From 6b3c7d562673318533942b2f3633f4373689d140 Mon Sep 17 00:00:00 2001 From: youngsu5582 <98307410+youngsu5582@users.noreply.github.com> Date: Wed, 6 May 2026 21:13:20 +0900 Subject: [PATCH 01/17] =?UTF-8?q?feat:=20DTO=20=EB=B0=8F=20Controller=20?= =?UTF-8?q?=EB=AA=85=EC=84=B8?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - DTO 및 Controller 선언 - 서비스 로직은 TODO --- .../FrequentlyQuestionChatApiController.java | 27 +++++++++++++++++++ .../com/cholog/bootcamp/data/TokenUsage.java | 8 ++++++ .../dto/FrequentlyQuestionChatRequestDto.java | 6 +++++ .../FrequentlyQuestionChatResponseDto.java | 6 +++++ 4 files changed, 47 insertions(+) create mode 100644 src/main/java/com/cholog/bootcamp/controller/FrequentlyQuestionChatApiController.java create mode 100644 src/main/java/com/cholog/bootcamp/data/TokenUsage.java create mode 100644 src/main/java/com/cholog/bootcamp/dto/FrequentlyQuestionChatRequestDto.java create mode 100644 src/main/java/com/cholog/bootcamp/dto/FrequentlyQuestionChatResponseDto.java diff --git a/src/main/java/com/cholog/bootcamp/controller/FrequentlyQuestionChatApiController.java b/src/main/java/com/cholog/bootcamp/controller/FrequentlyQuestionChatApiController.java new file mode 100644 index 0000000..6b1a362 --- /dev/null +++ b/src/main/java/com/cholog/bootcamp/controller/FrequentlyQuestionChatApiController.java @@ -0,0 +1,27 @@ +package com.cholog.bootcamp.controller; + +import com.cholog.bootcamp.dto.FrequentlyQuestionChatRequestDto; +import com.cholog.bootcamp.dto.FrequentlyQuestionChatResponseDto; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; +import org.springframework.http.ResponseEntity; +import org.springframework.web.bind.annotation.PostMapping; +import org.springframework.web.bind.annotation.RequestBody; +import org.springframework.web.bind.annotation.RequestMapping; +import org.springframework.web.bind.annotation.RestController; + +@RestController +@RequestMapping("/api/chat") +public class FrequentlyQuestionChatApiController { + + private static final Logger log = LoggerFactory.getLogger(FrequentlyQuestionChatApiController.class); + + @PostMapping + public ResponseEntity question( + @RequestBody FrequentlyQuestionChatRequestDto dto + ) { + log.info("FAQ 요청이 들어왔습니다. {}", dto.question()); + // TODO 서비스 레이어 및 응답 구현 + return ResponseEntity.ok(null); + } +} diff --git a/src/main/java/com/cholog/bootcamp/data/TokenUsage.java b/src/main/java/com/cholog/bootcamp/data/TokenUsage.java new file mode 100644 index 0000000..4747ef8 --- /dev/null +++ b/src/main/java/com/cholog/bootcamp/data/TokenUsage.java @@ -0,0 +1,8 @@ +package com.cholog.bootcamp.data; + +public record TokenUsage( + int promptTokens, + int completionTokens, + int totalTokens +) { +} \ No newline at end of file diff --git a/src/main/java/com/cholog/bootcamp/dto/FrequentlyQuestionChatRequestDto.java b/src/main/java/com/cholog/bootcamp/dto/FrequentlyQuestionChatRequestDto.java new file mode 100644 index 0000000..c5c9bde --- /dev/null +++ b/src/main/java/com/cholog/bootcamp/dto/FrequentlyQuestionChatRequestDto.java @@ -0,0 +1,6 @@ +package com.cholog.bootcamp.dto; + +public record FrequentlyQuestionChatRequestDto ( + String question +){ +} diff --git a/src/main/java/com/cholog/bootcamp/dto/FrequentlyQuestionChatResponseDto.java b/src/main/java/com/cholog/bootcamp/dto/FrequentlyQuestionChatResponseDto.java new file mode 100644 index 0000000..a4f2d89 --- /dev/null +++ b/src/main/java/com/cholog/bootcamp/dto/FrequentlyQuestionChatResponseDto.java @@ -0,0 +1,6 @@ +package com.cholog.bootcamp.dto; + +import com.cholog.bootcamp.data.TokenUsage; + +public record FrequentlyQuestionChatResponseDto(String answer, TokenUsage usage) { +} From b49fbb296f433cdc4542e39ba06d80adffdf4bae Mon Sep 17 00:00:00 2001 From: youngsu5582 <98307410+youngsu5582@users.noreply.github.com> Date: Sat, 9 May 2026 16:33:13 +0900 Subject: [PATCH 02/17] =?UTF-8?q?feat:=20gitignore=20=EC=B6=94=EA=B0=80=20?= =?UTF-8?q?=EB=B0=8F=20lombok=20=EC=9D=98=EC=A1=B4=EC=84=B1=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 - 개발 편의성 위한 lombok 추가 - local profile 용 gitignore 추가 --- .gitignore | 2 ++ build.gradle | 3 +++ 2 files changed, 5 insertions(+) diff --git a/.gitignore b/.gitignore index 23c550a..1c4fe0c 100644 --- a/.gitignore +++ b/.gitignore @@ -40,3 +40,5 @@ __pycache__/ # Matrix metadata **/.omc/ + +src/main/resources/application-*.yml \ No newline at end of file diff --git a/build.gradle b/build.gradle index 941e596..71883d3 100644 --- a/build.gradle +++ b/build.gradle @@ -26,8 +26,11 @@ dependencyManagement { dependencies { implementation 'org.springframework.boot:spring-boot-starter-web' implementation 'org.springframework.ai:spring-ai-starter-model-openai' + compileOnly 'org.projectlombok:lombok' testImplementation 'org.springframework.boot:spring-boot-starter-test' + + annotationProcessor 'org.projectlombok:lombok' } tasks.named('test') { From 8b092fa77ba4c93ec57477715a549d1ab277a487 Mon Sep 17 00:00:00 2001 From: youngsu5582 <98307410+youngsu5582@users.noreply.github.com> Date: Sat, 9 May 2026 16:34:46 +0900 Subject: [PATCH 03/17] =?UTF-8?q?feat:=20chat=20=EC=84=9C=EB=B9=84?= =?UTF-8?q?=EC=8A=A4=20=EB=A1=9C=EC=A7=81=20=EA=B5=AC=ED=98=84?