 + - 기존 SSE 동작(끊김/지연) 변화 없음 + +10. [주의/한계] + - 채팅 포맷 오버헤드는 모델별로 상이하며 근사치 사용. 최종 비용은 출력 토큰 카운트까지 반영해 오차 최소화 + - 스트리밍 API는 서버에서 사용량 메타를 즉시 제공하지 않으므로(비스트리밍과 달리), 응답 텍스트 기반 자체 카운트 수행 + - 함수 호출(tool_calls) 토큰은 인자 길이에 비례하여 증가. 누적 텍스트/인자 기반으로 동일하게 카운트 + - Cached Input 과금: 제공 API에서 캐시 히트 토큰 정보를 명시적으로 제공하는 경우에만 `cachedInputTokens`로 분리 산정. 그렇지 않으면 일반 입력으로 계산(보수적) + +11. [다음 단계(선택)] + - `console.log` → 구조화 로거(Pino/Winston)로 교체, 샘플링·보존 기간 설정 + - DB 또는 시계열(예: ClickHouse/Prometheus) 적재로 사용자별 비용 대시보드 구성 diff --git a/package.json b/package.json index 712d202..2911813 100644 --- a/package.json +++ b/package.json @@ -24,6 +24,7 @@ }, "dependencies": { "@dqbd/tiktoken": "^1.0.13", + "@google/genai": "^0.2.0", "cors": "^2.8.5", "dotenv": "^16.4.5", "express": "^4.19.2", diff --git a/src/config.ts b/src/config.ts index 18993cc..1be73a1 100644 --- a/src/config.ts +++ b/src/config.ts @@ -12,7 +12,10 @@ const configSchema = z.object({ TOKEN_AUDIENCE: z.string().default('bubblog'), ALGORITHM: z.string().default('HS256'), EMBED_MODEL: z.string().default('text-embedding-3-small'), - CHAT_MODEL: z.string().default('gpt-4o'), + CHAT_MODEL: z.string().default('gpt-5-mini'), + GEMINI_API_KEY: z.string().optional(), + GEMINI_CHAT_MODEL: z.string().default('gemini-2.5-flash'), + GEMINI_THINKING_BUDGET: z.string().optional(), }); const config = configSchema.parse(process.env); diff --git a/src/controllers/ai.controller.ts b/src/controllers/ai.controller.ts index e6ab029..1b9a593 100644 --- a/src/controllers/ai.controller.ts +++ b/src/controllers/ai.controller.ts @@ -49,13 +49,13 @@ export const askHandler = async ( next: NextFunction ) => { try { - const { question, user_id, category_id, speech_tone, post_id } = req.body; + const { question, user_id, category_id, speech_tone, post_id, llm } = req.body as any; res.setHeader('Content-Type', 'text/event-stream'); res.setHeader('Cache-Control', 'no-cache'); res.setHeader('Connection', 'keep-alive'); - const stream = await answerStream(question, user_id, category_id, speech_tone, post_id); + const stream = await answerStream(question, user_id, category_id, speech_tone, post_id, llm); stream.pipe(res); } catch (error) { diff --git a/src/llm/index.ts b/src/llm/index.ts new file mode 100644 index 0000000..9c39531 --- /dev/null +++ b/src/llm/index.ts @@ -0,0 +1,28 @@ +import { PassThrough } from 'stream'; +import { GenerateRequest } from './types'; +import { getDefaultChat } from './model-registry'; +import { generateOpenAIStream } from './providers/openai-responses'; +import { generateGeminiStream } from './providers/gemini'; + +export const generate = async (req: GenerateRequest): Promise => { + const merged = { ...req }; + if (!merged.provider || !merged.model) { + const def = getDefaultChat(); + merged.provider = merged.provider || def.provider; + merged.model = merged.model || def.modelId; + } + + if (merged.provider === 'openai') { + return generateOpenAIStream(merged); + } + + if (merged.provider === 'gemini') { + return generateGeminiStream(merged); + } + + const stream = new PassThrough(); + stream.write(`event: error\n`); + stream.write(`data: ${JSON.stringify({ message: 'Unknown provider' })}\n\n`); + stream.end(); + return stream; +}; diff --git a/src/llm/model-registry.ts b/src/llm/model-registry.ts new file mode 100644 index 0000000..14cbdfe --- /dev/null +++ b/src/llm/model-registry.ts @@ -0,0 +1,12 @@ +import { ProviderName } from './types'; + +type ModelEntry = { + provider: ProviderName; + modelId: string; +}; + +// Minimal registry for now; can expand with tokenizer/pricing later. +const DEFAULT_CHAT: ModelEntry = { provider: 'openai', modelId: 'gpt-5-mini' }; + +export const getDefaultChat = (): ModelEntry => DEFAULT_CHAT; + diff --git a/src/llm/providers/gemini.ts b/src/llm/providers/gemini.ts new file mode 100644 index 0000000..a399317 --- /dev/null +++ b/src/llm/providers/gemini.ts @@ -0,0 +1,75 @@ +import { PassThrough } from 'stream'; +import config from '../../config'; +import { GenerateRequest } from '../types'; + +// Using @google/genai per project plan; keep types loose for compatibility +// eslint-disable-next-line @typescript-eslint/no-var-requires +const { GoogleGenAI } = require('@google/genai'); + +const buildPromptFromMessages = (messages: { role: string; content: string }[]) => { + // Simple concatenation preserving roles + return messages + .map((m) => `[${m.role}]\n${m.content}`) + .join('\n\n'); +}; + +export const generateGeminiStream = async (req: GenerateRequest): Promise => { + const stream = new PassThrough(); + try { + const modelId = req.model || process.env.GEMINI_CHAT_MODEL || 'gemini-2.5-flash'; + const apiKey = (config as any).GEMINI_API_KEY || process.env.GEMINI_API_KEY; + if (!apiKey) { + stream.write(`event: error\n`); + stream.write(`data: ${JSON.stringify({ message: 'Gemini API key not configured' })}\n\n`); + stream.end(); + return stream; + } + + const ai = new GoogleGenAI({ apiKey }); + + const text = buildPromptFromMessages(req.messages || []); + + const generationConfig: any = {}; + if (req.options?.temperature != null) generationConfig.temperature = req.options.temperature; + if (req.options?.top_p != null) generationConfig.topP = req.options.top_p; + if (req.options?.max_output_tokens != null) generationConfig.maxOutputTokens = req.options.max_output_tokens; + + const thinkingBudget = parseInt(process.env.GEMINI_THINKING_BUDGET || '0', 10) || 0; + const configBlock: any = thinkingBudget > 0 ? { thinkingConfig: { thinkingBudget } } : {}; + + // Non-streaming first, then chunk SSE + const result = await ai.models.generateContent({ + model: modelId, + contents: [ + { + role: 'user', + parts: [{ text }], + }, + ], + generationConfig, + config: configBlock, + }); + + // Try common text access paths + const outputText = (result?.response?.text && result.response.text()) || (result?.text && result.text()) || ''; + + const finalText = typeof outputText === 'string' ? outputText : ''; + + const chunkSize = 400; + for (let i = 0; i < finalText.length; i += chunkSize) { + const chunk = finalText.slice(i, i + chunkSize); + stream.write(`event: answer\n`); + stream.write(`data: ${JSON.stringify(chunk)}\n\n`); + } + stream.write(`event: end\n`); + stream.write(`data: [DONE]\n\n`); + stream.end(); + return stream; + } catch (err) { + stream.write(`event: error\n`); + stream.write(`data: ${JSON.stringify({ message: 'Internal server error' })}\n\n`); + stream.end(); + return stream; + } +}; + diff --git a/src/llm/providers/openai-responses.ts b/src/llm/providers/openai-responses.ts new file mode 100644 index 0000000..1100db6 --- /dev/null +++ b/src/llm/providers/openai-responses.ts @@ -0,0 +1,158 @@ +import { PassThrough } from 'stream'; +import OpenAI from 'openai'; +import config from '../../config'; +import { GenerateRequest, OpenAIStyleMessage, OpenAIStyleTool } from '../types'; + +const openai = new OpenAI({ apiKey: config.OPENAI_API_KEY }); + +const toResponsesInput = (messages: OpenAIStyleMessage[] = []) => { + // Convert simple chat-style messages to Responses API input format + return messages.map((m) => ({ + role: m.role, + content: [{ type: 'text', text: m.content }], + })); +}; + +export const generateOpenAIStream = async (req: GenerateRequest): Promise => { + const stream = new PassThrough(); + const model = req.model || 'gpt-5-mini'; + const messages = req.messages || []; + const tools = (req.tools || []) as unknown as OpenAI.Responses.ResponseCreateParams['tools']; + + // For gpt-5-* prefer Responses API. For other models, fall back to Chat Completions streaming. + const isGpt5Family = /(^|\b)gpt-5/i.test(model); + + try { + if (isGpt5Family) { + // Prefer Responses API streaming for gpt-5 + try { + const responsesStream: any = await (openai as any).responses.stream({ + model, + input: toResponsesInput(messages) as any, + tools: tools as any, + temperature: req.options?.temperature, + top_p: req.options?.top_p, + max_output_tokens: req.options?.max_output_tokens, + }); + + responsesStream.on('response.output_text.delta', (delta: string) => { + if (delta) { + stream.write(`event: answer\n`); + stream.write(`data: ${JSON.stringify(delta)}\n\n`); + } + }); + + // Stream tool-call arguments as answer chunks to maintain SSE shape + responsesStream.on('response.tool_call.delta', (toolDelta: any) => { + const argsDelta = toolDelta?.arguments_delta || toolDelta?.arguments || ''; + if (argsDelta) { + stream.write(`event: answer\n`); + stream.write(`data: ${JSON.stringify(argsDelta)}\n\n`); + } + }); + + responsesStream.on('response.completed', () => { + stream.write(`event: end\n`); + stream.write(`data: [DONE]\n\n`); + stream.end(); + }); + + responsesStream.on('error', (e: any) => { + stream.write(`event: error\n`); + stream.write(`data: ${JSON.stringify({ message: 'Internal server error' })}\n\n`); + stream.end(); + }); + + // Ensure the stream starts and we await its completion + await responsesStream.done(); + return stream; + } catch (e) { + // Fallback to non-streaming Responses if streaming path fails + try { + const response = await openai.responses.create({ + model, + input: toResponsesInput(messages) as any, + // Avoid tools in non-streaming mode to ensure text output + temperature: req.options?.temperature, + top_p: req.options?.top_p, + max_output_tokens: req.options?.max_output_tokens, + }); + const text = (response as any).output_text ?? ''; + const answerText = typeof text === 'string' ? text : ''; + const fallbackText = (() => { + try { + const outputs = (response as any).output || []; + if (Array.isArray(outputs) && outputs.length > 0) { + const parts = outputs + .flatMap((o: any) => o.content || []) + .filter((c: any) => c.type === 'output_text') + .map((c: any) => c.text) + .join(''); + return parts || ''; + } + } catch { + // ignore + } + return ''; + })(); + const finalText = answerText || fallbackText; + const chunkSize = 400; + for (let i = 0; i < finalText.length; i += chunkSize) { + const chunk = finalText.slice(i, i + chunkSize); + stream.write(`event: answer\n`); + stream.write(`data: ${JSON.stringify(chunk)}\n\n`); + } + stream.write(`event: end\n`); + stream.write(`data: [DONE]\n\n`); + stream.end(); + return stream; + } catch (e2) { + // fall through to chat completions streaming below + } + } + } + + // Chat Completions streaming as universal fallback + const chatStream = await openai.chat.completions.create({ + model, + messages: messages as any, + tools: (req.tools as OpenAI.Chat.Completions.ChatCompletionTool[]) || undefined, + tool_choice: req.tools && req.tools.length > 0 ? 'auto' : undefined, + stream: true, + temperature: req.options?.temperature, + top_p: req.options?.top_p, + max_tokens: req.options?.max_output_tokens as any, + }); + + for await (const chunk of chatStream) { + const content = chunk.choices[0]?.delta?.content || ''; + const toolCalls = chunk.choices[0]?.delta?.tool_calls; + + if (toolCalls) { + for (const toolCall of toolCalls) { + if (toolCall.function?.arguments) { + stream.write(`event: answer\n`); + stream.write(`data: ${JSON.stringify(toolCall.function.arguments)}\n\n`); + } + } + } else if (content) { + stream.write(`event: answer\n`); + stream.write(`data: ${JSON.stringify(content)}\n\n`); + } + + if (chunk.choices[0]?.finish_reason) { + stream.write(`event: end\n`); + stream.write(`data: [DONE]\n\n`); + stream.end(); + break; + } + } + + return stream; + } catch (err) { + stream.write(`event: error\n`); + stream.write(`data: ${JSON.stringify({ message: 'Internal server error' })}\n\n`); + stream.end(); + return stream; + } +}; diff --git a/src/llm/types.ts b/src/llm/types.ts new file mode 100644 index 0000000..b17bc54 --- /dev/null +++ b/src/llm/types.ts @@ -0,0 +1,33 @@ +export type ProviderName = 'openai' | 'gemini'; + +export type OpenAIStyleMessage = { + role: 'system' | 'user' | 'assistant' | 'tool' | 'function'; + content: string; +}; + +export type OpenAIStyleTool = { + type: 'function'; + function: { + name: string; + description?: string; + parameters?: Record; + }; +}; + +export type GenerateRequest = { + provider?: ProviderName; + model?: string; + messages?: OpenAIStyleMessage[]; + tools?: OpenAIStyleTool[]; + options?: { + temperature?: number; + top_p?: number; + max_output_tokens?: number; + }; + meta?: { + userId?: string; + categoryId?: number; + postId?: number; + }; +}; + diff --git a/src/services/qa.service.ts b/src/services/qa.service.ts index 2361bbc..549a919 100644 --- a/src/services/qa.service.ts +++ b/src/services/qa.service.ts @@ -1,14 +1,10 @@ import { createEmbeddings } from './embedding.service'; import { PassThrough } from 'stream'; -import OpenAI from 'openai'; import config from '../config'; import * as postRepository from '../repositories/post.repository'; import * as personaRepository from '../repositories/persona.repository'; import * as qaPrompts from '../prompts/qa.prompts'; - -const openai = new OpenAI({ - apiKey: config.OPENAI_API_KEY, -}); +import { generate } from '../llm'; const preprocessContent = (content: string): string => { const plainText = content.replace(/<[^>]*>/g, ' ').replace(/\s+/g, ' ').trim(); @@ -27,20 +23,40 @@ const getSpeechTonePrompt = async (speechTone: number, userId: string): Promise< return "간결하고 명확한 말투로 답변해"; // Default } +type LlmOverride = { + provider?: 'openai' | 'gemini'; + model?: string; + options?: { temperature?: number; top_p?: number; max_output_tokens?: number }; +}; + export const answerStream = async ( question: string, userId: string, categoryId?: number, speechTone: number = -1, - postId?: number + postId?: number, + llm?: LlmOverride ): Promise => { const stream = new PassThrough(); - let messages: OpenAI.Chat.Completions.ChatCompletionMessageParam[] = []; - let tools: OpenAI.Chat.Completions.ChatCompletionTool[] | undefined = undefined; + let messages: { role: 'system' | 'user' | 'assistant' | 'tool' | 'function'; content: string }[] = []; + let tools: + | { + type: 'function'; + function: { name: string; description?: string; parameters?: Record }; + }[] + | undefined = undefined; (async () => { const speechTonePrompt = await getSpeechTonePrompt(speechTone, userId); + const toSimpleMessages = ( + raw: any[] + ): { role: 'system' | 'user' | 'assistant' | 'tool' | 'function'; content: string }[] => { + return (raw || []).map((m: any) => ({ + role: m.role, + content: typeof m.content === 'string' ? m.content : JSON.stringify(m.content), + })); + }; if (postId) { const post = await postRepository.findPostById(postId); @@ -61,7 +77,9 @@ export const answerStream = async ( stream.write(`event: exist_in_post_status\ndata: true\n\n`); stream.write(`event: context\ndata: ${JSON.stringify([{ postId: post.id, postTitle: post.title }])}\n\n`); - messages = qaPrompts.createPostContextPrompt(post, processedContent, question, speechTonePrompt); + messages = toSimpleMessages( + qaPrompts.createPostContextPrompt(post, processedContent, question, speechTonePrompt) + ); } else { const [questionEmbedding] = await createEmbeddings([question]); @@ -73,7 +91,9 @@ export const answerStream = async ( const context = similarChunks.map(chunk => ({ postId: chunk.postId, postTitle: chunk.postTitle })); stream.write(`event: context\ndata: ${JSON.stringify(context)}\n\n`); - messages = qaPrompts.createRagPrompt(question, similarChunks, speechTonePrompt); + messages = toSimpleMessages( + qaPrompts.createRagPrompt(question, similarChunks, speechTonePrompt) + ); tools = [ { type: "function", @@ -92,34 +112,21 @@ export const answerStream = async ( ]; } - const responseStream = await openai.chat.completions.create({ - model: config.CHAT_MODEL, + const llmStream = await generate({ + provider: llm?.provider || 'openai', + model: llm?.model || config.CHAT_MODEL, messages, tools, - tool_choice: tools ? 'auto' : undefined, - stream: true, + options: llm?.options, + meta: { userId, categoryId, postId }, }); - for await (const chunk of responseStream) { - const content = chunk.choices[0]?.delta?.content || ""; - const toolCalls = chunk.choices[0]?.delta?.tool_calls; - - if (toolCalls) { - for (const toolCall of toolCalls) { - if (toolCall.function?.arguments) { - stream.write(`event: answer\ndata: ${JSON.stringify(toolCall.function.arguments)}\n\n`); - } - } - } else if (content) { - stream.write(`event: answer\ndata: ${JSON.stringify(content)}\n\n`); - } - - if (chunk.choices[0]?.finish_reason) { - stream.write(`event: end\ndata: [DONE]\n\n`); - stream.end(); - break; - } - } + llmStream.on('data', (chunk) => { + stream.write(chunk); + }); + llmStream.on('end', () => { + stream.end(); + }); })().catch(err => { console.error('Stream process error:', err); diff --git a/src/types/ai.types.ts b/src/types/ai.types.ts index e596d1a..b9ed539 100644 --- a/src/types/ai.types.ts +++ b/src/types/ai.types.ts @@ -28,6 +28,19 @@ export const askSchema = z.object({ category_id: z.number().optional(), post_id: z.number().optional(), speech_tone: z.number().optional(), + llm: z + .object({ + provider: z.enum(['openai', 'gemini']).optional(), + model: z.string().optional(), + options: z + .object({ + temperature: z.number().optional(), + top_p: z.number().optional(), + max_output_tokens: z.number().optional(), + }) + .optional(), + }) + .optional(), }), }); From a3d6079c797c7eee1af582a070e1ad9bd909a67f Mon Sep 17 00:00:00 2001 From: chan000518 Date: Sat, 4 Oct 2025 11:35:22 +0900 Subject: [PATCH 02/14] =?UTF-8?q?=E2=9C=A8=20LLM=20=EB=B9=84=EC=9A=A9=20?= =?UTF-8?q?=EB=A1=9C=EA=B9=85=20=EB=B0=8F=20=EA=B0=80=EA=B2=A9=20=EA=B3=84?= =?UTF-8?q?=EC=82=B0=20=EA=B8=B0=EB=8A=A5=20=EC=B6=94=EA=B0=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/config.ts | 2 + src/llm/index.ts | 137 ++++++++++++++++++++++++++++++++++++++--- src/utils/cost.ts | 40 ++++++++++++ src/utils/tokenizer.ts | 32 ++++++++++ 4 files changed, 201 insertions(+), 10 deletions(-) create mode 100644 src/utils/cost.ts create mode 100644 src/utils/tokenizer.ts diff --git a/src/config.ts b/src/config.ts index 1be73a1..ba81564 100644 --- a/src/config.ts +++ b/src/config.ts @@ -16,6 +16,8 @@ const configSchema = z.object({ GEMINI_API_KEY: z.string().optional(), GEMINI_CHAT_MODEL: z.string().default('gemini-2.5-flash'), GEMINI_THINKING_BUDGET: z.string().optional(), + LLM_COST_LOG: z.string().default('false'), + LLM_COST_ROUND: z.coerce.number().default(4), }); const config = configSchema.parse(process.env); diff --git a/src/llm/index.ts b/src/llm/index.ts index 9c39531..ba30ce4 100644 --- a/src/llm/index.ts +++ b/src/llm/index.ts @@ -3,6 +3,10 @@ import { GenerateRequest } from './types'; import { getDefaultChat } from './model-registry'; import { generateOpenAIStream } from './providers/openai-responses'; import { generateGeminiStream } from './providers/gemini'; +import { countChatMessagesTokens, countTextTokens } from '../utils/tokenizer'; +import { calcCost, getModelPricing } from '../utils/cost'; +import config from '../config'; +import { randomUUID } from 'crypto'; export const generate = async (req: GenerateRequest): Promise => { const merged = { ...req }; @@ -12,17 +16,130 @@ export const generate = async (req: GenerateRequest): Promise => { merged.model = merged.model || def.modelId; } - if (merged.provider === 'openai') { - return generateOpenAIStream(merged); - } + const doLog = (config.LLM_COST_LOG || '').toString().toLowerCase() === 'true'; + const round = config.LLM_COST_ROUND ?? 4; + const corrId = randomUUID(); + const model = merged.model as string; + const provider = merged.provider as string; - if (merged.provider === 'gemini') { - return generateGeminiStream(merged); + // Pre-call logging: prompt tokens + estimated input cost + const messages = merged.messages || []; + let promptTokens = 0; + try { + promptTokens = countChatMessagesTokens(messages as any, model); + } catch { + // ignore + } + const pricing = getModelPricing(model); + const estInputCost = pricing ? calcCost(promptTokens, pricing.input_per_1k) : 0; + if (doLog) { + const pre = { + type: 'llm.request', + corrId, + provider, + model, + promptTokens, + estInputCost, + userId: merged.meta?.userId, + categoryId: merged.meta?.categoryId, + postId: merged.meta?.postId, + }; + console.log(JSON.stringify(pre)); } - const stream = new PassThrough(); - stream.write(`event: error\n`); - stream.write(`data: ${JSON.stringify({ message: 'Unknown provider' })}\n\n`); - stream.end(); - return stream; + const startedAt = Date.now(); + + const providerStream = + merged.provider === 'openai' + ? await generateOpenAIStream(merged) + : merged.provider === 'gemini' + ? await generateGeminiStream(merged) + : (() => { + const s = new PassThrough(); + s.write(`event: error\n`); + s.write(`data: ${JSON.stringify({ message: 'Unknown provider' })}\n\n`); + s.end(); + return s; + })(); + + // Wrap provider stream to accumulate output tokens + const outer = new PassThrough(); + let buffer = ''; + let outputText = ''; + + const flushBuffer = () => { + // Split by double newline to get SSE events + const chunks = buffer.split('\n\n'); + // Keep last partial + buffer = chunks.pop() || ''; + for (const block of chunks) { + const lines = block.split('\n'); + let evt: string | null = null; + let dataLine: string | null = null; + for (const line of lines) { + if (line.startsWith('event:')) evt = line.slice(6).trim(); + if (line.startsWith('data:')) dataLine = line.slice(5).trim(); + } + if (evt === 'answer' && dataLine) { + try { + const parsed = JSON.parse(dataLine); + if (typeof parsed === 'string') outputText += parsed; + else outputText += JSON.stringify(parsed); + } catch { + outputText += dataLine; + } + } + outer.write(block + '\n\n'); + } + }; + + providerStream.on('data', (chunk) => { + const str = Buffer.isBuffer(chunk) ? chunk.toString('utf8') : String(chunk); + buffer += str; + flushBuffer(); + }); + providerStream.on('end', () => { + if (buffer.length > 0) { + outer.write(buffer); + buffer = ''; + } + + const completionTokens = (() => { + try { + return countTextTokens(outputText, model); + } catch { + return 0; + } + })(); + const durationMs = Date.now() - startedAt; + if (doLog) { + const inputCost = pricing ? calcCost(promptTokens, pricing.input_per_1k) : 0; + const outputCost = pricing ? calcCost(completionTokens, pricing.output_per_1k) : 0; + const totalCost = inputCost + outputCost; + const post = { + type: 'llm.response', + corrId, + provider, + model, + promptTokens, + completionTokens, + inputCost, + outputCost, + totalCost, + durationMs, + }; + console.log(JSON.stringify(post)); + } + outer.end(); + }); + providerStream.on('error', (e) => { + if (doLog) { + console.log( + JSON.stringify({ type: 'llm.error', corrId, provider, model, message: (e as any)?.message || 'error' }) + ); + } + outer.emit('error', e); + }); + + return outer; }; diff --git a/src/utils/cost.ts b/src/utils/cost.ts new file mode 100644 index 0000000..62334db --- /dev/null +++ b/src/utils/cost.ts @@ -0,0 +1,40 @@ +export type Pricing = { + input_per_1k: number; + output_per_1k: number; + cached_input_per_1k?: number; + currency: 'USD' | 'KRW' | string; +}; + +const PRICING_TABLE: Record = { + // OpenAI + 'gpt-5-mini': { input_per_1k: 0.00025, output_per_1k: 0.002, cached_input_per_1k: 0.000025, currency: 'USD' }, + 'gpt-5-nano': { input_per_1k: 0.00005, output_per_1k: 0.0004, cached_input_per_1k: 0.000005, currency: 'USD' }, + 'gpt-4o': { input_per_1k: 0.005, output_per_1k: 0.015, currency: 'USD' }, + 'gpt-4o-mini': { input_per_1k: 0.0005, output_per_1k: 0.0015, currency: 'USD' }, + // Embeddings + 'text-embedding-3-small': { input_per_1k: 0.00002, output_per_1k: 0, currency: 'USD' }, + // Gemini (example values — update per official pricing if needed) + 'gemini-2.5-flash': { input_per_1k: 0.0001, output_per_1k: 0.0004, currency: 'USD' }, +}; + +export const getModelPricing = (model: string): Pricing | null => { + if (!model) return null; + const key = model.toLowerCase(); + if (PRICING_TABLE[key]) return PRICING_TABLE[key]; + // naive aliasing for common variants + if (key.startsWith('gpt-4o')) return PRICING_TABLE['gpt-4o']; + if (key.startsWith('gpt-5-mini')) return PRICING_TABLE['gpt-5-mini']; + return null; +}; + +export const calcCost = (tokens: number, per_1k: number): number => { + if (!per_1k) return 0; + return (tokens / 1000) * per_1k; +}; + +export const formatCost = (amount: number, currency: string, round: number = 4): string => { + const factor = Math.pow(10, Math.max(0, round)); + const rounded = Math.round(amount * factor) / factor; + return `${rounded} ${currency}`; +}; + diff --git a/src/utils/tokenizer.ts b/src/utils/tokenizer.ts new file mode 100644 index 0000000..48f8fbf --- /dev/null +++ b/src/utils/tokenizer.ts @@ -0,0 +1,32 @@ +import { get_encoding } from '@dqbd/tiktoken'; + +const encodingForModel = (model: string): string => { + const lower = (model || '').toLowerCase(); + if (lower.includes('gpt-5') || lower.includes('gpt-4o') || lower.includes('o1') || lower.includes('o3')) { + return 'o200k_base'; + } + return 'cl100k_base'; +}; + +export const countTextTokens = (text: string, model: string): number => { + const encKey = encodingForModel(model); + const enc = get_encoding(encKey); + try { + const tokens = enc.encode(text || ''); + return tokens.length; + } finally { + // no explicit free in @dqbd/tiktoken browser build; safe to let GC handle + } +}; + +type SimpleMessage = { role: string; content: string }; + +export const countChatMessagesTokens = (messages: SimpleMessage[], model: string): number => { + // Approximate: sum content token counts + minimal role overhead + const overheadPerMsg = 3; // rough + const roleOverhead = 1; + return messages.reduce((sum, m) => { + return sum + countTextTokens(m.content || '', model) + overheadPerMsg + roleOverhead; + }, 0); +}; + From 77c033fcb1cad0961aedab78b662575895a6f81a Mon Sep 17 00:00:00 2001 From: chan000518 Date: Sun, 5 Oct 2025 10:33:47 +0900 Subject: [PATCH 03/14] =?UTF-8?q?=F0=9F=90=9B=20=ED=83=80=EC=9E=85=20?= =?UTF-8?q?=EC=88=98=EC=A0=95?