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - spring ai 통한 기능 구현 --- .../bootcamp/config/ChatClientConfig.java | 16 +++++++ .../FrequentlyQuestionChatApiController.java | 8 +++- .../com/cholog/bootcamp/data/TokenUsage.java | 12 ++++++ .../FrequentlyQuestionChatApiService.java | 43 +++++++++++++++++++ 4 files changed, 78 insertions(+), 1 deletion(-) create mode 100644 src/main/java/com/cholog/bootcamp/config/ChatClientConfig.java create mode 100644 src/main/java/com/cholog/bootcamp/service/FrequentlyQuestionChatApiService.java diff --git a/src/main/java/com/cholog/bootcamp/config/ChatClientConfig.java b/src/main/java/com/cholog/bootcamp/config/ChatClientConfig.java new file mode 100644 index 0000000..b3a4ce2 --- /dev/null +++ b/src/main/java/com/cholog/bootcamp/config/ChatClientConfig.java @@ -0,0 +1,16 @@ +package com.cholog.bootcamp.config; + +import lombok.NoArgsConstructor; +import org.springframework.ai.chat.client.ChatClient; +import org.springframework.context.annotation.Bean; +import org.springframework.context.annotation.Configuration; + +@Configuration +@NoArgsConstructor +public class ChatClientConfig { + + @Bean + public ChatClient chatClient(ChatClient.Builder builder) { + return builder.build(); + } +} diff --git a/src/main/java/com/cholog/bootcamp/controller/FrequentlyQuestionChatApiController.java b/src/main/java/com/cholog/bootcamp/controller/FrequentlyQuestionChatApiController.java index 6b1a362..a3ab541 100644 --- a/src/main/java/com/cholog/bootcamp/controller/FrequentlyQuestionChatApiController.java +++ b/src/main/java/com/cholog/bootcamp/controller/FrequentlyQuestionChatApiController.java @@ -2,6 +2,8 @@ import com.cholog.bootcamp.dto.FrequentlyQuestionChatRequestDto; import com.cholog.bootcamp.dto.FrequentlyQuestionChatResponseDto; +import com.cholog.bootcamp.service.FrequentlyQuestionChatApiService; +import lombok.RequiredArgsConstructor; import org.slf4j.Logger; import org.slf4j.LoggerFactory; import org.springframework.http.ResponseEntity; @@ -12,16 +14,20 @@ @RestController @RequestMapping("/api/chat") +@RequiredArgsConstructor public class FrequentlyQuestionChatApiController { private static final Logger log = LoggerFactory.getLogger(FrequentlyQuestionChatApiController.class); + private final FrequentlyQuestionChatApiService frequentlyQuestionChatApiService; + @PostMapping public ResponseEntity question( @RequestBody FrequentlyQuestionChatRequestDto dto ) { log.info("FAQ 요청이 들어왔습니다. {}", dto.question()); // TODO 서비스 레이어 및 응답 구현 - return ResponseEntity.ok(null); + var response = frequentlyQuestionChatApiService.chat(dto); + return ResponseEntity.ok(response); } } diff --git a/src/main/java/com/cholog/bootcamp/data/TokenUsage.java b/src/main/java/com/cholog/bootcamp/data/TokenUsage.java index 4747ef8..ee7d271 100644 --- a/src/main/java/com/cholog/bootcamp/data/TokenUsage.java +++ b/src/main/java/com/cholog/bootcamp/data/TokenUsage.java @@ -1,8 +1,20 @@ package com.cholog.bootcamp.data; +import org.springframework.ai.chat.metadata.Usage; + public record TokenUsage( int promptTokens, int completionTokens, int totalTokens ) { + + public static TokenUsage EMPTY = new TokenUsage(0, 0, 0); + + public static TokenUsage from(Usage usage) { + return new TokenUsage( + usage.getPromptTokens(), + usage.getCompletionTokens(), + usage.getTotalTokens() + ); + } } \ No newline at end of file diff --git a/src/main/java/com/cholog/bootcamp/service/FrequentlyQuestionChatApiService.java b/src/main/java/com/cholog/bootcamp/service/FrequentlyQuestionChatApiService.java new file mode 100644 index 0000000..95fbb8b --- /dev/null +++ b/src/main/java/com/cholog/bootcamp/service/FrequentlyQuestionChatApiService.java @@ -0,0 +1,43 @@ +package com.cholog.bootcamp.service; + +import com.cholog.bootcamp.data.TokenUsage; +import com.cholog.bootcamp.dto.FrequentlyQuestionChatRequestDto; +import com.cholog.bootcamp.dto.FrequentlyQuestionChatResponseDto; +import jakarta.annotation.PostConstruct; +import lombok.RequiredArgsConstructor; +import lombok.extern.slf4j.Slf4j; +import org.springframework.ai.chat.client.ChatClient; +import org.springframework.ai.chat.metadata.Usage; +import org.springframework.ai.chat.prompt.Prompt; +import org.springframework.stereotype.Service; + +@Slf4j +@Service +@RequiredArgsConstructor +public class FrequentlyQuestionChatApiService { + + private final ChatClient chatClient; + + public FrequentlyQuestionChatResponseDto chat(FrequentlyQuestionChatRequestDto requestDto) { + var prompt = Prompt.builder() + .content(requestDto.question()) + .build(); + + var response = chatClient.prompt(prompt) + .call() + .chatResponse(); + + if (response == null) { + return new FrequentlyQuestionChatResponseDto( + "응답이 없습니다.", TokenUsage.EMPTY + ); + } + + var generation = response.getResult().getOutput(); + var metadata = response.getMetadata(); + Usage usage = metadata.getUsage(); + + log.info("[{}] 결과: {}, 토큰 사용량: {}", metadata.getModel(), generation.getText(), usage); + return new FrequentlyQuestionChatResponseDto(generation.getText(), TokenUsage.from(usage)); + } +} From 77010104e0cfe0021576c14c106a2808940a5db1 Mon Sep 17 00:00:00 2001 From: youngsu5582 <98307410+youngsu5582@users.noreply.github.com> Date: Sun, 10 May 2026 00:38:24 +0900 Subject: [PATCH 04/17] =?UTF-8?q?chore:=20=ED=85=8C=EC=8A=A4=ED=8A=B8=20?= =?UTF-8?q?=EC=8A=A4=ED=81=AC=EB=A6=BD=ED=8A=B8=20=EB=B3=91=EB=A0=AC=20?= =?UTF-8?q?=EA=B0=80=EB=8A=A5=ED=95=98=EA=B2=8C=20=EB=B3=80=EA=B2=BD,=20?= =?UTF-8?q?=EB=AC=B8=EC=84=9C=EB=82=B4=20=EC=9E=98=EB=AA=BB=EB=90=9C=20?= =?UTF-8?q?=EB=B6=80=EB=B6=84=20=EC=88=98=EC=A0=95?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - requirements 파일 추가 --- README.md | 7 +- data/evaluate.py | 163 +++++++++++++++++++++++++++++++----------- data/requirements.txt | 3 + 3 files changed, 131 insertions(+), 42 deletions(-) create mode 100644 data/requirements.txt diff --git a/README.md b/README.md index 7a0f14b..d88d313 100644 --- a/README.md +++ b/README.md @@ -45,10 +45,13 @@ cd data # Python 환경 준비 python -m venv .venv -.venv/bin/pip install openai qdrant-client python-dotenv +.venv/bin/pip install -r requirements.txt -# 평가 실행 (judge 모델 gpt-4o-mini 사용, 100문항 기준 약 $0.5~1 추가 비용) +# 평가 실행 (judge 모델 gpt-4o-mini 사용, 150문항 기준 약 $1~1.5 추가 비용) .venv/bin/python evaluate.py + +# 빠르게 돌리려면 병렬 워커 활성화 (default: 1) +.venv/bin/python evaluate.py --parallel 10 ``` --- diff --git a/data/evaluate.py b/data/evaluate.py index ed941cd..ee02239 100644 --- a/data/evaluate.py +++ b/data/evaluate.py @@ -11,8 +11,9 @@ 실행: # 서버가 localhost:8080에서 실행 중이어야 합니다 .venv/bin/python evaluate.py - .venv/bin/python evaluate.py --verbose # 질문별 상세 출력 - .venv/bin/python evaluate.py --limit 10 # 처음 10개만 평가 + .venv/bin/python evaluate.py --verbose # 질문별 상세 출력 + .venv/bin/python evaluate.py --limit 10 # 처음 10개만 평가 + .venv/bin/python evaluate.py --parallel 10 # 병렬 워커 10개로 가속 비용: judge 모델(gpt-4o-mini) 사용, 100문항 기준 약 $0.3~0.5 @@ -22,6 +23,7 @@ import os import argparse import time +from concurrent.futures import ThreadPoolExecutor, as_completed from pathlib import Path import requests @@ -102,12 +104,43 @@ def judge_answer(question: str, expected: str, actual: str) -> dict: return {"score": 0, "reason": "판정 파싱 실패"} +# ─── 워커 ───────────────────────────────────────────────────────────────────── + +def process_question(q: dict, idx: int) -> dict: + """질문 1건을 처리해 결과 dict를 반환합니다. (스레드 안전)""" + start = time.time() + qid = q.get("id", f"Q{idx+1}") + question_ko = q["question_ko"] + expected = q["expected_answer"] + tier = q.get("tier", "unknown") + + response = ask_server(question_ko) + if response is None: + return {"qid": qid, "tier": tier, "status": "error", "question": question_ko, + "duration": time.time() - start} + + actual_answer = response.get("answer", "") + judgment = judge_answer(question_ko, expected, actual_answer) + score = judgment.get("score", 0) + + return { + "qid": qid, + "tier": tier, + "status": "ok", + "score": score, + "reason": judgment.get("reason", ""), + "question": question_ko, + "duration": time.time() - start, + } + + # ─── 메인 ───────────────────────────────────────────────────────────────────── def main(): parser = argparse.ArgumentParser(description="챗봇 품질 평가") parser.add_argument("--verbose", action="store_true", help="질문별 상세 출력") parser.add_argument("--limit", type=int, default=0, help="평가할 질문 수 제한 (0=전체)") + parser.add_argument("--parallel", type=int, default=1, help="병렬 워커 수 (default: 1, 순차 실행)") args = parser.parse_args() # 테스트 질문 로드 @@ -122,6 +155,8 @@ def main(): print(f"서버: {SERVER_URL}") print(f"질문 수: {len(questions)}") print(f"판정 모델: {JUDGE_MODEL}") + if args.parallel > 1: + print(f"병렬 워커: {args.parallel}") print() # 서버 연결 확인 @@ -133,48 +168,93 @@ def main(): results = {"correct": 0, "incorrect": 0, "error": 0} tier_results = {} + durations = [] start_time = time.time() - for i, q in enumerate(questions): - qid = q.get("id", f"Q{i+1}") - question_ko = q["question_ko"] - expected = q["expected_answer"] - tier = q.get("tier", "unknown") + if args.parallel > 1: + # ─── 병렬 실행 (워커는 결과만 반환, 집계는 메인 스레드에서) ─── + completed = 0 + with ThreadPoolExecutor(max_workers=args.parallel) as executor: + futures = [executor.submit(process_question, q, i) for i, q in enumerate(questions)] + + for fut in as_completed(futures): + r = fut.result() + durations.append(r["duration"]) + completed += 1 + tier = r["tier"] + + if tier not in tier_results: + tier_results[tier] = {"correct": 0, "total": 0} + tier_results[tier]["total"] += 1 + + if r["status"] == "error": + results["error"] += 1 + if args.verbose: + print(f"[{r['qid']}] ERROR — 서버 응답 없음") + else: + score = r["score"] + if score == 1: + results["correct"] += 1 + tier_results[tier]["correct"] += 1 + marker = "✓" + else: + results["incorrect"] += 1 + marker = "✗" + + if args.verbose: + print(f"[{r['qid']}] {marker} ({tier}) {r['question'][:40]}...") + if score == 0: + print(f" 이유: {r['reason'][:80]}") + + # 진행률 (10개마다) + if not args.verbose and completed % 10 == 0: + print(f" 진행: {completed}/{len(questions)}") + else: + # ─── 순차 실행 (기본) ─── + for i, q in enumerate(questions): + q_start = time.time() + qid = q.get("id", f"Q{i+1}") + question_ko = q["question_ko"] + expected = q["expected_answer"] + tier = q.get("tier", "unknown") + + if tier not in tier_results: + tier_results[tier] = {"correct": 0, "total": 0} + tier_results[tier]["total"] += 1 + + # 서버에 질문 + response = ask_server(question_ko) + if response is None: + results["error"] += 1 + durations.append(time.time() - q_start) + if args.verbose: + print(f"[{qid}] ERROR — 서버 응답 없음") + continue + + actual_answer = response.get("answer", "") + + # LLM 판정 + judgment = judge_answer(question_ko, expected, actual_answer) + score = judgment.get("score", 0) + + if score == 1: + results["correct"] += 1 + tier_results[tier]["correct"] += 1 + marker = "✓" + else: + results["incorrect"] += 1 + marker = "✗" - if tier not in tier_results: - tier_results[tier] = {"correct": 0, "total": 0} - tier_results[tier]["total"] += 1 - - # 서버에 질문 - response = ask_server(question_ko) - if response is None: - results["error"] += 1 if args.verbose: - print(f"[{qid}] ERROR — 서버 응답 없음") - continue - - actual_answer = response.get("answer", "") - - # LLM 판정 - judgment = judge_answer(question_ko, expected, actual_answer) - score = judgment.get("score", 0) - - if score == 1: - results["correct"] += 1 - tier_results[tier]["correct"] += 1 - marker = "✓" - else: - results["incorrect"] += 1 - marker = "✗" + print(f"[{qid}] {marker} ({tier}) {question_ko[:40]}...") + if score == 0: + print(f" 이유: {judgment.get('reason', '')[:80]}") - if args.verbose: - print(f"[{qid}] {marker} ({tier}) {question_ko[:40]}...") - if score == 0: - print(f" 이유: {judgment.get('reason', '')[:80]}") + durations.append(time.time() - q_start) - # 진행률 (10개마다) - if not args.verbose and (i + 1) % 10 == 0: - print(f" 진행: {i+1}/{len(questions)}") + # 진행률 (10개마다) + if not args.verbose and (i + 1) % 10 == 0: + print(f" 진행: {i+1}/{len(questions)}") # 결과 출력 elapsed = time.time() - start_time @@ -194,8 +274,10 @@ def main(): if results["error"] > 0: print(f"\n 에러: {results['error']}건") - print(f"\n소요 시간: {elapsed:.1f}초") - print(f"평균 응답: {elapsed/max(total,1):.1f}초/질문") + print(f"\n벽시계 시간: {elapsed:.1f}초") + if durations: + avg_response = sum(durations) / len(durations) + print(f"평균 응답: {avg_response:.1f}초/질문") # 결과 저장 result_file = DATA_DIR / "eval_result.json" @@ -208,6 +290,7 @@ def main(): "accuracy": results["correct"] / max(total, 1), "tier_results": tier_results, "elapsed_seconds": elapsed, + "avg_response_seconds": (sum(durations) / len(durations)) if durations else 0, }, f, indent=2, ensure_ascii=False) print(f"\n결과 저장: {result_file}") diff --git a/data/requirements.txt b/data/requirements.txt new file mode 100644 index 0000000..521ddde --- /dev/null +++ b/data/requirements.txt @@ -0,0 +1,3 @@ +requests +openai +python-dotenv From 41fe308f75ab83f222eb1d341cb8c9b0530632d7 Mon Sep 17 00:00:00 2001 From: youngsu5582 <98307410+youngsu5582@users.noreply.github.com> Date: Sun, 10 May 2026 14:01:24 +0900 Subject: [PATCH 05/17] =?UTF-8?q?feat:=20=EA=B8=B0=EB=8A=A5=20=EB=B9=84?