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- package-lock.json | 219 ++++++++++++++++++++++++++++++++++++++++- src/utils/tokenizer.ts | 10 +- 2 files changed, 223 insertions(+), 6 deletions(-) diff --git a/package-lock.json b/package-lock.json index 65109c4..5814cd7 100644 --- a/package-lock.json +++ b/package-lock.json @@ -10,6 +10,7 @@ "license": "ISC", "dependencies": { "@dqbd/tiktoken": "^1.0.13", + "@google/genai": "^0.2.0", "cors": "^2.8.5", "dotenv": "^16.4.5", "express": "^4.19.2", @@ -47,6 +48,18 @@ "resolved": "https://registry.npmjs.org/@dqbd/tiktoken/-/tiktoken-1.0.22.tgz", "integrity": 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"*" + } + }, "node_modules/binary-extensions": { "version": "2.3.0", "resolved": "https://registry.npmjs.org/binary-extensions/-/binary-extensions-2.3.0.tgz", @@ -535,7 +583,6 @@ "version": "4.4.1", "resolved": "https://registry.npmjs.org/debug/-/debug-4.4.1.tgz", "integrity": "sha512-KcKCqiftBJcZr++7ykoDIEwSa3XWowTfNPo92BYxjXiyYEVrUQh2aLyhxBCwww+heortUFxEJYcRzosstTEBYQ==", - "dev": true, "dependencies": { "ms": "^2.1.3" }, @@ -747,6 +794,11 @@ "resolved": "https://registry.npmjs.org/ms/-/ms-2.0.0.tgz", "integrity": "sha512-Tpp60P6IUJDTuOq/5Z8cdskzJujfwqfOTkrwIwj7IRISpnkJnT6SyJ4PCPnGMoFjC9ddhal5KVIYtAt97ix05A==" }, + "node_modules/extend": { + "version": "3.0.2", + "resolved": "https://registry.npmjs.org/extend/-/extend-3.0.2.tgz", + "integrity": "sha512-fjquC59cD7CyW6urNXK0FBufkZcoiGG80wTuPujX590cB5Ttln20E2UB4S/WARVqhXffZl2LNgS+gQdPIIim/g==" + }, "node_modules/fill-range": { "version": "7.1.1", "resolved": "https://registry.npmjs.org/fill-range/-/fill-range-7.1.1.tgz", @@ -859,6 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"https://registry.npmjs.org/get-intrinsic/-/get-intrinsic-1.3.0.tgz", @@ -906,6 +986,49 @@ "node": ">= 6" } }, + "node_modules/google-auth-library": { + "version": "9.15.1", + "resolved": "https://registry.npmjs.org/google-auth-library/-/google-auth-library-9.15.1.tgz", + "integrity": "sha512-Jb6Z0+nvECVz+2lzSMt9u98UsoakXxA2HGHMCxh+so3n90XgYWkq5dur19JAJV7ONiJY22yBTyJB1TSkvPq9Ng==", + "dependencies": { + "base64-js": "^1.3.0", + "ecdsa-sig-formatter": "^1.0.11", + "gaxios": "^6.1.1", + "gcp-metadata": "^6.1.0", + "gtoken": "^7.0.0", + "jws": "^4.0.0" + }, + "engines": { + "node": ">=14" + } + }, + "node_modules/google-auth-library/node_modules/jwa": { + "version": "2.0.1", + "resolved": "https://registry.npmjs.org/jwa/-/jwa-2.0.1.tgz", + "integrity": "sha512-hRF04fqJIP8Abbkq5NKGN0Bbr3JxlQ+qhZufXVr0DvujKy93ZCbXZMHDL4EOtodSbCWxOqR8MS1tXA5hwqCXDg==", + "dependencies": { + "buffer-equal-constant-time": "^1.0.1", + "ecdsa-sig-formatter": "1.0.11", + "safe-buffer": "^5.0.1" + } + }, + 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"https://registry.npmjs.org/has-flag/-/has-flag-3.0.0.tgz", @@ -977,6 +1131,18 @@ "node": ">= 0.8" } }, + "node_modules/https-proxy-agent": { + "version": "7.0.6", + "resolved": "https://registry.npmjs.org/https-proxy-agent/-/https-proxy-agent-7.0.6.tgz", + "integrity": "sha512-vK9P5/iUfdl95AI+JVyUuIcVtd4ofvtrOr3HNtM2yxC9bnMbEdp3x01OhQNnjb8IJYi38VlTE3mBXwcfvywuSw==", + "dependencies": { + "agent-base": "^7.1.2", + "debug": "4" + }, + "engines": { + "node": ">= 14" + } + }, "node_modules/humanize-ms": { "version": "1.2.1", "resolved": "https://registry.npmjs.org/humanize-ms/-/humanize-ms-1.2.1.tgz", @@ -1057,6 +1223,25 @@ "node": ">=0.12.0" } }, + "node_modules/is-stream": { + "version": "2.0.1", + "resolved": "https://registry.npmjs.org/is-stream/-/is-stream-2.0.1.tgz", + "integrity": "sha512-hFoiJiTl63nn+kstHGBtewWSKnQLpyb155KHheA1l39uvtO9nWIop1p3udqPcUd/xbF1VLMO4n7OI6p7RbngDg==", + "engines": { + "node": ">=8" + }, + "funding": { + "url": "https://github.com/sponsors/sindresorhus" + } + }, + "node_modules/json-bigint": { + "version": "1.0.0", + "resolved": "https://registry.npmjs.org/json-bigint/-/json-bigint-1.0.0.tgz", + "integrity": "sha512-SiPv/8VpZuWbvLSMtTDU8hEfrZWg/mH/nV/b4o0CYbSxu1UIQPLdwKOCIyLQX+VIPO5vrLX3i8qtqFyhdPSUSQ==", + "dependencies": { + "bignumber.js": "^9.0.0" + } + }, "node_modules/jsonwebtoken": { "version": "9.0.2", "resolved": "https://registry.npmjs.org/jsonwebtoken/-/jsonwebtoken-9.0.2.tgz", @@ -1922,6 +2107,18 @@ "node": ">= 0.4.0" } }, + "node_modules/uuid": { + "version": "9.0.1", + "resolved": "https://registry.npmjs.org/uuid/-/uuid-9.0.1.tgz", + "integrity": "sha512-b+1eJOlsR9K8HJpow9Ok3fiWOWSIcIzXodvv0rQjVoOVNpWMpxf1wZNpt4y9h10odCNrqnYp1OBzRktckBe3sA==", + "funding": [ + "https://github.com/sponsors/broofa", + "https://github.com/sponsors/ctavan" + ], + "bin": { + "uuid": "dist/bin/uuid" + } + }, "node_modules/v8-compile-cache-lib": { "version": "3.0.1", "resolved": "https://registry.npmjs.org/v8-compile-cache-lib/-/v8-compile-cache-lib-3.0.1.tgz", @@ -1958,6 +2155,26 @@ "webidl-conversions": "^3.0.0" } }, + "node_modules/ws": { + "version": "8.18.3", + "resolved": "https://registry.npmjs.org/ws/-/ws-8.18.3.tgz", + "integrity": "sha512-PEIGCY5tSlUt50cqyMXfCzX+oOPqN0vuGqWzbcJ2xvnkzkq46oOpz7dQaTDBdfICb4N14+GARUDw2XV2N4tvzg==", + "engines": { + "node": ">=10.0.0" + }, + "peerDependencies": { + "bufferutil": "^4.0.1", + "utf-8-validate": ">=5.0.2" + }, + "peerDependenciesMeta": { + "bufferutil": { + "optional": true + }, + "utf-8-validate": { + "optional": true + } + } + }, "node_modules/xtend": { "version": "4.0.2", "resolved": "https://registry.npmjs.org/xtend/-/xtend-4.0.2.tgz", diff --git a/src/utils/tokenizer.ts b/src/utils/tokenizer.ts index 48f8fbf..bc662f4 100644 --- a/src/utils/tokenizer.ts +++ b/src/utils/tokenizer.ts @@ -1,11 +1,11 @@ -import { get_encoding } from '@dqbd/tiktoken'; +import { get_encoding, type TiktokenEncoding } from '@dqbd/tiktoken'; -const encodingForModel = (model: string): string => { - const lower = (model || '').toLowerCase(); +const encodingForModel = (model?: string): TiktokenEncoding => { + const lower = (model ?? '').toLowerCase(); if (lower.includes('gpt-5') || lower.includes('gpt-4o') || lower.includes('o1') || lower.includes('o3')) { - return 'o200k_base'; + return 'o200k_base' as TiktokenEncoding; } - return 'cl100k_base'; + return 'cl100k_base' as TiktokenEncoding; }; export const countTextTokens = (text: string, model: string): number => { From 7ee29e4f4da8ff876814b6d8547d70b050452c18 Mon Sep 17 00:00:00 2001 From: chan000518 Date: Sun, 5 Oct 2025 14:00:48 +0900 Subject: [PATCH 04/14] =?UTF-8?q?=F0=9F=90=9B=20=EB=94=94=EB=B2=84?= =?UTF-8?q?=EA=B9=85=20=EC=B6=94=EA=B0=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/config.ts | 1 + src/llm/index.ts | 17 +++ src/llm/providers/openai-responses.ts | 161 ++++++++++++++++++++------ src/llm/types.ts | 4 +- src/prompts/qa.prompts.ts | 17 ++- src/services/qa.service.ts | 32 +++++ 6 files changed, 191 insertions(+), 41 deletions(-) diff --git a/src/config.ts b/src/config.ts index ba81564..5bd402a 100644 --- a/src/config.ts +++ b/src/config.ts @@ -12,6 +12,7 @@ const configSchema = z.object({ TOKEN_AUDIENCE: z.string().default('bubblog'), ALGORITHM: z.string().default('HS256'), EMBED_MODEL: z.string().default('text-embedding-3-small'), + // 기본 LLM 모델: GPT-5 계열 CHAT_MODEL: z.string().default('gpt-5-mini'), GEMINI_API_KEY: z.string().optional(), GEMINI_CHAT_MODEL: z.string().default('gemini-2.5-flash'), diff --git a/src/llm/index.ts b/src/llm/index.ts index ba30ce4..3c67eb4 100644 --- a/src/llm/index.ts +++ b/src/llm/index.ts @@ -67,6 +67,13 @@ export const generate = async (req: GenerateRequest): Promise => { let buffer = ''; let outputText = ''; + // Debug: start info + try { + console.log( + JSON.stringify({ type: 'debug.llm.start', provider, model, messages: (messages || []).length }) + ); + } catch {} + const flushBuffer = () => { // Split by double newline to get SSE events const chunks = buffer.split('\n\n'); @@ -130,6 +137,11 @@ export const generate = async (req: GenerateRequest): Promise => { }; console.log(JSON.stringify(post)); } + try { + console.log( + JSON.stringify({ type: 'debug.llm.end', provider, model, durationMs, outputChars: outputText.length }) + ); + } catch {} outer.end(); }); providerStream.on('error', (e) => { @@ -138,6 +150,11 @@ export const generate = async (req: GenerateRequest): Promise => { JSON.stringify({ type: 'llm.error', corrId, provider, model, message: (e as any)?.message || 'error' }) ); } + try { + console.error( + JSON.stringify({ type: 'debug.llm.error', provider, model, message: (e as any)?.message || 'error' }) + ); + } catch {} outer.emit('error', e); }); diff --git a/src/llm/providers/openai-responses.ts b/src/llm/providers/openai-responses.ts index 1100db6..430728c 100644 --- a/src/llm/providers/openai-responses.ts +++ b/src/llm/providers/openai-responses.ts @@ -7,38 +7,103 @@ const openai = new OpenAI({ apiKey: config.OPENAI_API_KEY }); const toResponsesInput = (messages: OpenAIStyleMessage[] = []) => { // Convert simple chat-style messages to Responses API input format + // Responses API expects 'input_text' as the content type (not 'text'). return messages.map((m) => ({ role: m.role, - content: [{ type: 'text', text: m.content }], + content: [{ type: 'input_text', text: m.content }], })); }; +const toResponsesTools = (tools: OpenAIStyleTool[] = []) => { + // Map Chat Completions style tool definitions to Responses API format + // Chat: { type: 'function', function: { name, description, parameters } } + // Responses: { type: 'function', name, description, parameters } + return tools + .filter((t) => t && (t as any).type === 'function' && (t as any).function?.name) + .map((t) => ({ + type: 'function', + name: t.function.name, + description: t.function.description, + parameters: t.function.parameters, + })); +}; + export const generateOpenAIStream = async (req: GenerateRequest): Promise => { const stream = new PassThrough(); + // Guard to avoid writing after stream end + let closed = false; + const safeWrite = (chunk: string) => { + if (!closed && !stream.writableEnded && !stream.destroyed) { + stream.write(chunk); + } + }; + const safeEnd = () => { + if (!closed && !stream.writableEnded && !stream.destroyed) { + closed = true; + stream.end(); + } else { + closed = true; + } + }; const model = req.model || 'gpt-5-mini'; const messages = req.messages || []; - const tools = (req.tools || []) as unknown as OpenAI.Responses.ResponseCreateParams['tools']; + const toolsChat = (req.tools || []) as OpenAIStyleTool[]; // For gpt-5-* prefer Responses API. For other models, fall back to Chat Completions streaming. const isGpt5Family = /(^|\b)gpt-5/i.test(model); + // Debug: basic call info + try { + console.log( + JSON.stringify({ + type: 'debug.openai.start', + model, + isGpt5Family, + hasTools: Array.isArray(req.tools) && req.tools.length > 0, + options: { + temperature: req.options?.temperature, + top_p: req.options?.top_p, + max_output_tokens: req.options?.max_output_tokens, + reasoning_effort: (req as any)?.options?.reasoning_effort, + text_verbosity: (req as any)?.options?.text_verbosity, + }, + }) + ); + } catch {} + try { if (isGpt5Family) { // Prefer Responses API streaming for gpt-5 try { - const responsesStream: any = await (openai as any).responses.stream({ + const respParams: any = { model, input: toResponsesInput(messages) as any, - tools: tools as any, - temperature: req.options?.temperature, - top_p: req.options?.top_p, + tools: toolsChat && toolsChat.length > 0 ? toResponsesTools(toolsChat) : undefined, max_output_tokens: req.options?.max_output_tokens, - }); + }; + // GPT-5 family: omit temperature/top_p; allow reasoning/text controls + if (req.options?.reasoning_effort) { + respParams.reasoning = { effort: req.options.reasoning_effort }; + } + if (req.options?.text_verbosity) { + respParams.text = { verbosity: req.options.text_verbosity }; + } + try { + console.log( + JSON.stringify({ type: 'debug.openai.path', path: 'responses.stream', paramsKeys: Object.keys(respParams) }) + ); + } catch {} + const responsesStream: any = await (openai as any).responses.stream(respParams); + let loggedFirstDelta = false; responsesStream.on('response.output_text.delta', (delta: string) => { if (delta) { - stream.write(`event: answer\n`); - stream.write(`data: ${JSON.stringify(delta)}\n\n`); + safeWrite(`event: answer\n`); + safeWrite(`data: ${JSON.stringify(delta)}\n\n`); + if (!loggedFirstDelta) { + try { console.log(JSON.stringify({ type: 'debug.openai.delta', len: delta.length })); } catch {} + loggedFirstDelta = true; + } } }); @@ -46,21 +111,27 @@ export const generateOpenAIStream = async (req: GenerateRequest): Promise { const argsDelta = toolDelta?.arguments_delta || toolDelta?.arguments || ''; if (argsDelta) { - stream.write(`event: answer\n`); - stream.write(`data: ${JSON.stringify(argsDelta)}\n\n`); + safeWrite(`event: answer\n`); + safeWrite(`data: ${JSON.stringify(argsDelta)}\n\n`); } }); responsesStream.on('response.completed', () => { - stream.write(`event: end\n`); - stream.write(`data: [DONE]\n\n`); - stream.end(); + safeWrite(`event: end\n`); + safeWrite(`data: [DONE]\n\n`); + safeEnd(); + try { console.log(JSON.stringify({ type: 'debug.openai.completed' })); } catch {} }); responsesStream.on('error', (e: any) => { - stream.write(`event: error\n`); - stream.write(`data: ${JSON.stringify({ message: 'Internal server error' })}\n\n`); - stream.end(); + safeWrite(`event: error\n`); + safeWrite(`data: ${JSON.stringify({ message: 'Internal server error' })}\n\n`); + safeEnd(); + try { + console.error( + JSON.stringify({ type: 'debug.openai.error', path: 'responses.stream', message: (e as any)?.message }) + ); + } catch {} }); // Ensure the stream starts and we await its completion @@ -69,14 +140,20 @@ export const generateOpenAIStream = async (req: GenerateRequest): Promise { @@ -99,20 +176,26 @@ export const generateOpenAIStream = async (req: GenerateRequest): Promise { - const systemPrompt = `너는 사용자의 블로그 글 컨텍스트만으로 답변한다. 컨텍스트에 없는 사실은 추정하지 말고 “문서에 없음”이라고 말한다. 말투는 speech_tone 지시에 따른다.`; + const systemPrompt = ` 당신은 블로그 운영자 AI입니다. 사용자의 블로그에 대한 질문에 답변합니다. + 블로그 운영자 AI는 사용자의 질문에 대해 블로그 본문 컨텍스트를 참고하여 답변합니다. + 모든 한국어 응답은 무슨일이 있어도 반드시 답변 말투 및 규칙을 따릅니다. + 또한 주어진 내용외의 내용을 지어내지 마십시오. + + [응답 규칙] + 1. 만약 제목과 본문을 활용해 답변할 수 있다면 답변 말투 및 규칙을 지켜 직접 답변하고, 마지막에 추가적인 내용에 대한 질문을 유도하는 문장을 추가합니다. + 2. 만약 질문이 욕설·비난·무관·부적절하거나 주어진 제목, 본문과 관련이 없다면 사과와 블로그 관련된 내용만 답변 가능하다는 내용을 답변 말투 및 규칙을 지켜 답합니다. + 3. 질문이 블로그 카테고리나 사용자 블로그에는 부합하지만 제공된 본문 컨텍스트의 내용이 매우 부족하거나 적절하지 않다고 판단되면 report_content_insufficient 함수를 호출하고 답변 말투 및 규칙을 지켜 해당 내용이 아직 부족하다는 안내를 합니다. 그 후 본문 컨텍스트를 참고해 질문과 관련된 답변할 수 있는 내용을 언급하고 해당 내용에 대한 질문을 직접적으로 유도합니다. + `; const userMessage = ` +[instruction] +답변 말투: "${speechTonePrompt}" + [context] 제목: ${post.title} 작성일: ${post.created_at} @@ -18,9 +30,6 @@ ${processedContent} [user] ${question} - -[instruction] -답변 말투: "${speechTonePrompt}" `; return [ diff --git a/src/services/qa.service.ts b/src/services/qa.service.ts index 549a919..eef997f 100644 --- a/src/services/qa.service.ts +++ b/src/services/qa.service.ts @@ -38,6 +38,11 @@ export const answerStream = async ( llm?: LlmOverride ): Promise => { const stream = new PassThrough(); + try { + console.log( + JSON.stringify({ type: 'debug.qa.start', questionLen: question?.length || 0, userId, categoryId, postId, speechTone, llm }) + ); + } catch {} let messages: { role: 'system' | 'user' | 'assistant' | 'tool' | 'function'; content: string }[] = []; let tools: @@ -64,18 +69,25 @@ export const answerStream = async ( if (!post) { stream.write(`event: error\ndata: ${JSON.stringify({ code: 404, message: 'Post not found' })}\n\n`); stream.end(); + try { console.warn(JSON.stringify({ type: 'debug.qa.post', status: 'not_found', postId })); } catch {} return; } if (post.user_id !== userId) { stream.write(`event: error\ndata: ${JSON.stringify({ code: 403, message: 'Forbidden' })}\n\n`); stream.end(); + try { console.warn(JSON.stringify({ type: 'debug.qa.post', status: 'forbidden', postId })); } catch {} return; } const processedContent = preprocessContent(post.content); stream.write(`event: exist_in_post_status\ndata: true\n\n`); stream.write(`event: context\ndata: ${JSON.stringify([{ postId: post.id, postTitle: post.title }])}\n\n`); + try { + console.log( + JSON.stringify({ type: 'debug.qa.path', mode: 'post', postId: post.id, processedLen: processedContent.length }) + ); + } catch {} messages = toSimpleMessages( qaPrompts.createPostContextPrompt(post, processedContent, question, speechTonePrompt) @@ -90,6 +102,11 @@ export const answerStream = async ( const context = similarChunks.map(chunk => ({ postId: chunk.postId, postTitle: chunk.postTitle })); stream.write(`event: context\ndata: ${JSON.stringify(context)}\n\n`); + try { + console.log( + JSON.stringify({ type: 'debug.qa.path', mode: 'rag', similarChunks: similarChunks.length, contextPreview: context.slice(0, 3) }) + ); + } catch {} messages = toSimpleMessages( qaPrompts.createRagPrompt(question, similarChunks, speechTonePrompt) @@ -120,6 +137,18 @@ export const answerStream = async ( options: llm?.options, meta: { userId, categoryId, postId }, }); + try { + console.log( + JSON.stringify({ + type: 'debug.qa.call', + provider: llm?.provider || 'openai', + model: llm?.model || config.CHAT_MODEL, + messages: messages.length, + tools: (tools || []).length, + hasOptions: !!llm?.options, + }) + ); + } catch {} llmStream.on('data', (chunk) => { stream.write(chunk); @@ -127,6 +156,9 @@ export const answerStream = async ( llmStream.on('end', () => { stream.end(); }); + llmStream.on('error', (e) => { + try { console.error(JSON.stringify({ type: 'debug.qa.llmError', message: (e as any)?.message || 'error' })); } catch {} + }); })().catch(err => { console.error('Stream process error:', err); From cf88ceb0091b5b75db7ad6443a7bfb7940e03a3c Mon Sep 17 00:00:00 2001 From: chan000518 Date: Sun, 5 Oct 2025 14:50:30 +0900 Subject: [PATCH 05/14] =?UTF-8?q?=E2=9C=A8=20ask=20V2=20=EA=B5=AC=ED=98=84?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- REPORT-askv2.md | 92 +++++ TASK.md | 481 +++++++++++++++--------- src/app.ts | 2 + src/controllers/ai.v2.controller.ts | 29 ++ src/prompts/qa.v2.prompts.ts | 142 +++++++ src/repositories/post.repository.ts | 95 ++++- src/routes/ai.v2.routes.ts | 14 + src/services/qa.v2.service.ts | 208 ++++++++++ src/services/search-plan.service.ts | 102 +++++ src/services/semantic-search.service.ts | 36 ++ src/types/ai.v2.types.ts | 79 ++++ src/utils/time.ts | 82 ++++ 12 files changed, 1173 insertions(+), 189 deletions(-) create mode 100644 REPORT-askv2.md create mode 100644 src/controllers/ai.v2.controller.ts create mode 100644 src/prompts/qa.v2.prompts.ts create mode 100644 src/routes/ai.v2.routes.ts create mode 100644 src/services/qa.v2.service.ts create mode 100644 src/services/search-plan.service.ts create mode 100644 src/services/semantic-search.service.ts create mode 100644 src/types/ai.v2.types.ts create mode 100644 src/utils/time.ts diff --git a/REPORT-askv2.md b/REPORT-askv2.md new file mode 100644 index 0000000..b0d5e12 --- /dev/null +++ b/REPORT-askv2.md @@ -0,0 +1,92 @@ +# 보고서: /ai/v2/ask 구현 내역과 구조(한국어) + +## 1) 개요 +- 목적: v1의 고정형 RAG 흐름을 개선하기 위해, 사용자의 질문을 LLM이 “검색 계획(JSON)”으로 구조화 → 서버가 안전한 시맨틱 SQL 검색 수행 → v1과 동일한 방식으로 최종 답변을 SSE로 스트리밍하는 v2 파이프라인을 추가했습니다. +- 상태: v1은 수정하지 않고 유지. v2 엔드포인트 `POST /ai/v2/ask`(SSE) 신규 추가 및 전체 흐름 구현 완료. + +## 2) v1의 주요 문제점(한계) +- 고정 파라미터 검색: 임계치(0.2), LIMIT(5)가 고정되어 질문 의도(기간/정렬/가중치/갯수 등) 반영이 어려움. +- 시간/정렬 표현 미흡: “최근 글/9월 글/지난주” 등 자연어 시간 요구를 반영할 수 없음. +- 컨텍스트 제어 부족: LLM 입력 컨텍스트의 개수/범위를 질문 의도나 비용/토큰 예산에 맞춰 유연하게 조절하기 어려움. +- 관측성 부족: 어떤 기준/전략으로 검색되었는지(계획/파라미터)를 클라이언트에서 파악하기 어려움. +- 보안/안전 제약: 프롬프트/검색 로직이 단순해, 미래에 복잡한 동적 쿼리로 확장 시 안전한 경계(화이트리스트/스키마 강제)가 필요. + +## 3) v2의 도입/해결 방안과 가능해진 것 +- LLM 검색 계획(JSON) 도입 + - OpenAI Responses API + JSON Schema 강제 → LLM이 오직 스키마에 맞는 JSON만 출력. + - 계획 항목: `mode`(rag/post), `top_k`, `threshold`, `weights{chunk,title}`, `filters{user_id, category_ids?, post_id?, time?}`, `sort`, `limit`. +- 시간/정렬 의도 반영 + - 자연어 시간(최근/이번 달/지난주/9월/작년 등)을 `filters.time`에 구조화. + - 서버는 KST 기준 절대 기간으로 표준화(from/to) 후 안전 SQL에 반영. + - 최신순/오래된순 정렬(`sort`) 지원. +- 안전한 서버 표준화/실행 + - Zod로 계획 검증 + 범위 강제(top_k 1..10, limit 1..20, threshold 0..1, weights 합=1). + - SQL은 화이트리스트 템플릿 + 파라미터 바인딩으로만 구성(문자열 결합 금지). +- 관측성 강화 + - SSE 메타 이벤트(`search_plan`, `search_result`)를 추가해, 검색 계획과 결과 요약을 클라이언트에 투명하게 제공. +- 컨텍스트 최적화 + - 청크 검색 후보(top_k)를 넓게 가져온 뒤, 포스트 단위 중복 제거 + `limit`개까지만 컨텍스트/노출로 사용(토큰/비용 관리). +- 폴백 안전성 + - 계획 생성 실패 시 v1 RAG 경로로 자동 폴백하여 기능 연속성 보장. + +## 4) v2 구조(파일과 역할) +- 라우팅/컨트롤러 + - `src/routes/ai.v2.routes.ts`: `/ai/v2/ask` SSE 라우트. + - `src/controllers/ai.v2.controller.ts`: SSE 헤더 설정, 서비스 호출/파이프. + - `src/app.ts`: `/ai/v2` 마운트. +- 플래너 LLM & 타입 + - `src/types/ai.v2.types.ts`: v2 요청/검색 계획 Zod 스키마. + - `src/prompts/qa.v2.prompts.ts`: 검색 계획 프롬프트/JSON 스키마. + - `src/services/search-plan.service.ts`: Responses API 호출 → 계획 JSON 검증/정규화. +- 시간 표준화 + - `src/utils/time.ts`: KST 기준 상대/월/분기/연도 → 절대기간 변환. +- 시맨틱 서치 + - `src/services/semantic-search.service.ts`: 질문 임베딩 생성 → 동적 저장소 호출. + - `src/repositories/post.repository.ts`: `findSimilarChunksV2`(가중치/임계치/기간/정렬/topK 파라미터 반영). +- 오케스트레이션 + - `src/services/qa.v2.service.ts`: `search_plan`→`search_result` 발행, v1 프롬프트 재사용으로 최종 LLM 스트림 중계. + +## 5) v2 요청→응답 흐름 +1) 클라이언트: `POST /ai/v2/ask` (JWT 필요) +2) 컨트롤러: SSE 헤더 설정 → `answerStreamV2` 호출 +3) post 모드(post_id 존재): 소유자 검증 → 계획/결과 이벤트 발행 → 본문 컨텍스트로 메시지 구성 → 답변 스트림 +4) rag 모드: 플래너 LLM으로 계획 생성 → Zod 검증/시간 표준화 → `search_plan` 이벤트 발행 → 시맨틱 서치 실행 → `search_result`/`exist_in_post_status`/`context` 발행 → RAG 프롬프트 구성 → 답변 스트림 +5) 공통: `src/llm` 레이어를 통해 `answer` 청크 스트리밍, 종료 시 `end` 이벤트 + +SSE 이벤트 순서 +- `search_plan` → `search_result` → `exist_in_post_status` → `context` → `answer`* → `end` +- 오류 시: `error` + +## 6) 모델/설정 +- 기본 LLM: `openai/gpt-5-mini`(요청 `llm` 또는 `CHAT_MODEL`로 변경 가능) +- 임베딩: `text-embedding-3-small` +- Gemini: 응답 스트림은 지원(계획 LLM은 현재 OpenAI 사용) + +## 7) 보안/안전 +- 계획 출력은 JSON Schema로 제한, 서버에서 Zod 검증 및 값 범위 강제. +- SQL은 파라미터 바인딩/화이트리스트 템플릿만 사용. +- post 모드에서 소유권 체크. +- SSE에는 민감정보 제외(제목/ID 등만 노출). + +## 8) 현재 동작과 향후 확장 +- 현재 + - `top_k`: 청크 단위 후보 개수(리콜 폭)로 SQL LIMIT 적용(v1과 동일). + - `limit`: 현재는 미적용(v1과 동일 동작 유지). 컨텍스트는 청크 Top-K를 그대로 사용하며, 포스트 단위 중복 제거도 하지 않음. + - `sort`: 유사도 우선 + 최신순/오래된순 보조 정렬. +- 향후 + - `limit` 적용을 재도입하여 컨텍스트 길이/비용 최적화(옵션화 가능). + - 포스트별 상위 N청크 추출(윈도우 함수) 설계로 깊이/다양성 균형. + - `search_sql` 이벤트(템플릿 ID + 파라미터 프리뷰)로 관측성 강화. + - 계획 LLM의 프로바이더 확장(Gemini) 및 프롬프트 튜닝 고도화. + +## 9) 사용 예시 +- 요청 바디 예시 +``` +{ + "question": "9월 글 2개만 보여줘", + "user_id": "", + "speech_tone": -1, + "llm": { "provider": "openai", "model": "gpt-5-mini" } +} +``` +- 기대 SSE 순서: `search_plan` → `search_result` → `exist_in_post_status` → `context` → `answer*` → `end` diff --git a/TASK.md b/TASK.md index 3bb0eca..6016664 100644 --- a/TASK.md +++ b/TASK.md @@ -1,188 +1,293 @@ -## 작업 계획 - -작업 순서 -- 1) LLM 모듈화(퍼사드/프로바이더/모델 레지스트리) + GPT-5 mini 기본 적용 -- 2) Gemini 도입: 병행 사용(전용) + 대체 사용(퍼사드) -- 3) 토큰 카운트 및 비용 로깅 추가(양 프로바이더 공통) - -참고: 아래 문서의 섹션 순서와 무관하게 실제 구현 순서는 위의 "작업 순서"를 따릅니다. - - -### 1) LLM 모듈화(퍼사드/프로바이더/모델 레지스트리) + GPT-5 mini 기본 적용 - -목적: LLM 호출을 모듈화하여 옵션 기반으로 모델/프로바이더를 교체 가능하게 만들고, 기본 모델을 `gpt-5-mini`로 전환합니다. 토크나이저/가격표는 3단계에서 처리합니다. - -1. [구조] 파일/모듈 구성 - - 디렉토리: `src/llm/` - - `src/llm/types.ts` — 공통 인터페이스 정의 - - `GenerateRequest`: `{ provider?: 'openai'|'gemini', model?: string, messages?: OpenAIStyleMessages, contents?: GeminiStyleContents, stream?: boolean, tools?, options?: { temperature?, top_p?, max_output_tokens?, reasoning?, text? }, meta?: { userId?, categoryId?, postId? } }` - - `GenerateStream`: `onToken(text)`, `onToolCall(json)`, `onEnd()`, `onError(err)`(또는 AsyncIterable) - - `src/llm/modelRegistry.ts` — 모델 레지스트리/기본값 - - 논리 모델 키 → `{ provider, modelId, defaults, tokenizerKey?, pricingKey? }` - - 기본값: `defaultChat = { provider: 'openai', modelId: 'gpt-5-mini' }` - - `src/llm/providers/openaiResponses.ts` — OpenAI Responses API 구현 - - `src/llm/providers/gemini.ts` — @google/gemini 구현 - - `src/llm/index.ts` — 퍼사드: `generate(req: GenerateRequest): GenerateStream` 선택 라우팅 - - 기존 서비스(`qa.service.ts`)는 퍼사드만 사용하도록 변경 - -2. [기본 모델] GPT-5 mini 적용(Responses API) - - `src/config.ts`의 `CHAT_MODEL` 기본값을 `gpt-5-mini`로 변경 - - OpenAI 경로: `openai.responses.create/stream`로 마이그레이션(SSE 어댑터 포함) - - 기존 Chat Completions 경로는 임시 백업/옵션으로 유지 가능(필요 시) - -3. [옵션 기반 모델/프로바이더 선택] - - 요청 바디에 `llm?: { provider?: 'openai'|'gemini', model?: string, options?: {...} }` 허용 - - 미지정 시 레지스트리의 기본값 사용(`gpt-5-mini` on OpenAI) - - 향후 기능(Reasoning/Text 옵션, tool calls, timeout 등) 확장 용이 - -4. [검증/수용 기준] - - `/ai/ask` SSE 정상 동작(중단/지연 없음) - - 기존 프롬프트/툴 호출이 동일하게 동작(필요 시 어댑터) - - 로그/오류 처리 기존 수준 유지 - -### 2) Gemini 도입: 병행 사용(전용) + 대체 사용(퍼사드) - -목적: Gemini를 독립 엔드포인트로 직접 쓰는 경로와, 기존 GPT 경로의 대체 제공자로 모두 사용할 수 있게 합니다(퍼사드 경유). 이후 3단계에서 토큰/비용 로깅을 공통 적용합니다. - -1. [Config] Gemini 키/모델 설정 - - `.env` - - `GEMINI_API_KEY=...` - - `GEMINI_CHAT_MODEL=gemini-2.5-flash` (예: 변경 가능) - - `src/config.ts`에 항목 반영 및 기본값/검증 추가(Provider 고정 ENV는 사용하지 않음) - -2. [Provider] 퍼사드에 Gemini 구현 추가 - - 1단계에서 만든 LLM 퍼사드(`src/llm/index.ts`)에 Gemini 프로바이더를 추가 - - 구현 위치: `src/llm/providers/gemini.ts` (OpenAI 구현은 `src/llm/providers/openaiResponses.ts`) - - 퍼사드 인터페이스로 라우팅되어 기존 `qa.service.ts`는 퍼사드만 사용(교체 투명) - -3. [Gemini 호출] @google/genai SDK 적용 및 스트리밍 - - 의존성: `@google/genai` 추가 (설치 커맨드: `npm i @google/genai`) - - 클라이언트: `import { GoogleGenAI } from "@google/genai"; const ai = new GoogleGenAI({});` (`GEMINI_API_KEY`는 환경변수에서 자동 주입) - - 비스트리밍(우선 적용): - - `ai.models.generateContent({ model: GEMINI_CHAT_MODEL, contents, config: { thinkingConfig: { thinkingBudget }}})` - - 기본값으로 `thinkingBudget=0`(생각 비활성화) 적용, `.env`에서 오버라이드 가능 - - 응답 텍스트를 한번에 수신한 뒤 SSE로 순차 chunk 분할하여 `answer` 이벤트로 전송(간단 구현) - - 스트리밍(선택 적용): - - SDK 제공 시 스트리밍 API 사용(예: `generateContentStream` 유사 기능)으로 델타를 받아 즉시 SSE로 전달 - - SDK에서 미지원일 경우, 비스트리밍으로 우선 릴리즈 후 스트리밍 전환 - - (옵션) Safety 설정, generationConfig(temperature/topP/maxOutputTokens) 파라미터는 설정값으로 노출 - -5. [토큰 카운팅] Gemini 대응 - - 사전 카운트(가능 시): SDK의 토큰 카운트 API(`tokens:count`/`countTokens`)가 제공되면 이를 사용해 프롬프트 토큰 계산 → 비용 선로깅 - - 네트워크 요청이므로 로깅 토글이 켜져 있을 때만 수행하도록 옵션화 - - 사후 카운트: 응답 텍스트 기준 동일 API로 출력 토큰 계산(또는 비가용 시 근사치) - - 폴백 전략: 카운트 API가 불가한 환경에서는 근사치 사용(문자수/4), 추후 정확도 개선 시 교체 - -6. [가격 정책] Gemini 추가 - - `src/config/pricing.ts`의 `PRICING_TABLE`에 Gemini 모델(`gemini-2.5-flash`, 임베딩 모델 등) 단가 추가 - - 동일한 `calcCost`, `formatCost` 로직 재사용 - -7. [생각(Thinking) 설정] 기본 비활성화 - - Gemini 2.5 Flash의 생각 기능은 응답 품질 대신 비용/지연이 증가하므로 기본 `thinkingBudget=0`으로 비활성화 - - `.env`에 `GEMINI_THINKING_BUDGET`를 두어 필요 시 활성화(정수값) - -8. [도구/함수 호출] 호환성 계획(선택) - - 현재 OpenAI `tool_calls`를 사용 중. Gemini는 `functionDeclarations`/`toolConfig` 형태로 유사 기능 제공 - - 1단계: Gemini 경로에서는 도구 호출 비활성화(빠른 도입) - - 2단계: 필요 시 `report_content_insufficient`를 Gemini `functionDeclarations`로 매핑하여 동일 동작 구현 - -9. [Wiring] 사용 패턴 - - 독립 사용(A): `POST /ai/gemini/ask`로 직접 호출(옵션: thinkingBudget 등) - - 대체 사용(B): 기존 `POST /ai/ask`에 `llm.provider?: 'openai'|'gemini'`, `llm.model?` 허용 → 퍼사드가 라우팅 - - 로깅 시 `provider` 필드를 포함(3단계에서 적용) - -10. [검증/수용 기준] - - OpenAI/Gemini 각각에서 동일한 SSE 응답 형식으로 동작 - - 요청 전/후 토큰·비용 로그가 두 프로바이더 모두에서 출력 - - 로깅 토글이 정상 작동, 스트리밍 성능 저하 없음 - -설정 확정 -- `GEMINI_CHAT_MODEL=gemini-2.5-flash` -- `GEMINI_THINKING_BUDGET=0` (기본값으로 비활성화) - -### 토큰 카운트 및 비용 로깅 추가 - -목적: LLM에 요청을 보내기 직전에 프롬프트(메시지) 토큰 수를 계산해 예상 입력 비용을 콘솔로 로깅하고, 스트리밍 응답 완료 후 실제 출력 토큰 수 기반 최종 비용을 추가 로깅합니다. 