= =?UTF-8?q?=EC=9A=A9=EA=B3=84=EC=82=B0=20=EB=A1=9C=EA=B7=B8=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 - litellm github 기반 비용 JSON 로드해서 사용 - spring ai 표준 기반 처리(캐시 비용 처리X) --- .../com/cholog/bootcamp/data/ModelPrice.java | 22 + .../com/cholog/bootcamp/data/TokenUsage.java | 2 + .../FrequentlyQuestionChatApiService.java | 22 +- .../bootcamp/service/PricingCalculator.java | 77 + src/main/resources/model_prices.json | 40041 ++++++++++++++++ 5 files changed, 40159 insertions(+), 5 deletions(-) create mode 100644 src/main/java/com/cholog/bootcamp/data/ModelPrice.java create mode 100644 src/main/java/com/cholog/bootcamp/service/PricingCalculator.java create mode 100644 src/main/resources/model_prices.json diff --git a/src/main/java/com/cholog/bootcamp/data/ModelPrice.java b/src/main/java/com/cholog/bootcamp/data/ModelPrice.java new file mode 100644 index 0000000..59a807f --- /dev/null +++ b/src/main/java/com/cholog/bootcamp/data/ModelPrice.java @@ -0,0 +1,22 @@ +package com.cholog.bootcamp.data; + +import com.fasterxml.jackson.annotation.JsonIgnoreProperties; +import com.fasterxml.jackson.annotation.JsonProperty; +import org.springframework.ai.chat.metadata.Usage; +import org.springframework.lang.Nullable; + +import java.math.BigDecimal; + +@JsonIgnoreProperties(ignoreUnknown = true) +public record ModelPrice( + @JsonProperty("input_cost_per_token") BigDecimal inputCostPerToken, + @JsonProperty("output_cost_per_token") BigDecimal outputCostPerToken, + @Nullable @JsonProperty("cache_read_input_token_cost") BigDecimal cacheReadCost, + @Nullable @JsonProperty("cache_creation_input_token_cost") BigDecimal cacheWriteCost, + @JsonProperty("supports_prompt_caching") Boolean supportsPromptCaching +) { + public BigDecimal calculate(TokenUsage usage) { + return inputCostPerToken.multiply(BigDecimal.valueOf(usage.promptTokens())) + .add(outputCostPerToken.multiply(BigDecimal.valueOf(usage.completionTokens()))); + } +} \ No newline at end of file diff --git a/src/main/java/com/cholog/bootcamp/data/TokenUsage.java b/src/main/java/com/cholog/bootcamp/data/TokenUsage.java index ee7d271..adbfecf 100644 --- a/src/main/java/com/cholog/bootcamp/data/TokenUsage.java +++ b/src/main/java/com/cholog/bootcamp/data/TokenUsage.java @@ -1,7 +1,9 @@ package com.cholog.bootcamp.data; +import com.fasterxml.jackson.annotation.JsonInclude; import org.springframework.ai.chat.metadata.Usage; +@JsonInclude(JsonInclude.Include.NON_NULL) public record TokenUsage( int promptTokens, int completionTokens, diff --git a/src/main/java/com/cholog/bootcamp/service/FrequentlyQuestionChatApiService.java b/src/main/java/com/cholog/bootcamp/service/FrequentlyQuestionChatApiService.java index 95fbb8b..85acae4 100644 --- a/src/main/java/com/cholog/bootcamp/service/FrequentlyQuestionChatApiService.java +++ b/src/main/java/com/cholog/bootcamp/service/FrequentlyQuestionChatApiService.java @@ -3,20 +3,21 @@ import com.cholog.bootcamp.data.TokenUsage; import com.cholog.bootcamp.dto.FrequentlyQuestionChatRequestDto; import com.cholog.bootcamp.dto.FrequentlyQuestionChatResponseDto; -import jakarta.annotation.PostConstruct; import lombok.RequiredArgsConstructor; import lombok.extern.slf4j.Slf4j; import org.springframework.ai.chat.client.ChatClient; -import org.springframework.ai.chat.metadata.Usage; import org.springframework.ai.chat.prompt.Prompt; import org.springframework.stereotype.Service; +import java.math.BigDecimal; + @Slf4j @Service @RequiredArgsConstructor public class FrequentlyQuestionChatApiService { private final ChatClient chatClient; + private final PricingCalculator pricingCalculator; public FrequentlyQuestionChatResponseDto chat(FrequentlyQuestionChatRequestDto requestDto) { var prompt = Prompt.builder() @@ -35,9 +36,20 @@ public FrequentlyQuestionChatResponseDto chat(FrequentlyQuestionChatRequestDto r var generation = response.getResult().getOutput(); var metadata = response.getMetadata(); - Usage usage = metadata.getUsage(); - log.info("[{}] 결과: {}, 토큰 사용량: {}", metadata.getModel(), generation.getText(), usage); - return new FrequentlyQuestionChatResponseDto(generation.getText(), TokenUsage.from(usage)); + var usage = TokenUsage.from(metadata.getUsage()); + var price = calculateModelPrice(metadata.getModel(), usage); + + log.info("[{}] 토큰 사용량: {}, 토큰 비용: {}$\n결과: {}", metadata.getModel(), usage, price, generation.getText()); + return new FrequentlyQuestionChatResponseDto(generation.getText(), usage); + } + + private BigDecimal calculateModelPrice(String model, TokenUsage usage) { + try { + return pricingCalculator.calculatePrice(model, usage); + } catch (Exception e) { + log.info("토큰 비용 계산에 실패했습니다. 모델: {}, 메시지: {}", model, e.getMessage(), e); + return BigDecimal.ZERO; + } } } diff --git a/src/main/java/com/cholog/bootcamp/service/PricingCalculator.java b/src/main/java/com/cholog/bootcamp/service/PricingCalculator.java new file mode 100644 index 0000000..7cadbb4 --- /dev/null +++ b/src/main/java/com/cholog/bootcamp/service/PricingCalculator.java @@ -0,0 +1,77 @@ +package com.cholog.bootcamp.service; + +import com.cholog.bootcamp.data.ModelPrice; +import com.cholog.bootcamp.data.TokenUsage; +import com.fasterxml.jackson.databind.ObjectMapper; +import jakarta.annotation.PostConstruct; +import lombok.extern.slf4j.Slf4j; +import org.springframework.core.io.ClassPathResource; +import org.springframework.stereotype.Service; + +import java.math.BigDecimal; +import java.util.Map; +import java.util.Optional; + +/** + * 모델별 토큰 사용량 기반으로 API 호출 비용을 계산하는 서비스. + * + *