초기에는 `console.log`만 사용합니다. - -1. [Utils] 토크나이저 유틸 추가 - - 파일: `src/utils/tokenizer.ts` - - 내용: - - `getTokenizerForModel(model: string)` → 모델명에 따라 적절한 인코딩을 선택 - - `gpt-5*` → 자료 제공 전까지 임시로 `o200k_base` 사용(TBD, 전환 시 교체) - - `gpt-4o`, `gpt-4o-mini`, 기타 `o`계열 → `o200k_base` - - `countTextTokens(text: string, model: string): number` - - `countChatMessagesTokens(messages: OpenAI.Chat.Completions.ChatCompletionMessageParam[], model: string): number` - - 메시지 `content`들을 토크나이즈하여 합산하고, 채팅 포맷 오버헤드(메시지당 소량, 모델별 상수)를 보정치로 가산 - - 주의: 보정치는 근사치이며, 정확한 정산은 응답 토큰 합산으로 후처리 - - 비고: 이미 프로젝트에 `@dqbd/tiktoken`이 포함되어 있으므로 이를 사용합니다. - -2. [Config] 가격 정책 맵 구조 설계 (임시 하드코딩 + ENV 오버라이드) - - 파일: `src/config/pricing.ts` - - 내용: - - `export type Pricing = { input_per_1k: number; output_per_1k: number; cached_input_per_1k?: number; currency: 'USD' | 'KRW' }` - - `PRICING_TABLE: Record`: 모델명 키에 따른 단가 설정 - - 선택: `LLM_PRICING_OVERRIDES`(JSON) 환경변수로 런타임 오버라이드 허용 - - 초기값은 사용자 제공 정책으로 채울 예정. 제공 전까지는 로깅에 `N/A` 표기 또는 0 처리. - - - 초기 PRICING_TABLE(제공 정책 반영, 단위: per 1K tokens, 통화: USD) - - `gpt-5`: { input_per_1k: 0.00125, cached_input_per_1k: 0.000125, output_per_1k: 0.01, currency: 'USD' } - - `gpt-5-mini`: { input_per_1k: 0.00025, cached_input_per_1k: 0.000025, output_per_1k: 0.002, currency: 'USD' } - - `gpt-5-nano`: { input_per_1k: 0.00005, cached_input_per_1k: 0.000005, output_per_1k: 0.0004, currency: 'USD' } - -3. [Utils] 비용 계산 유틸 추가 - - 파일: `src/utils/cost.ts` - - 내용: - - `getModelPricing(model: string): Pricing | null` - - `calcCost(tokens: number, per_1k: number): number` → 반올림 1~4자리(옵션) - - 화폐 표기 함수(선택): `formatCost(amount: number, currency: string)` - -4. [Facade] LLM 퍼사드에 비용 로깅 통합 - - 위치: `src/llm/index.ts` 퍼사드 내부에서 공통 로깅 수행 - - 기능 흐름(공통): - 1) 요청 전: 메시지/콘텐츠 토큰 카운트 → `promptTokens` - - OpenAI: `countChatMessagesTokens`(토크나이저) - - Gemini: `countTokens` API 가능 시 사용(불가 시 근사치) - 2) 단가 조회: `getModelPricing(model)` → `estInputCost` - 3) 선로깅: `{type:'llm.request', provider, model, promptTokens, estInputCost, corrId, userId, categoryId, postId}` - 4) 실제 호출: 등록된 프로바이더(OpenAI Responses 또는 Gemini)로 위임, 스트림은 그대로 중계 - 5) 스트림 종료 후: 출력 텍스트/함수인자 토큰 합산 → `completionTokens` - 6) 비용 계산: 입력/출력(+cached 입력이 있으면 분리) → `totalCost` - 7) 후로깅: `{type:'llm.response', provider, model, promptTokens, completionTokens, inputCost, outputCost, totalCost, durationMs, corrId, cachedInputTokens}` - - 주의: 기존 SSE 흐름(이벤트명/포맷) 불변 유지. 퍼사드는 원본 델타를 그대로 전달. - - 상관관계 ID(`corrId`)는 `uuid` 생성(또는 요청별 식별자 전달 시 사용). - -5. [Wiring] `qa.service.ts`에서 퍼사드 사용 - - 기존 직접 호출부를 LLM 퍼사드로 교체(`generate(req)`) - - 요청 바디의 `llm` 옵션을 퍼사드에 그대로 전달(provider/model/options) - - 출력 토큰 카운트/비용 로깅은 퍼사드 내부에서 처리 - -6. [옵션] 임베딩 호출 비용 로깅(확장) - - 파일: `src/services/embedding.service.ts` - - `createEmbeddings` 호출 직전 `input` 텍스트 전체 토큰 수 계산(`countTextTokens` 누적) → 입력 비용 로깅 - - 임베딩 모델 단가(`text-embedding-3-*`)도 `PRICING_TABLE`에 포함 - -7. [환경변수] 로깅 토글 및 라운딩 - - `.env` 키 추가(기본값은 off) - - `LLM_COST_LOG=true|false` (기본: true로 해도 무방) - - `LLM_COST_ROUND=2` (소수점 자리수, 선택) - - 로깅은 토글 꺼져 있으면 수행하지 않음 - -8. [로그 포맷] 예시(JSON 라인) - - 요청 전: `{ "type": "llm.request", "corrId": "...", "provider": "openai", "model": "gpt-5-mini", "promptTokens": 1234, "estInputCost": 0.00031, "userId": "...", "categoryId": 1, "postId": 42 }` - - 응답 후: `{ "type": "llm.response", "corrId": "...", "provider": "openai", "model": "gpt-5-mini", "promptTokens": 1234, "completionTokens": 456, "inputCost": 0.00031, "outputCost": 0.00091, "totalCost": 0.00122, "durationMs": 987, "cachedInputTokens": 0 }` - -9. [검증/수용 기준] - - `POST /ai/ask` 호출 시 콘솔에 요청 전/후 로그 각각 1회 출력 - - 모델/프롬프트/토큰 수/예상 비용/총 비용/시간(ms)이 포함되어야 함 - - 로깅 on/off 토글 동작, 라운딩 반영 확인 - - 기존 SSE 동작(끊김/지연) 변화 없음 - -10. [주의/한계] - - 채팅 포맷 오버헤드는 모델별로 상이하며 근사치 사용. 최종 비용은 출력 토큰 카운트까지 반영해 오차 최소화 - - 스트리밍 API는 서버에서 사용량 메타를 즉시 제공하지 않으므로(비스트리밍과 달리), 응답 텍스트 기반 자체 카운트 수행 - - 함수 호출(tool_calls) 토큰은 인자 길이에 비례하여 증가. 누적 텍스트/인자 기반으로 동일하게 카운트 - - Cached Input 과금: 제공 API에서 캐시 히트 토큰 정보를 명시적으로 제공하는 경우에만 `cachedInputTokens`로 분리 산정. 그렇지 않으면 일반 입력으로 계산(보수적) - -11. [다음 단계(선택)] - - `console.log` → 구조화 로거(Pino/Winston)로 교체, 샘플링·보존 기간 설정 - - DB 또는 시계열(예: ClickHouse/Prometheus) 적재로 사용자별 비용 대시보드 구성 +# v2 설계 및 작업 계획 (LLM 주도 SQL 기반 시맨틱 서치 → 응답 스트리밍) + +## 목표 +- v1 API/흐름은 그대로 유지. +- v2에서 질의를 LLM이 “검색 계획(계획 JSON)”으로 변환 → 서버가 안전하게 실행하여 컨텍스트 확보 → 확보한 컨텍스트와 함께 LLM에 넘겨 최종 답변을 SSE로 스트리밍. + +## 적합성 평가 (요약) +- 장점 + - LLM이 질문 의도를 기반으로 동적 필터/가중치/Top-K/Threshold를 튜닝한 “검색 계획”을 생산하여 검색 적합도를 끌어올릴 수 있음. + - DB 스키마/비즈니스 제약을 반영해 검색 전략을 바꾸기 쉬움(프롬프트/제약 업데이트로 최적화 가능). +- 리스크 및 보완 + - “LLM이 직접 SQL 문자열”은 주입/스키마 일탈 위험. 안전하게 “계획(JSON)”을 생성하게 하고, 서버가 화이트리스트 템플릿/파라미터 바인딩으로 SQL을 구성하는 방식 권장. + - 벡터 연산 상 LLM은 임베딩을 생성할 수 없으므로, 서버가 질문 임베딩을 계산하여 파라미터로 주입해야 함. + - 실패/부적합 시 v1 RAG 경로로 폴백 필요. + +결론: “LLM → 검색 계획(JSON) → 서버가 안전 SQL 구성/실행 → 결과 컨텍스트로 최종 LLM”의 변형 설계가 적합하며 안전/유지보수에도 유리. + +## v2 전체 흐름 +1) 클라이언트가 `POST /ai/v2/ask`로 질문 전송(SSE 응답). +2) 서버가 LLM에 “검색 계획” 생성을 요청(계획 JSON; SQL 문자열 생성은 비권장, 옵션으로 지원). +3) 서버가 계획(JSON)을 검증/정규화 후 파라미터 바인딩으로 안전 SQL 실행(벡터 임베딩은 서버가 생성). +4) 검색 결과(문맥) 메타 이벤트 송신(`search_plan`, `search_result`). +5) 기존 v1과 동일한 형식의 최종 LLM 호출을 수행하고 `answer`를 스트리밍. +6) 종료 시 `end: [DONE]` 송신. + +## API 설계 +- Route: `POST /ai/v2/ask` (SSE) +- Auth: 기존 `authMiddleware` 재사용 +- Request Body (제안) + - `question: string` + - `user_id: string` + - `category_id?: number` + - `post_id?: number` (지정 시 해당 글 컨텍스트 중심) + - `speech_tone?: number` + - `llm?: { provider?: 'openai'|'gemini', model?: string, options?: { temperature?: number, top_p?: number, max_output_tokens?: number } }` +- SSE 이벤트(순서 권장) + - `event: search_plan` / `data: { ...계획JSON }` + - `event: search_sql` / `data: { templateId, paramsPreview }` (SQL 템플릿 경로 선택 시, 민감정보 제외 프리뷰) + - `event: search_result` / `data: [{ postId, postTitle }]` + - `event: exist_in_post_status` / `data: true|false` (v1과 동일) + - `event: context` / `data: [{ postId, postTitle }]` (v1과 동일) + - `event: answer` (여러 번) + - `event: end` / `data: [DONE]` + - `event: error` (오류 시) + +## LLM “검색 계획” 출력 스키마(권장) +```json +{ + "mode": "rag", // "rag" | "post" + "top_k": 5, // 1~20 범위 권장 + "threshold": 0.2, // 0.0~1.0, 유사도 하한 + "weights": { "chunk": 0.7, "title": 0.3 }, + "filters": { + "user_id": "", + "category_ids": [7], // 필요 시 + "post_id": 123, // mode=post 시 + "time": { // 시간/날짜 필터(선택) + "type": "relative", // "relative" | "absolute" | "month" | "year" | "quarter" + "unit": "day", // relative: "day" | "week" | "month" | "year" + "value": 30 // relative: 정수(양수) + // absolute: { from: ISO8601, to: ISO8601 } + // month: { month: 1..12, year?: number } + // year: { year: number } + // quarter: { quarter: 1..4, year?: number } + } + }, + "sort": "created_at_desc", // "created_at_desc" | "created_at_asc" + "limit": 5 // 결과 행 상한(서버 상한 적용) +} +``` +- 서버에서 Zod로 검증 후 기본값/상한선 적용(top_k max 10 등), 불일치 시 안전 폴백값 적용. +- SQL 템플릿은 서버가 선택/생성하며, 파라미터는 모두 바인딩 처리. + +### 시간/날짜 필터 처리 규칙 +- 서버 표준화 단계에서만 절대 기간으로 변환(Asia/Seoul 기준 권장): + - relative: 현재 시각 기준으로 from/to 계산(예: 최근 30일) + - month: 연도 미지정 시 현재 연도 사용(예: “9월” → 올해 9월 1일 00:00:00 ~ 9월 말 23:59:59) + - quarter/year: 분기·연도 경계 계산 +- 검증 실패·모호 값: 날짜 필터 제외 혹은 기본 30일 적용 후 로그 남기고 폴백 +- 모든 상수값은 서버에서 범위 강제(limit<=20, top_k<=10, threshold 0..1, weights 합=1) + +## SQL 실행(안전 구성) +- 벡터 임베딩: 서버가 `createEmbeddings([question])`로 생성. +- 화이트리스트 템플릿(예시) + - 공통: 사용자/카테고리/소유권 필터를 CTE에서 먼저 적용. + - 스코어링: `(w_chunk * (1 - (pc.embedding <=> $embed))) + (w_title * (1 - (pte.embedding <=> $embed))) AS score` + - 임계치: `WHERE (1 - (pc.embedding <=> $embed)) > $threshold` + - 날짜 필터: `AND bp.created_at BETWEEN $from AND $to` (표준화된 절대 기간 사용) + - 정렬/상한: `ORDER BY score DESC` + 옵션 정렬(`created_at_desc|asc`) 병합, `LIMIT $limit` +- 모든 동적 값은 파라미터로 바인딩하고, 가중치/상한 등은 서버에서 범위 제한. + +## 최종 LLM 호출(응답 생성) +- v1의 `generate` 재사용(프로바이더/모델 동일 정책). +- 프롬프트: v1의 `qa.prompts`를 재사용하되, v2에서는 `search_result` 컨텍스트를 그대로 투입. +- v1과 동일한 `report_content_insufficient` 툴 전략 유지(필요 시). + +## 디렉터리/컴포넌트 추가 계획 +- `src/routes/ai.v2.routes.ts` — v2 라우터(`POST /ask`). +- `src/controllers/ai.v2.controller.ts` — SSE 헤더 설정, 서비스 호출/파이프. +- `src/services/search-plan.service.ts` — LLM에 검색 계획 요청 및 스키마 검증. +- `src/services/semantic-search.service.ts` — 계획(JSON)→SQL 템플릿 매핑 및 안전 실행. +- `src/services/qa.v2.service.ts` — 전체 orchestrator: 계획 생성→검색→컨텍스트 이벤트→최종 LLM 스트림. +- `src/prompts/qa.v2.prompts.ts` — 검색 계획 LLM 프롬프트(스키마/제약 포함). +- `src/types/ai.v2.types.ts` — 요청/계획/응답 타입/Zod 스키마. +- `src/routes/ai.routes.ts`는 그대로, `app.ts`에 `/ai/v2` 라우트 추가. + +## 프롬프트 가이드(검색 계획용, 요지) +- 역할: “블로그 시맨틱 검색 플래너”. +- 입력: 질문, 사용자 메타(user_id/category_id/post_id), 스키마/제약 요약, 기본값. +- 출력: 상기 계획 JSON 스키마만 생성(그 외 텍스트 금지). +- 제약: 값 범위(Top-K, threshold, weights 합=1), 허용된 필드만. +- 실패 시: 기본값으로 귀결되는 최소 계획을 출력하도록 유도. +- 시간/날짜 처리 지침: + - 시스템 메시지에 현재 날짜/시간과 타임존(예: Asia/Seoul)을 제공 + - “최근/이번 달/지난주/9월/작년 9월” 등 자연어 시간을 위 스키마의 time 필드로 구조화 + - 연도 미지정 시 현재 연도 가정, 모호하면 relative 30일로 유도 + +## 예시 +- “최근 글 보여줘” + - LLM 계획(JSON): `{ "mode": "rag", "top_k": 5, "threshold": 0.2, "weights": { "chunk": 0.7, "title": 0.3 }, "filters": { "user_id": "", "time": { "type": "relative", "unit": "day", "value": 30 } }, "sort": "created_at_desc", "limit": 5 }` + - 서버 표준화: `time → absolute { from: <30일 전 00:00+09:00>, to: <지금+09:00> }`, `limit=5` + +- “9월 글 2개” + - LLM 계획(JSON): `{ "mode": "rag", "top_k": 5, "threshold": 0.2, "weights": { "chunk": 0.7, "title": 0.3 }, "filters": { "user_id": "", "time": { "type": "month", "month": 9 } }, "sort": "created_at_desc", "limit": 2 }` + - 서버 표준화: `time → absolute { from: YYYY-09-01T00:00:00+09:00, to: YYYY-09-30T23:59:59+09:00 } (연도 미지정 시 올해)`, `limit=2` + +## 폴백 전략 +- 계획 JSON 검증 실패/LLM 오류 → v1 RAG 경로로 폴백. +- 검색 결과 0건 → v1과 동일하게 `exist_in_post_status=false` 전송 후, 부족 안내 규칙에 맞춰 답변 유도. + +## 텔레메트리/비용 +- `llm.request/llm.response` 로그 기존 그대로 사용. +- v2 전용 디버그 이벤트: `debug.plan.start`, `debug.plan.json`, `debug.plan.sql`, `debug.plan.result` 등. + +## 마이그레이션/DB +- 추가 테이블 불필요(기존 `post_chunks`, `post_title_embeddings` 재사용). +- 필요 시 뷰/인덱스 최적화 검토: `post_chunks(post_id, embedding)`, `post_title_embeddings(post_id, embedding)`. + +## 보안/안전장치 +- LLM은 “계획 JSON”만 생성(기본). SQL은 서버가 템플릿/파라미터 바인딩으로 생성. +- 만약 SQL 문자열 모드가 필요하면: 템플릿 ID+파라미터만 허용하거나, 정규식/AST로 화이트리스트 검증 후 실행. +- SSE에 민감 값(user_id/token 등) 노출 금지. + +## 단계별 작업(체크리스트) +1) 스켈레톤 + - [ ] 라우터/컨트롤러 v2 추가(`/ai/v2/ask`) + - [ ] SSE 헤더/에러 핸들링 공통화 +2) 검색 계획 + - [ ] `search-plan.service` + `qa.v2.prompts` + Zod 스키마 + - [ ] 계획 실패 시 v1 경로 폴백 +3) 검색 실행 + - [ ] 임베딩 생성(질문) + - [ ] 템플릿 기반 안전 SQL 실행 + 파라미터 바인딩 + - [ ] `search_plan`, `search_result` SSE 송신 +4) 최종 응답 스트림 + - [ ] v1 `generate` 재사용하여 LLM 스트림 + - [ ] v1과 동일한 `answer`/`end`/`error` 이벤트 유지 +5) 관측성/테스트 + - [ ] 디버그/비용 로그 연결 + - [ ] 단위 테스트: 계획 검증/템플릿 빌더/리포지토리 + - [ ] 통합 테스트: SSE 이벤트 순서/형식 + +## 수용 기준(AC) +- `/ai/v2/ask`가 SSE로 동작하고, `search_plan`→`search_result`→`answer` 순으로 이벤트가 수신된다. +- 계획 JSON이 범위를 벗어나거나 실패해도 서버는 안전 폴백으로 정상 응답을 스트리밍한다. +- SQL은 파라미터 바인딩으로만 구성되며, 화이트리스트 템플릿을 벗어난 질의가 실행되지 않는다. +- v1과 동일한 최종 응답 품질을 유지하거나 개선한다. + +## 검색 계획 LLM 프롬프트(초안) + +목표: LLM이 “검색 계획 JSON”만 출력하도록 강제하여 서버가 안전하게 SQL 템플릿을 선택·실행할 수 있도록 한다. 자연어 시간(최근/이번 달/지난주/9월/작년 9월 등)을 구조화하고, 정렬/상한/가중치/임계치 값 제약을 지키도록 한다. + +입력 변수(서버가 주입) +- now_utc, now_kst: 현재 시간(ISO8601) +- timezone: 문자열(예: Asia/Seoul) +- user_id: 요청자의 사용자 ID +- category_id?: 선택, 존재 시 조상 기준 필터 +- post_id?: 선택, 존재 시 mode=post 우선 +- defaults: { top_k: 5, threshold: 0.2, weights: { chunk: 0.7, title: 0.3 }, sort: created_at_desc, limit: 5 } +- question: 사용자 질문 원문 + +출력 형식(반드시 엄수) +- 오직 하나의 JSON 객체만 출력. 추가 텍스트/주석/마크다운/설명 금지. +- 키/값은 스키마 내 필드만 사용. 불필요 필드 생성 금지. + +스키마(재확인) +{ + "mode": "rag" | "post", + "top_k": number, // 1..10 + "threshold": number, // 0..1 + "weights": { "chunk": number, "title": number }, // 0..1, 합=1 + "filters": { + "user_id": string, + "category_ids"?: number[], // 제공 시 우선 적용(카테고리 조상 ID) + "post_id"?: number, // mode=post 시 필수 + "time"?: { // 하나만 선택 + "type": "relative" | "absolute" | "month" | "year" | "quarter", + // relative + "unit"?: "day" | "week" | "month" | "year", + "value"?: number, + // absolute + "from"?: string, // ISO8601 + "to"?: string, // ISO8601 + // month + "month"?: number, // 1..12, year 없으면 현재 연도 가정 + "year"?: number, + // quarter + "quarter"?: number // 1..4, year 없으면 현재 연도 가정 + } + }, + "sort": "created_at_desc" | "created_at_asc", + "limit": number // 1..20 +} + +규칙(핵심) +- post_id가 존재하면 mode="post"를 사용하고 filters.post_id를 설정. 그렇지 않으면 mode="rag". +- category_id가 있으면 filters.category_ids에 포함(단일 ID여도 배열 사용 허용). +- 가중치 합은 1이 되도록 조정(기본 chunk 0.7, title 0.3). 범위를 넘을 경우 기본값 사용. +- top_k는 1..10, limit는 1..20, threshold는 0..1 범위로 제한. 미지정 시 기본값 사용. +- 시간 표현 해석: + - “최근/요즘/최근 N개” → relative(단, “N개”는 limit=N으로 반영; 기간은 기본 30일 유지) + - “최근 N일/주/달/년” → relative + { unit, value } + - “이번 달/이번 주/올해/올해 9월” → month/year/quarter로 구조화 + - “9월”처럼 연도 미지정 → 현재 연도 가정 + - “지난주/지난달/작년” → relative 또는 해당 단위 기간으로 구조화 + - 모호하거나 충돌 시 time 생략(서버 기본 30일 적용 예상) +- 정렬: “최근/최신/새로운” 등은 created_at_desc. “오래된”은 created_at_asc. +- 질문에 “N개”가 있으면 limit=N 반영(상한 20). +- JSON 이외 어떤 텍스트도 출력하지 말 것. + +시스템 프롬프트 템플릿 +""" +You are a Search Plan Generator for a Korean blogging platform. +Your task is to read the user question and output ONLY a JSON object that defines a safe search plan. + +Context: +- now_utc: {now_utc} +- now_kst: {now_kst} +- timezone: {timezone} +- user_id: {user_id} +- category_id: {category_id} +- post_id: {post_id} +- defaults: {"top_k":5,"threshold":0.2,"weights":{"chunk":0.7,"title":0.3},"sort":"created_at_desc","limit":5} + +Rules: +1) Output ONLY a single JSON object matching the schema below. No extra text. +2) Respect bounds: top_k 1..10, limit 1..20, threshold 0..1, weights in [0,1] and sum to 1. +3) If post_id exists, use mode="post" and include filters.post_id; else mode="rag". +4) If category_id exists, include it in filters.category_ids. +5) Interpret Korean temporal phrases into filters.time using the provided timezone. + - “최근/최신/요즘”: prefer sort=created_at_desc. If a concrete period is given (e.g., 최근 30일), use relative. + - “이번 달/이번 주/올해/올해 9월”: use month/year/quarter forms. + - Month without year (e.g., “9월”): assume current year. + - “지난주/지난달/작년”: use the respective period. + - If ambiguous, omit time (server applies defaults). +6) If the question asks for N items (e.g., “N개”), set limit=N within bounds. +7) Keep the weights to defaults unless a clear need implies otherwise. + +Schema: +{SCHEMA_JSON} + +Question (Korean): +{question} + +Respond with ONLY the JSON object. No markdown, no explanation. +""" + +Few-shot 예시 +- Q: "최근 글 보여줘" + 출력: + {"mode":"rag","top_k":5,"threshold":0.2,"weights":{"chunk":0.7,"title":0.3},"filters":{"user_id":"{user_id}","time":{"type":"relative","unit":"day","value":30}},"sort":"created_at_desc","limit":5} + +- Q: "9월 글 2개" + 출력: + {"mode":"rag","top_k":5,"threshold":0.2,"weights":{"chunk":0.7,"title":0.3},"filters":{"user_id":"{user_id}","time":{"type":"month","month":9}},"sort":"created_at_desc","limit":2} + +- Q: "카테고리 7의 최신 글 3개" + 출력: + {"mode":"rag","top_k":5,"threshold":0.2,"weights":{"chunk":0.7,"title":0.3},"filters":{"user_id":"{user_id}","category_ids":[7]},"sort":"created_at_desc","limit":3} + +- Q: "지난주에 쓴 포스트 중 추천해줘" + 출력: + {"mode":"rag","top_k":5,"threshold":0.2,"weights":{"chunk":0.7,"title":0.3},"filters":{"user_id":"{user_id}","time":{"type":"relative","unit":"week","value":1}},"sort":"created_at_desc","limit":5} + +- Q: "이 포스트(123) 기준으로 답해줘" + 출력: + {"mode":"post","top_k":5,"threshold":0.2,"weights":{"chunk":0.7,"title":0.3},"filters":{"user_id":"{user_id}","post_id":123},"sort":"created_at_desc","limit":5} + +검증 포인트(서버 측) +- JSON 파싱 실패 → v1 폴백 +- 값 범위 초과 → 기본값/상한으로 교정 +- time 표준화(절대 from/to 변환), 타임존 적용 +- category_ids/post_id 소유권 확인 diff --git a/src/app.ts b/src/app.ts index 6968488..dfd79c4 100644 --- a/src/app.ts +++ b/src/app.ts @@ -1,6 +1,7 @@ import express, { Express, Request, Response, NextFunction } from 'express'; import cors from 'cors'; import aiRouter from './routes/ai.routes'; +import aiV2Router from './routes/ai.v2.routes'; const app: Express = express(); @@ -21,6 +22,7 @@ app.get('/', (request: Request, response: Response) => { }); app.use('/ai', aiRouter); +app.use('/ai/v2', aiV2Router); // Central Error Handler app.use((err: Error, req: Request, res: Response, next: NextFunction) => { diff --git a/src/controllers/ai.v2.controller.ts b/src/controllers/ai.v2.controller.ts new file mode 100644 index 0000000..ed1f579 --- /dev/null +++ b/src/controllers/ai.v2.controller.ts @@ -0,0 +1,29 @@ +import { Request, Response, NextFunction } from 'express'; +import { AskV2Request } from '../types/ai.v2.types'; +import { answerStreamV2 } from '../services/qa.v2.service'; + +export const askV2Handler = async ( + req: Request<{}, {}, AskV2Request>, + res: Response, + next: NextFunction +) => { + try { + const { question, user_id, category_id, speech_tone, post_id, llm } = req.body as any; + res.setHeader('Content-Type', 'text/event-stream'); + res.setHeader('Cache-Control', 'no-cache'); + res.setHeader('Connection', 'keep-alive'); + + const stream = await answerStreamV2( + question, + user_id, + category_id, + speech_tone, + post_id, + llm + ); + stream.pipe(res); + } catch (error) { + next(error); + } +}; + diff --git a/src/prompts/qa.v2.prompts.ts b/src/prompts/qa.v2.prompts.ts new file mode 100644 index 0000000..da03657 --- /dev/null +++ b/src/prompts/qa.v2.prompts.ts @@ -0,0 +1,142 @@ +// Search Plan prompt templates for v2 + +import { SearchPlan } from '../types/ai.v2.types'; + +export const getSearchPlanSchemaJson = (): Record => ({ + type: 'object', + additionalProperties: false, + properties: { + mode: { enum: ['rag', 'post'] }, + top_k: { type: 'integer', minimum: 1, maximum: 10 }, + threshold: { type: 'number', minimum: 0, maximum: 1 }, + weights: { + type: 'object', + additionalProperties: false, + properties: { + chunk: { type: 'number', minimum: 0, maximum: 1 }, + title: { type: 'number', minimum: 0, maximum: 1 }, + }, + required: ['chunk', 'title'], + }, + filters: { + type: 'object', + additionalProperties: false, + properties: { + user_id: { type: 'string' }, + category_ids: { type: 'array', items: { type: 'integer' } }, + post_id: { type: 'integer' }, + time: { + oneOf: [ + { + type: 'object', + additionalProperties: false, + properties: { + type: { const: 'relative' }, + unit: { enum: ['day', 'week', 'month', 'year'] }, + value: { type: 'integer', minimum: 1 }, + }, + required: ['type', 'unit', 'value'], + }, + { + type: 'object', + additionalProperties: false, + properties: { + type: { const: 'absolute' }, + from: { type: 'string' }, + to: { type: 'string' }, + }, + required: ['type', 'from', 'to'], + }, + { + type: 'object', + additionalProperties: false, + properties: { + type: { const: 'month' }, + month: { type: 'integer', minimum: 1, maximum: 12 }, + year: { type: 'integer' }, + }, + required: ['type', 'month'], + }, + { + type: 'object', + additionalProperties: false, + properties: { + type: { const: 'year' }, + year: { type: 'integer' }, + }, + required: ['type', 'year'], + }, + { + type: 'object', + additionalProperties: false, + properties: { + type: { const: 'quarter' }, + quarter: { type: 'integer', minimum: 1, maximum: 4 }, + year: { type: 'integer' }, + }, + required: ['type', 'quarter'], + }, + ], + }, + }, + required: ['user_id'], + }, + sort: { enum: ['created_at_desc', 'created_at_asc'] }, + limit: { type: 'integer', minimum: 1, maximum: 20 }, + }, + required: ['mode', 'top_k', 'threshold', 'weights', 'filters', 'sort', 'limit'], +}); + +export const buildSearchPlanPrompt = (params: { + now_utc: string; + now_kst: string; + timezone: string; + user_id: string; + category_id?: number; + post_id?: number; + defaults?: Partial; + question: string; +}): string => { + const defaults = JSON.stringify( + params.defaults || { + top_k: 5, + threshold: 0.2, + weights: { chunk: 0.7, title: 0.3 }, + sort: 'created_at_desc', + limit: 5, + mode: 'rag', + filters: { user_id: params.user_id }, + }, + ); + + const schemaHint = JSON.stringify(getSearchPlanSchemaJson()); + + return [ + 'You are a Search Plan Generator for a Korean blogging platform.', + 'Your task is to read the user question and output ONLY a JSON object that defines a safe search plan.', + '', + `now_utc: ${params.now_utc}`, + `now_kst: ${params.now_kst}`, + `timezone: ${params.timezone}`, + `user_id: ${params.user_id}`, + `category_id: ${params.category_id ?? ''}`, + `post_id: ${params.post_id ?? ''}`, + `defaults: ${defaults}`, + '', + 'Rules:', + '1) Output ONLY a single JSON object matching the schema. No extra text.', + '2) Respect bounds: top_k 1..10, limit 1..20, threshold 0..1, weights in [0,1] and sum to 1.', + '3) If post_id exists, use mode="post" and include filters.post_id; else mode="rag".', + '4) If category_id exists, include it in filters.category_ids.', + '5) Interpret Korean temporal phrases into filters.time using the provided timezone. Month without year assumes current year.', + '6) If the question asks for N items (e.g., “N개”), set limit=N within bounds.', + '7) Keep weights to defaults unless a clear need implies otherwise.', + '', + `Schema: ${schemaHint}`, + '', + `Question (Korean):\n${params.question}`, + '', + 'Respond with ONLY the JSON object. No markdown, no explanation.', + ].join('\n'); +}; + diff --git a/src/repositories/post.repository.ts b/src/repositories/post.repository.ts index 545dc57..6af3af3 100644 --- a/src/repositories/post.repository.ts +++ b/src/repositories/post.repository.ts @@ -131,4 +131,97 @@ export const storeContentEmbeddings = async ( await pool.query('ROLLBACK'); throw error; } -}; \ No newline at end of file +}; + +// ========= READ QUERIES (V2 dynamic) ========= +export const findSimilarChunksV2 = async (params: { + userId: string; + embedding: number[]; + categoryId?: number; + from?: string; // ISO UTC + to?: string; // ISO UTC + threshold?: number; // 0..1 + topK?: number; // default 5, max 10 + weights?: { chunk: number; title: number }; + sort?: 'created_at_desc' | 'created_at_asc'; +}): Promise => { + const pool = getDb(); + const wChunk = Math.max(0, Math.min(1, params.weights?.chunk ?? 0.7)); + const wTitle = Math.max(0, Math.min(1, params.weights?.title ?? 0.3)); + const thr = params.threshold != null ? Math.max(0, Math.min(1, params.threshold)) : 0.2; + const limit = Math.min(10, Math.max(1, params.topK ?? 5)); + + const parts: string[] = []; + const values: any[] = []; + + // $1: userId, $2: embedding + values.push(params.userId); + values.push(pgvector.toSql(params.embedding)); + + const hasCategory = typeof params.categoryId === 'number'; + const hasTime = !!(params.from && params.to); + + if (hasCategory) { + const catParam = values.length + 1; // next index + parts.push(` + WITH category_ids AS ( + SELECT DISTINCT cc.descendant_id + FROM category_closure cc + WHERE cc.ancestor_id = $${catParam} + ), + filtered_posts AS ( + SELECT bp.id AS post_id, bp.title AS post_title, bp.created_at + FROM blog_post bp + WHERE bp.user_id = $1 AND bp.category_id IN (SELECT descendant_id FROM category_ids) + )`); + values.push(params.categoryId); + } else { + parts.push(` + WITH filtered_posts AS ( + SELECT id AS post_id, title AS post_title, created_at + FROM blog_post + WHERE user_id = $1 + )`); + } + + // base select and threshold + const thrParam = values.length + 1; + parts.push(` + SELECT + fp.post_id, + fp.post_title, + pc.content AS post_chunk, + (${wChunk} * (1.0 - (pc.embedding <=> $2))) + (${wTitle} * (1.0 - (pte.embedding <=> $2))) AS similarity_score, + fp.created_at + FROM filtered_posts fp + JOIN post_chunks pc ON pc.post_id = fp.post_id + JOIN post_title_embeddings pte ON pte.post_id = fp.post_id + WHERE (1.0 - (pc.embedding <=> $2)) > $${thrParam} + `); + values.push(thr); + + if (hasTime) { + const fromParam = values.length + 1; + const toParam = values.length + 2; + parts.push(` AND fp.created_at BETWEEN $${fromParam} AND $${toParam}`); + values.push(params.from, params.to); + } + + let orderBy = 'similarity_score DESC'; + if (params.sort === 'created_at_desc') orderBy = 'similarity_score DESC, fp.created_at DESC'; + if (params.sort === 'created_at_asc') orderBy = 'similarity_score DESC, fp.created_at ASC'; + + const limitParam = values.length + 1; + parts.push(` ORDER BY ${orderBy} LIMIT $${limitParam}`); + values.push(limit); + + const sql = parts.join('\n'); + + const { rows } = await pool.query(sql, values); + return rows.map((row) => ({ + postId: row.post_id, + postTitle: row.post_title, + postChunk: row.post_chunk, + similarityScore: row.similarity_score, + })); +}; diff --git a/src/routes/ai.v2.routes.ts b/src/routes/ai.v2.routes.ts new file mode 100644 index 0000000..2558023 --- /dev/null +++ b/src/routes/ai.v2.routes.ts @@ -0,0 +1,14 @@ +import { Router } from 'express'; +import { askV2Handler } from '../controllers/ai.v2.controller'; +import { authMiddleware } from '../middlewares/auth.middleware'; + +const aiV2Router = Router(); + +aiV2Router.get('/health', (req, res) => { + res.status(200).json({ status: 'ok', v: 'v2' }); +}); + +aiV2Router.post('/ask', authMiddleware, askV2Handler); + +export default aiV2Router; + diff --git a/src/services/qa.v2.service.ts b/src/services/qa.v2.service.ts new file mode 100644 index 0000000..ef2db41 --- /dev/null +++ b/src/services/qa.v2.service.ts @@ -0,0 +1,208 @@ +import { PassThrough } from 'stream'; +import { generate } from '../llm'; +import config from '../config'; +import * as qaPrompts from '../prompts/qa.prompts'; +import * as postRepository from '../repositories/post.repository'; +import * as personaRepository from '../repositories/persona.repository'; +import { generateSearchPlan } from './search-plan.service'; +import { runSemanticSearch } from './semantic-search.service'; + +const preprocessContent = (content: string): string => { + const plainText = content.replace(/<[^>]*>/g, ' ').replace(/\s+/g, ' ').trim(); + return plainText.length > 40000 ? plainText.substring(0, 40000) : plainText; +}; + +const getSpeechTonePrompt = async (speechTone: number, userId: string): Promise => { + if (speechTone === -1) return '간결하고 명확한 말투로 답변해'; + if (speechTone === -2) + return '아래의 블로그 본문 컨텍스트를 참고하여 본문의 말투를 파악해 최대한 비슷한 말투로 답변해'; + + const persona = await personaRepository.findPersonaById(speechTone, userId); + if (persona) return `${persona.name}: ${persona.description}`; + return '간결하고 명확한 말투로 답변해'; +}; + +type LlmOverride = { + provider?: 'openai' | 'gemini'; + model?: string; + options?: { temperature?: number; top_p?: number; max_output_tokens?: number }; +}; + +export const answerStreamV2 = async ( + question: string, + userId: string, + categoryId?: number, + speechTone: number = -1, + postId?: number, + llm?: LlmOverride +): Promise => { + const stream = new PassThrough(); + + (async () => { + const speechTonePrompt = await getSpeechTonePrompt(speechTone, userId); + + let messages: { role: 'system' | 'user' | 'assistant' | 'tool' | 'function'; content: string }[] = []; + let tools: + | { + type: 'function'; + function: { name: string; description?: string; parameters?: Record }; + }[] + | undefined = undefined; + + const toSimpleMessages = ( + raw: any[] + ): { role: 'system' | 'user' | 'assistant' | 'tool' | 'function'; content: string }[] => { + return (raw || []).map((m: any) => ({ + role: m.role, + content: typeof m.content === 'string' ? m.content : JSON.stringify(m.content), + })); + }; + + if (postId) { + // Post-centric path (same as v1 with added v2 pre-events) + const post = await postRepository.findPostById(postId); + if (!post || post.user_id !