가격 정보는 클래스패스의 {@code model_prices.json} 파일에서 로드된다. + * 이 파일은 BerriAI/litellm 저장소의 가격 데이터를 그대로 사용하며, + * 빌드 전 아래 명령으로 수동 갱신해야 한다. + * + * 

{@code
+ * curl -o src/main/resources/model_prices.json \
+ *   https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json
+ * }
+ * + *

출처: + * + * model_prices_and_context_window.json (BerriAI/litellm) + * + *

{@link PostConstruct} 시점에 한 번 로드되어 메모리에 캐시되며, + * 런타임 중에는 외부 네트워크 호출이 발생하지 않는다. + */ +@Slf4j +@Service +public class PricingCalculator { + + private final ObjectMapper mapper = new ObjectMapper(); + private Map prices; + + private static final String CLASSPATH_FILE = "model_prices.json"; + + @PostConstruct + public void init() throws Exception { + var resource = new ClassPathResource(CLASSPATH_FILE); + try (var is = resource.getInputStream()) { + prices = mapper.readValue(is, mapper.getTypeFactory() + .constructMapType(Map.class, String.class, ModelPrice.class)); + } + log.info("모델 JSON 을 로딩완료. 개수:{}", prices.size()); + } + + /** + * 지정한 모델의 토큰 사용량으로 비용을 계산한다. + * + * @param model 모델명 (예: {@code "gpt-4o-mini"}) + * @param usage 입출력 토큰 사용량 + * @return 계산된 비용 (USD) + * @throws IllegalArgumentException {@code model}이 가격 데이터에 없을 때 + */ + public BigDecimal calculatePrice(String model, TokenUsage usage) { + var modelPrice = find(model); + return modelPrice.calculate(usage); + } + + /** + * 모델명으로 가격 정보를 조회한다. + * + * @param model 모델명 + * @return 해당 모델의 가격 정보 + * @throws IllegalArgumentException {@code model}이 가격 데이터에 없을 때 + */ + public ModelPrice find(String model) { + return Optional.ofNullable(prices.get(model)) + .orElseThrow(() -> new IllegalArgumentException("데이터에 해당 모델이 없습니다. 모델명: %s".formatted(model))); + } +} diff --git a/src/main/resources/model_prices.json b/src/main/resources/model_prices.json new file mode 100644 index 0000000..e66ad8e --- /dev/null +++ b/src/main/resources/model_prices.json @@ -0,0 +1,40041 @@ +{ + "sample_spec": { + "code_interpreter_cost_per_session": 0.0, + "computer_use_input_cost_per_1k_tokens": 0.0, + "computer_use_output_cost_per_1k_tokens": 0.0, + "deprecation_date": "date when the model becomes deprecated in the format YYYY-MM-DD", + "file_search_cost_per_1k_calls": 0.0, + "file_search_cost_per_gb_per_day": 0.0, + "input_cost_per_audio_token": 0.0, + "input_cost_per_token": 0.0, + "litellm_provider": "one of https://docs.litellm.ai/docs/providers", + "max_input_tokens": "max input tokens, if the provider specifies it. if not default to max_tokens", + "max_output_tokens": "max output tokens, if the provider specifies it. if not default to max_tokens", + "max_tokens": "LEGACY parameter. set to max_output_tokens if provider specifies it. IF not set to max_input_tokens, if provider specifies it.", + "mode": "one of: chat, embedding, completion, image_generation, audio_transcription, audio_speech, image_generation, moderation, rerank, search", + "output_cost_per_reasoning_token": 0.0, + "output_cost_per_token": 0.0, + "search_context_cost_per_query": { + "search_context_size_high": 0.0, + "search_context_size_low": 0.0, + "search_context_size_medium": 0.0 + }, + "supported_regions": [ + "global", + "us-west-2", + "eu-west-1", + "ap-southeast-1", + "ap-northeast-1" + ], + "supports_audio_input": true, + "supports_audio_output": true, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_vision": true, + "supports_web_search": true, + "vector_store_cost_per_gb_per_day": 0.0 + }, + "1024-x-1024/50-steps/bedrock/amazon.nova-canvas-v1:0": { + "litellm_provider": "bedrock", + "max_input_tokens": 2600, + "mode": "image_generation", + "output_cost_per_image": 0.06 + }, + "1024-x-1024/50-steps/stability.stable-diffusion-xl-v1": { + "litellm_provider": "bedrock", + "max_input_tokens": 77, + "max_tokens": 77, + "mode": "image_generation", + "output_cost_per_image": 0.04 + }, + "1024-x-1024/dall-e-2": { + "input_cost_per_pixel": 1.9e-08, + "litellm_provider": "openai", + "mode": "image_generation", + "output_cost_per_pixel": 0.0 + }, + "1024-x-1024/max-steps/stability.stable-diffusion-xl-v1": { + "litellm_provider": "bedrock", + "max_input_tokens": 77, + "max_tokens": 77, + "mode": "image_generation", + "output_cost_per_image": 0.08 + }, + "256-x-256/dall-e-2": { + "input_cost_per_pixel": 2.4414e-07, + "litellm_provider": "openai", + "mode": "image_generation", + "output_cost_per_pixel": 0.0 + }, + "512-x-512/50-steps/stability.stable-diffusion-xl-v0": { + "litellm_provider": "bedrock", + "max_input_tokens": 77, + "max_tokens": 77, + "mode": "image_generation", + "output_cost_per_image": 0.018 + }, + "512-x-512/dall-e-2": { + "input_cost_per_pixel": 6.86e-08, + "litellm_provider": "openai", + "mode": "image_generation", + "output_cost_per_pixel": 0.0 + }, + "512-x-512/max-steps/stability.stable-diffusion-xl-v0": { + "litellm_provider": "bedrock", + "max_input_tokens": 77, + "max_tokens": 77, + "mode": "image_generation", + "output_cost_per_image": 0.036 + }, + "ai21.j2-mid-v1": { + "input_cost_per_token": 1.25e-05, + "litellm_provider": "bedrock", + "max_input_tokens": 8191, + "max_output_tokens": 8191, + "max_tokens": 8191, + "mode": "chat", + "output_cost_per_token": 1.25e-05 + }, + "ai21.j2-ultra-v1": { + "input_cost_per_token": 1.88e-05, + "litellm_provider": "bedrock", + "max_input_tokens": 8191, + "max_output_tokens": 8191, + "max_tokens": 8191, + "mode": "chat", + "output_cost_per_token": 1.88e-05 + }, + "ai21.jamba-1-5-large-v1:0": { + "input_cost_per_token": 2e-06, + "litellm_provider": "bedrock", + "max_input_tokens": 256000, + "max_output_tokens": 256000, + "max_tokens": 