== userId) { + const code = !post ? 404 : 403; + stream.write(`event: error\n`); + stream.write(`data: ${JSON.stringify({ code, message: !post ? 'Post not found' : 'Forbidden' })}\n\n`); + stream.end(); + return; + } + // Emit plan event for transparency + stream.write(`event: search_plan\n`); + stream.write( + `data: ${JSON.stringify({ mode: 'post', filters: { post_id: postId, user_id: userId } })}\n\n` + ); + + const processed = preprocessContent(post.content); + const ctx = [{ postId: post.id, postTitle: post.title }]; + stream.write(`event: search_result\n`); + stream.write(`data: ${JSON.stringify(ctx)}\n\n`); + stream.write(`event: exist_in_post_status\n`); + stream.write(`data: true\n\n`); + stream.write(`event: context\n`); + stream.write(`data: ${JSON.stringify(ctx)}\n\n`); + + messages = toSimpleMessages( + qaPrompts.createPostContextPrompt(post, processed, question, speechTonePrompt) + ); + } else { + // Plan generation + const planPair = await generateSearchPlan(question, { user_id: userId, category_id: categoryId }); + if (!planPair) { + // Fallback to v1 RAG silently + const [questionEmbedding] = await (await import('./embedding.service')).createEmbeddings([ + question, + ]); + const similarChunks = await postRepository.findSimilarChunks(userId, questionEmbedding, categoryId); + const context = similarChunks.map((c) => ({ postId: c.postId, postTitle: c.postTitle })); + stream.write(`event: search_plan\n`); + stream.write(`data: ${JSON.stringify({ mode: 'rag', fallback: true })}\n\n`); + stream.write(`event: search_result\n`); + stream.write(`data: ${JSON.stringify(context)}\n\n`); + stream.write(`event: exist_in_post_status\n`); + stream.write(`data: ${JSON.stringify(similarChunks.length > 0)}\n\n`); + stream.write(`event: context\n`); + stream.write(`data: ${JSON.stringify(context)}\n\n`); + + messages = toSimpleMessages( + qaPrompts.createRagPrompt(question, similarChunks, speechTonePrompt) + ); + tools = [ + { + type: 'function', + function: { + name: 'report_content_insufficient', + description: '카테고리는 맞지만 본문 컨텍스트가 부족할 때 호출', + parameters: { + type: 'object', + properties: { + text: { + type: 'string', + description: + '답변 말투 및 규칙을 지켜 해당 내용이 아직 부족하다는 안내를 합니다. 그 후 본문 컨텍스트를 참고해 질문과 관련된 답변할 수 있는 내용을 언급하고 해당 내용에 대한 질문을 직접적으로 유도합니다.', + }, + }, + required: ['text'], + }, + }, + }, + ]; + } else { + const plan = planPair.normalized; + stream.write(`event: search_plan\n`); + stream.write(`data: ${JSON.stringify(plan)}\n\n`); + + // Execute semantic search per plan + const rows = await runSemanticSearch(question, userId, plan); + // v1과 동일하게: 청크 Top-K를 그대로 사용(포스트 단위 dedupe/limit 미적용) + const context = rows.map((r) => ({ postId: r.postId, postTitle: r.postTitle })); + stream.write(`event: search_result\n`); + stream.write(`data: ${JSON.stringify(context)}\n\n`); + stream.write(`event: exist_in_post_status\n`); + stream.write(`data: ${JSON.stringify(rows.length > 0)}\n\n`); + stream.write(`event: context\n`); + stream.write(`data: ${JSON.stringify(context)}\n\n`); + + messages = toSimpleMessages( + qaPrompts.createRagPrompt( + question, + rows.map((r) => ({ + postId: r.postId, + postTitle: r.postTitle, + postChunk: r.postChunk, + similarityScore: r.similarityScore, + })) as any, + speechTonePrompt + ) + ); + tools = [ + { + type: 'function', + function: { + name: 'report_content_insufficient', + description: '카테고리는 맞지만 본문 컨텍스트가 부족할 때 호출', + parameters: { + type: 'object', + properties: { + text: { + type: 'string', + description: + '답변 말투 및 규칙을 지켜 해당 내용이 아직 부족하다는 안내를 합니다. 그 후 본문 컨텍스트를 참고해 질문과 관련된 답변할 수 있는 내용을 언급하고 해당 내용에 대한 질문을 직접적으로 유도합니다.', + }, + }, + required: ['text'], + }, + }, + }, + ]; + } + } + + const llmStream = await generate({ + provider: llm?.provider || 'openai', + model: llm?.model || config.CHAT_MODEL, + messages, + tools, + options: llm?.options, + meta: { userId, categoryId, postId }, + }); + + llmStream.on('data', (chunk) => stream.write(chunk)); + llmStream.on('end', () => stream.end()); + llmStream.on('error', () => { + stream.write(`event: error\n`); + stream.write(`data: ${JSON.stringify({ message: 'Internal server error' })}\n\n`); + stream.end(); + }); + })().catch((err) => { + try { + console.error('v2 Stream process error:', err); + } catch {} + stream.write(`event: error\n`); + stream.write(`data: ${JSON.stringify({ message: 'Internal server error' })}\n\n`); + stream.end(); + }); + + return stream; +}; diff --git a/src/services/search-plan.service.ts b/src/services/search-plan.service.ts new file mode 100644 index 0000000..f2861da --- /dev/null +++ b/src/services/search-plan.service.ts @@ -0,0 +1,102 @@ +import OpenAI from 'openai'; +import config from '../config'; +import { buildSearchPlanPrompt, getSearchPlanSchemaJson } from '../prompts/qa.v2.prompts'; +import { planSchema, type SearchPlan } from '../types/ai.v2.types'; +import { nowUtc, toAbsoluteRangeKst } from '../utils/time'; + +const openai = new OpenAI({ apiKey: config.OPENAI_API_KEY }); + +export type PlanContext = { + user_id: string; + category_id?: number; + post_id?: number; + timezone?: string; // default Asia/Seoul +}; + +export const generateSearchPlan = async ( + question: string, + ctx: PlanContext +): Promise<{ plan: SearchPlan; normalized: SearchPlan } | null> => { + const timezone = ctx.timezone || 'Asia/Seoul'; + const now = nowUtc(); + const nowUtcIso = now.toISOString(); + const nowKstIso = new Date(now.getTime() + 9 * 3600 * 1000).toISOString(); + + const prompt = buildSearchPlanPrompt({ + now_utc: nowUtcIso, + now_kst: nowKstIso, + timezone, + user_id: ctx.user_id, + category_id: ctx.category_id, + post_id: ctx.post_id, + question, + }); + + const schema = getSearchPlanSchemaJson(); + + try { + const response: any = await (openai as any).responses.create({ + model: config.CHAT_MODEL || 'gpt-5-mini', + input: [{ role: 'user', content: [{ type: 'input_text', text: prompt }] }], + response_format: { type: 'json_schema', json_schema: { name: 'SearchPlan', schema, strict: true } }, + max_output_tokens: 600, + }); + + const text = (response as any)?.output_text || ''; + const raw = text && text.trim().startsWith('{') ? text : (() => { + // Fallback: try to extract JSON from response.output + try { + const outputs = (response as any).output || []; + const parts = outputs + .flatMap((o: any) => o.content || []) + .filter((c: any) => typeof c?.text === 'string') + .map((c: any) => c.text) + .join(''); + return parts; + } catch { + return ''; + } + })(); + const parsed = JSON.parse(raw); + const plan = planSchema.parse(parsed); + + // Normalize weights sum to 1 + const sum = (plan.weights?.chunk ?? 0) + (plan.weights?.title ?? 0); + const weights = sum > 0 ? { chunk: plan.weights.chunk / sum, title: plan.weights.title / sum } : { chunk: 0.7, title: 0.3 }; + + // Normalize time range to absolute if provided + let normPlan: SearchPlan = { ...plan, weights }; + if (plan.filters?.time) { + const abs = toAbsoluteRangeKst(plan.filters.time as any, now); + if (abs) { + normPlan = { + ...normPlan, + filters: { ...normPlan.filters, time: { type: 'absolute', from: abs.from, to: abs.to } as any }, + }; + } else { + // drop invalid time + const { time, ...rest } = normPlan.filters || ({} as any); + normPlan = { ...normPlan, filters: rest as any }; + } + } + + // Enforce bounds just in case + normPlan.top_k = Math.min(10, Math.max(1, normPlan.top_k || 5)); + normPlan.limit = Math.min(20, Math.max(1, normPlan.limit || 5)); + normPlan.threshold = Math.min(1, Math.max(0, normPlan.threshold ?? 0.2)); + if (!normPlan.mode) normPlan.mode = (ctx.post_id ? 'post' : 'rag') as any; + if (ctx.category_id && !normPlan.filters.category_ids) normPlan.filters.category_ids = [ctx.category_id]; + if (ctx.post_id) { + normPlan.mode = 'post'; + normPlan.filters.post_id = ctx.post_id; + } + + return { plan, normalized: normPlan }; + } catch (e) { + try { + console.error(JSON.stringify({ type: 'debug.plan.error', message: (e as any)?.message || 'error' })); + } catch {} + return null; + } +}; + diff --git a/src/services/semantic-search.service.ts b/src/services/semantic-search.service.ts new file mode 100644 index 0000000..05561ac --- /dev/null +++ b/src/services/semantic-search.service.ts @@ -0,0 +1,36 @@ +import { createEmbeddings } from './embedding.service'; +import * as postRepository from '../repositories/post.repository'; +import { SearchPlan } from '../types/ai.v2.types'; + +export type SemanticSearchResult = { + postId: string; + postTitle: string; + postChunk: string; + similarityScore: number; +}[]; + +export const runSemanticSearch = async ( + question: string, + userId: string, + plan: SearchPlan +): Promise => { + const [embedding] = await createEmbeddings([question]); + + const from = (plan.filters as any)?.time?.type === 'absolute' ? (plan.filters as any).time.from : undefined; + const to = (plan.filters as any)?.time?.type === 'absolute' ? (plan.filters as any).time.to : undefined; + + const rows = await postRepository.findSimilarChunksV2({ + userId, + embedding, + categoryId: (plan.filters as any)?.category_ids?.[0], + from, + to, + threshold: plan.threshold, + topK: plan.top_k, + weights: plan.weights, + sort: plan.sort, + }); + + return rows; +}; + diff --git a/src/types/ai.v2.types.ts b/src/types/ai.v2.types.ts new file mode 100644 index 0000000..f10debb --- /dev/null +++ b/src/types/ai.v2.types.ts @@ -0,0 +1,79 @@ +import { z } from 'zod'; + +// ===== Plan JSON Schema (Zod) ===== + +export const timeFilterSchema = z.discriminatedUnion('type', [ + z.object({ + type: z.literal('relative'), + unit: z.enum(['day', 'week', 'month', 'year']), + value: z.number().int().positive(), + }), + z.object({ + type: z.literal('absolute'), + from: z.string(), // ISO8601 + to: z.string(), // ISO8601 + }), + z.object({ + type: z.literal('month'), + month: z.number().int().min(1).max(12), + year: z.number().int().optional(), + }), + z.object({ + type: z.literal('year'), + year: z.number().int(), + }), + z.object({ + type: z.literal('quarter'), + quarter: z.number().int().min(1).max(4), + year: z.number().int().optional(), + }), +]); + +export const planSchema = z.object({ + mode: z.enum(['rag', 'post']).default('rag'), + top_k: z.number().int().min(1).max(10).default(5), + threshold: z.number().min(0).max(1).default(0.2), + weights: z + .object({ chunk: z.number().min(0).max(1), title: z.number().min(0).max(1) }) + .default({ chunk: 0.7, title: 0.3 }), + filters: z + .object({ + user_id: z.string(), + category_ids: z.array(z.number().int()).optional(), + post_id: z.number().int().optional(), + time: timeFilterSchema.optional(), + }) + .default({ user_id: '' }), + sort: z.enum(['created_at_desc', 'created_at_asc']).default('created_at_desc'), + limit: z.number().int().min(1).max(20).default(5), +}); + +export type SearchPlan = z.infer; + +// ===== API: /ai/v2/ask ===== + +export const askV2Schema = z.object({ + body: z.object({ + question: z.string(), + user_id: z.string(), + category_id: z.number().optional(), + post_id: z.number().optional(), + speech_tone: z.number().optional(), + llm: z + .object({ + provider: z.enum(['openai', 'gemini']).optional(), + model: z.string().optional(), + options: z + .object({ + temperature: z.number().optional(), + top_p: z.number().optional(), + max_output_tokens: z.number().optional(), + }) + .optional(), + }) + .optional(), + }), +}); + +export type AskV2Request = z.infer['body']; + diff --git a/src/utils/time.ts b/src/utils/time.ts new file mode 100644 index 0000000..406a16e --- /dev/null +++ b/src/utils/time.ts @@ -0,0 +1,82 @@ +// Minimal KST time utilities and range normalization + +const KST_OFFSET_MINUTES = 9 * 60; // UTC+9 + +const toDate = (isoOrDate: string | Date): Date => (isoOrDate instanceof Date ? isoOrDate : new Date(isoOrDate)); + +export const nowUtc = (): Date => new Date(); + +export const toKst = (d: Date): Date => { + // Convert UTC date to KST by adding offset + return new Date(d.getTime() + KST_OFFSET_MINUTES * 60 * 1000); +}; + +export const fromKstToUtc = (d: Date): Date => { + return new Date(d.getTime() - KST_OFFSET_MINUTES * 60 * 1000); +}; + +const startOfDay = (d: Date): Date => new Date(d.getFullYear(), d.getMonth(), d.getDate(), 0, 0, 0, 0); +const endOfDay = (d: Date): Date => new Date(d.getFullYear(), d.getMonth(), d.getDate(), 23, 59, 59, 999); + +export const startOfMonth = (year: number, monthIndex0: number): Date => new Date(year, monthIndex0, 1, 0, 0, 0, 0); +export const endOfMonth = (year: number, monthIndex0: number): Date => new Date(year, monthIndex0 + 1, 0, 23, 59, 59, 999); + +export const startOfQuarter = (year: number, quarter: number): Date => { + const m0 = (quarter - 1) * 3; // 0-based month index + return new Date(year, m0, 1, 0, 0, 0, 0); +}; +export const endOfQuarter = (year: number, quarter: number): Date => { + const m0 = quarter * 3 - 1; // end month index + return new Date(year, m0 + 1, 0, 23, 59, 59, 999); +}; + +export type AbsoluteRange = { from: string; to: string }; + +export const toAbsoluteRangeKst = (input: { type: string; [k: string]: any }, base: Date = nowUtc()): AbsoluteRange | null => { + try { + const baseKst = toKst(base); + const year = baseKst.getFullYear(); + if (input.type === 'relative') { + const unit = String(input.unit); + const value = Math.max(1, parseInt(String(input.value || '1'), 10)); + const toK = endOfDay(baseKst); + const fromK = new Date(toK.getTime()); + if (unit === 'day') fromK.setDate(fromK.getDate() - value + 1); + else if (unit === 'week') fromK.setDate(fromK.getDate() - value * 7 + 1); + else if (unit === 'month') fromK.setMonth(fromK.getMonth() - value); + else if (unit === 'year') fromK.setFullYear(fromK.getFullYear() - value); + const fromUtc = fromKstToUtc(startOfDay(fromK)); + const toUtc = fromKstToUtc(toK); + return { from: fromUtc.toISOString(), to: toUtc.toISOString() }; + } + if (input.type === 'absolute') { + const from = new Date(input.from); + const to = new Date(input.to); + return { from: from.toISOString(), to: to.toISOString() }; + } + if (input.type === 'month') { + const m = Math.max(1, Math.min(12, parseInt(String(input.month), 10))); + const y = input.year ? parseInt(String(input.year), 10) : year; + const fromK = startOfMonth(y, m - 1); + const toK = endOfMonth(y, m - 1); + return { from: fromKstToUtc(fromK).toISOString(), to: fromKstToUtc(toK).toISOString() }; + } + if (input.type === 'year') { + const y = parseInt(String(input.year), 10); + const fromK = startOfMonth(y, 0); + const toK = endOfMonth(y, 11); + return { from: fromKstToUtc(fromK).toISOString(), to: fromKstToUtc(toK).toISOString() }; + } + if (input.type === 'quarter') { + const q = Math.max(1, Math.min(4, parseInt(String(input.quarter), 10))); + const y = input.year ? parseInt(String(input.year), 10) : year; + const fromK = startOfQuarter(y, q); + const toK = endOfQuarter(y, q); + return { from: fromKstToUtc(fromK).toISOString(), to: fromKstToUtc(toK).toISOString() }; + } + } catch { + // ignore + } + return null; +}; + From f7e09a6a0c5c8011f218d5aa6b662cf54f91dfb6 Mon Sep 17 00:00:00 2001 From: chan000518 Date: Sun, 5 Oct 2025 15:09:54 +0900 Subject: [PATCH 06/14] =?UTF-8?q?=F0=9F=93=9D=20askV2=20=EB=AC=B8=EC=84=9C?= =?UTF-8?q?=20=EC=B6=94=EA=B0=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../reports/REPORT-askv2.md | 7 +++++-- docs/structureDiagram/askV1-structure.png | Bin 0 -> 149241 bytes docs/structureDiagram/askV2-structure.png | Bin 0 -> 131020 bytes 3 files changed, 5 insertions(+), 2 deletions(-) rename REPORT-askv2.md => docs/reports/REPORT-askv2.md (97%) create mode 100644 docs/structureDiagram/askV1-structure.png create mode 100644 docs/structureDiagram/askV2-structure.png diff --git a/REPORT-askv2.md b/docs/reports/REPORT-askv2.md similarity index 97% rename from REPORT-askv2.md rename to docs/reports/REPORT-askv2.md index b0d5e12..6bc5641 100644 --- a/REPORT-askv2.md +++ b/docs/reports/REPORT-askv2.md @@ -1,4 +1,4 @@ -# 보고서: /ai/v2/ask 구현 내역과 구조(한국어) +# 보고서: /ai/v2/ask 구현 내역과 구조 ## 1) 개요 - 목적: v1의 고정형 RAG 흐름을 개선하기 위해, 사용자의 질문을 LLM이 “검색 계획(JSON)”으로 구조화 → 서버가 안전한 시맨틱 SQL 검색 수행 → v1과 동일한 방식으로 최종 답변을 SSE로 스트리밍하는 v2 파이프라인을 추가했습니다. @@ -11,6 +11,8 @@ - 관측성 부족: 어떤 기준/전략으로 검색되었는지(계획/파라미터)를 클라이언트에서 파악하기 어려움. - 보안/안전 제약: 프롬프트/검색 로직이 단순해, 미래에 복잡한 동적 쿼리로 확장 시 안전한 경계(화이트리스트/스키마 강제)가 필요. +![alt text](../structureDiagram/askV1-structure.png) + ## 3) v2의 도입/해결 방안과 가능해진 것 - LLM 검색 계획(JSON) 도입 - OpenAI Responses API + JSON Schema 강제 → LLM이 오직 스키마에 맞는 JSON만 출력. @@ -28,7 +30,7 @@ - 청크 검색 후보(top_k)를 넓게 가져온 뒤, 포스트 단위 중복 제거 + `limit`개까지만 컨텍스트/노출로 사용(토큰/비용 관리). - 폴백 안전성 - 계획 생성 실패 시 v1 RAG 경로로 자동 폴백하여 기능 연속성 보장. - +![alt text](../structureDiagram/askV2-structure.png) ## 4) v2 구조(파일과 역할) - 라우팅/컨트롤러 - `src/routes/ai.v2.routes.ts`: `/ai/v2/ask` SSE 라우트. @@ -90,3 +92,4 @@ SSE 이벤트 순서 } ``` - 기대 SSE 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zBbNp!>ZY}f4M5e!4BWu<`3a2}AzsjJ1E?1U)lhGNRRR<;Wj;WCtU(uHazY3a+q6g3_pTK_O^oC%4IoLTJZMNmONCAh$oIifVGg=Ss_P-F za?b6n#Um@aw?jdM!t*P!ThN6dU8hhBC!6LvuNgy&2vE+PB?8?+7uKk$-?nuNc_kSk zGll|E50U&saK{{c1gRfVL>`y%RS8TbxZKYOWE4*5JZlEP?k=S3uo&QNdPf7AO@OC; zbRzRy2CF(N8*sIDz(M=@f$X6Gl8f}TjQ8`~YzX2at%_^|)Z%G&sB)6N zte@m*oug(nLc%b7Dc#3NArR@GuLfFVuKxM*Uin8;L;7y?>;FhYNPiv{`OlmG{;4UF z{NbN>5THOt{m<7|R}$>`=Nm1gs=m+lW*Qd{(Y!@Kz;b@`k9NTJ%nD6eY#oiPPyB2bYP z*~uNGy!`v?5~6D)eR===Z_`If{Er^vq#iq@fABY{ffMPsf0BC1kbe8$w}ol=?N2H&;cV)kzij=#S&X;)Suj z*FpKFzwHdQc=&hIE98&=%l!45>e)l6jd@7$%H4NN*8QWbjOfA}jV=55NDbTIe9+G& zmuS&L!*Ruy3i^i(LRYp#6jD1%^f>cVf6q?;6McMM4pA%2c>SEW;qQicq`&!ZxPxi< ze|8cCnix=DhYHynyB9W$w8MH-No%_K+^Ww+(Yx$(Un#}m67j}Kxs!_oy Date: Sun, 5 Oct 2025 16:04:11 +0900 Subject: [PATCH 07/14] =?UTF-8?q?=F0=9F=90=9B=20ask=EC=9D=91=EB=8B=B5=20?= =?UTF-8?q?=ED=8C=8C=EC=8B=B1=20=EC=97=90=EB=9F=AC=20=EB=B0=8F=20sse=20?= =?UTF-8?q?=EB=8D=B8=ED=83=80=20=EB=8B=A8=EC=9C=84=20=EC=A0=84=EB=8B=AC=20?= =?UTF-8?q?=EB=B2=84=EA=B7=B8=20=ED=95=B4=EA=B2=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/controllers/ai.controller.ts | 40 +++++++++- src/llm/providers/openai-responses.ts | 103 +++++++++++++++++++------- src/services/qa.service.ts | 11 +++ 3 files changed, 125 insertions(+), 29 deletions(-) diff --git a/src/controllers/ai.controller.ts b/src/controllers/ai.controller.ts index 1b9a593..d276f23 100644 --- a/src/controllers/ai.controller.ts +++ b/src/controllers/ai.controller.ts @@ -51,12 +51,46 @@ export const askHandler = async ( try { const { question, user_id, category_id, speech_tone, post_id, llm } = req.body as any; - res.setHeader('Content-Type', 'text/event-stream'); - res.setHeader('Cache-Control', 'no-cache'); + // SSE headers and anti-buffering hints + res.setHeader('Content-Type', 'text/event-stream; charset=utf-8'); + res.setHeader('Cache-Control', 'no-cache, no-transform'); res.setHeader('Connection', 'keep-alive'); + // Nginx buffering off + res.setHeader('X-Accel-Buffering', 'no'); + // Flush headers early so clients start processing immediately + (res as any).flushHeaders?.(); + // Reduce Nagle’s algorithm buffering on the socket for faster flush + (res.socket as any)?.setNoDelay?.(true); + // Prime the SSE stream to break proxy buffering thresholds + res.write(':ok\n\n'); const stream = await answerStream(question, user_id, category_id, speech_tone, post_id, llm); - stream.pipe(res); + // Manually bridge to ensure flushing of SSE deltas + stream.on('data', (chunk) => { + const buf = Buffer.isBuffer(chunk) ? chunk : Buffer.from(String(chunk)); + res.write(buf); + const canFlush = typeof (res as any).flush === 'function'; + // try to flush if supported by runtime/middleware + (res as any).flush?.(); + try { + console.log( + JSON.stringify({ type: 'debug.sse.write', at: Date.now(), bytes: buf.length, flushed: canFlush }) + ); + } catch {} + }); + stream.on('end', () => { + res.end(); + }); + stream.on('error', () => { + res.end(); + }); + + // Cleanup if client disconnects + req.on('close', () => { + try { + stream.destroy(); + } catch {} + }); } catch (error) { next(error); diff --git a/src/llm/providers/openai-responses.ts b/src/llm/providers/openai-responses.ts index 430728c..3a2e86b 100644 --- a/src/llm/providers/openai-responses.ts +++ b/src/llm/providers/openai-responses.ts @@ -84,9 +84,15 @@ export const generateOpenAIStream = async (req: GenerateRequest): Promise { - if (delta) { + responsesStream.on('response.output_text.delta', (ev: any) => { + const text = typeof ev === 'string' ? ev : ev?.delta ?? ''; + if (text) { safeWrite(`event: answer\n`); - safeWrite(`data: ${JSON.stringify(delta)}\n\n`); + safeWrite(`data: ${JSON.stringify(text)}\n\n`); + try { console.log(JSON.stringify({ type: 'debug.openai.delta', len: String(text).length, at: Date.now() })); } catch {} if (!loggedFirstDelta) { - try { console.log(JSON.stringify({ type: 'debug.openai.delta', len: delta.length })); } catch {} + try { console.log(JSON.stringify({ type: 'debug.openai.delta', len: String(text).length })); } catch {} loggedFirstDelta = true; } } }); // Stream tool-call arguments as answer chunks to maintain SSE shape - responsesStream.on('response.tool_call.delta', (toolDelta: any) => { - const argsDelta = toolDelta?.arguments_delta || toolDelta?.arguments || ''; + responsesStream.on('response.tool_call.delta', (ev: any) => { + const argsDelta = ev?.arguments_delta || ev?.arguments || ev?.delta || ''; if (argsDelta) { safeWrite(`event: answer\n`); safeWrite(`data: ${JSON.stringify(argsDelta)}\n\n`); } }); + // Catch-all messages to ensure we don't miss alternative text events + responsesStream.on('message', (msg: any) => { + try { + const m = typeof msg === 'string' ? JSON.parse(msg) : msg; + if (!m) return; + // Prefer explicit output_text delta + if (m.type === 'response.output_text.delta' && m.delta) { + safeWrite(`event: answer\n`); + safeWrite(`data: ${JSON.stringify(m.delta)}\n\n`); + } + // Some SDKs may emit full output_text chunk at once + else if (m.type === 'response.output_text' && typeof m.text === 'string') { + safeWrite(`event: answer\n`); + safeWrite(`data: ${JSON.stringify(m.text)}\n\n`); + } + // Generic delta fallback + else if (m.type === 'response.delta' && typeof m.delta === 'string') { + safeWrite(`event: answer\n`); + safeWrite(`data: ${JSON.stringify(m.delta)}\n\n`); + } + // Log for visibility + console.log( + JSON.stringify({ type: 'debug.openai.msg', mtype: m.type, keys: Object.keys(m || {}) }) + ); + } catch (e) { + try { console.log(JSON.stringify({ type: 'debug.openai.msg_parse_error' })); } catch {} + } + }); + responsesStream.on('response.completed', () => { safeWrite(`event: end\n`); safeWrite(`data: [DONE]\n\n`); @@ -134,8 +171,12 @@ export const generateOpenAIStream = async (req: GenerateRequest): Promise { + try { + await responsesStream.done(); + } catch {} + })(); return stream; } catch (e) { // Fallback to non-streaming Responses if streaming path fails @@ -147,6 +188,7 @@ export const generateOpenAIStream = async (req: GenerateRequest): Promise { + try { + for await (const chunk of chatStream) { + const content = chunk.choices[0]?.delta?.content || ''; + const toolCalls = chunk.choices[0]?.delta?.tool_calls; - if (toolCalls) { - for (const toolCall of toolCalls) { - if (toolCall.function?.arguments) { + if (toolCalls) { + for (const toolCall of toolCalls) { + if (toolCall.function?.arguments) { + safeWrite(`event: answer\n`); + safeWrite(`data: ${JSON.stringify(toolCall.function.arguments)}\n\n`); + } + } + } else if (content) { safeWrite(`event: answer\n`); - safeWrite(`data: ${JSON.stringify(toolCall.function.arguments)}\n\n`); + safeWrite(`data: ${JSON.stringify(content)}\n\n`); } - } - } else if (content) { - safeWrite(`event: answer\n`); - safeWrite(`data: ${JSON.stringify(content)}\n\n`); - } - if (chunk.choices[0]?.finish_reason) { - safeWrite(`event: end\n`); - safeWrite(`data: [DONE]\n\n`); + if (chunk.choices[0]?.finish_reason) { + safeWrite(`event: end\n`); + safeWrite(`data: [DONE]\n\n`); + safeEnd(); + try { console.log(JSON.stringify({ type: 'debug.openai.completed', path: 'chat.completions.stream' })); } catch {} + break; + } + } + } catch (e) { + safeWrite(`event: error\n`); + safeWrite(`data: ${JSON.stringify({ message: 'Internal server error' })}\n\n`); safeEnd(); - try { console.log(JSON.stringify({ type: 'debug.openai.completed', path: 'chat.completions.stream' })); } catch {} - break; } - } + })(); return stream; } catch (err) { diff --git a/src/services/qa.service.ts b/src/services/qa.service.ts index eef997f..0d8ffe0 100644 --- a/src/services/qa.service.ts +++ b/src/services/qa.service.ts @@ -151,6 +151,17 @@ export const answerStream = async ( } catch {} llmStream.on('data', (chunk) => { + try { + const str = Buffer.isBuffer(chunk) ? chunk.toString('utf8') : String(chunk); + console.log( + JSON.stringify({ + type: 'debug.qa.chunk', + at: Date.now(), + bytes: Buffer.byteLength(str, 'utf8'), + preview: str.slice(0, 40).replace(/\n/g, '\\n'), + }) + ); + } catch {} stream.write(chunk); }); llmStream.on('end', () => { From 1a073c36efa83890b671c5e12b38a47b0751b463 Mon Sep 17 00:00:00 2001 From: chan000518 Date: Tue, 7 Oct 2025 16:21:35 +0900 Subject: [PATCH 08/14] =?UTF-8?q?=E2=9C=A8=20askV2=20=ED=95=98=EC=9D=B4?= =?UTF-8?q?=EB=B8=8C=EB=A6=AC=EB=93=9C=20=EC=84=9C=EC=B9=98=20=EC=B6=94?= =?UTF-8?q?=EA=B0=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- TASK.md | 352 +++++--------------------- docs/api.md | 148 +++++++++++ docs/migrations/2025-01-pgtrgm.sql | 9 + docs/migrations/README.md | 27 ++ package.json | 3 +- readme.md | 42 +++ src/prompts/qa.v2.prompts.ts | 17 +- src/repositories/post.repository.ts | 118 +++++++++ src/services/hybrid-search.service.ts | 104 ++++++++ src/services/qa.v2.service.ts | 31 ++- src/services/search-plan.service.ts | 39 ++- src/types/ai.v2.types.ts | 11 +- 12 files changed, 606 insertions(+), 295 deletions(-) create mode 100644 docs/api.md create mode 100644 docs/migrations/2025-01-pgtrgm.sql create mode 100644 docs/migrations/README.md create mode 100644 src/services/hybrid-search.service.ts diff --git a/TASK.md b/TASK.md index 6016664..4104ee2 100644 --- a/TASK.md +++ b/TASK.md @@ -1,293 +1,71 @@ -# v2 설계 및 작업 계획 (LLM 주도 SQL 기반 시맨틱 서치 → 응답 스트리밍) +## 하이브리드 서치 확장(질문 재작성 + 키워드) -## 목표 -- v1 API/흐름은 그대로 유지. -- v2에서 질의를 LLM이 “검색 계획(계획 JSON)”으로 변환 → 서버가 안전하게 실행하여 컨텍스트 확보 → 확보한 컨텍스트와 함께 LLM에 넘겨 최종 답변을 SSE로 스트리밍. +목표 +- 리콜 향상: 질문을 LLM이 재작성(rewrites)하고, 핵심 키워드를 생성하여 벡터+텍스트 양쪽에서 검색. +- 정밀/비용 제어: 재작성/키워드 개수를 제한하고, 융합 가중치로 결과를 안정적으로 선별. -## 적합성 평가 (요약) -- 장점 - - LLM이 질문 의도를 기반으로 동적 필터/가중치/Top-K/Threshold를 튜닝한 “검색 계획”을 생산하여 검색 적합도를 끌어올릴 수 있음. - - DB 스키마/비즈니스 제약을 반영해 검색 전략을 바꾸기 쉬움(프롬프트/제약 업데이트로 최적화 가능). -- 리스크 및 보완 - - “LLM이 직접 SQL 문자열”은 주입/스키마 일탈 위험. 안전하게 “계획(JSON)”을 생성하게 하고, 서버가 화이트리스트 템플릿/파라미터 바인딩으로 SQL을 구성하는 방식 권장. - - 벡터 연산 상 LLM은 임베딩을 생성할 수 없으므로, 서버가 질문 임베딩을 계산하여 파라미터로 주입해야 함. - - 실패/부적합 시 v1 RAG 경로로 폴백 필요. - -결론: “LLM → 검색 계획(JSON) → 서버가 안전 SQL 구성/실행 → 결과 컨텍스트로 최종 LLM”의 변형 설계가 적합하며 안전/유지보수에도 유리. - -## v2 전체 흐름 -1) 클라이언트가 `POST /ai/v2/ask`로 질문 전송(SSE 응답). -2) 서버가 LLM에 “검색 계획” 생성을 요청(계획 JSON; SQL 문자열 생성은 비권장, 옵션으로 지원). -3) 서버가 계획(JSON)을 검증/정규화 후 파라미터 바인딩으로 안전 SQL 실행(벡터 임베딩은 서버가 생성). -4) 검색 결과(문맥) 메타 이벤트 송신(`search_plan`, `search_result`). -5) 기존 v1과 동일한 형식의 최종 LLM 호출을 수행하고 `answer`를 스트리밍. -6) 종료 시 `end: [DONE]` 송신. - -## API 설계 -- Route: `POST /ai/v2/ask` (SSE) -- Auth: 기존 `authMiddleware` 재사용 -- Request Body (제안) - - `question: string` - - `user_id: string` - - `category_id?: number` - - `post_id?: number` (지정 시 해당 글 컨텍스트 중심) - - `speech_tone?: number` - - `llm?: { provider?: 'openai'|'gemini', model?: string, options?: { temperature?: number, top_p?: number, max_output_tokens?: number } }` -- SSE 이벤트(순서 권장) - - `event: search_plan` / `data: { ...계획JSON }` - - `event: search_sql` / `data: { templateId, paramsPreview }` (SQL 템플릿 경로 선택 시, 민감정보 제외 프리뷰) - - `event: search_result` / `data: [{ postId, postTitle }]` - - `event: exist_in_post_status` / `data: true|false` (v1과 동일) - - `event: context` / `data: [{ postId, postTitle }]` (v1과 동일) - - `event: answer` (여러 번) - - `event: end` / `data: [DONE]` - - `event: error` (오류 시) - -## LLM “검색 계획” 출력 스키마(권장) +Plan JSON 확장(초안) ```json { - "mode": "rag", // "rag" | "post" - "top_k": 5, // 1~20 범위 권장 - "threshold": 0.2, // 0.0~1.0, 유사도 하한 - "weights": { "chunk": 0.7, "title": 0.3 }, - "filters": { - "user_id": "", - "category_ids": [7], // 필요 시 - "post_id": 123, // mode=post 시 - "time": { // 시간/날짜 필터(선택) - "type": "relative", // "relative" | "absolute" | "month" | "year" | "quarter" - "unit": "day", // relative: "day" | "week" | "month" | "year" - "value": 30 // relative: 정수(양수) - // absolute: { from: ISO8601, to: ISO8601 } - // month: { month: 1..12, year?: number } - // year: { year: number } - // quarter: { quarter: 1..4, year?: number } - } - }, - "sort": "created_at_desc", // "created_at_desc" | "created_at_asc" - "limit": 5 // 결과 행 상한(서버 상한 적용) + "rewrites": ["..."], + "keywords": ["..."], + "hybrid": { "enabled": true, "alpha": 0.7, "max_rewrites": 4, "max_keywords": 8 } } ``` -- 서버에서 Zod로 검증 후 기본값/상한선 적용(top_k max 10 등), 불일치 시 안전 폴백값 적용. -- SQL 템플릿은 서버가 선택/생성하며, 파라미터는 모두 바인딩 처리. - -### 시간/날짜 필터 처리 규칙 -- 서버 표준화 단계에서만 절대 기간으로 변환(Asia/Seoul 기준 권장): - - relative: 현재 시각 기준으로 from/to 계산(예: 최근 30일) - - month: 연도 미지정 시 현재 연도 사용(예: “9월” → 올해 9월 1일 00:00:00 ~ 9월 말 23:59:59) - - quarter/year: 분기·연도 경계 계산 -- 검증 실패·모호 값: 날짜 필터 제외 혹은 기본 30일 적용 후 로그 남기고 폴백 -- 모든 상수값은 서버에서 범위 강제(limit<=20, top_k<=10, threshold 0..1, weights 합=1) - -## SQL 실행(안전 구성) -- 벡터 임베딩: 서버가 `createEmbeddings([question])`로 생성. -- 화이트리스트 템플릿(예시) - - 공통: 사용자/카테고리/소유권 필터를 CTE에서 먼저 적용. - - 스코어링: `(w_chunk * (1 - (pc.embedding <=> $embed))) + (w_title * (1 - (pte.embedding <=> $embed))) AS score` - - 임계치: `WHERE (1 - (pc.embedding <=> $embed)) > $threshold` - - 날짜 필터: `AND bp.created_at BETWEEN $from AND $to` (표준화된 절대 기간 사용) - - 정렬/상한: `ORDER BY score DESC` + 옵션 정렬(`created_at_desc|asc`) 병합, `LIMIT $limit` -- 모든 동적 값은 파라미터로 바인딩하고, 가중치/상한 등은 서버에서 범위 제한. - -## 최종 LLM 호출(응답 생성) -- v1의 `generate` 재사용(프로바이더/모델 동일 정책). -- 프롬프트: v1의 `qa.prompts`를 재사용하되, v2에서는 `search_result` 컨텍스트를 그대로 투입. -- v1과 동일한 `report_content_insufficient` 툴 전략 유지(필요 시). - -## 디렉터리/컴포넌트 추가 계획 -- `src/routes/ai.v2.routes.ts` — v2 라우터(`POST /ask`). -- `src/controllers/ai.v2.controller.ts` — SSE 헤더 설정, 서비스 호출/파이프. -- `src/services/search-plan.service.ts` — LLM에 검색 계획 요청 및 스키마 검증. -- `src/services/semantic-search.service.ts` — 계획(JSON)→SQL 템플릿 매핑 및 안전 실행. -- `src/services/qa.v2.service.ts` — 전체 orchestrator: 계획 생성→검색→컨텍스트 이벤트→최종 LLM 스트림. -- `src/prompts/qa.v2.prompts.ts` — 검색 계획 LLM 프롬프트(스키마/제약 포함). -- `src/types/ai.v2.types.ts` — 요청/계획/응답 타입/Zod 스키마. -- `src/routes/ai.routes.ts`는 그대로, `app.ts`에 `/ai/v2` 라우트 추가. - -## 프롬프트 가이드(검색 계획용, 요지) -- 역할: “블로그 시맨틱 검색 플래너”. -- 입력: 질문, 사용자 메타(user_id/category_id/post_id), 스키마/제약 요약, 기본값. -- 출력: 상기 계획 JSON 스키마만 생성(그 외 텍스트 금지). -- 제약: 값 범위(Top-K, threshold, weights 합=1), 허용된 필드만. -- 실패 시: 기본값으로 귀결되는 최소 계획을 출력하도록 유도. -- 시간/날짜 처리 지침: - - 시스템 메시지에 현재 날짜/시간과 타임존(예: Asia/Seoul)을 제공 - - “최근/이번 달/지난주/9월/작년 9월” 등 자연어 시간을 위 스키마의 time 필드로 구조화 - - 연도 미지정 시 현재 연도 가정, 모호하면 relative 30일로 유도 - -## 예시 -- “최근 글 보여줘” - - LLM 계획(JSON): `{ "mode": "rag", "top_k": 5, "threshold": 0.2, "weights": { "chunk": 0.7, "title": 0.3 }, "filters": { "user_id": "", "time": { "type": "relative", "unit": "day", "value": 30 } }, "sort": "created_at_desc", "limit": 5 }` - - 서버 표준화: `time → absolute { from: <30일 전 00:00+09:00>, to: <지금+09:00> }`, `limit=5` - -- “9월 글 2개” - - LLM 계획(JSON): `{ "mode": "rag", "top_k": 5, "threshold": 0.2, "weights": { "chunk": 0.7, "title": 0.3 }, "filters": { "user_id": "", "time": { "type": "month", "month": 9 } }, "sort": "created_at_desc", "limit": 2 }` - - 서버 표준화: `time → absolute { from: YYYY-09-01T00:00:00+09:00, to: YYYY-09-30T23:59:59+09:00 } (연도 미지정 시 올해)`, `limit=2` - -## 폴백 전략 -- 계획 JSON 검증 실패/LLM 오류 → v1 RAG 경로로 폴백. -- 검색 결과 0건 → v1과 동일하게 `exist_in_post_status=false` 전송 후, 부족 안내 규칙에 맞춰 답변 유도. - -## 텔레메트리/비용 -- `llm.request/llm.response` 로그 기존 그대로 사용. -- v2 전용 디버그 이벤트: `debug.plan.start`, `debug.plan.json`, `debug.plan.sql`, `debug.plan.result` 등. - -## 마이그레이션/DB -- 추가 테이블 불필요(기존 `post_chunks`, `post_title_embeddings` 재사용). -- 필요 시 뷰/인덱스 최적화 검토: `post_chunks(post_id, embedding)`, `post_title_embeddings(post_id, embedding)`. - -## 보안/안전장치 -- LLM은 “계획 JSON”만 생성(기본). SQL은 서버가 템플릿/파라미터 바인딩으로 생성. -- 만약 SQL 문자열 모드가 필요하면: 템플릿 ID+파라미터만 허용하거나, 정규식/AST로 화이트리스트 검증 후 실행. -- SSE에 민감 값(user_id/token 등) 노출 금지. - -## 단계별 작업(체크리스트) -1) 스켈레톤 - - [ ] 라우터/컨트롤러 v2 추가(`/ai/v2/ask`) - - [ ] SSE 헤더/에러 핸들링 공통화 -2) 검색 계획 - - [ ] `search-plan.service` + `qa.v2.prompts` + Zod 스키마 - - [ ] 계획 실패 시 v1 경로 폴백 -3) 검색 실행 - - [ ] 임베딩 생성(질문) - - [ ] 템플릿 기반 안전 SQL 실행 + 파라미터 바인딩 - - [ ] `search_plan`, `search_result` SSE 송신 -4) 최종 응답 스트림 - - [ ] v1 `generate` 재사용하여 LLM 스트림 - - [ ] v1과 동일한 `answer`/`end`/`error` 이벤트 유지 -5) 관측성/테스트 - - [ ] 디버그/비용 로그 연결 - - [ ] 단위 테스트: 계획 검증/템플릿 빌더/리포지토리 - - [ ] 통합 테스트: SSE 이벤트 순서/형식 - -## 수용 기준(AC) -- `/ai/v2/ask`가 SSE로 동작하고, `search_plan`→`search_result`→`answer` 순으로 이벤트가 수신된다. -- 계획 JSON이 범위를 벗어나거나 실패해도 서버는 안전 폴백으로 정상 응답을 스트리밍한다. -- SQL은 파라미터 바인딩으로만 구성되며, 화이트리스트 템플릿을 벗어난 질의가 실행되지 않는다. -- v1과 동일한 최종 응답 품질을 유지하거나 개선한다. - -## 검색 계획 LLM 프롬프트(초안) - -목표: LLM이 “검색 계획 JSON”만 출력하도록 강제하여 서버가 안전하게 SQL 템플릿을 선택·실행할 수 있도록 한다. 자연어 시간(최근/이번 달/지난주/9월/작년 9월 등)을 구조화하고, 정렬/상한/가중치/임계치 값 제약을 지키도록 한다. - -입력 변수(서버가 주입) -- now_utc, now_kst: 현재 시간(ISO8601) -- timezone: 문자열(예: Asia/Seoul) -- user_id: 요청자의 사용자 ID -- category_id?: 선택, 존재 시 조상 기준 필터 -- post_id?: 선택, 존재 시 mode=post 우선 -- defaults: { top_k: 5, threshold: 0.2, weights: { chunk: 0.7, title: 0.3 }, sort: created_at_desc, limit: 5 } -- question: 사용자 질문 원문 - -출력 형식(반드시 엄수) -- 오직 하나의 JSON 객체만 출력. 추가 텍스트/주석/마크다운/설명 금지. -- 키/값은 스키마 내 필드만 사용. 불필요 필드 생성 금지. - -스키마(재확인) -{ - "mode": "rag" | "post", - "top_k": number, // 1..10 - "threshold": number, // 0..1 - "weights": { "chunk": number, "title": number }, // 0..1, 합=1 - "filters": { - "user_id": string, - "category_ids"?: number[], // 제공 시 우선 적용(카테고리 조상 ID) - "post_id"?: number, // mode=post 시 필수 - "time"?: { // 하나만 선택 - "type": "relative" | "absolute" | "month" | "year" | "quarter", - // relative - "unit"?: "day" | "week" | "month" | "year", - "value"?: number, - // absolute - "from"?: string, // ISO8601 - "to"?: string, // ISO8601 - // month - "month"?: number, // 1..12, year 없으면 현재 연도 가정 - "year"?: number, - // quarter - "quarter"?: number // 1..4, year 없으면 현재 연도 가정 - } - }, - "sort": "created_at_desc" | "created_at_asc", - "limit": number // 1..20 -} - -규칙(핵심) -- post_id가 존재하면 mode="post"를 사용하고 filters.post_id를 설정. 그렇지 않으면 mode="rag". -- category_id가 있으면 filters.category_ids에 포함(단일 ID여도 배열 사용 허용). -- 가중치 합은 1이 되도록 조정(기본 chunk 0.7, title 0.3). 범위를 넘을 경우 기본값 사용. -- top_k는 1..10, limit는 1..20, threshold는 0..1 범위로 제한. 미지정 시 기본값 사용. -- 시간 표현 해석: - - “최근/요즘/최근 N개” → relative(단, “N개”는 limit=N으로 반영; 기간은 기본 30일 유지) - - “최근 N일/주/달/년” → relative + { unit, value } - - “이번 달/이번 주/올해/올해 9월” → month/year/quarter로 구조화 - - “9월”처럼 연도 미지정 → 현재 연도 가정 - - “지난주/지난달/작년” → relative 또는 해당 단위 기간으로 구조화 - - 모호하거나 충돌 시 time 생략(서버 기본 30일 적용 예상) -- 정렬: “최근/최신/새로운” 등은 created_at_desc. “오래된”은 created_at_asc. -- 질문에 “N개”가 있으면 limit=N 반영(상한 20). -- JSON 이외 어떤 텍스트도 출력하지 말 것. - -시스템 프롬프트 템플릿 -""" -You are a Search Plan Generator for a Korean blogging platform. -Your task is to read the user question and output ONLY a JSON object that defines a safe search plan. - -Context: -- now_utc: {now_utc} -- now_kst: {now_kst} -- timezone: {timezone} -- user_id: {user_id} -- category_id: {category_id} -- post_id: {post_id} -- defaults: {"top_k":5,"threshold":0.2,"weights":{"chunk":0.7,"title":0.3},"sort":"created_at_desc","limit":5} - -Rules: -1) Output ONLY a single JSON object matching the schema below. No extra text. -2) Respect bounds: top_k 1..10, limit 1..20, threshold 0..1, weights in [0,1] and sum to 1. -3) If post_id exists, use mode="post" and include filters.post_id; else mode="rag". -4) If category_id exists, include it in filters.category_ids. -5) Interpret Korean temporal phrases into filters.time using the provided timezone. - - “최근/최신/요즘”: prefer sort=created_at_desc. If a concrete period is given (e.g., 최근 30일), use relative. - - “이번 달/이번 주/올해/올해 9월”: use month/year/quarter forms. - - Month without year (e.g., “9월”): assume current year. - - “지난주/지난달/작년”: use the respective period. - - If ambiguous, omit time (server applies defaults). -6) If the question asks for N items (e.g., “N개”), set limit=N within bounds. -7) Keep the weights to defaults unless a clear need implies otherwise. - -Schema: -{SCHEMA_JSON} - -Question (Korean): -{question} - -Respond with ONLY the JSON object. No markdown, no explanation. -""" - -Few-shot 예시 -- Q: "최근 글 보여줘" - 출력: - {"mode":"rag","top_k":5,"threshold":0.2,"weights":{"chunk":0.7,"title":0.3},"filters":{"user_id":"{user_id}","time":{"type":"relative","unit":"day","value":30}},"sort":"created_at_desc","limit":5} - -- Q: "9월 글 2개" - 출력: - {"mode":"rag","top_k":5,"threshold":0.2,"weights":{"chunk":0.7,"title":0.3},"filters":{"user_id":"{user_id}","time":{"type":"month","month":9}},"sort":"created_at_desc","limit":2} - -- Q: "카테고리 7의 최신 글 3개" - 출력: - {"mode":"rag","top_k":5,"threshold":0.2,"weights":{"chunk":0.7,"title":0.3},"filters":{"user_id":"{user_id}","category_ids":[7]},"sort":"created_at_desc","limit":3} - -- Q: "지난주에 쓴 포스트 중 추천해줘" - 출력: - {"mode":"rag","top_k":5,"threshold":0.2,"weights":{"chunk":0.7,"title":0.3},"filters":{"user_id":"{user_id}","time":{"type":"relative","unit":"week","value":1}},"sort":"created_at_desc","limit":5} - -- Q: "이 포스트(123) 기준으로 답해줘" - 출력: - {"mode":"post","top_k":5,"threshold":0.2,"weights":{"chunk":0.7,"title":0.3},"filters":{"user_id":"{user_id}","post_id":123},"sort":"created_at_desc","limit":5} - -검증 포인트(서버 측) -- JSON 파싱 실패 → v1 폴백 -- 값 범위 초과 → 기본값/상한으로 교정 -- time 표준화(절대 from/to 변환), 타임존 적용 -- category_ids/post_id 소유권 확인 +- 기존 필드(`top_k`, `threshold`, `weights`, `filters.time`, `sort`, `limit`)와 공존. +- 서버에서 상한 강제(rewrites<=4, keywords<=8), 품질이 낮거나 중복인 항목 제거. + +프롬프트 가이드(확장) +- 역할: ‘검색 계획 + 재작성/키워드 생성’ +- 출력: 기존 계획 JSON + `rewrites[]`, `keywords[]`, `hybrid{}` +- 규칙: + - 불용/범용 단어(예: "글", "포스트", "블로그") 지양 + - 과도한 시기/주제 확장 금지(사용자 블로그 컨텍스트 벗어나지 않기) + - 중복/동의어 반복 최소화(문장 유사도 과다 시 제거) + +하이브리드 검색 파이프라인 +1) 멀티 벡터 검색: 원 질문 + rewrites 각각 임베딩 → pgvector Top-K 검색(합집합) +2) 텍스트 검색: keywords로 `post_chunks.content`/`blog_post.title` 텍스트 매칭 → Top-K 추출 +3) 랭킹 융합: 점수 정규화 후 `score = α·vec + (1-α)·text` 또는 RRF로 병합 → 상위 N 청크 선택 +4) 컨텍스트 구성: v1과 동일 프롬프트로 최종 LLM 호출 + +저장소/인덱스(제안) +- PostgreSQL 확장: `pg_trgm`(간단/범용) 또는 `tsvector`(정교) +- 1차안(pg_trgm) + - DDL: + - `CREATE EXTENSION IF NOT EXISTS pg_trgm;` + - `CREATE INDEX IF NOT EXISTS idx_pc_content_trgm ON post_chunks USING gin (content gin_trgm_ops);` + - `CREATE INDEX IF NOT EXISTS idx_bp_title_trgm ON blog_post USING gin (title gin_trgm_ops);` + - 질의: `similarity(content, $q) > $min OR content ILIKE ANY($patterns)` 등으로 text_score 계산 + +SSE 확장(선택) +- `event: rewrite` / `data: ["..."]` +- `event: keywords` / `data: ["..."]` +- `event: hybrid_result` / `data: [{ postId, postTitle }]` + +보안/안전 +- 재작성/키워드 출력 스키마 강제(JSON), 길이/개수 상한 +- 금칙어/민감어 필터(선택), 카테고리/기간 필터는 서버가 최종 결정 + +세부 구현 계획(하이브리드 서치) +1) 스키마/프롬프트 + - [ ] `src/types/ai.v2.types.ts`: Plan 스키마에 `rewrites[]`, `keywords[]`, `hybrid{enabled,alpha,max_rewrites,max_keywords}` 추가 + - [ ] `src/prompts/qa.v2.prompts.ts`: 프롬프트/JSON Schema에 확장 필드 반영 + few-shot 보강 +2) 플래너 서비스 + - [ ] `search-plan.service.ts`: 확장 필드 파싱/검증, 중복/불용어 필터링, 상한 강제 +3) 텍스트 검색 저장소 + - [ ] `post.repository.ts`: `textSearchChunksV2({ userId, query, keywords[], from?, to?, topK })` + - [ ] (옵션) DDL 문서화: pg_trgm 인덱스 생성 스크립트 추가 +4) 하이브리드 서비스 + - [ ] `hybrid-search.service.ts` 구현: 멀티 임베딩, 텍스트 검색, 정규화, α 융합/RRF, 상위 N 반환 +5) 오케스트레이션/이벤트 + - [ ] `qa.v2.service.ts`: plan.hybrid.enabled 시 하이브리드 경로 분기, (선택) `rewrite`/`keywords`/`hybrid_result` 송신 +6) 테스트/튜닝 + - [ ] 통합 테스트: 재작성/키워드 포함 질의에서 리콜↑ 확인 + - [ ] 가중치 α, Top-K 상수, 불용어/중복 필터 기준 튜닝 + - [ ] 0건/오류 폴백(v1 RAG) 검증 + +권장 단계적 도입 +- Phase 1: 스키마/프롬프트/하이브리드 파이프라인 구현(기본 α=0.7, rewrites<=3, keywords<=6) +- Phase 2: SSE 관측성 이벤트 추가, 품질 튜닝 +- Phase 3: 인덱스/성능 최적화, 불용어 사전/NER 보정 diff --git a/docs/api.md b/docs/api.md new file mode 100644 index 0000000..4ed3103 --- /dev/null +++ b/docs/api.md @@ -0,0 +1,148 @@ +# Bubblog AI API 문서 (v1 ~ v2) + +본 문서는 `/ai` (v1)와 `/ai/v2` (v2) 엔드포인트를 정리합니다. 서버는 Express 기반이며, `POST /ask` 류는 Server‑Sent Events(SSE)로 답변을 스트리밍합니다. + +## 기본 정보 +- Base Path + - v1: `/ai` + - v2: `/ai/v2` +- 인증 + - `POST /ask` 엔드포인트는 `Authorization: Bearer ` 필요 + - 임베딩 생성 엔드포인트는 인증 없이 사용 가능 +- 본문 형식: `application/json` +- SSE 수신: `Content-Type: text/event-stream` + - 이벤트명은 `event:` 라인으로, 데이터는 `data:` 라인으로 전송됩니다. + - 일반 텍스트 콘텐츠는 `event: answer`로 분할 전송되며, 종료 시 `event: end` + `data: [DONE]`가 송신됩니다. + +## v1 엔드포인트 (`/ai`) + +### GET `/ai/health` +- 인증: 불필요 +- 응답(200): `{ "status": "ok" }` + +### POST `/ai/embeddings/title` +- 인증: 불필요 +- 요청 Body + - `post_id`(number, required) + - `title`(string, required) +- 동작: 제목 임베딩 생성 후 저장 +- 응답(200): `{ "ok": true }` + +### POST `/ai/embeddings/content` +- 인증: 불필요 +- 요청 Body + - `post_id`(number, required) + - `content`(string, required) +- 동작: 본문을 약 512 토큰 단위로 중첩(50) 청킹 → 임베딩 생성/저장 +- 응답(200): `{ "post_id": number, "chunk_count": number, "success": true }` + +### POST `/ai/ask` (SSE) +- 인증: 필요 (`Authorization: Bearer `) +- 요청 Body + - `question`(string, required) + - `user_id`(string, required) + - `category_id`(number, optional) + - `post_id`(number, optional) — 지정 시 해당 글 컨텍스트에 국한하여 답변 + - `speech_tone`(number, optional) + - `-1`: 간결하고 명확한 말투(기본) + - `-2`: 해당 글의 말투를 최대한 모사 + - 양의 정수: 페르소나 ID(해당 유저의 등록된 페르소나 참조) + - `llm`(object, optional) + - `provider`: `openai` | `gemini` + - `model`: string (미지정 시 서버 기본값 사용) + - `options`: `{ temperature?, top_p?, max_output_tokens? }` +- SSE 이벤트(주요) + - `exist_in_post_status`: `true|false` — 관련 컨텍스트 존재 여부 + - `context`: `[ { postId, postTitle }, ... ]` — 검색/선택된 컨텍스트 요약 + - `answer`: 모델의 부분 응답 텍스트(여러 번 전송) + - `end`: 종료 시 `data: [DONE]` + - `error`: `{ code?, message }` — 예: `post_id`가 없거나 권한 없음(403), 없음(404) +- 예시(curl) + ```bash + curl -N \ + -H "Authorization: Bearer " \ + -H "Content-Type: application/json" \ + -X POST http://localhost:3000/ai/ask \ + -d '{ + "question": "카테고리 A 관련 요약 해줘", + "user_id": "u_123", + "category_id": 1, + "speech_tone": -1 + }' + ``` + +## v2 엔드포인트 (`/ai/v2`) + +### GET `/ai/v2/health` +- 인증: 불필요 +- 응답(200): `{ "status": "ok", "v": "v2" }` + +### POST `/ai/v2/ask` (SSE) +- 인증: 필요 (`Authorization: Bearer `) +- 요청 Body (v1과 동일 스키마) + - `question`(string, required) + - `user_id`(string, required) + - `category_id`(number, optional) + - `post_id`(number, optional) + - `speech_tone`(number, optional) + - `llm`(object, optional) +- v2의 추가/변경 사항 + - 검색 계획 수립과 결과를 사전 이벤트로 투명하게 송신합니다. + - `post_id` 지정 시에도 동일한 형태의 사전 이벤트를 간략히 제공합니다. +- SSE 이벤트 순서(일반적인 흐름) + 1) `search_plan`: 검색 계획 정보 + - 예시 데이터(정규화된 계획): + ```json + { + "mode": "rag" | "post", + "top_k": 5, + "threshold": 0.2, + "weights": { "chunk": 0.7, "title": 0.3 }, + "filters": { "user_id": "u_123", "category_ids": [1], "post_id": 10?, "time": {...}? }, + "sort": "created_at_desc", + "limit": 5 + } + ``` + - `time` 필터는 `relative|absolute|month|year|quarter` 타입을 지원하며, 값은 ISO8601 또는 숫자 조합입니다. + 2) (하이브리드 사용 시) `rewrite`: `["재작성 질의", ...]` + 3) (하이브리드 사용 시) `keywords`: `["핵심 키워드", ...]` + 4) (하이브리드 사용 시) `hybrid_result`: `[ { postId, postTitle }, ... ]` — 융합 기준 상위 결과 요약 + 5) `search_result`: `[ { postId, postTitle }, ... ]` — 최종 컨텍스트 요약 + 6) `exist_in_post_status`: `true|false` + 7) `context`: `[ { postId, postTitle }, ... ]` — 모델 프롬프트에 사용되는 컨텍스트 요약 + 8) `answer` 스트리밍(여러 번) + 9) `end` with `data: [DONE]` + - 오류 시 `error`: `{ code?, message }` + +- 예시(curl) + ```bash + curl -N \ + -H "Authorization: Bearer " \ + -H "Content-Type: application/json" \ + -X POST http://localhost:3000/ai/v2/ask \ + -d '{ + "question": "최근 한 달 블로그에서 프로젝트 X 관련 내용 요약", + "user_id": "u_123", + "category_id": 3, + "llm": { "provider": "openai", "options": { "temperature": 0.2 } } + }' + ``` + +## 참고 사항 +- `post_id`가 지정된 요청에서 해당 글이 존재하지 않거나, 글의 `user_id`가 요청 `user_id`와 다르면 SSE로 `error` 이벤트가 송신되고 스트림이 종료됩니다. +- v1/v2 모두 모델 응답 텍스트는 `answer` 이벤트로 분할 전송됩니다. 클라이언트는 누적하여 최종 답변을 구성해야 합니다. +- EventSource(브라우저) 사용 예시 + ```js + const es = new EventSource('/ai/v2/ask', { withCredentials: true }); // 헤더 인증이 필요한 경우 fetch/XHR 권장 + es.addEventListener('search_plan', (e) => console.log('plan', e.data)); + es.addEventListener('search_result', (e) => console.log('result', e.data)); + es.addEventListener('context', (e) => console.log('ctx', e.data)); + es.addEventListener('answer', (e) => renderAppend(JSON.parse(e.data))); + es.addEventListener('end', () => es.close()); + es.addEventListener('error', (e) => es.close()); + ``` + +## 요약 +- v1 `/ai/ask`: 컨텍스트 존재 여부와 요약(`exist_in_post_status`, `context`) 후 답변 스트리밍 +- v2 `/ai/v2/ask`: 위 흐름에 더해 검색 계획(`search_plan`)과 검색 결과 요약(`search_result`)을 추가로 제공 +- 임베딩 API(v1): 게시물 제목/본문 임베딩 생성 및 저장 diff --git a/docs/migrations/2025-01-pgtrgm.sql b/docs/migrations/2025-01-pgtrgm.sql new file mode 100644 index 0000000..168a891 --- /dev/null +++ b/docs/migrations/2025-01-pgtrgm.sql @@ -0,0 +1,9 @@ +-- Enable pg_trgm and add GIN indexes for text search on content and title +CREATE EXTENSION IF NOT EXISTS pg_trgm; + +CREATE INDEX IF NOT EXISTS idx_pc_content_trgm + ON post_chunks USING gin (content gin_trgm_ops); + +CREATE INDEX IF NOT EXISTS idx_bp_title_trgm + ON blog_post USING gin (title gin_trgm_ops); + diff --git a/docs/migrations/README.md b/docs/migrations/README.md new file mode 100644 index 0000000..3f4de87 --- /dev/null +++ b/docs/migrations/README.md @@ -0,0 +1,27 @@ +# Migrations Guide + +This folder contains SQL scripts for optional indexes/extensions used by the AI services. + +## Apply pg_trgm for text search + +File: `2025-01-pgtrgm.sql` + +Purpose: +- Enable `pg_trgm` extension and add GIN indexes on `post_chunks.content` and `blog_post.title` to accelerate partial/fuzzy text search used in hybrid search. + +Run (with `DATABASE_URL`): + +```bash +psql "$DATABASE_URL" -f docs/migrations/2025-01-pgtrgm.sql +``` + +Or inside `psql`: + +```sql +\i docs/migrations/2025-01-pgtrgm.sql +``` + +Notes: +- Indexes increase disk usage and write overhead; create only on columns used for text search. +- The extension must be installed once per database. + diff --git a/package.json b/package.json index 2911813..f4594d4 100644 --- a/package.json +++ b/package.json @@ -7,7 +7,8 @@ "dev": "nodemon --watch 'src/**/*.ts' --exec 'ts-node' src/server.ts", "build": "tsc", "start": "node dist/server.js", - "test": "echo \"Error: no test specified\" && exit 1" + "test": "echo \"Error: no test specified\" && exit 1", + "db:migrate:pgtrgm": "psql \"$DATABASE_URL\" -f docs/migrations/2025-01-pgtrgm.sql" }, "keywords": [], "author": "", diff --git a/readme.md b/readme.md index a656468..fa301f2 100644 --- a/readme.md +++ b/readme.md @@ -87,3 +87,45 @@ CREATE INDEX idx_post_title_embeddings_embedding ## 4. AI 서버 작동 ### `.env` 파일 작성 (루트 디렉토리 `BUBBLOG_AI`에 위치) + +--- + +## 5. 텍스트 검색 인덱스 (pg_trgm) + +하이브리드 검색의 키워드/부분일치 성능 향상을 위해 `pg_trgm` 확장 및 GIN 인덱스를 추가합니다. + +### 5.1 확장 및 인덱스 생성 스크립트 + +프로젝트에 제공된 스크립트를 사용하세요: + +`docs/migrations/2025-01-pgtrgm.sql` + +내용: + +```sql +CREATE EXTENSION IF NOT EXISTS pg_trgm; +CREATE INDEX IF NOT EXISTS idx_pc_content_trgm ON post_chunks USING gin (content gin_trgm_ops); +CREATE INDEX IF NOT EXISTS idx_bp_title_trgm ON blog_post USING gin (title gin_trgm_ops); +``` + +### 5.2 적용 방법 + +- 환경변수 `DATABASE_URL`이 설정된 경우: + +```bash +psql "$DATABASE_URL" -f docs/migrations/2025-01-pgtrgm.sql +``` + +- 또는 npm 스크립트 사용: + +```bash +npm run db:migrate:pgtrgm +``` + +- 또는 수동 실행(PostgreSQL 쉘 접속 후): + +```sql +\i docs/migrations/2025-01-pgtrgm.sql +``` + +주의: 인덱스는 쓰기 비용과 디스크 사용량을 증가시킵니다. 텍스트 검색에 사용하는 컬럼(`post_chunks.content`, `blog_post.title`)에만 생성하세요. diff --git a/src/prompts/qa.v2.prompts.ts b/src/prompts/qa.v2.prompts.ts index da03657..48ef58c 100644 --- a/src/prompts/qa.v2.prompts.ts +++ b/src/prompts/qa.v2.prompts.ts @@ -18,6 +18,19 @@ export const getSearchPlanSchemaJson = (): Record => ({ }, required: ['chunk', 'title'], }, + rewrites: { type: 'array', items: { type: 'string' } }, + keywords: { type: 'array', items: { type: 'string' } }, + hybrid: { + type: 'object', + additionalProperties: false, + properties: { + enabled: { type: 'boolean' }, + alpha: { type: 'number', minimum: 0, maximum: 1 }, + max_rewrites: { type: 'integer', minimum: 0, maximum: 4 }, + max_keywords: { type: 'integer', minimum: 0, maximum: 8 }, + }, + required: ['enabled', 'alpha', 'max_rewrites', 'max_keywords'], + }, filters: { type: 'object', additionalProperties: false, @@ -131,6 +144,9 @@ export const buildSearchPlanPrompt = (params: { '5) Interpret Korean temporal phrases into filters.time using the provided timezone. Month without year assumes current year.', '6) If the question asks for N items (e.g., “N개”), set limit=N within bounds.', '7) Keep weights to defaults unless a clear need implies otherwise.', + '8) When helpful for recall, set hybrid.enabled=true and generate concise rewrites (<= max_rewrites) and focused keywords (<= max_keywords).', + '9) Avoid stop/common words (예: "글", "포스트", "블로그", "소개", "정리"). Keep within the user context; avoid over-broad topics or time spans.', + '10) Remove near-duplicates: if rewrites/keywords are synonymous or highly similar, include only one.', '', `Schema: ${schemaHint}`, '', @@ -139,4 +155,3 @@ export const buildSearchPlanPrompt = (params: { 'Respond with ONLY the JSON object. No markdown, no explanation.', ].join('\n'); }; - diff --git a/src/repositories/post.repository.ts b/src/repositories/post.repository.ts index 6af3af3..1fdec4c 100644 --- a/src/repositories/post.repository.ts +++ b/src/repositories/post.repository.ts @@ -18,6 +18,13 @@ export interface SimilarChunk { similarityScore: number; } +export interface TextSearchHit { + postId: string; + postTitle: string; + postChunk: string; + textScore: number; +} + // ========= READ QUERIES ========= export const findPostById = async (postId: number): Promise => { const pool = getDb(); @@ -225,3 +232,114 @@ export const findSimilarChunksV2 = async (params: { similarityScore: row.similarity_score, })); }; + +export const textSearchChunksV2 = async (params: { + userId: string; + query?: string; + keywords?: string[]; + categoryId?: number; + from?: string; + to?: string; + topK?: number; + sort?: 'created_at_desc' | 'created_at_asc'; +}): Promise => { + const pool = getDb(); + const limit = Math.min(10, Math.max(1, params.topK ?? 5)); + + const values: any[] = []; + values.push(params.userId); + + const hasCategory = typeof params.categoryId === 'number'; + const hasTime = !!(params.from && params.to); + const hasQuery = !!params.query && params.query.trim().length > 0; + const keywords = (params.keywords || []).filter((k) => typeof k === 'string' && k.trim().length > 0); + + const withParts: string[] = []; + if (hasCategory) { + const catParam = values.length + 1; + withParts.push(` + category_ids AS ( + SELECT DISTINCT cc.descendant_id FROM category_closure cc WHERE cc.ancestor_id = $${catParam} + ), + filtered_posts AS ( + SELECT bp.id AS post_id, bp.title AS post_title, bp.created_at + FROM blog_post bp + WHERE bp.user_id = $1 AND bp.category_id IN (SELECT descendant_id FROM category_ids) + )`); + values.push(params.categoryId); + } else { + withParts.push(` + filtered_posts AS ( + SELECT id AS post_id, title AS post_title, created_at + FROM blog_post + WHERE user_id = $1 + )`); + } + + let base = ` + SELECT + fp.post_id, + fp.post_title, + pc.content AS post_chunk, + 0::float8 AS content_sim, + 0::float8 AS title_sim, + fp.created_at + FROM filtered_posts fp + JOIN post_chunks pc ON pc.post_id = fp.post_id + `; + if (hasQuery) { + const qParam = values.length + 1; + base = ` + SELECT + fp.post_id, + fp.post_title, + pc.content AS post_chunk, + COALESCE(similarity(pc.content, $${qParam}), 0) AS content_sim, + COALESCE(similarity(fp.post_title, $${qParam}), 0) AS title_sim, + fp.created_at + FROM filtered_posts fp + JOIN post_chunks pc ON pc.post_id = fp.post_id + `; + values.push(params.query); + } + + const whereParts: string[] = []; + if (hasTime) { + const fromParam = values.length + 1; + const toParam = values.length + 2; + whereParts.push(`fp.created_at BETWEEN $${fromParam} AND $${toParam}`); + values.push(params.from, params.to); + } + + const likePatterns: string[] = []; + for (const k of keywords) { + likePatterns.push(`%${k}%`); + } + if (likePatterns.length > 0) { + const arrParam = values.length + 1; + whereParts.push(`(pc.content ILIKE ANY($${arrParam}) OR fp.post_title ILIKE ANY($${arrParam}))`); + values.push(likePatterns); + } + + const whereSql = whereParts.length > 0 ? `WHERE ${whereParts.join(' AND ')}` : ''; + + let orderBy = 'content_sim DESC'; + if (params.sort === 'created_at_desc') orderBy = 'content_sim DESC, fp.created_at DESC'; + if (params.sort === 'created_at_asc') orderBy = 'content_sim DESC, fp.created_at ASC'; + + const limitParam = values.length + 1; + const sql = `${withParts.length > 0 ? 