256000, + "mode": "chat", + "output_cost_per_token": 8e-06 + }, + "ai21.jamba-1-5-mini-v1:0": { + "input_cost_per_token": 2e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 256000, + "max_output_tokens": 256000, + "max_tokens": 256000, + "mode": "chat", + "output_cost_per_token": 4e-07 + }, + "ai21.jamba-instruct-v1:0": { + "input_cost_per_token": 5e-07, + "litellm_provider": "bedrock", + "max_input_tokens": 70000, + "max_output_tokens": 4096, + "max_tokens": 4096, + "mode": "chat", + "output_cost_per_token": 7e-07, + "supports_system_messages": true + }, + "aiml/dall-e-2": { + "litellm_provider": "aiml", + "metadata": { + "notes": "DALL-E 2 via AI/ML API - Reliable text-to-image generation" + }, + "mode": "image_generation", + "output_cost_per_image": 0.026, + "source": "https://docs.aimlapi.com/", + "supported_endpoints": [ + "/v1/images/generations" + ] + }, + "aiml/dall-e-3": { + "litellm_provider": "aiml", + "metadata": { + "notes": "DALL-E 3 via AI/ML API - High-quality text-to-image generation" + }, + "mode": "image_generation", + "output_cost_per_image": 0.052, + "source": "https://docs.aimlapi.com/", + "supported_endpoints": [ + "/v1/images/generations" + ] + }, + "aiml/flux-pro": { + "litellm_provider": "aiml", + "metadata": { + "notes": "Flux Dev - Development version optimized for experimentation" + }, + "mode": "image_generation", + "output_cost_per_image": 0.065, + "source": "https://docs.aimlapi.com/", + "supported_endpoints": [ + "/v1/images/generations" + ] + }, + "aiml/flux-pro/v1.1": { + "litellm_provider": "aiml", + "mode": "image_generation", + "output_cost_per_image": 0.052, + "supported_endpoints": [ + "/v1/images/generations" + ] + }, + "aiml/flux-pro/v1.1-ultra": { + "litellm_provider": "aiml", + "mode": "image_generation", + "output_cost_per_image": 0.063, + "supported_endpoints": [ + "/v1/images/generations" + ] + }, + "aiml/flux-realism": { + "litellm_provider": "aiml", + "metadata": { + "notes": "Flux Pro - Professional-grade image generation model" + }, + "mode": "image_generation", + "output_cost_per_image": 0.046, + "source": "https://docs.aimlapi.com/", + "supported_endpoints": [ + "/v1/images/generations" + ] + }, + "aiml/flux/dev": { + "litellm_provider": "aiml", + "metadata": { + "notes": "Flux Dev - Development version optimized for experimentation" + }, + "mode": "image_generation", + "output_cost_per_image": 0.033, + "source": "https://docs.aimlapi.com/", + "supported_endpoints": [ + "/v1/images/generations" + ] + }, + "aiml/flux/kontext-max/text-to-image": { + "litellm_provider": "aiml", + "metadata": { + "notes": "Flux Pro v1.1 - Enhanced version with improved capabilities and 6x faster inference speed" + }, + "mode": "image_generation", + "output_cost_per_image": 0.104, + "source": "https://docs.aimlapi.com/", + "supported_endpoints": [ + "/v1/images/generations" + ] + }, + "aiml/flux/kontext-pro/text-to-image": { + "litellm_provider": "aiml", + "metadata": { + "notes": "Flux Pro v1.1 - Enhanced version with improved capabilities and 6x faster inference speed" + }, + "mode": "image_generation", + "output_cost_per_image": 0.052, + "source": "https://docs.aimlapi.com/", + "supported_endpoints": [ + "/v1/images/generations" + ] + }, + "aiml/flux/schnell": { + "litellm_provider": "aiml", + "metadata": { + "notes": "Flux Schnell - Fast generation model optimized for speed" + }, + "mode": "image_generation", + "output_cost_per_image": 0.004, + "source": "https://docs.aimlapi.com/", + "supported_endpoints": [ + "/v1/images/generations" + ] + }, + "aiml/google/imagen-4.0-ultra-generate-001": { + "litellm_provider": "aiml", + "metadata": { + "notes": "Imagen 4.0 Ultra Generate API - Photorealistic image generation with precise text rendering" + }, + "mode": "image_generation", + "output_cost_per_image": 0.078, + "source": "https://docs.aimlapi.com/api-references/image-models/google/imagen-4-ultra-generate", + "supported_endpoints": [ + "/v1/images/generations" + ] + }, + "aiml/google/nano-banana-pro": { + "litellm_provider": "aiml", + "metadata": { + "notes": "Gemini 3 Pro Image (Nano Banana Pro) - Advanced text-to-image generation with reasoning and 4K resolution support" + }, + "mode": "image_generation", + "output_cost_per_image": 0.195, + "source": "https://docs.aimlapi.com/api-references/image-models/google/gemini-3-pro-image-preview", + "supported_endpoints": [ + "/v1/images/generations" + ] + }, + "amazon.nova-canvas-v1:0": { + "litellm_provider": "bedrock", + "max_input_tokens": 2600, + "mode": "image_generation", + "output_cost_per_image": 0.06, + "supports_nova_canvas_image_edit": true + }, + "us.amazon.nova-canvas-v1:0": { + "litellm_provider": "bedrock", + "max_input_tokens": 2600, + "mode": "image_generation", + "output_cost_per_image": 0.06, + "supports_nova_canvas_image_edit": true + }, + 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Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "embedding", + "output_cost_per_token": 0.0, + "output_dbu_cost_per_token": 0.0, + "output_vector_size": 1024, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving" + }, + "databricks/databricks-claude-3-7-sonnet": { + "input_cost_per_token": 2.9999900000000002e-06, + "input_dbu_cost_per_token": 4.2857e-05, + "litellm_provider": "databricks", + "max_input_tokens": 200000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 1.5000020000000002e-05, + "output_dbu_cost_per_token": 0.000214286, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "databricks/databricks-claude-haiku-4-5": { + "input_cost_per_token": 1.00002e-06, + "input_dbu_cost_per_token": 1.4286e-05, + "litellm_provider": "databricks", + "max_input_tokens": 200000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 5.00003e-06, + "output_dbu_cost_per_token": 7.1429e-05, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "databricks/databricks-claude-opus-4": { + "input_cost_per_token": 