'WITH ' + withParts.join(',\n') : ''} +${base} +${whereSql} +ORDER BY ${orderBy} +LIMIT $${limitParam}`; + values.push(limit); + + const { rows } = await pool.query(sql, values); + return rows.map((row) => ({ + postId: row.post_id, + postTitle: row.post_title, + postChunk: row.post_chunk, + textScore: Math.max(Number(row.content_sim) || 0, Number(row.title_sim) || 0), + })); +}; diff --git a/src/services/hybrid-search.service.ts b/src/services/hybrid-search.service.ts new file mode 100644 index 0000000..592e723 --- /dev/null +++ b/src/services/hybrid-search.service.ts @@ -0,0 +1,104 @@ +import { createEmbeddings } from './embedding.service'; +import * as postRepository from '../repositories/post.repository'; +import { SearchPlan } from '../types/ai.v2.types'; + +export type HybridSearchResult = { + postId: string; + postTitle: string; + postChunk: string; + similarityScore: number; +}[]; + +type Candidate = { + postId: string; + postTitle: string; + postChunk: string; + vecScore?: number; + textScore?: number; +}; + +export const runHybridSearch = async ( + question: string, + userId: string, + plan: SearchPlan +): Promise => { + const queries = [question, ...((plan.rewrites as string[]) || [])]; + const alpha = Math.max(0, Math.min(1, (plan.hybrid as any)?.alpha ?? 0.7)); + + const from = (plan.filters as any)?.time?.type === 'absolute' ? (plan.filters as any).time.from : undefined; + const to = (plan.filters as any)?.time?.type === 'absolute' ? (plan.filters as any).time.to : undefined; + const categoryId = (plan.filters as any)?.category_ids?.[0]; + + const embeddings = await createEmbeddings(queries); + + const byKey = new Map(); + + for (let i = 0; i < embeddings.length; i++) { + const emb = embeddings[i]; + const rows = await postRepository.findSimilarChunksV2({ + userId, + embedding: emb, + categoryId, + from, + to, + threshold: plan.threshold, + topK: plan.top_k, + weights: plan.weights, + sort: plan.sort, + }); + for (const r of rows) { + const key = `${r.postId}:${r.postChunk}`; + const prev = byKey.get(key); + const curVec = Number(r.similarityScore) || 0; + if (!prev) { + byKey.set(key, { postId: r.postId, postTitle: r.postTitle, postChunk: r.postChunk, vecScore: curVec }); + } else { + prev.vecScore = Math.max(prev.vecScore || 0, curVec); + } + } + } + + const textRows = await postRepository.textSearchChunksV2({ + userId, + query: question, + keywords: (plan.keywords as string[]) || [], + categoryId, + from, + to, + topK: plan.top_k, + sort: plan.sort, + }); + for (const r of textRows) { + const key = `${r.postId}:${r.postChunk}`; + const prev = byKey.get(key); + const curText = Number(r.textScore) || 0; + if (!prev) { + byKey.set(key, { postId: r.postId, postTitle: r.postTitle, postChunk: r.postChunk, textScore: curText }); + } else { + prev.textScore = Math.max(prev.textScore || 0, curText); + } + } + + const list = Array.from(byKey.values()); + const vecVals = list.map((c) => c.vecScore || 0); + const textVals = list.map((c) => c.textScore || 0); + const vMin = Math.min(...vecVals, 0); + const vMax = Math.max(...vecVals, 0); + const tMin = Math.min(...textVals, 0); + const tMax = Math.max(...textVals, 0); + + const norm = (v: number, lo: number, hi: number) => (hi > lo ? (v - lo) / (hi - lo) : 0); + + const fused = list + .map((c) => { + const v = norm(c.vecScore || 0, vMin, vMax); + const t = norm(c.textScore || 0, tMin, tMax); + const score = alpha * v + (1 - alpha) * t; + return { postId: c.postId, postTitle: c.postTitle, postChunk: c.postChunk, similarityScore: score }; + }) + .sort((a, b) => b.similarityScore - a.similarityScore) + .slice(0, Math.min(10, Math.max(1, plan.top_k || 5))); + + return fused; +}; + diff --git a/src/services/qa.v2.service.ts b/src/services/qa.v2.service.ts index ef2db41..fd5b459 100644 --- a/src/services/qa.v2.service.ts +++ b/src/services/qa.v2.service.ts @@ -129,13 +129,36 @@ export const answerStreamV2 = async ( }, ]; } else { - const plan = planPair.normalized; + const plan: any = planPair.normalized; stream.write(`event: search_plan\n`); stream.write(`data: ${JSON.stringify(plan)}\n\n`); - // Execute semantic search per plan - const rows = await runSemanticSearch(question, userId, plan); - // v1과 동일하게: 청크 Top-K를 그대로 사용(포스트 단위 dedupe/limit 미적용) + let rows: { postId: string; postTitle: string; postChunk: string; similarityScore: number }[] = []; + if (plan.hybrid?.enabled) { + if (Array.isArray(plan.rewrites) && plan.rewrites.length > 0) { + stream.write(`event: rewrite\n`); + stream.write(`data: ${JSON.stringify(plan.rewrites)}\n\n`); + } + if (Array.isArray(plan.keywords) && plan.keywords.length > 0) { + stream.write(`event: keywords\n`); + stream.write(`data: ${JSON.stringify(plan.keywords)}\n\n`); + } + rows = await (await import('./hybrid-search.service')).runHybridSearch( + question, + userId, + plan + ); + const hybridContext = rows.map((r) => ({ postId: r.postId, postTitle: r.postTitle })); + stream.write(`event: hybrid_result\n`); + stream.write(`data: ${JSON.stringify(hybridContext)}\n\n`); + + if (!rows.length) { + rows = await runSemanticSearch(question, userId, plan); + } + } else { + rows = await runSemanticSearch(question, userId, plan); + } + const context = rows.map((r) => ({ postId: r.postId, postTitle: r.postTitle })); stream.write(`event: search_result\n`); stream.write(`data: ${JSON.stringify(context)}\n\n`); diff --git a/src/services/search-plan.service.ts b/src/services/search-plan.service.ts index f2861da..c26ac5a 100644 --- a/src/services/search-plan.service.ts +++ b/src/services/search-plan.service.ts @@ -66,6 +66,34 @@ export const generateSearchPlan = async ( // Normalize time range to absolute if provided let normPlan: SearchPlan = { ...plan, weights }; + + const clamp = (n: number, lo: number, hi: number) => Math.min(hi, Math.max(lo, n)); + + const stopwords = new Set([ + '글', + '포스트', + '블로그', + '소개', + '정리', + '내용', + '최신', + '최근', + '정보', + ]); + + const cleanList = (arr: string[] | undefined, max: number) => { + const uniq = new Set(); + for (const s of arr || []) { + const t = String(s || '').trim(); + if (!t) continue; + if (t.length < 2) continue; + if (stopwords.has(t)) continue; + const key = t.toLowerCase(); + if (uniq.has(key)) continue; + uniq.add(key); + } + return Array.from(uniq).slice(0, max); + }; if (plan.filters?.time) { const abs = toAbsoluteRangeKst(plan.filters.time as any, now); if (abs) { @@ -84,6 +112,16 @@ export const generateSearchPlan = async ( normPlan.top_k = Math.min(10, Math.max(1, normPlan.top_k || 5)); normPlan.limit = Math.min(20, Math.max(1, normPlan.limit || 5)); normPlan.threshold = Math.min(1, Math.max(0, normPlan.threshold ?? 0.2)); + const maxRewrites = clamp(plan.hybrid?.max_rewrites ?? 3, 0, 4); + const maxKeywords = clamp(plan.hybrid?.max_keywords ?? 6, 0, 8); + normPlan.hybrid = { + enabled: !!plan.hybrid?.enabled, + alpha: clamp(plan.hybrid?.alpha ?? 0.7, 0, 1), + max_rewrites: maxRewrites, + max_keywords: maxKeywords, + } as any; + normPlan.rewrites = cleanList(plan.rewrites, maxRewrites) as any; + normPlan.keywords = cleanList(plan.keywords, maxKeywords) as any; if (!normPlan.mode) normPlan.mode = (ctx.post_id ? 'post' : 'rag') as any; if (ctx.category_id && !normPlan.filters.category_ids) normPlan.filters.category_ids = [ctx.category_id]; if (ctx.post_id) { @@ -99,4 +137,3 @@ export const generateSearchPlan = async ( return null; } }; - diff --git a/src/types/ai.v2.types.ts b/src/types/ai.v2.types.ts index f10debb..1027ba4 100644 --- a/src/types/ai.v2.types.ts +++ b/src/types/ai.v2.types.ts @@ -36,6 +36,16 @@ export const planSchema = z.object({ weights: z .object({ chunk: z.number().min(0).max(1), title: z.number().min(0).max(1) }) .default({ chunk: 0.7, title: 0.3 }), + rewrites: z.array(z.string()).default([]), + keywords: z.array(z.string()).default([]), + hybrid: z + .object({ + enabled: z.boolean().default(false), + alpha: z.number().min(0).max(1).default(0.7), + max_rewrites: z.number().int().min(0).max(4).default(3), + max_keywords: z.number().int().min(0).max(8).default(6), + }) + .default({ enabled: false, alpha: 0.7, max_rewrites: 3, max_keywords: 6 }), filters: z .object({ user_id: z.string(), @@ -76,4 +86,3 @@ export const askV2Schema = z.object({ }); export type AskV2Request = z.infer['body']; - From 4915fee496f7ff76b2fcf6b6cf73694fa64cc81f Mon Sep 17 00:00:00 2001 From: chan000518 Date: Wed, 8 Oct 2025 16:15:52 +0900 Subject: [PATCH 09/14] =?UTF-8?q?=F0=9F=90=9B=20=EA=B8=80=20=EC=86=8C?= =?UTF-8?q?=EC=9C=A0=EA=B6=8C=20=EB=B0=8F=20=EC=9E=84=ED=8F=AC=ED=8A=B8=20?= =?UTF-8?q?=EC=97=90=EB=9F=AC=20=EC=88=98=EC=A0=95?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/api.md | 3 +- docs/reports/REPORT-askv2.md | 185 ++++++++++++++++------------ src/prompts/qa.v2.prompts.ts | 8 +- src/repositories/post.repository.ts | 3 +- src/services/qa.service.ts | 14 ++- src/services/qa.v2.service.ts | 19 +-- 6 files changed, 137 insertions(+), 95 deletions(-) diff --git a/docs/api.md b/docs/api.md index 4ed3103..a61eb38 100644 --- a/docs/api.md +++ b/docs/api.md @@ -129,7 +129,8 @@ ``` ## 참고 사항 -- `post_id`가 지정된 요청에서 해당 글이 존재하지 않거나, 글의 `user_id`가 요청 `user_id`와 다르면 SSE로 `error` 이벤트가 송신되고 스트림이 종료됩니다. +- `post_id`가 지정된 요청에서 해당 글이 존재하지 않으면 SSE로 `error` 이벤트(404)가 송신되고 스트림이 종료됩니다. +- `post.is_public`이 `false`인 글은 요청 `user_id`가 글 소유자와 다르면 `error` 이벤트(403)로 차단됩니다. `post.is_public`이 `true`면 누구나 접근 가능합니다. - v1/v2 모두 모델 응답 텍스트는 `answer` 이벤트로 분할 전송됩니다. 클라이언트는 누적하여 최종 답변을 구성해야 합니다. - EventSource(브라우저) 사용 예시 ```js diff --git a/docs/reports/REPORT-askv2.md b/docs/reports/REPORT-askv2.md index 6bc5641..ea77fc2 100644 --- a/docs/reports/REPORT-askv2.md +++ b/docs/reports/REPORT-askv2.md @@ -1,95 +1,124 @@ -# 보고서: /ai/v2/ask 구현 내역과 구조 +# 보고서: /ai/v2/ask — 동작 방식, 하이브리드 서치, 검색 계획 ## 1) 개요 -- 목적: v1의 고정형 RAG 흐름을 개선하기 위해, 사용자의 질문을 LLM이 “검색 계획(JSON)”으로 구조화 → 서버가 안전한 시맨틱 SQL 검색 수행 → v1과 동일한 방식으로 최종 답변을 SSE로 스트리밍하는 v2 파이프라인을 추가했습니다. -- 상태: v1은 수정하지 않고 유지. v2 엔드포인트 `POST /ai/v2/ask`(SSE) 신규 추가 및 전체 흐름 구현 완료. +- 목표: v1의 고정형 RAG 한계를 보완. “검색 계획(JSON)”을 LLM이 생성 → 서버가 안전하게 표준화/검증 → 시맨틱/하이브리드 검색 수행 → 최종 답변을 SSE로 스트리밍. +- 상태: `POST /ai/v2/ask`(SSE) 운영 중. v1은 유지하며, v2에서 계획 기반 검색·관측성을 추가. -## 2) v1의 주요 문제점(한계) -- 고정 파라미터 검색: 임계치(0.2), LIMIT(5)가 고정되어 질문 의도(기간/정렬/가중치/갯수 등) 반영이 어려움. -- 시간/정렬 표현 미흡: “최근 글/9월 글/지난주” 등 자연어 시간 요구를 반영할 수 없음. -- 컨텍스트 제어 부족: LLM 입력 컨텍스트의 개수/범위를 질문 의도나 비용/토큰 예산에 맞춰 유연하게 조절하기 어려움. -- 관측성 부족: 어떤 기준/전략으로 검색되었는지(계획/파라미터)를 클라이언트에서 파악하기 어려움. -- 보안/안전 제약: 프롬프트/검색 로직이 단순해, 미래에 복잡한 동적 쿼리로 확장 시 안전한 경계(화이트리스트/스키마 강제)가 필요. +## 2) v1의 한계와 v2가 해결한 점 +- 고정 파라미터 문제 → 동적 계획 + - v1: 임계치(0.2), LIMIT(5), 가중치(0.7/0.3) 고정. + - v2: `top_k`, `threshold`, `weights`, `sort`, `limit`을 계획으로 동적 제어(서버가 범위 강제). +- 시간/정렬 의도 미흡 → 자연어 시간 해석 + - v1: “최근/지난주/9월/작년” 반영 불가. + - v2: `filters.time`에 상대/월/분기/연도 표현 → `KST 절대범위(from/to)`로 변환해 쿼리에 반영. +- 컨텍스트 제어/비용 → 토큰 예산 컨트롤 + - v1: 컨텍스트 폭 조절 한계. + - v2: `top_k`(리콜 폭), `limit`(노출 개수; 현재 미적용)로 조절 가능. 향후 dedupe/limit 적용 예정. +- 관측성 부족 → SSE 메타 이벤트 확장 + - v1: 검색 근거 불투명. + - v2: `search_plan`, `rewrite`, `keywords`, `hybrid_result`, `search_result` 등으로 의사결정 가시화. +- 검색 방식 단일 → 하이브리드 도입 + - v1: 임베딩 기반 RAG만. + - v2: 벡터+텍스트 하이브리드 융합으로 재현율/정확도 개선. ![alt text](../structureDiagram/askV1-structure.png) -## 3) v2의 도입/해결 방안과 가능해진 것 -- LLM 검색 계획(JSON) 도입 - - OpenAI Responses API + JSON Schema 강제 → LLM이 오직 스키마에 맞는 JSON만 출력. - - 계획 항목: `mode`(rag/post), `top_k`, `threshold`, `weights{chunk,title}`, `filters{user_id, category_ids?, post_id?, time?}`, `sort`, `limit`. -- 시간/정렬 의도 반영 - - 자연어 시간(최근/이번 달/지난주/9월/작년 등)을 `filters.time`에 구조화. - - 서버는 KST 기준 절대 기간으로 표준화(from/to) 후 안전 SQL에 반영. - - 최신순/오래된순 정렬(`sort`) 지원. -- 안전한 서버 표준화/실행 - - Zod로 계획 검증 + 범위 강제(top_k 1..10, limit 1..20, threshold 0..1, weights 합=1). - - SQL은 화이트리스트 템플릿 + 파라미터 바인딩으로만 구성(문자열 결합 금지). -- 관측성 강화 - - SSE 메타 이벤트(`search_plan`, `search_result`)를 추가해, 검색 계획과 결과 요약을 클라이언트에 투명하게 제공. -- 컨텍스트 최적화 - - 청크 검색 후보(top_k)를 넓게 가져온 뒤, 포스트 단위 중복 제거 + `limit`개까지만 컨텍스트/노출로 사용(토큰/비용 관리). -- 폴백 안전성 - - 계획 생성 실패 시 v1 RAG 경로로 자동 폴백하여 기능 연속성 보장. -![alt text](../structureDiagram/askV2-structure.png) -## 4) v2 구조(파일과 역할) -- 라우팅/컨트롤러 - - `src/routes/ai.v2.routes.ts`: `/ai/v2/ask` SSE 라우트. - - `src/controllers/ai.v2.controller.ts`: SSE 헤더 설정, 서비스 호출/파이프. - - `src/app.ts`: `/ai/v2` 마운트. -- 플래너 LLM & 타입 - - `src/types/ai.v2.types.ts`: v2 요청/검색 계획 Zod 스키마. - - `src/prompts/qa.v2.prompts.ts`: 검색 계획 프롬프트/JSON 스키마. - - `src/services/search-plan.service.ts`: Responses API 호출 → 계획 JSON 검증/정규화. -- 시간 표준화 - - `src/utils/time.ts`: KST 기준 상대/월/분기/연도 → 절대기간 변환. -- 시맨틱 서치 - - `src/services/semantic-search.service.ts`: 질문 임베딩 생성 → 동적 저장소 호출. - - `src/repositories/post.repository.ts`: `findSimilarChunksV2`(가중치/임계치/기간/정렬/topK 파라미터 반영). -- 오케스트레이션 - - `src/services/qa.v2.service.ts`: `search_plan`→`search_result` 발행, v1 프롬프트 재사용으로 최종 LLM 스트림 중계. +## 3) 검색 계획(Planner)과 안전 표준화 +- 스키마(`src/types/ai.v2.types.ts`) + - `mode`: `rag|post` + - `top_k`(1..10), `threshold`(0..1), `weights`(`chunk`,`title`) + - `filters`: `{ user_id, category_ids?, post_id?, time? }` + - `sort`: `created_at_desc|created_at_asc`, `limit`(1..20) + - `hybrid`: `{ enabled, alpha, max_rewrites, max_keywords }`, `rewrites`: string[], `keywords`: string[] +- 프롬프트 규칙 강화(`src/prompts/qa.v2.prompts.ts`) + - 서버 제공 필터(`user_id`, `category_id`, `post_id`)는 고정(FIXED)이며 변경 금지. + - 플래너는 `top_k`, `threshold`, `filters.time`, `sort`, `limit`, `hybrid`, `rewrites`, `keywords`만 결정. +- 생성/정규화(`src/services/search-plan.service.ts`) + - OpenAI Responses API(JSON Schema strict) → Zod 파싱/검증. + - 값 범위 강제, 가중치 합 1로 정규화, 불용어/중복 제거로 `rewrites`/`keywords` 정제. + - 시간 필터를 `KST 절대범위`로 변환(`src/utils/time.ts`). + - 카테고리/포스트 ID는 서버가 최종 주입(정합성 보장). 실패 시 v1 RAG 폴백. + +## 4) 검색 엔진: 시맨틱 + 하이브리드 +- 시맨틱(`src/services/semantic-search.service.ts` → `findSimilarChunksV2`) + - 입력: 질문 임베딩, `threshold/top_k/weights/sort`, 카테고리/시간 필터. + - 점수: `w_chunk*(1 - chunk_dist) + w_title*(1 - title_dist)`. + - SQL: 파라미터 바인딩/화이트리스트 템플릿. +- 하이브리드(`src/services/hybrid-search.service.ts`) + - 입력: 원 질문 + `rewrites`(재작성), `keywords`(키워드), `alpha`. + - 벡터 경로: 각 query 임베딩 → 시맨틱 검색 → 동일 청크는 최대 vecScore로 병합. + - 텍스트 경로: ILIKE/텍스트 유사도 기반 `textSearchChunksV2` → 최대 textScore로 병합. + - 정규화/융합: min-max 정규화 후 `score = alpha*vec + (1-alpha)*text`로 랭킹 → 상위 `top_k` 반환. + - SSE: `rewrite`, `keywords`, `hybrid_result` 이벤트로 중간 산출물/요약 제공. +- 폴백: 하이브리드 결과가 비어있으면 시맨틱 경로로 재시도. -## 5) v2 요청→응답 흐름 -1) 클라이언트: `POST /ai/v2/ask` (JWT 필요) -2) 컨트롤러: SSE 헤더 설정 → `answerStreamV2` 호출 -3) post 모드(post_id 존재): 소유자 검증 → 계획/결과 이벤트 발행 → 본문 컨텍스트로 메시지 구성 → 답변 스트림 -4) rag 모드: 플래너 LLM으로 계획 생성 → Zod 검증/시간 표준화 → `search_plan` 이벤트 발행 → 시맨틱 서치 실행 → `search_result`/`exist_in_post_status`/`context` 발행 → RAG 프롬프트 구성 → 답변 스트림 -5) 공통: `src/llm` 레이어를 통해 `answer` 청크 스트리밍, 종료 시 `end` 이벤트 +![alt text](../structureDiagram/askV2-structure.png) -SSE 이벤트 순서 -- `search_plan` → `search_result` → `exist_in_post_status` → `context` → `answer`* → `end` -- 오류 시: `error` +## 5) 파이프라인(SSE 이벤트)과 모드 +- 컨트롤러/오케스트레이터 + - `src/controllers/ai.v2.controller.ts`: SSE 헤더 설정, 서비스 파이프. + - `src/services/qa.v2.service.ts`: 계획 생성→검색→LLM 호출까지 오케스트레이션. +- post 모드(post_id 존재) + - 접근 정책: `post.is_public=false`면 소유자만(403), `true`면 공개. 미존재 404. + - 이벤트: `search_plan`(합성) → `search_result` → `exist_in_post_status:true` → `context` → `answer`* → `end`. + - 컨텍스트: 단일 글 본문(가공) 사용. RAG/하이브리드 미사용. +- rag/하이브리드 모드 + - 일반 흐름: + 1) `search_plan`(정규화된 계획) + 2) `rewrite`(선택) + 3) `keywords`(선택) + 4) `hybrid_result`(선택) + 5) `search_result` + 6) `exist_in_post_status` + 7) `context` + 8) `answer`* → `end` + - 오류 시: `error` 송신. -## 6) 모델/설정 -- 기본 LLM: `openai/gpt-5-mini`(요청 `llm` 또는 `CHAT_MODEL`로 변경 가능) -- 임베딩: `text-embedding-3-small` -- Gemini: 응답 스트림은 지원(계획 LLM은 현재 OpenAI 사용) +## 6) LLM/비용/프로바이더 +- 기본 모델: `openai/gpt-5-mini`(환경변수로 변경 가능), 임베딩: `text-embedding-3-small`. +- OpenAI: GPT‑5 Responses API 스트리밍 우선, 실패 시 논스트림/Chat Completions 폴백. 툴콜 인자도 `answer` 청크로 송신. +- Gemini: 현재 논스트림 결과를 SSE 청크로 전송. +- 비용 로깅: 프롬프트/완료 토큰, 추정 비용 로깅(`src/llm/index.ts`). -## 7) 보안/안전 -- 계획 출력은 JSON Schema로 제한, 서버에서 Zod 검증 및 값 범위 강제. -- SQL은 파라미터 바인딩/화이트리스트 템플릿만 사용. -- post 모드에서 소유권 체크. -- SSE에는 민감정보 제외(제목/ID 등만 노출). +## 7) 보안·정합성 +- JSON Schema + Zod 검증 + 범위 강제. +- 서버가 `user_id/category_id/post_id` 필터를 최종 강제(LLM 변조 무력화). +- SQL 파라미터 바인딩/템플릿 화이트리스트. +- SSE 데이터 최소화(제목/ID/요약만). +- post 모드 접근 제어: `is_public` 조건 기반. -## 8) 현재 동작과 향후 확장 +## 8) 현재 동작과 한계(향후 과제) - 현재 - - `top_k`: 청크 단위 후보 개수(리콜 폭)로 SQL LIMIT 적용(v1과 동일). - - `limit`: 현재는 미적용(v1과 동일 동작 유지). 컨텍스트는 청크 Top-K를 그대로 사용하며, 포스트 단위 중복 제거도 하지 않음. - - `sort`: 유사도 우선 + 최신순/오래된순 보조 정렬. + - `top_k`로 리콜 폭 제어, 시맨틱/하이브리드 모두 적용. + - `limit`은 계획에 있으나 컨텍스트 축약에는 아직 미적용(Top‑K 청크를 그대로 사용). + - 청크 기준 컨텍스트로 포스트 중복 가능. + - 카테고리 배열은 첫 항목만 반영. - 향후 - - `limit` 적용을 재도입하여 컨텍스트 길이/비용 최적화(옵션화 가능). - - 포스트별 상위 N청크 추출(윈도우 함수) 설계로 깊이/다양성 균형. - - `search_sql` 이벤트(템플릿 ID + 파라미터 프리뷰)로 관측성 강화. - - 계획 LLM의 프로바이더 확장(Gemini) 및 프롬프트 튜닝 고도화. + - 포스트 단위 dedupe + `limit` 적용(윈도우 함수 등)으로 비용/다양성 균형. + - 다중 카테고리 필터 완전 반영. + - `search_sql`/`search_debug` 이벤트로 쿼리/파라미터 노출. + - 텍스트 경로 고도화(순위 함수/전처리/키워드 확장), 모델 프로바이더 다양화. -## 9) 사용 예시 -- 요청 바디 예시 -``` +## 9) 파일 맵(핵심) +- 라우트/컨트롤러: `src/routes/ai.v2.routes.ts`, `src/controllers/ai.v2.controller.ts`, `src/app.ts` +- 플래너/타입/프롬프트: `src/services/search-plan.service.ts`, `src/types/ai.v2.types.ts`, `src/prompts/qa.v2.prompts.ts` +- 시간 유틸: `src/utils/time.ts` +- 검색: `src/services/semantic-search.service.ts`, `src/services/hybrid-search.service.ts`, `src/repositories/post.repository.ts` +- 오케스트레이션: `src/services/qa.v2.service.ts` +- LLM: `src/llm/*` + +## 10) 예시 +- 요청 +```json { - "question": "9월 글 2개만 보여줘", - "user_id": "", - "speech_tone": -1, - "llm": { "provider": "openai", "model": "gpt-5-mini" } + "question": "지난달 프로젝트 X 관련 핵심만 3개 보여줘", + "user_id": "u_123", + "category_id": 3, + "llm": { "provider": "openai", "options": { "temperature": 0.2 } } } ``` -- 기대 SSE 순서: `search_plan` → `search_result` → `exist_in_post_status` → `context` → `answer*` → `end` - +- 이벤트 예시 + - `search_plan`(rag, time=지난달, top_k/threshold/weights/sort/limit/hybrid 포함) + - `rewrite`/`keywords`(선택) + - `hybrid_result`(선택) + - `search_result` → `exist_in_post_status` → `context` → `answer`* → `end` diff --git a/src/prompts/qa.v2.prompts.ts b/src/prompts/qa.v2.prompts.ts index 48ef58c..ef6c0f9 100644 --- a/src/prompts/qa.v2.prompts.ts +++ b/src/prompts/qa.v2.prompts.ts @@ -139,8 +139,12 @@ export const buildSearchPlanPrompt = (params: { 'Rules:', '1) Output ONLY a single JSON object matching the schema. No extra text.', '2) Respect bounds: top_k 1..10, limit 1..20, threshold 0..1, weights in [0,1] and sum to 1.', - '3) If post_id exists, use mode="post" and include filters.post_id; else mode="rag".', - '4) If category_id exists, include it in filters.category_ids.', + '3) Server-provided filters are FIXED constraints. Do NOT change or omit them:', + ' - Always keep filters.user_id as provided (unchanged).', + ' - If category_id exists, include ONLY that id in filters.category_ids (no additions/removals).', + ' - If post_id exists, include exactly that as filters.post_id (no substitutions).', + ' - Your job is to decide top_k, threshold, filters.time, sort, and limit only; do NOT invent other filters.', + '4) Mode must follow constraints: if post_id exists, use mode="post"; otherwise use mode="rag".', '5) Interpret Korean temporal phrases into filters.time using the provided timezone. Month without year assumes current year.', '6) If the question asks for N items (e.g., “N개”), set limit=N within bounds.', '7) Keep weights to defaults unless a clear need implies otherwise.', diff --git a/src/repositories/post.repository.ts b/src/repositories/post.repository.ts index 1fdec4c..eade868 100644 --- a/src/repositories/post.repository.ts +++ b/src/repositories/post.repository.ts @@ -9,6 +9,7 @@ export interface Post { tags: string[]; created_at: string; user_id: string; + is_public:boolean; } export interface SimilarChunk { @@ -29,7 +30,7 @@ export interface TextSearchHit { export const findPostById = async (postId: number): Promise => { const pool = getDb(); const { rows } = await pool.query( - 'SELECT id, title, content, tags, created_at, user_id FROM blog_post WHERE id = $1', + 'SELECT id, title, content, tags, created_at, user_id, is_public FROM blog_post WHERE id = $1', [postId] ); return rows.length > 0 ? rows[0] : null; diff --git a/src/services/qa.service.ts b/src/services/qa.service.ts index 0d8ffe0..e0b308f 100644 --- a/src/services/qa.service.ts +++ b/src/services/qa.service.ts @@ -72,12 +72,14 @@ export const answerStream = async ( try { console.warn(JSON.stringify({ type: 'debug.qa.post', status: 'not_found', postId })); } catch {} return; } - - if (post.user_id !== userId) { - stream.write(`event: error\ndata: ${JSON.stringify({ code: 403, message: 'Forbidden' })}\n\n`); - stream.end(); - try { console.warn(JSON.stringify({ type: 'debug.qa.post', status: 'forbidden', postId })); } catch {} - return; + + // Enforce conditional ownership: if post is not public, require owner + if (!post.is_public && post.user_id !== userId) { + stream.write(`event: error\n`); + stream.write(`data: ${JSON.stringify({ code: 403, message: 'Forbidden' })}\n\n`); + stream.end(); + try { console.warn(JSON.stringify({ type: 'debug.qa.post', status: 'forbidden', postId })); } catch {} + return; } const processedContent = preprocessContent(post.content); diff --git a/src/services/qa.v2.service.ts b/src/services/qa.v2.service.ts index fd5b459..9027c36 100644 --- a/src/services/qa.v2.service.ts +++ b/src/services/qa.v2.service.ts @@ -6,6 +6,8 @@ import * as postRepository from '../repositories/post.repository'; import * as personaRepository from '../repositories/persona.repository'; import { generateSearchPlan } from './search-plan.service'; import { runSemanticSearch } from './semantic-search.service'; +import { runHybridSearch } from './hybrid-search.service'; +import { createEmbeddings } from './embedding.service'; const preprocessContent = (content: string): string => { const plainText = content.replace(/<[^>]*>/g, ' ').replace(/\s+/g, ' ').trim(); @@ -61,10 +63,15 @@ export const answerStreamV2 = async ( if (postId) { // Post-centric path (same as v1 with added v2 pre-events) const post = await postRepository.findPostById(postId); - if (!post || post.user_id !== userId) { - const code = !post ? 404 : 403; + if (!post) { stream.write(`event: error\n`); - stream.write(`data: ${JSON.stringify({ code, message: !post ? 'Post not found' : 'Forbidden' })}\n\n`); + stream.write(`data: ${JSON.stringify({ code: 404, message: 'Post not found' })}\n\n`); + stream.end(); + return; + } + if (!post.is_public && post.user_id !== userId) { + stream.write(`event: error\n`); + stream.write(`data: ${JSON.stringify({ code: 403, message: 'Forbidden' })}\n\n`); stream.end(); return; } @@ -91,9 +98,7 @@ export const answerStreamV2 = async ( const planPair = await generateSearchPlan(question, { user_id: userId, category_id: categoryId }); if (!planPair) { // Fallback to v1 RAG silently - const [questionEmbedding] = await (await import('./embedding.service')).createEmbeddings([ - question, - ]); + const [questionEmbedding] = await createEmbeddings([question]); const similarChunks = await postRepository.findSimilarChunks(userId, questionEmbedding, categoryId); const context = similarChunks.map((c) => ({ postId: c.postId, postTitle: c.postTitle })); stream.write(`event: search_plan\n`); @@ -143,7 +148,7 @@ export const answerStreamV2 = async ( stream.write(`event: keywords\n`); stream.write(`data: ${JSON.stringify(plan.keywords)}\n\n`); } - rows = await (await import('./hybrid-search.service')).runHybridSearch( + rows = await runHybridSearch( question, userId, plan From 0b616dacf1e6da54d8c4dfcc8a11a23aa669ed28 Mon Sep 17 00:00:00 2001 From: chan000518 Date: Wed, 8 Oct 2025 16:17:06 +0900 Subject: [PATCH 10/14] =?UTF-8?q?=E2=9A=A1=EF=B8=8F=20=ED=95=98=EC=9D=B4?= =?UTF-8?q?=EB=B8=8C=EB=A6=AC=EB=93=9C=20=EA=B2=80=EC=83=89=20=EA=B3=84?= =?UTF-8?q?=ED=9A=8D=20=EC=8B=9C=20=EC=95=8C=ED=8C=8C=EA=B0=92=20=EB=8C=80?= =?UTF-8?q?=EC=8B=A0=20=EB=9D=BC=EB=B2=A8=EC=9D=84=20=ED=86=B5=ED=95=B4=20?= =?UTF-8?q?=EB=A7=A4=ED=95=91?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/api.md | 56 +++++++++++++++++++---------- src/prompts/qa.v2.prompts.ts | 9 +++-- src/services/search-plan.service.ts | 9 ++++- src/types/ai.v2.types.ts | 6 ++-- 4 files changed, 55 insertions(+), 25 deletions(-) diff --git a/docs/api.md b/docs/api.md index a61eb38..fcce131 100644 --- a/docs/api.md +++ b/docs/api.md @@ -79,40 +79,58 @@ ### POST `/ai/v2/ask` (SSE) - 인증: 필요 (`Authorization: Bearer `) -- 요청 Body (v1과 동일 스키마) +- 요청 Body - `question`(string, required) - `user_id`(string, required) - `category_id`(number, optional) - `post_id`(number, optional) - `speech_tone`(number, optional) + - `-1`: 기본 말투(간결/명확) + - `-2`: 해당 글(post 모드) 말투 모사 + - 양수: 페르소나 ID(해당 유저의 등록 페르소나) - `llm`(object, optional) -- v2의 추가/변경 사항 - - 검색 계획 수립과 결과를 사전 이벤트로 투명하게 송신합니다. - - `post_id` 지정 시에도 동일한 형태의 사전 이벤트를 간략히 제공합니다. + - `provider`: `openai` | `gemini` + - `model`: string (미지정 시 서버 기본값 사용) + - `options`: `{ temperature?: number, top_p?: number, max_output_tokens?: number }` +- 동작 개요 + - 서버가 질문을 토대로 “검색 계획(JSON)”을 생성·검증·정규화한 뒤, 계획에 따라 시맨틱/하이브리드 검색을 수행하고 결과를 SSE로 스트리밍합니다. + - `post_id`가 있으면 post 모드(단일 글 컨텍스트)로 처리하며, 간략한 `search_plan`/`search_result` 이벤트 후 본문 기반 답변을 스트리밍합니다. +- 하이브리드 검색(벡터+텍스트) + - 계획에 `hybrid.enabled: true`인 경우 활성화됩니다. + - `rewrites`(재작성 질의)와 `keywords`(핵심 키워드)를 생성하여 벡터/텍스트 두 경로로 후보를 수집하고, `alpha` 가중으로 점수를 융합해 상위 `top_k`를 선택합니다. + - SSE로 `rewrite`, `keywords`, `hybrid_result` 이벤트가 순차 송신됩니다. 하이브리드 결과가 없으면 시맨틱 검색으로 폴백합니다. - SSE 이벤트 순서(일반적인 흐름) - 1) `search_plan`: 검색 계획 정보 - - 예시 데이터(정규화된 계획): + 1) `search_plan`: 정규화된 검색 계획(JSON) + - 예시 데이터(정규화): ```json { - "mode": "rag" | "post", + "mode": "rag", "top_k": 5, "threshold": 0.2, "weights": { "chunk": 0.7, "title": 0.3 }, - "filters": { "user_id": "u_123", "category_ids": [1], "post_id": 10?, "time": {...}? }, + "filters": { + "user_id": "u_123", + "category_ids": [3], + "time": { "type": "absolute", "from": "2025-09-01T00:00:00.000Z", "to": "2025-09-30T23:59:59.999Z" } + }, "sort": "created_at_desc", - "limit": 5 + "limit": 5, + "hybrid": { "enabled": true, "alpha": 0.7, "max_rewrites": 3, "max_keywords": 6 }, + "rewrites": ["프로젝트 X 요약", "프로젝트 X 핵심"], + "keywords": ["프로젝트 X", "핵심", "요약"] } ``` - - `time` 필터는 `relative|absolute|month|year|quarter` 타입을 지원하며, 값은 ISO8601 또는 숫자 조합입니다. - 2) (하이브리드 사용 시) `rewrite`: `["재작성 질의", ...]` - 3) (하이브리드 사용 시) `keywords`: `["핵심 키워드", ...]` - 4) (하이브리드 사용 시) `hybrid_result`: `[ { postId, postTitle }, ... ]` — 융합 기준 상위 결과 요약 - 5) `search_result`: `[ { postId, postTitle }, ... ]` — 최종 컨텍스트 요약 + - 비고: 카테고리는 단일 값만 지원하며 서버는 첫 번째 항목만 사용합니다. + - post 모드에서는 간략한 형태(`{ mode: "post", filters: { post_id, user_id } }`)가 송신될 수 있습니다. + 2) (하이브리드 사용 시) `rewrite`: `string[]` + 3) (하이브리드 사용 시) `keywords`: `string[]` + 4) (하이브리드 사용 시) `hybrid_result`: `[ { postId, postTitle }, ... ]` + 5) `search_result`: `[ { postId, postTitle }, ... ]` — 최종 컨텍스트 요약(하이브리드 또는 시맨틱) 6) `exist_in_post_status`: `true|false` - 7) `context`: `[ { postId, postTitle }, ... ]` — 모델 프롬프트에 사용되는 컨텍스트 요약 - 8) `answer` 스트리밍(여러 번) - 9) `end` with `data: [DONE]` - - 오류 시 `error`: `{ code?, message }` + 7) `context`: `[ { postId, postTitle }, ... ]` + 8) `answer` — 모델 부분 응답(여러 번) + 9) `end` — `data: [DONE]` + - 오류 시 `error`: `{ code?: number, message: string }` - 예시(curl) ```bash @@ -124,7 +142,7 @@ "question": "최근 한 달 블로그에서 프로젝트 X 관련 내용 요약", "user_id": "u_123", "category_id": 3, - "llm": { "provider": "openai", "options": { "temperature": 0.2 } } + "llm": { "provider": "openai", "model": "gpt-5-mini", "options": { "temperature": 0.2, "top_p": 0.9, "max_output_tokens": 800 } } }' ``` diff --git a/src/prompts/qa.v2.prompts.ts b/src/prompts/qa.v2.prompts.ts index ef6c0f9..ad82feb 100644 --- a/src/prompts/qa.v2.prompts.ts +++ b/src/prompts/qa.v2.prompts.ts @@ -25,11 +25,11 @@ export const getSearchPlanSchemaJson = (): Record => ({ additionalProperties: false, properties: { enabled: { type: 'boolean' }, - alpha: { type: 'number', minimum: 0, maximum: 1 }, + retrieval_bias: { enum: ['lexical', 'balanced', 'semantic'] }, max_rewrites: { type: 'integer', minimum: 0, maximum: 4 }, max_keywords: { type: 'integer', minimum: 0, maximum: 8 }, }, - required: ['enabled', 'alpha', 'max_rewrites', 'max_keywords'], + required: ['enabled', 'retrieval_bias', 'max_rewrites', 'max_keywords'], }, filters: { type: 'object', @@ -148,7 +148,10 @@ export const buildSearchPlanPrompt = (params: { '5) Interpret Korean temporal phrases into filters.time using the provided timezone. Month without year assumes current year.', '6) If the question asks for N items (e.g., “N개”), set limit=N within bounds.', '7) Keep weights to defaults unless a clear need implies otherwise.', - '8) When helpful for recall, set hybrid.enabled=true and generate concise rewrites (<= max_rewrites) and focused keywords (<= max_keywords).', + '8) When helpful for recall, set hybrid.enabled=true and choose hybrid.retrieval_bias ∈ {lexical, balanced, semantic}. Then generate concise rewrites (<= max_rewrites) and focused keywords (<= max_keywords).', + ' - lexical: keyword/정확 매칭이 중요할 때 (숫자, 버전, 고유명사 등).', + ' - balanced: 일반 질의.', + ' - semantic: 개념/요약/의도 중심일 때.', '9) Avoid stop/common words (예: "글", "포스트", "블로그", "소개", "정리"). Keep within the user context; avoid over-broad topics or time spans.', '10) Remove near-duplicates: if rewrites/keywords are synonymous or highly similar, include only one.', '', diff --git a/src/services/search-plan.service.ts b/src/services/search-plan.service.ts index c26ac5a..ee9a1fe 100644 --- a/src/services/search-plan.service.ts +++ b/src/services/search-plan.service.ts @@ -114,9 +114,16 @@ export const generateSearchPlan = async ( normPlan.threshold = Math.min(1, Math.max(0, normPlan.threshold ?? 0.2)); const maxRewrites = clamp(plan.hybrid?.max_rewrites ?? 3, 0, 4); const maxKeywords = clamp(plan.hybrid?.max_keywords ?? 6, 0, 8); + + // Map retrieval_bias -> alpha (fallback to provided alpha or default) + const bias = (plan.hybrid as any)?.retrieval_bias || 'balanced'; + const biasAlpha = bias === 'lexical' ? 0.3 : bias === 'semantic' ? 0.75 : 0.5; + const alpha = clamp(((plan.hybrid as any)?.alpha ?? biasAlpha) as number, 0, 1); + normPlan.hybrid = { enabled: !!plan.hybrid?.enabled, - alpha: clamp(plan.hybrid?.alpha ?? 0.7, 0, 1), + retrieval_bias: bias, + alpha, max_rewrites: maxRewrites, max_keywords: maxKeywords, } as any; diff --git a/src/types/ai.v2.types.ts b/src/types/ai.v2.types.ts index 1027ba4..c944b54 100644 --- a/src/types/ai.v2.types.ts +++ b/src/types/ai.v2.types.ts @@ -41,11 +41,13 @@ export const planSchema = z.object({ hybrid: z .object({ enabled: z.boolean().default(false), - alpha: z.number().min(0).max(1).default(0.7), + // LLM outputs retrieval_bias label; server maps to alpha + retrieval_bias: z.enum(['lexical', 'balanced', 'semantic']).default('balanced'), + alpha: z.number().min(0).max(1).optional(), max_rewrites: z.number().int().min(0).max(4).default(3), max_keywords: z.number().int().min(0).max(8).default(6), }) - .default({ enabled: false, alpha: 0.7, max_rewrites: 3, max_keywords: 6 }), + .default({ enabled: false, retrieval_bias: 'balanced', max_rewrites: 3, max_keywords: 6 }), filters: z .object({ user_id: z.string(), From aaaf4c483707f6eba99e47ddb8aeac13a893bf4a Mon Sep 17 00:00:00 2001 From: chan000518 Date: Wed, 8 Oct 2025 16:31:23 +0900 Subject: [PATCH 11/14] =?UTF-8?q?=F0=9F=93=9D=20v2=EB=AC=B8=EC=84=9C=20?= =?UTF-8?q?=EC=B5=9C=EC=8B=A0=ED=99=94?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/api.md | 10 +- docs/reports/REPORT-askv2.md | 142 ++++++++++++---------- docs/structureDiagram/askV1-structure.png | Bin 149241 -> 131020 bytes docs/structureDiagram/askV2-structure.png | Bin 131020 -> 149241 bytes 4 files changed, 83 insertions(+), 69 deletions(-) diff --git a/docs/api.md b/docs/api.md index fcce131..7b08af3 100644 --- a/docs/api.md +++ b/docs/api.md @@ -97,7 +97,9 @@ - `post_id`가 있으면 post 모드(단일 글 컨텍스트)로 처리하며, 간략한 `search_plan`/`search_result` 이벤트 후 본문 기반 답변을 스트리밍합니다. - 하이브리드 검색(벡터+텍스트) - 계획에 `hybrid.enabled: true`인 경우 활성화됩니다. - - `rewrites`(재작성 질의)와 `keywords`(핵심 키워드)를 생성하여 벡터/텍스트 두 경로로 후보를 수집하고, `alpha` 가중으로 점수를 융합해 상위 `top_k`를 선택합니다. + - `rewrites`(재작성 질의)와 `keywords`(핵심 키워드)를 생성하여 벡터/텍스트 두 경로로 후보를 수집하고, `hybrid.retrieval_bias` 라벨을 서버가 `alpha` 값으로 매핑해 점수를 융합하여 상위 `top_k`를 선택합니다. + - 매핑(기본): `lexical → 0.3`, `balanced → 0.5`, `semantic → 0.75` + - 정규화/결합식: `score = alpha*vec + (1-alpha)*text` (min-max 정규화 후) - SSE로 `rewrite`, `keywords`, `hybrid_result` 이벤트가 순차 송신됩니다. 하이브리드 결과가 없으면 시맨틱 검색으로 폴백합니다. - SSE 이벤트 순서(일반적인 흐름) 1) `search_plan`: 정규화된 검색 계획(JSON) @@ -115,12 +117,14 @@ }, "sort": "created_at_desc", "limit": 5, - "hybrid": { "enabled": true, "alpha": 0.7, "max_rewrites": 3, "max_keywords": 6 }, + "hybrid": { "enabled": true, "retrieval_bias": "balanced", "alpha": 0.5, "max_rewrites": 3, "max_keywords": 6 }, "rewrites": ["프로젝트 X 요약", "프로젝트 X 핵심"], "keywords": ["프로젝트 X", "핵심", "요약"] } ``` - - 비고: 카테고리는 단일 값만 지원하며 서버는 첫 번째 항목만 사용합니다. + - 비고: + - `hybrid.retrieval_bias`는 LLM이 결정하는 라벨이며, 서버는 이를 `alpha`로 변환해 사용합니다. + - 카테고리는 단일 값만 지원하며 서버는 첫 번째 항목만 사용합니다. - post 모드에서는 간략한 형태(`{ mode: "post", filters: { post_id, user_id } }`)가 송신될 수 있습니다. 2) (하이브리드 사용 시) `rewrite`: `string[]` 3) (하이브리드 사용 시) `keywords`: `string[]` diff --git a/docs/reports/REPORT-askv2.md b/docs/reports/REPORT-askv2.md index ea77fc2..c9d4229 100644 --- a/docs/reports/REPORT-askv2.md +++ b/docs/reports/REPORT-askv2.md @@ -1,103 +1,113 @@ -# 보고서: /ai/v2/ask — 동작 방식, 하이브리드 서치, 검색 계획 +# 보고서: /ai/v2/ask — 구조, 동작, 하이브리드 검색 계획 ## 1) 개요 - 목표: v1의 고정형 RAG 한계를 보완. “검색 계획(JSON)”을 LLM이 생성 → 서버가 안전하게 표준화/검증 → 시맨틱/하이브리드 검색 수행 → 최종 답변을 SSE로 스트리밍. -- 상태: `POST /ai/v2/ask`(SSE) 운영 중. v1은 유지하며, v2에서 계획 기반 검색·관측성을 추가. +- 상태: `POST /ai/v2/ask`(SSE) 운영 중. v1은 유지하며, v2에서 계획 기반 검색과 관측성을 강화. -## 2) v1의 한계와 v2가 해결한 점 -- 고정 파라미터 문제 → 동적 계획 - - v1: 임계치(0.2), LIMIT(5), 가중치(0.7/0.3) 고정. - - v2: `top_k`, `threshold`, `weights`, `sort`, `limit`을 계획으로 동적 제어(서버가 범위 강제). -- 시간/정렬 의도 미흡 → 자연어 시간 해석 - - v1: “최근/지난주/9월/작년” 반영 불가. - - v2: `filters.time`에 상대/월/분기/연도 표현 → `KST 절대범위(from/to)`로 변환해 쿼리에 반영. -- 컨텍스트 제어/비용 → 토큰 예산 컨트롤 - - v1: 컨텍스트 폭 조절 한계. - - v2: `top_k`(리콜 폭), `limit`(노출 개수; 현재 미적용)로 조절 가능. 향후 dedupe/limit 적용 예정. -- 관측성 부족 → SSE 메타 이벤트 확장 - - v1: 검색 근거 불투명. - - v2: `search_plan`, `rewrite`, `keywords`, `hybrid_result`, `search_result` 등으로 의사결정 가시화. -- 검색 방식 단일 → 하이브리드 도입 +## 2) v1의 한계와 v2 도입 효과 +- 고정 파라미터 → 동적 계획 + - v1: 임계치(0.2), LIMIT(5), 가중치(0.7/0.3) 등 고정. + - v2: `top_k`, `threshold`, `weights`, `sort`, `limit`을 질문/맥락에 맞게 동적 제어(서버가 범위 강제). +- 시간/정렬 의도 미반영 → 자연어 시간 해석 + - v1: “최근/지난주/9월/작년” 같은 시간 의도 반영 불가. + - v2: `filters.time`(상대/월/분기/연도)을 받아 `KST 절대범위(from/to)`로 변환해 쿼리에 반영. +- 리콜/정밀도 균형 한계 → 하이브리드 - v1: 임베딩 기반 RAG만. - - v2: 벡터+텍스트 하이브리드 융합으로 재현율/정확도 개선. + - v2: 벡터+텍스트 하이브리드 융합으로 재현율 향상 및 키워드 민감 질의 대응. +- 관측성 부족 → SSE 메타 이벤트 + - v1: 검색/선택 근거가 불투명. + - v2: `search_plan`, `rewrite`, `keywords`, `hybrid_result`, `search_result` 등으로 의사결정 가시화. +- 안전성/정합성 + - v1: 클라이언트 입력 이탈 감지 어려움. + - v2: JSON Schema(strict) + Zod 검증 + 필터 강제 주입으로 안전한 실행계획만 수행. +- 비용/토큰 관리 + - v1: 고정 상수로 세밀한 제어 어려움. + - v2: `top_k`/향후 `limit`/dedupe로 토큰 예산을 상황별 최적화 가능. -![alt text](../structureDiagram/askV1-structure.png) +![v1 구조](../structureDiagram/askV1-structure.png) ## 3) 검색 계획(Planner)과 안전 표준화 -- 스키마(`src/types/ai.v2.types.ts`) - - `mode`: `rag|post` - - `top_k`(1..10), `threshold`(0..1), `weights`(`chunk`,`title`) +- 타입/스키마(`src/types/ai.v2.types.ts`) + - `mode`: `rag | post` + - `top_k`(1..10), `threshold`(0..1), `weights`(`chunk`,`title`; 합 1로 정규화) - `filters`: `{ user_id, category_ids?, post_id?, time? }` - - `sort`: `created_at_desc|created_at_asc`, `limit`(1..20) - - `hybrid`: `{ enabled, alpha, max_rewrites, max_keywords }`, `rewrites`: string[], `keywords`: string[] -- 프롬프트 규칙 강화(`src/prompts/qa.v2.prompts.ts`) + - `sort`: `created_at_desc | created_at_asc`, `limit`(1..20) + - `hybrid`: `{ enabled, retrieval_bias, alpha?, max_rewrites, max_keywords }` + - `rewrites`: string[], `keywords`: string[] +- 프롬프트 규칙(`src/prompts/qa.v2.prompts.ts`) - 서버 제공 필터(`user_id`, `category_id`, `post_id`)는 고정(FIXED)이며 변경 금지. - 플래너는 `top_k`, `threshold`, `filters.time`, `sort`, `limit`, `hybrid`, `rewrites`, `keywords`만 결정. + - 하이브리드 시 `hybrid.retrieval_bias ∈ {lexical, balanced, semantic}` 라벨을 결정. - 생성/정규화(`src/services/search-plan.service.ts`) - - OpenAI Responses API(JSON Schema strict) → Zod 파싱/검증. - - 값 범위 강제, 가중치 합 1로 정규화, 불용어/중복 제거로 `rewrites`/`keywords` 정제. + - OpenAI Responses(JSON Schema strict) → Zod 파싱/검증. + - 값 범위 강제, `weights` 합 1로 정규화. + - 불용어/중복 제거로 `rewrites`/`keywords` 정제. - 시간 필터를 `KST 절대범위`로 변환(`src/utils/time.ts`). - - 카테고리/포스트 ID는 서버가 최종 주입(정합성 보장). 실패 시 v1 RAG 폴백. + - `retrieval_bias → alpha` 매핑(서버): `lexical→0.3`, `balanced→0.5`, `semantic→0.75`(필요 시 서버가 `alpha`를 직접 클램프/주입). + - `user_id/category_id/post_id`는 서버가 최종 주입(정합성 보장). 실패 시 v1 RAG 폴백. ## 4) 검색 엔진: 시맨틱 + 하이브리드 - 시맨틱(`src/services/semantic-search.service.ts` → `findSimilarChunksV2`) - 입력: 질문 임베딩, `threshold/top_k/weights/sort`, 카테고리/시간 필터. - 점수: `w_chunk*(1 - chunk_dist) + w_title*(1 - title_dist)`. - - SQL: 파라미터 바인딩/화이트리스트 템플릿. + - 저장소: `postRepository.findSimilarChunksV2(...)` 호출, 파라미터 바인딩 기반 안전 쿼리. - 하이브리드(`src/services/hybrid-search.service.ts`) - - 입력: 원 질문 + `rewrites`(재작성), `keywords`(키워드), `alpha`. - - 벡터 경로: 각 query 임베딩 → 시맨틱 검색 → 동일 청크는 최대 vecScore로 병합. - - 텍스트 경로: ILIKE/텍스트 유사도 기반 `textSearchChunksV2` → 최대 textScore로 병합. - - 정규화/융합: min-max 정규화 후 `score = alpha*vec + (1-alpha)*text`로 랭킹 → 상위 `top_k` 반환. - - SSE: `rewrite`, `keywords`, `hybrid_result` 이벤트로 중간 산출물/요약 제공. -- 폴백: 하이브리드 결과가 비어있으면 시맨틱 경로로 재시도. + - 입력: 원 질문 + `rewrites`(재작성), `keywords`(키워드), `alpha`(서버 매핑). + - 벡터 경로: 각 query 임베딩 → 시맨틱 후보 수집 → 동일 청크는 최대 vecScore로 병합. + - 텍스트 경로: `textSearchChunksV2`(키워드/질의 기반 텍스트 검색) → 동일 청크는 최대 textScore로 병합. + - 정규화/융합: min-max 정규화 후 `score = alpha*vec + (1-alpha)*text`로 결합 → 상위 `top_k`를 반환. + - 폴백: 하이브리드 결과 비었을 때 시맨틱 경로로 재시도. + - SSE 메타: `rewrite`, `keywords`, `hybrid_result`로 중간 산출물/요약을 별도 송신. -![alt text](../structureDiagram/askV2-structure.png) +![v2 구조](../structureDiagram/askV2-structure.png) ## 5) 파이프라인(SSE 이벤트)과 모드 -- 컨트롤러/오케스트레이터 - - `src/controllers/ai.v2.controller.ts`: SSE 헤더 설정, 서비스 파이프. - - `src/services/qa.v2.service.ts`: 계획 생성→검색→LLM 호출까지 오케스트레이션. -- post 모드(post_id 존재) - - 접근 정책: `post.is_public=false`면 소유자만(403), `true`면 공개. 미존재 404. - - 이벤트: `search_plan`(합성) → `search_result` → `exist_in_post_status:true` → `context` → `answer`* → `end`. - - 컨텍스트: 단일 글 본문(가공) 사용. RAG/하이브리드 미사용. +- 컨트롤/오케스트레이션 + - `src/controllers/ai.v2.controller.ts`: SSE 헤더, 스트림 라우팅. + - `src/services/qa.v2.service.ts`: 계획 생성 → 검색 실행(하이브리드/시맨틱) → LLM 호출까지 오케스트레이션. +- post 모드(`post_id` 존재) + - 접근 정책: `post.is_public=false`면 소유자만(403), 미존재 시 404. + - 이벤트: `search_plan`(간략) → `search_result` → `exist_in_post_status:true` → `context` → `answer`* → `end`. + - 컨텍스트: 해당 글 본문만 사용(전처리 후). 하이브리드 미사용. - rag/하이브리드 모드 - - 일반 흐름: - 1) `search_plan`(정규화된 계획) - 2) `rewrite`(선택) - 3) `keywords`(선택) - 4) `hybrid_result`(선택) - 5) `search_result` - 6) `exist_in_post_status` - 7) `context` - 8) `answer`* → `end` + - 이벤트 순서(일반): + 1) `search_plan`: 정규화된 계획(JSON). `hybrid.enabled`가 true면 `retrieval_bias`와 서버 계산 `alpha`가 함께 포함. + 2) `rewrite`(선택): 계획의 재작성 질의 목록. + 3) `keywords`(선택): 계획의 핵심 키워드 목록. + 4) `hybrid_result`(선택): 하이브리드 후보 요약. + 5) `search_result`: 최종 컨텍스트 요약. + 6) `exist_in_post_status`: `true | false`. + 7) `context`: `[ { postId, postTitle }, ... ]`. + 8) `answer`*: 모델 부분 응답. + 9) `end`: 종료 시그널(`[DONE]`). - 오류 시: `error` 송신. ## 6) LLM/비용/프로바이더 - 기본 모델: `openai/gpt-5-mini`(환경변수로 변경 가능), 임베딩: `text-embedding-3-small`. -- OpenAI: GPT‑5 Responses API 스트리밍 우선, 실패 시 논스트림/Chat Completions 폴백. 툴콜 인자도 `answer` 청크로 송신. -- Gemini: 현재 논스트림 결과를 SSE 청크로 전송. -- 비용 로깅: 프롬프트/완료 토큰, 추정 비용 로깅(`src/llm/index.ts`). +- OpenAI: Responses API 스트리밍 우선, 실패 시 논스트림/Chat Completions 폴백. +- Gemini: 현재 논스트림 결과를 SSE 청크로 분할 전송. +- 비용 로깅: 프롬프트/완료 토큰 및 추정 비용 로깅(`src/llm/*`). +- 툴콜: 컨텍스트 부족 시 `report_content_insufficient` 툴을 통해 안내 문구 유도. ## 7) 보안·정합성 -- JSON Schema + Zod 검증 + 범위 강제. -- 서버가 `user_id/category_id/post_id` 필터를 최종 강제(LLM 변조 무력화). -- SQL 파라미터 바인딩/템플릿 화이트리스트. -- SSE 데이터 최소화(제목/ID/요약만). -- post 모드 접근 제어: `is_public` 조건 기반. +- 생성 계획 방어선: JSON Schema(strict) + Zod 검증 + 서버 측 범위 강제. +- 필터 주입: `user_id/category_id/post_id`는 서버가 최종 주입하여 일탈 방지. +- SQL 안전성: 파라미터 바인딩/화이트리스트 템플릿. +- 데이터 최소화: SSE에는 ID/제목 위주 요약만 전송. +- 접근 제어: post 모드에서 `is_public`/소유자 검사. ## 8) 현재 동작과 한계(향후 과제) - 현재 - `top_k`로 리콜 폭 제어, 시맨틱/하이브리드 모두 적용. - - `limit`은 계획에 있으나 컨텍스트 축약에는 아직 미적용(Top‑K 청크를 그대로 사용). - - 청크 기준 컨텍스트로 포스트 중복 가능. + - `limit`은 계획에는 존재하나 컨텍스트 축약에는 미적용(향후 포스트 단위 dedupe와 함께 적용 예정). + - 청크 단위 랭킹 → 동일 포스트 다수 노출 가능. - 카테고리 배열은 첫 항목만 반영. - 향후 - - 포스트 단위 dedupe + `limit` 적용(윈도우 함수 등)으로 비용/다양성 균형. - - 다중 카테고리 필터 완전 반영. - - `search_sql`/`search_debug` 이벤트로 쿼리/파라미터 노출. - - 텍스트 경로 고도화(순위 함수/전처리/키워드 확장), 모델 프로바이더 다양화. + - 포스트 단위 dedupe + `limit` 적용으로 다양성/비용 균형. + - `retrieval_bias → alpha` 매핑의 AB 테스트/텔레메트리 튜닝(예: {0.25,0.5,0.8}). + - 점수 정규화 고도화(z-score/quantile) 및 RRF 옵션 도입 검토. + - 텍스트 경로 향상(전처리/순위 함수/키워드 확장) 및 프로바이더 다변화. + - SSE `search_sql`/`search_debug`로 투명성 강화. ## 9) 파일 맵(핵심) - 라우트/컨트롤러: `src/routes/ai.v2.routes.ts`, `src/controllers/ai.v2.controller.ts`, `src/app.ts` @@ -118,7 +128,7 @@ } ``` - 이벤트 예시 - - `search_plan`(rag, time=지난달, top_k/threshold/weights/sort/limit/hybrid 포함) + - `search_plan`(rag, time=지난달, top_k/threshold/weights/sort/limit/hybrid 포함; hybrid.retrieval_bias와 서버 주입 alpha 함께 표기) - 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=?UTF-8?q?=EC=9E=84)=20=EA=B5=AC=EC=B2=B4=ED=99=94?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/llm/providers/openai-responses.ts | 12 +- src/prompts/qa.v2.prompts.ts | 89 ++------- src/services/qa.v2.service.ts | 26 +++ src/services/search-plan.service.ts | 277 +++++++++++++++++++++++--- src/types/ai.v2.types.ts | 72 ++++--- src/utils/time.ts | 131 +++++++++++- 6 files changed, 480 insertions(+), 127 deletions(-) diff --git a/src/llm/providers/openai-responses.ts b/src/llm/providers/openai-responses.ts index 3a2e86b..d45d2db 100644 --- a/src/llm/providers/openai-responses.ts +++ b/src/llm/providers/openai-responses.ts @@ -101,17 +101,17 @@ export const generateOpenAIStream = async (req: GenerateRequest): Promise { const text = typeof ev === 'string' ? ev : ev?.delta ?? ''; if (text) { safeWrite(`event: answer\n`); safeWrite(`data: ${JSON.stringify(text)}\n\n`); - try { console.log(JSON.stringify({ type: 'debug.openai.delta', len: String(text).length, at: Date.now() })); } catch {} - if (!loggedFirstDelta) { - try { console.log(JSON.stringify({ type: 'debug.openai.delta', len: String(text).length })); } catch {} - loggedFirstDelta = true; - } + // try { console.log(JSON.stringify({ type: 'debug.openai.delta', len: String(text).length, at: Date.now() })); } catch {} + // if (!loggedFirstDelta) { + // try { console.log(JSON.stringify({ type: 'debug.openai.delta', len: String(text).length })); } catch {} + // loggedFirstDelta = true; + // } } }); diff --git a/src/prompts/qa.v2.prompts.ts b/src/prompts/qa.v2.prompts.ts index ad82feb..a752324 100644 --- a/src/prompts/qa.v2.prompts.ts +++ b/src/prompts/qa.v2.prompts.ts @@ -31,73 +31,28 @@ export const getSearchPlanSchemaJson = (): Record => ({ }, required: ['enabled', 'retrieval_bias', 'max_rewrites', 'max_keywords'], }, + // Only time is allowed under filters. Responses JSON Schema requires closed objects + // with explicit required fields at each level. We constrain time to label-form only. filters: { type: 'object', additionalProperties: false, properties: { - user_id: { type: 'string' }, - category_ids: { type: 'array', items: { type: 'integer' } }, - post_id: { type: 'integer' }, time: { - oneOf: [ - { - type: 'object', - additionalProperties: false, - properties: { - type: { const: 'relative' }, - unit: { enum: ['day', 'week', 'month', 'year'] }, - value: { type: 'integer', minimum: 1 }, - }, - required: ['type', 'unit', 'value'], - }, - { - type: 'object', - additionalProperties: false, - properties: { - type: { const: 'absolute' }, - from: { type: 'string' }, - to: { type: 'string' }, - }, - required: ['type', 'from', 'to'], - }, - { - type: 'object', - additionalProperties: false, - properties: { - type: { const: 'month' }, - month: { type: 'integer', minimum: 1, maximum: 12 }, - year: { type: 'integer' }, - }, - required: ['type', 'month'], - }, - { - type: 'object', - additionalProperties: false, - properties: { - type: { const: 'year' }, - year: { type: 'integer' }, - }, - required: ['type', 'year'], - }, - { - type: 'object', - additionalProperties: false, - properties: { - type: { const: 'quarter' }, - quarter: { type: 'integer', minimum: 1, maximum: 4 }, - year: { type: 'integer' }, - }, - required: ['type', 'quarter'], - }, - ], + type: 'object', + additionalProperties: false, + properties: { + type: { type: 'string', enum: ['label'] }, + label: { type: 'string', minLength: 1 }, + }, + required: ['type', 'label'], }, }, - required: ['user_id'], + required: ['time'], }, sort: { enum: ['created_at_desc', 'created_at_asc'] }, limit: { type: 'integer', minimum: 1, maximum: 20 }, }, - required: ['mode', 'top_k', 'threshold', 'weights', 'filters', 'sort', 'limit'], + required: ['mode', 'top_k', 'threshold', 'weights', 'rewrites', 'keywords', 'hybrid', 'filters', 'sort', 'limit'], }); export const buildSearchPlanPrompt = (params: { @@ -117,8 +72,6 @@ export const buildSearchPlanPrompt = (params: { weights: { chunk: 0.7, title: 0.3 }, sort: 'created_at_desc', limit: 5, - mode: 'rag', - filters: { user_id: params.user_id }, }, ); @@ -134,20 +87,20 @@ export const buildSearchPlanPrompt = (params: { `user_id: ${params.user_id}`, `category_id: ${params.category_id ?? ''}`, `post_id: ${params.post_id ?? ''}`, - `defaults: ${defaults}`, '', 'Rules:', '1) Output ONLY a single JSON object matching the schema. No extra text.', - '2) Respect bounds: top_k 1..10, limit 1..20, threshold 0..1, weights in [0,1] and sum to 1.', - '3) Server-provided filters are FIXED constraints. Do NOT change or omit them:', - ' - Always keep filters.user_id as provided (unchanged).', - ' - If category_id exists, include ONLY that id in filters.category_ids (no additions/removals).', - ' - If post_id exists, include exactly that as filters.post_id (no substitutions).', - ' - Your job is to decide top_k, threshold, filters.time, sort, and limit only; do NOT invent other filters.', + '2) Respect bounds: top_k 1..10, limit 1..20, threshold 0..1, weights in [0,1]. The server normalizes their sum.', + '3) Do NOT output any filters other than filters.time. The server injects user_id/category_id/post_id.', + ' - Your job: decide top_k, threshold, sort, limit; and optionally rewrites/keywords/hybrid only.', '4) Mode must follow constraints: if post_id exists, use mode="post"; otherwise use mode="rag".', - '5) Interpret Korean temporal phrases into filters.time using the provided timezone. Month without year assumes current year.', - '6) If the question asks for N items (e.g., “N개”), set limit=N within bounds.', - '7) Keep weights to defaults unless a clear need implies otherwise.', + '5) Time MUST be provided via a label only: filters.time = { "type": "label", "label": "..." }', + ' - Allowed labels (examples): "all_time"(no filter), "today", "yesterday", "last_7_days", "last_14_days", "last_30_days", "this_month", "last_month",', + ' or structured: "2006_to_now", "2019-2022", "2024-Q3", "Q3-2024", "2024-09", "2024".', + ' - For queries like "최근 글", prefer a short window label such as "last_7_days" or "last_30_days" (choose appropriately).', + '6) Do NOT include any temporal words or ranges inside rewrites/keywords. Temporal intent must live ONLY in filters.time.', + '7) If the question asks for N items (e.g., “N개”), set limit=N within bounds.', + '8) Keep weights to defaults unless a clear need implies otherwise.', '8) When helpful for recall, set hybrid.enabled=true and choose hybrid.retrieval_bias ∈ {lexical, balanced, semantic}. Then generate concise rewrites (<= max_rewrites) and focused keywords (<= max_keywords).', ' - lexical: keyword/정확 매칭이 중요할 때 (숫자, 버전, 고유명사 등).', ' - balanced: 일반 질의.', diff --git a/src/services/qa.v2.service.ts b/src/services/qa.v2.service.ts index 9027c36..294195e 100644 --- a/src/services/qa.v2.service.ts +++ b/src/services/qa.v2.service.ts @@ -137,6 +137,32 @@ export const answerStreamV2 = async ( const plan: any = planPair.normalized; stream.write(`event: search_plan\n`); stream.write(`data: ${JSON.stringify(plan)}\n\n`); + // Console debug for emitted search plan + try { + console.log( + JSON.stringify( + { + type: 'debug.sse.search_plan', + userId, + categoryId, + plan_summary: { + mode: plan.mode, + top_k: plan.top_k, + threshold: plan.threshold, + weights: plan.weights, + sort: plan.sort, + limit: plan.limit, + hybrid: plan.hybrid, + time: plan?.filters?.time || null, + rewrites_len: Array.isArray(plan.rewrites) ? plan.rewrites.length : 0, + keywords_len: Array.isArray(plan.keywords) ? plan.keywords.length : 0, + }, + }, + null, + 0, + ), + ); + } catch {} let rows: { postId: string; postTitle: string; postChunk: string; similarityScore: number }[] = []; if (plan.hybrid?.enabled) { diff --git a/src/services/search-plan.service.ts b/src/services/search-plan.service.ts index ee9a1fe..22fa98a 100644 --- a/src/services/search-plan.service.ts +++ b/src/services/search-plan.service.ts @@ -32,32 +32,228 @@ export const generateSearchPlan = async ( question, }); - const schema = getSearchPlanSchemaJson(); try { + // Debug prompt before request + try { + console.log( + JSON.stringify( + { + type: 'debug.plan.prompt', + model: config.CHAT_MODEL || 'gpt-5-mini', + prompt_len: prompt.length, + head: prompt.slice(0, 600), + tail: prompt.slice(Math.max(0, prompt.length - 600)), + }, + null, + 0, + ), + ); + } catch {} + const response: any = await (openai as any).responses.create({ model: config.CHAT_MODEL || 'gpt-5-mini', - input: [{ role: 'user', content: [{ type: 'input_text', text: prompt }] }], - response_format: { type: 'json_schema', json_schema: { name: 'SearchPlan', schema, strict: true } }, - max_output_tokens: 600, + input: prompt, + text: { format: { type: 'json_schema', name: 'SearchPlan', schema: getSearchPlanSchemaJson()} }, + max_output_tokens: 1500, }); - const text = (response as any)?.output_text || ''; - const raw = text && text.trim().startsWith('{') ? text : (() => { - // Fallback: try to extract JSON from response.output + // Debug peek: log response shapes before JSON extraction + try { + const outputs = (response as any)?.output || []; + const outputSummary = outputs.map((o: any) => ({ + role: o?.role, + content: (o?.content || []).map((c: any) => ({ + type: c?.type, + hasText: typeof c?.text === 'string', + textLen: typeof c?.text === 'string' ? (c.text as string).length : undefined, + hasJson: !!c?.json, + })), + })); + const outputText = (response as any)?.output_text; + console.log( + JSON.stringify( + { type: 'debug.plan.response.peek', has_output_text: !!outputText, output_text_len: typeof outputText === 'string' ? outputText.length : undefined, output_summary: outputSummary }, + null, + 0, + ), + ); + } catch {} + + // Extract structured JSON if available, otherwise parse text + let parsed: any = null; + try { + const outputs = (response as any)?.output || []; + for (const o of outputs) { + for (const c of (o?.content || [])) { + if (c && (c.type === 'json' || c.type === 'output_json') && c.json) { + parsed = c.json; + break; + } + } + if (parsed) break; + } + } catch {} + // Also check output_text for JSON string if using Responses API response_format + if (!parsed && typeof (response as any)?.output_text === 'string') { + const s = ((response as any).output_text as string).trim(); + if (s.startsWith('{')) { + try { parsed = JSON.parse(s); } catch {} + } + } + if (!parsed) { + const texts: string[] = []; + const ot = (response as any)?.output_text; + if (typeof ot === 'string') texts.push(ot); + try { + const outputs = (response as any)?.output || []; + for (const o of outputs) { + for (const c of (o?.content || [])) { + const t = typeof c?.text === 'string' ? c.text : undefined; + if (t) texts.push(t); + } + } + } catch {} + const raw = texts.join('').trim(); + // Debug: log raw text before JSON.parse + try { + console.log( + JSON.stringify( + { type: 'debug.plan.raw_text', len: raw.length, head: raw.slice(0, 200) }, + null, + 0, + ), + ); + } catch {} + if (!raw) { + // Graceful fallback: unable to parse structured output + return null; + } + + // Robust extraction of first balanced JSON object + const tryParse = (s: string): any | null => { + try { return JSON.parse(s); } catch { return null; } + }; + let candidate = tryParse(raw); + if (!candidate) { + const start = raw.indexOf('{'); + if (start >= 0) { + let depth = 0; + let inStr = false; + let esc = false; + for (let i = start; i < raw.length; i++) { + const ch = raw[i]; + if (inStr) { + if (esc) esc = false; + else if (ch === '\\') esc = true; + else if (ch === '"') inStr = false; + } else { + if (ch === '"') inStr = true; + else if (ch === '{') depth++; + else if (ch === '}') { + depth--; + if (depth === 0) { + const sub = raw.slice(start, i + 1); + candidate = tryParse(sub); + if (candidate) break; + } + } + } + } + if (!candidate) { + const last = raw.lastIndexOf('}'); + if (last > start) candidate = tryParse(raw.slice(start, last + 1)); + } + } + } + if (!candidate) { + try { + console.warn(JSON.stringify({ type: 'debug.plan.parse_fail', note: 'could not extract JSON from raw' })); + } catch {} + return null; + } + parsed = candidate; + } + + // If still no parsed plan at this point, try a fallback call without text.format + if (!parsed) { try { - const outputs = (response as any).output || []; - const parts = outputs - .flatMap((o: any) => o.content || []) - .filter((c: any) => typeof c?.text === 'string') - .map((c: any) => c.text) - .join(''); - return parts; + const response2: any = await (openai as any).responses.create({ + model: config.CHAT_MODEL || 'gpt-5-mini', + input: prompt, + max_output_tokens: 700, + }); + // Debug peek for fallback + try { + const outputs = (response2 as any)?.output || []; + const outputSummary = outputs.map((o: any) => ({ + role: o?.role, + content: (o?.content || []).map((c: any) => ({ type: c?.type, hasText: typeof c?.text === 'string', textLen: typeof c?.text === 'string' ? (c.text as string).length : undefined })) + })); + const outputText = (response2 as any)?.output_text; + console.log(JSON.stringify({ type: 'debug.plan.fallback.peek', has_output_text: !!outputText, output_text_len: typeof outputText === 'string' ? outputText.length : undefined, output_summary: outputSummary })); + } catch {} + + // Parse fallback response + let parsed2: any = null; + try { + const outputs = (response2 as any)?.output || []; + for (const o of outputs) { + for (const c of (o?.content || [])) { + const t = typeof c?.text === 'string' ? c.text : undefined; + if (t) { + const s = t.trim(); + if (s.startsWith('{')) { try { parsed2 = JSON.parse(s); } catch {} } + if (parsed2) break; + } + } + if (parsed2) break; + } + } catch {} + if (!parsed2) { + const ot = (response2 as any)?.output_text; + if (typeof ot === 'string') { + const s = ot.trim(); + try { parsed2 = JSON.parse(s); } catch {} + } + } + if (!parsed2) { + console.warn(JSON.stringify({ type: 'debug.plan.fallback.parse_fail' })); + // proceed to chat completions fallback + } + if (parsed2) parsed = parsed2; } catch { - return ''; + // continue to chat completions fallback } - })(); - const parsed = JSON.parse(raw); + } + + // Final fallback: Chat Completions with JSON object mode + if (!parsed) { + try { + const sys = 'You output ONLY a single JSON object matching the SearchPlan shape. No extra text.'; + const userMsg = prompt; + const cc: any = await (openai as any).chat.completions.create({ + model: config.CHAT_MODEL || 'gpt-5-mini', + messages: [ + { role: 'system', content: sys }, + { role: 'user', content: userMsg }, + ], + response_format: { type: 'json_object' }, + max_tokens: 700, + }); + // Debug + try { + console.log(JSON.stringify({ type: 'debug.plan.cc.peek', choices: (cc as any)?.choices?.length || 0 })); + } catch {} + const content = (cc as any)?.choices?.