1.5000020000000002e-05, + "input_dbu_cost_per_token": 0.000214286, + "litellm_provider": "databricks", + "max_input_tokens": 200000, + "max_output_tokens": 32000, + "max_tokens": 32000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 7.500003000000001e-05, + "output_dbu_cost_per_token": 0.001071429, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "databricks/databricks-claude-opus-4-1": { + "input_cost_per_token": 1.5000020000000002e-05, + "input_dbu_cost_per_token": 0.000214286, + "litellm_provider": "databricks", + "max_input_tokens": 200000, + "max_output_tokens": 32000, + "max_tokens": 32000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 7.500003000000001e-05, + "output_dbu_cost_per_token": 0.001071429, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "databricks/databricks-claude-opus-4-5": { + "input_cost_per_token": 5.00003e-06, + "input_dbu_cost_per_token": 7.1429e-05, + "litellm_provider": "databricks", + "max_input_tokens": 200000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 2.5000010000000002e-05, + "output_dbu_cost_per_token": 0.000357143, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_minimal_reasoning_effort": true, + "supports_tool_choice": true + }, + "databricks/databricks-claude-sonnet-4": { + "input_cost_per_token": 2.9999900000000002e-06, + "input_dbu_cost_per_token": 4.2857e-05, + "litellm_provider": "databricks", + "max_input_tokens": 200000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 1.5000020000000002e-05, + "output_dbu_cost_per_token": 0.000214286, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "databricks/databricks-claude-sonnet-4-1": { + "input_cost_per_token": 2.9999900000000002e-06, + "input_dbu_cost_per_token": 4.2857e-05, + "litellm_provider": "databricks", + "max_input_tokens": 200000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 1.5000020000000002e-05, + "output_dbu_cost_per_token": 0.000214286, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "databricks/databricks-claude-sonnet-4-5": { + "input_cost_per_token": 2.9999900000000002e-06, + "input_dbu_cost_per_token": 4.2857e-05, + "litellm_provider": "databricks", + "max_input_tokens": 200000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 1.5000020000000002e-05, + "output_dbu_cost_per_token": 0.000214286, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "databricks/databricks-gemini-2-5-flash": { + "input_cost_per_token": 3.0001999999999996e-07, + "input_dbu_cost_per_token": 4.285999999999999e-06, + "litellm_provider": "databricks", + "max_input_tokens": 1048576, + "max_output_tokens": 65535, + "max_tokens": 65535, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 2.49998e-06, + "output_dbu_cost_per_token": 3.5714e-05, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_function_calling": true, + "supports_tool_choice": true + }, + "databricks/databricks-gemini-2-5-pro": { + "input_cost_per_token": 1.24999e-06, + "input_dbu_cost_per_token": 1.7857e-05, + "litellm_provider": "databricks", + "max_input_tokens": 1048576, + "max_output_tokens": 65536, + "max_tokens": 65536, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 9.999990000000002e-06, + "output_dbu_cost_per_token": 0.000142857, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_function_calling": true, + "supports_tool_choice": true + }, + "databricks/databricks-gemma-3-12b": { + "input_cost_per_token": 1.5000999999999998e-07, + "input_dbu_cost_per_token": 2.1429999999999996e-06, + "litellm_provider": "databricks", + "max_input_tokens": 128000, + "max_output_tokens": 32000, + "max_tokens": 32000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 5.0001e-07, + "output_dbu_cost_per_token": 7.143e-06, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving" + }, + "databricks/databricks-gpt-5": { + "input_cost_per_token": 1.24999e-06, + "input_dbu_cost_per_token": 1.7857e-05, + "litellm_provider": "databricks", + "max_input_tokens": 272000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 9.999990000000002e-06, + "output_dbu_cost_per_token": 0.000142857, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving" + }, + "databricks/databricks-gpt-5-1": { + "input_cost_per_token": 1.24999e-06, + "input_dbu_cost_per_token": 1.7857e-05, + "litellm_provider": "databricks", + "max_input_tokens": 272000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 9.999990000000002e-06, + "output_dbu_cost_per_token": 0.000142857, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving" + }, + "databricks/databricks-gpt-5-mini": { + "input_cost_per_token": 2.4997000000000006e-07, + "input_dbu_cost_per_token": 3.571e-06, + "litellm_provider": "databricks", + "max_input_tokens": 272000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 1.9999700000000004e-06, + "output_dbu_cost_per_token": 2.8571e-05, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving" + }, + "databricks/databricks-gpt-5-nano": { + "input_cost_per_token": 4.998e-08, + "input_dbu_cost_per_token": 7.14e-07, + "litellm_provider": "databricks", + "max_input_tokens": 272000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 3.9998000000000007e-07, + "output_dbu_cost_per_token": 5.714000000000001e-06, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving" + }, + "databricks/databricks-gpt-oss-120b": { + "input_cost_per_token": 1.5000999999999998e-07, + "input_dbu_cost_per_token": 2.1429999999999996e-06, + "litellm_provider": "databricks", + "max_input_tokens": 131072, + "max_output_tokens": 131072, + "max_tokens": 131072, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 5.9997e-07, + "output_dbu_cost_per_token": 8.571e-06, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving" + }, + "databricks/databricks-gpt-oss-20b": { + "input_cost_per_token": 7e-08, + "input_dbu_cost_per_token": 1e-06, + "litellm_provider": "databricks", + "max_input_tokens": 