[0]?.message?.content || ''; + if (typeof content === 'string' && content.trim().startsWith('{')) { + parsed = JSON.parse(content); + } + } catch (e) { + try { console.warn(JSON.stringify({ type: 'debug.plan.cc.error', message: (e as any)?.message || 'error' })); } catch {} + return null; + } + } const plan = planSchema.parse(parsed); // Normalize weights sum to 1 @@ -94,8 +290,8 @@ export const generateSearchPlan = async ( } return Array.from(uniq).slice(0, max); }; - if (plan.filters?.time) { - const abs = toAbsoluteRangeKst(plan.filters.time as any, now); + if ((plan as any)?.filters?.time) { + const abs = toAbsoluteRangeKst(plan.filters?.time as any, now); if (abs) { normPlan = { ...normPlan, @@ -130,11 +326,44 @@ export const generateSearchPlan = async ( normPlan.rewrites = cleanList(plan.rewrites, maxRewrites) as any; normPlan.keywords = cleanList(plan.keywords, maxKeywords) as any; if (!normPlan.mode) normPlan.mode = (ctx.post_id ? 'post' : 'rag') as any; - if (ctx.category_id && !normPlan.filters.category_ids) normPlan.filters.category_ids = [ctx.category_id]; - if (ctx.post_id) { - normPlan.mode = 'post'; - normPlan.filters.post_id = ctx.post_id; - } + + // Note: Only filters.time is kept here to satisfy the SearchPlan schema. + // user_id/category_ids/post_id will be injected later by the query layer. + + // Console debug: final parsed + normalized plan + try { + const timeInfo = (normPlan as any)?.filters?.time; + console.log( + JSON.stringify( + { + type: 'debug.plan.final', + ctx: { user_id: ctx.user_id, category_id: ctx.category_id, post_id: ctx.post_id }, + summary: { + mode: normPlan.mode, + top_k: normPlan.top_k, + threshold: normPlan.threshold, + weights: normPlan.weights, + sort: normPlan.sort, + limit: normPlan.limit, + hybrid: { + enabled: !!normPlan.hybrid?.enabled, + retrieval_bias: normPlan.hybrid?.retrieval_bias, + alpha: normPlan.hybrid?.alpha, + max_rewrites: normPlan.hybrid?.max_rewrites, + max_keywords: normPlan.hybrid?.max_keywords, + }, + time: timeInfo ? { type: timeInfo.type, from: timeInfo.from, to: timeInfo.to } : null, + rewrites_len: (normPlan.rewrites || []).length, + keywords_len: (normPlan.keywords || []).length, + }, + plan, + normalized: normPlan, + }, + null, + 0, + ), + ); + } catch {} return { plan, normalized: normPlan }; } catch (e) { diff --git a/src/types/ai.v2.types.ts b/src/types/ai.v2.types.ts index c944b54..6e3353b 100644 --- a/src/types/ai.v2.types.ts +++ b/src/types/ai.v2.types.ts @@ -2,31 +2,49 @@ import { z } from 'zod'; // ===== Plan JSON Schema (Zod) ===== +// Time filter schema: support multiple shapes to reduce LLM fragility export const timeFilterSchema = z.discriminatedUnion('type', [ - z.object({ - type: z.literal('relative'), - unit: z.enum(['day', 'week', 'month', 'year']), - value: z.number().int().positive(), - }), - z.object({ - type: z.literal('absolute'), - from: z.string(), // ISO8601 - to: z.string(), // ISO8601 - }), - z.object({ - type: z.literal('month'), - month: z.number().int().min(1).max(12), - year: z.number().int().optional(), - }), - z.object({ - type: z.literal('year'), - year: z.number().int(), - }), - z.object({ - type: z.literal('quarter'), - quarter: z.number().int().min(1).max(4), - year: z.number().int().optional(), - }), + // Absolute ISO range + z + .object({ type: z.literal('absolute'), from: z.string(), to: z.string() }) + .strict(), + // Relative window: N units up to today (KST) + z + .object({ + type: z.literal('relative'), + unit: z.enum(['day', 'week', 'month', 'year']), + value: z.number().int().min(1).max(365), + }) + .strict(), + // Month of a year (default year=now) + z + .object({ type: z.literal('month'), year: z.number().int().optional(), month: z.number().int().min(1).max(12) }) + .strict(), + // Quarter of a year (default year=now) + z + .object({ type: z.literal('quarter'), year: z.number().int().optional(), quarter: z.number().int().min(1).max(4) }) + .strict(), + // Single year + z.object({ type: z.literal('year'), year: z.number().int() }).strict(), + // Named presets (limited set) + z + .object({ + type: z.literal('named'), + preset: z.enum([ + 'all_time', + 'all', + 'today', + 'yesterday', + 'last_7_days', + 'last_14_days', + 'last_30_days', + 'this_month', + 'last_month', + ]), + }) + .strict(), + // Free-form label, e.g., "2006_to_now", "2024-Q3", "2019-2022", "2024-09" + z.object({ type: z.literal('label'), label: z.string().min(1) }).strict(), ]); export const planSchema = z.object({ @@ -50,12 +68,10 @@ export const planSchema = z.object({ .default({ enabled: false, retrieval_bias: 'balanced', max_rewrites: 3, max_keywords: 6 }), filters: z .object({ - user_id: z.string(), - category_ids: z.array(z.number().int()).optional(), - post_id: z.number().int().optional(), time: timeFilterSchema.optional(), }) - .default({ user_id: '' }), + .strict() + .optional(), sort: z.enum(['created_at_desc', 'created_at_asc']).default('created_at_desc'), limit: z.number().int().min(1).max(20).default(5), }); diff --git a/src/utils/time.ts b/src/utils/time.ts index 406a16e..b950068 100644 --- a/src/utils/time.ts +++ b/src/utils/time.ts @@ -36,6 +36,44 @@ export const toAbsoluteRangeKst = (input: { type: string; [k: string]: any }, ba try { const baseKst = toKst(base); const year = baseKst.getFullYear(); + // Named presets + if (input.type === 'named') { + const p = String(input.preset || '').toLowerCase(); + const endK = endOfDay(baseKst); + const beginOfTodayK = startOfDay(baseKst); + const endUtc = fromKstToUtc(endK).toISOString(); + const todayStartUtc = fromKstToUtc(beginOfTodayK).toISOString(); + if (p === 'all' || p === 'all_time') return null; // no time filter + if (p === 'today') return { from: todayStartUtc, to: endUtc }; + if (p === 'yesterday') { + const yK = new Date(beginOfTodayK.getTime()); + yK.setDate(yK.getDate() - 1); + return { from: fromKstToUtc(startOfDay(yK)).toISOString(), to: fromKstToUtc(endOfDay(yK)).toISOString() }; + } + const daysBack = (n: number) => { + const toK = endK; + const fromK = new Date(toK.getTime()); + fromK.setDate(fromK.getDate() - (n - 1)); + return { from: fromKstToUtc(startOfDay(fromK)).toISOString(), to: fromKstToUtc(toK).toISOString() }; + }; + if (p === 'last_7_days') return daysBack(7); + if (p === 'last_14_days') return daysBack(14); + if (p === 'last_30_days') return daysBack(30); + if (p === 'this_month') { + const fromK = startOfMonth(year, baseKst.getMonth()); + const toK = endOfMonth(year, baseKst.getMonth()); + return { from: fromKstToUtc(fromK).toISOString(), to: fromKstToUtc(toK).toISOString() }; + } + if (p === 'last_month') { + const m = baseKst.getMonth(); + const yAdj = m === 0 ? year - 1 : year; + const mAdj = m === 0 ? 11 : m - 1; + const fromK = startOfMonth(yAdj, mAdj); + const toK = endOfMonth(yAdj, mAdj); + return { from: fromKstToUtc(fromK).toISOString(), to: fromKstToUtc(toK).toISOString() }; + } + return null; // unknown named: drop filter + } if (input.type === 'relative') { const unit = String(input.unit); const value = Math.max(1, parseInt(String(input.value || '1'), 10)); @@ -74,9 +112,100 @@ export const toAbsoluteRangeKst = (input: { type: string; [k: string]: any }, ba const toK = endOfQuarter(y, q); return { from: fromKstToUtc(fromK).toISOString(), to: fromKstToUtc(toK).toISOString() }; } + if (input.type === 'label') { + const raw = String(input.label || '').trim(); + if (!raw) return null; + const s = raw.replace(/\s+/g, '').toLowerCase(); + const endK = endOfDay(baseKst); + // Support common named tokens expressed as labels + const startTodayK = startOfDay(baseKst); + const toUtcStr = fromKstToUtc(endK).toISOString(); + const fromTodayUtcStr = fromKstToUtc(startTodayK).toISOString(); + const daysBack = (n: number) => { + const toK = endK; + const fromK = new Date(toK.getTime()); + fromK.setDate(fromK.getDate() - (n - 1)); + return { from: fromKstToUtc(startOfDay(fromK)).toISOString(), to: fromKstToUtc(toK).toISOString() }; + }; + if (s === 'all' || s === 'all_time') return null; // drop filter + if (s === 'today') return { from: fromTodayUtcStr, to: toUtcStr }; + if (s === 'yesterday') { + const yK = new Date(startTodayK.getTime()); + yK.setDate(yK.getDate() - 1); + return { from: fromKstToUtc(startOfDay(yK)).toISOString(), to: fromKstToUtc(endOfDay(yK)).toISOString() }; + } + if (s === 'last_7_days') return daysBack(7); + if (s === 'last_14_days') return daysBack(14); + if (s === 'last_30_days') return daysBack(30); + if (s === 'this_month') { + const fromK = startOfMonth(year, baseKst.getMonth()); + const toK = endOfMonth(year, baseKst.getMonth()); + return { from: fromKstToUtc(fromK).toISOString(), to: fromKstToUtc(toK).toISOString() }; + } + if (s === 'last_month') { + const m = baseKst.getMonth(); + const yAdj = m === 0 ? year - 1 : year; + const mAdj = m === 0 ? 11 : m - 1; + const fromK = startOfMonth(yAdj, mAdj); + const toK = endOfMonth(yAdj, mAdj); + return { from: fromKstToUtc(fromK).toISOString(), to: fromKstToUtc(toK).toISOString() }; + } + // 1) YYYY_to_now / YYYY-to-now + let m = s.match(/^(\d{4})(?:_|-|to)+now$/); + if (m) { + const y = parseInt(m[1], 10); + const fromK = startOfMonth(y, 0); + return { from: fromKstToUtc(fromK).toISOString(), to: fromKstToUtc(endK).toISOString() }; + } + // 2) YYYY-YYYY / YYYY..YYYY / YYYY_to_YYYY + m = s.match(/^(\d{4})(?:\.|_|-|to){1,2}(\d{4})$/); + if (m) { + const y1 = parseInt(m[1], 10); + const y2 = parseInt(m[2], 10); + const a = Math.min(y1, y2); + const b = Math.max(y1, y2); + const fromK = startOfMonth(a, 0); + const toK = endOfMonth(b, 11); + return { from: fromKstToUtc(fromK).toISOString(), to: fromKstToUtc(toK).toISOString() }; + } + // 3) YYYY-Qn or Qn-YYYY + m = s.match(/^(\d{4})(?:-|_)q([1-4])$/); + if (m) { + const y = parseInt(m[1], 10); + const q = parseInt(m[2], 10); + const fromK = startOfQuarter(y, q); + const toK = endOfQuarter(y, q); + return { from: fromKstToUtc(fromK).toISOString(), to: fromKstToUtc(toK).toISOString() }; + } + m = s.match(/^q([1-4])(?:-|_)?(\d{4})$/); + if (m) { + const q = parseInt(m[1], 10); + const y = parseInt(m[2], 10); + const fromK = startOfQuarter(y, q); + const toK = endOfQuarter(y, q); + return { from: fromKstToUtc(fromK).toISOString(), to: fromKstToUtc(toK).toISOString() }; + } + // 4) YYYY-MM + m = s.match(/^(\d{4})(?:-|_)?(\d{1,2})$/); + if (m) { + const y = parseInt(m[1], 10); + const month = Math.max(1, Math.min(12, parseInt(m[2], 10))); + const fromK = startOfMonth(y, month - 1); + const toK = endOfMonth(y, month - 1); + return { from: fromKstToUtc(fromK).toISOString(), to: fromKstToUtc(toK).toISOString() }; + } + // 5) YYYY + m = s.match(/^(\d{4})$/); + if (m) { + const y = parseInt(m[1], 10); + const fromK = startOfMonth(y, 0); + const toK = endOfMonth(y, 11); + return { from: fromKstToUtc(fromK).toISOString(), to: fromKstToUtc(toK).toISOString() }; + } + return null; // unrecognized label + } } catch { // ignore } return null; }; - From 45fad46852b271ba4ce1d21167e87e494d7ce1ef Mon Sep 17 00:00:00 2001 From: chan000518 Date: Thu, 9 Oct 2025 00:36:56 +0900 Subject: [PATCH 13/14] =?UTF-8?q?=F0=9F=90=9B=20=EB=A7=90=ED=88=AC=20?= =?UTF-8?q?=EB=B0=8F=20=ED=8E=91=EC=85=98=20=EC=BD=9C=EB=A7=81=20=EC=97=90?= =?UTF-8?q?=EB=9F=AC=20=EC=88=98=EC=A0=95?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/api.md | 16 +++--- src/llm/providers/openai-responses.ts | 42 +++++++++++---- src/prompts/qa.prompts.ts | 17 ++++--- src/services/qa.v2.service.ts | 73 +++++++++++++++------------ src/services/search-plan.service.ts | 5 +- 5 files changed, 94 insertions(+), 59 deletions(-) diff --git a/docs/api.md b/docs/api.md index 7b08af3..2e55bef 100644 --- a/docs/api.md +++ b/docs/api.md @@ -93,14 +93,14 @@ - `model`: string (미지정 시 서버 기본값 사용) - `options`: `{ temperature?: number, top_p?: number, max_output_tokens?: number }` - 동작 개요 - - 서버가 질문을 토대로 “검색 계획(JSON)”을 생성·검증·정규화한 뒤, 계획에 따라 시맨틱/하이브리드 검색을 수행하고 결과를 SSE로 스트리밍합니다. + - 서버가 질문을 토대로 “검색 계획(JSON)”을 생성·검증·정규화한 뒤, 계획에 따라 시맨틱 또는 하이브리드 검색을 수행하고 결과를 SSE로 스트리밍합니다. - `post_id`가 있으면 post 모드(단일 글 컨텍스트)로 처리하며, 간략한 `search_plan`/`search_result` 이벤트 후 본문 기반 답변을 스트리밍합니다. - 하이브리드 검색(벡터+텍스트) - 계획에 `hybrid.enabled: true`인 경우 활성화됩니다. - `rewrites`(재작성 질의)와 `keywords`(핵심 키워드)를 생성하여 벡터/텍스트 두 경로로 후보를 수집하고, `hybrid.retrieval_bias` 라벨을 서버가 `alpha` 값으로 매핑해 점수를 융합하여 상위 `top_k`를 선택합니다. - 매핑(기본): `lexical → 0.3`, `balanced → 0.5`, `semantic → 0.75` - - 정규화/결합식: `score = alpha*vec + (1-alpha)*text` (min-max 정규화 후) - - SSE로 `rewrite`, `keywords`, `hybrid_result` 이벤트가 순차 송신됩니다. 하이브리드 결과가 없으면 시맨틱 검색으로 폴백합니다. + - 결합식: `score = alpha*vec + (1-alpha)*text` (각 경로 점수 min-max 정규화 후) + - SSE로 `rewrite`, `keywords`, `hybrid_result` 이벤트가 필요한 경우에만 송신됩니다. 하이브리드 결과가 없으면 시맨틱 검색으로 폴백합니다. - SSE 이벤트 순서(일반적인 흐름) 1) `search_plan`: 정규화된 검색 계획(JSON) - 예시 데이터(정규화): @@ -111,8 +111,6 @@ "threshold": 0.2, "weights": { "chunk": 0.7, "title": 0.3 }, "filters": { - "user_id": "u_123", - "category_ids": [3], "time": { "type": "absolute", "from": "2025-09-01T00:00:00.000Z", "to": "2025-09-30T23:59:59.999Z" } }, "sort": "created_at_desc", @@ -123,9 +121,9 @@ } ``` - 비고: - - `hybrid.retrieval_bias`는 LLM이 결정하는 라벨이며, 서버는 이를 `alpha`로 변환해 사용합니다. - - 카테고리는 단일 값만 지원하며 서버는 첫 번째 항목만 사용합니다. - - post 모드에서는 간략한 형태(`{ mode: "post", filters: { post_id, user_id } }`)가 송신될 수 있습니다. + - `filters.time`만 포함됩니다. `user_id`/`category_id`/`post_id` 등은 서버가 검색 시 내부적으로 적용합니다. + - `hybrid.retrieval_bias`는 LLM 라벨이며 서버가 `alpha`로 변환해 사용합니다. + - post 모드에서는 간략한 형태 예: `{ "mode": "post", "filters": { "post_id": 123, "user_id": "u_123" } }`. 2) (하이브리드 사용 시) `rewrite`: `string[]` 3) (하이브리드 사용 시) `keywords`: `string[]` 4) (하이브리드 사용 시) `hybrid_result`: `[ { postId, postTitle }, ... ]` @@ -135,6 +133,8 @@ 8) `answer` — 모델 부분 응답(여러 번) 9) `end` — `data: [DONE]` - 오류 시 `error`: `{ code?: number, message: string }` +- 폴백 동작 + - 플래너 실패 시 `search_plan`으로 `{ "mode": "rag", "fallback": true }`가 송신되며, v1 스타일 RAG로 컨텍스트를 구성합니다. - 예시(curl) ```bash diff --git a/src/llm/providers/openai-responses.ts b/src/llm/providers/openai-responses.ts index d45d2db..65cc1ec 100644 --- a/src/llm/providers/openai-responses.ts +++ b/src/llm/providers/openai-responses.ts @@ -123,6 +123,14 @@ export const generateOpenAIStream = async (req: GenerateRequest): Promise { + const args = ev?.arguments || ev?.arguments_delta || ''; + if (args) { + safeWrite(`event: answer\n`); + safeWrite(`data: ${JSON.stringify(args)}\n\n`); + } + }); // Catch-all messages to ensure we don't miss alternative text events responsesStream.on('message', (msg: any) => { @@ -238,17 +246,31 @@ export const generateOpenAIStream = async (req: GenerateRequest): Promise 0 ? 'auto' : undefined, - stream: true, - temperature: req.options?.temperature, - top_p: req.options?.top_p, - max_tokens: req.options?.max_output_tokens as any, - }); + let chatStream: any; + // temperature/top_p are not supported on reasoning models (e.g., GPT-5 family) + if (!isGpt5Family) { + chatStream = await openai.chat.completions.create({ + model, + messages: messages as any, + tools: (req.tools as OpenAI.Chat.Completions.ChatCompletionTool[]) || undefined, + tool_choice: req.tools && req.tools.length > 0 ? 'auto' : undefined, + stream: true, + temperature: req.options?.temperature, + top_p: req.options?.top_p, + max_tokens: req.options?.max_output_tokens as any, + }); + } else { + chatStream = await openai.chat.completions.create({ + model, + messages: messages as any, + tools: (req.tools as OpenAI.Chat.Completions.ChatCompletionTool[]) || undefined, + tool_choice: req.tools && req.tools.length > 0 ? 'auto' : undefined, + stream: true, + max_tokens: req.options?.max_output_tokens as any, + }); + } + // Iterate asynchronously; return stream immediately to allow real-time consumption (async () => { try { diff --git a/src/prompts/qa.prompts.ts b/src/prompts/qa.prompts.ts index 8f93f75..599e46d 100644 --- a/src/prompts/qa.prompts.ts +++ b/src/prompts/qa.prompts.ts @@ -12,15 +12,16 @@ export const createPostContextPrompt = ( 모든 한국어 응답은 무슨일이 있어도 반드시 답변 말투 및 규칙을 따릅니다. 또한 주어진 내용외의 내용을 지어내지 마십시오. + [말투 지침] + ${speechTonePrompt} + - 위 말투/지시문을 출력에 절대 노출하지 말고(예: "말투", "규칙" 등 언급 금지), 실제 답변 내용에만 반영하십시오. + [응답 규칙] 1. 만약 제목과 본문을 활용해 답변할 수 있다면 답변 말투 및 규칙을 지켜 직접 답변하고, 마지막에 추가적인 내용에 대한 질문을 유도하는 문장을 추가합니다. 2. 만약 질문이 욕설·비난·무관·부적절하거나 주어진 제목, 본문과 관련이 없다면 사과와 블로그 관련된 내용만 답변 가능하다는 내용을 답변 말투 및 규칙을 지켜 답합니다. - 3. 질문이 블로그 카테고리나 사용자 블로그에는 부합하지만 제공된 본문 컨텍스트의 내용이 매우 부족하거나 적절하지 않다고 판단되면 report_content_insufficient 함수를 호출하고 답변 말투 및 규칙을 지켜 해당 내용이 아직 부족하다는 안내를 합니다. 그 후 본문 컨텍스트를 참고해 질문과 관련된 답변할 수 있는 내용을 언급하고 해당 내용에 대한 질문을 직접적으로 유도합니다. + 3. 질문이 블로그 카테고리나 사용자 블로그에는 부합하지만 제공된 본문 컨텍스트의 내용이 매우 부족하거나 적절하지 않다고 판단되면, 함수명이나 함수 호출을 절대 언급하지 말고(예: "report_content_insufficient" 같은 문자열이나 괄호 "()" 출력 금지), 답변 말투 및 규칙을 지켜 자연스럽게 다음을 수행합니다: (a) 현재로서는 본문 컨텍스트가 부족함을 간단히 안내하고, (b) 질문과 직접 관련된 정보를 구체적으로 요청합니다(예: 게시일, 최근 글 목록, 최신 포스트의 제목과 날짜 등). 서버가 내부 함수를 제공하는 경우에도 사용자가 보는 답변에는 함수명/호출을 절대 노출하지 않습니다. `; const userMessage = ` -[instruction] -답변 말투: "${speechTonePrompt}" - [context] 제목: ${post.title} 작성일: ${post.created_at} @@ -49,14 +50,16 @@ export const createRagPrompt = ( 모든 한국어 응답은 무슨일이 있어도 반드시 답변 말투 및 규칙을 따릅니다. 또한 주어진 내용외의 내용을 지어내지 마십시오. + [말투 지침] + ${speechTonePrompt} + - 위 말투/지시문을 출력에 절대 노출하지 말고(예: "말투", "규칙" 등 언급 금지), 실제 답변 내용에만 반영하십시오. + [응답 규칙] 1. 만약 제목과 본문을 활용해 답변할 수 있다면 답변 말투 및 규칙을 지켜 직접 답변하고, 마지막에 추가적인 내용에 대한 질문을 유도하는 문장을 추가합니다. 2. 만약 질문이 욕설·비난·무관·부적절하거나 주어진 제목, 본문과 관련이 없다면 사과와 블로그 관련된 내용만 답변 가능하다는 내용을 답변 말투 및 규칙을 지켜 답합니다. - 3. 질문이 블로그 카테고리나 사용자 블로그에는 부합하지만 제공된 본문 컨텍스트의 내용이 매우 부족하거나 적절하지 않다고 판단되면 report_content_insufficient 함수를 호출하고 답변 말투 및 규칙을 지켜 해당 내용이 아직 부족하다는 안내를 합니다. 그 후 본문 컨텍스트를 참고해 질문과 관련된 답변할 수 있는 내용을 언급하고 해당 내용에 대한 질문을 직접적으로 유도합니다. + 3. 질문이 블로그 카테고리나 사용자 블로그에는 부합하지만 제공된 본문 컨텍스트의 내용이 매우 부족하거나 적절하지 않다고 판단되면, 함수명이나 함수 호출을 절대 언급하지 말고(예: "report_content_insufficient" 같은 문자열이나 괄호 "()" 출력 금지), 답변 말투 및 규칙을 지켜 자연스럽게 다음을 수행합니다: (a) 현재로서는 본문 컨텍스트가 부족함을 간단히 안내하고, (b) 질문과 직접 관련된 정보를 구체적으로 요청합니다(예: 게시일, 최근 글 목록, 최신 포스트의 제목과 날짜 등). 서버가 내부 함수를 제공하는 경우에도 사용자가 보는 답변에는 함수명/호출을 절대 노출하지 않습니다. `; const userMessage = ` - 답변 말투 및 규칙: "${speechTonePrompt}" - 반드시 말투 및 규칙에 따라 대답하세요! 아래의 질문과 블로그 본문 컨텍스트를 참고하여 답변하세요. 사용자의 질문: ${question} 가장 근접한 블로그 본문 컨텍스트: diff --git a/src/services/qa.v2.service.ts b/src/services/qa.v2.service.ts index 294195e..8a8e651 100644 --- a/src/services/qa.v2.service.ts +++ b/src/services/qa.v2.service.ts @@ -113,26 +113,30 @@ export const answerStreamV2 = async ( messages = toSimpleMessages( qaPrompts.createRagPrompt(question, similarChunks, speechTonePrompt) ); - tools = [ - { - type: 'function', - function: { - name: 'report_content_insufficient', - description: '카테고리는 맞지만 본문 컨텍스트가 부족할 때 호출', - parameters: { - type: 'object', - properties: { - text: { - type: 'string', - description: - '답변 말투 및 규칙을 지켜 해당 내용이 아직 부족하다는 안내를 합니다. 그 후 본문 컨텍스트를 참고해 질문과 관련된 답변할 수 있는 내용을 언급하고 해당 내용에 대한 질문을 직접적으로 유도합니다.', + if (similarChunks.length === 0) { + tools = undefined; + } else { + tools = [ + { + type: 'function', + function: { + name: 'report_content_insufficient', + description: '카테고리는 맞지만 본문 컨텍스트가 부족할 때 호출', + parameters: { + type: 'object', + properties: { + text: { + type: 'string', + description: + '답변 말투 및 규칙을 지켜 해당 내용이 아직 부족하다는 안내를 합니다. 그 후 본문 컨텍스트를 참고해 질문과 관련된 답변할 수 있는 내용을 언급하고 해당 내용에 대한 질문을 직접적으로 유도합니다.', + }, }, + required: ['text'], }, - required: ['text'], }, }, - }, - ]; + ]; + } } else { const plan: any = planPair.normalized; stream.write(`event: search_plan\n`); @@ -210,26 +214,31 @@ export const answerStreamV2 = async ( speechTonePrompt ) ); - tools = [ - { - type: 'function', - function: { - name: 'report_content_insufficient', - description: '카테고리는 맞지만 본문 컨텍스트가 부족할 때 호출', - parameters: { - type: 'object', - properties: { - text: { - type: 'string', - description: - '답변 말투 및 규칙을 지켜 해당 내용이 아직 부족하다는 안내를 합니다. 그 후 본문 컨텍스트를 참고해 질문과 관련된 답변할 수 있는 내용을 언급하고 해당 내용에 대한 질문을 직접적으로 유도합니다.', + // If no context was found, avoid tool-calls to force direct natural-language guidance + if (rows.length === 0) { + tools = undefined; + } else { + tools = [ + { + type: 'function', + function: { + name: 'report_content_insufficient', + description: '카테고리는 맞지만 본문 컨텍스트가 부족할 때 호출', + parameters: { + type: 'object', + properties: { + text: { + type: 'string', + description: + '답변 말투 및 규칙을 지켜 해당 내용이 아직 부족하다는 안내를 합니다. 그 후 본문 컨텍스트를 참고해 질문과 관련된 답변할 수 있는 내용을 언급하고 해당 내용에 대한 질문을 직접적으로 유도합니다.', + }, }, + required: ['text'], }, - required: ['text'], }, }, - }, - ]; + ]; + } } } diff --git a/src/services/search-plan.service.ts b/src/services/search-plan.service.ts index 22fa98a..507a1c4 100644 --- a/src/services/search-plan.service.ts +++ b/src/services/search-plan.service.ts @@ -40,7 +40,7 @@ export const generateSearchPlan = async ( JSON.stringify( { type: 'debug.plan.prompt', - model: config.CHAT_MODEL || 'gpt-5-mini', + model: 'gpt-5-mini', prompt_len: prompt.length, head: prompt.slice(0, 600), tail: prompt.slice(Math.max(0, prompt.length - 600)), @@ -52,9 +52,10 @@ export const generateSearchPlan = async ( } catch {} const response: any = await (openai as any).responses.create({ - model: config.CHAT_MODEL || 'gpt-5-mini', + model: 'gpt-5-mini', input: prompt, text: { format: { type: 'json_schema', name: 'SearchPlan', schema: getSearchPlanSchemaJson()} }, + reasoning: { effort: 'minimal' }, max_output_tokens: 1500, }); From 3bfb8ea234aac5a694a1ca242147a7e862042959 Mon Sep 17 00:00:00 2001 From: chan000518 Date: Thu, 9 Oct 2025 00:37:16 +0900 Subject: [PATCH 14/14] =?UTF-8?q?=F0=9F=90=9B=20=ED=83=9C=EA=B7=B8=20?= =?UTF-8?q?=EA=B4=80=EB=A0=A8=20=EC=A1=B0=ED=9A=8C=20=EC=97=90=EB=9F=AC=20?= =?UTF-8?q?=EC=88=98=EC=A0=95?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/repositories/post.repository.ts | 21 ++++++++++++++++++--- 1 file changed, 18 insertions(+), 3 deletions(-) diff --git a/src/repositories/post.repository.ts b/src/repositories/post.repository.ts index eade868..ffc17c0 100644 --- a/src/repositories/post.repository.ts +++ b/src/repositories/post.repository.ts @@ -29,11 +29,26 @@ export interface TextSearchHit { // ========= READ QUERIES ========= export const findPostById = async (postId: number): Promise => { const pool = getDb(); - const { rows } = await pool.query( - 'SELECT id, title, content, tags, created_at, user_id, is_public FROM blog_post WHERE id = $1', + // Some databases may not have a `tags` column on blog_post. + // Select existing columns and populate `tags` as an empty array fallback. + const { rows } = await pool.query( + 'SELECT id, title, content, created_at, user_id, is_public FROM blog_post WHERE id = $1', [postId] ); - return rows.length > 0 ? rows[0] : null; + if (rows.length === 0) return null; + + const row = rows[0] as any; + const post: Post = { + id: row.id, + title: row.title, + content: row.content, + // Fallback: DB has no tags column; keep empty list so prompts render gracefully + tags: Array.isArray(row.tags) ? row.tags : [], + created_at: row.created_at, + user_id: row.user_id, + is_public: row.is_public, + }; + return post; }; export const findSimilarChunks = async (