131072, + "max_output_tokens": 131072, + "max_tokens": 131072, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 3.0001999999999996e-07, + "output_dbu_cost_per_token": 4.285999999999999e-06, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving" + }, + "databricks/databricks-gte-large-en": { + "input_cost_per_token": 1.2999000000000001e-07, + "input_dbu_cost_per_token": 1.857e-06, + "litellm_provider": "databricks", + "max_input_tokens": 8192, + "max_tokens": 8192, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "embedding", + "output_cost_per_token": 0.0, + "output_dbu_cost_per_token": 0.0, + "output_vector_size": 1024, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving" + }, + "databricks/databricks-llama-2-70b-chat": { + "input_cost_per_token": 5.0001e-07, + "input_dbu_cost_per_token": 7.143e-06, + "litellm_provider": "databricks", + "max_input_tokens": 4096, + "max_output_tokens": 4096, + "max_tokens": 4096, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 1.5000300000000002e-06, + "output_dbu_cost_per_token": 2.1429e-05, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "supports_tool_choice": true + }, + "databricks/databricks-llama-4-maverick": { + "input_cost_per_token": 5.0001e-07, + "input_dbu_cost_per_token": 7.143e-06, + "litellm_provider": "databricks", + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "metadata": { + "notes": "Databricks documentation now provides both DBU costs (_dbu_cost_per_token) and dollar costs(_cost_per_token)." + }, + "mode": "chat", + "output_cost_per_token": 1.5000300000000002e-06, + "output_dbu_cost_per_token": 2.1429e-05, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "supports_tool_choice": true + }, + "databricks/databricks-meta-llama-3-1-405b-instruct": { + "input_cost_per_token": 5.00003e-06, + "input_dbu_cost_per_token": 7.1429e-05, + "litellm_provider": "databricks", + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 1.5000020000000002e-05, + "output_dbu_cost_per_token": 0.000214286, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "supports_tool_choice": true + }, + "databricks/databricks-meta-llama-3-1-8b-instruct": { + "input_cost_per_token": 1.5000999999999998e-07, + "input_dbu_cost_per_token": 2.1429999999999996e-06, + "litellm_provider": "databricks", + "max_input_tokens": 200000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 4.5003000000000007e-07, + "output_dbu_cost_per_token": 6.429000000000001e-06, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving" + }, + "databricks/databricks-meta-llama-3-3-70b-instruct": { + "input_cost_per_token": 5.0001e-07, + "input_dbu_cost_per_token": 7.143e-06, + "litellm_provider": "databricks", + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 1.5000300000000002e-06, + "output_dbu_cost_per_token": 2.1429e-05, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "supports_tool_choice": true + }, + "databricks/databricks-meta-llama-3-70b-instruct": { + "input_cost_per_token": 1.00002e-06, + "input_dbu_cost_per_token": 1.4286e-05, + "litellm_provider": "databricks", + "max_input_tokens": 128000, + "max_output_tokens": 128000, + "max_tokens": 128000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 2.9999900000000002e-06, + "output_dbu_cost_per_token": 4.2857e-05, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "supports_tool_choice": true + }, + "databricks/databricks-mixtral-8x7b-instruct": { + "input_cost_per_token": 5.0001e-07, + "input_dbu_cost_per_token": 7.143e-06, + "litellm_provider": "databricks", + "max_input_tokens": 4096, + "max_output_tokens": 4096, + "max_tokens": 4096, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 1.00002e-06, + "output_dbu_cost_per_token": 1.4286e-05, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "supports_tool_choice": true + }, + "databricks/databricks-mpt-30b-instruct": { + "input_cost_per_token": 1.00002e-06, + "input_dbu_cost_per_token": 1.4286e-05, + "litellm_provider": "databricks", + "max_input_tokens": 8192, + "max_output_tokens": 8192, + "max_tokens": 8192, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 1.00002e-06, + "output_dbu_cost_per_token": 1.4286e-05, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "supports_tool_choice": true + }, + "databricks/databricks-mpt-7b-instruct": { + "input_cost_per_token": 5.0001e-07, + "input_dbu_cost_per_token": 7.143e-06, + "litellm_provider": "databricks", + "max_input_tokens": 8192, + "max_output_tokens": 8192, + "max_tokens": 8192, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 0.0, + "output_dbu_cost_per_token": 0.0, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "supports_tool_choice": true + }, + "dataforseo/search": { + "input_cost_per_query": 0.003, + "litellm_provider": "dataforseo", + "mode": "search" + }, + "davinci-002": { + "input_cost_per_token": 2e-06, + "litellm_provider": "text-completion-openai", + "max_input_tokens": 16384, + "max_output_tokens": 4096, + "max_tokens": 4096, + "mode": "completion", + "output_cost_per_token": 2e-06 + }, + "deepgram/base": { + "input_cost_per_second": 0.00020833, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0125/60 seconds = $0.00020833 per second", + "original_pricing_per_minute": 0.0125 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/base-conversationalai": { + "input_cost_per_second": 0.00020833, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0125/60 seconds = $0.00020833 per second", + "original_pricing_per_minute": 0.0125 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/base-finance": { + "input_cost_per_second": 0.00020833, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0125/60 seconds = $0.00020833 per second", + "original_pricing_per_minute": 0.0125 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/base-general": { + "input_cost_per_second": 0.00020833, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0125/60 seconds = $0.00020833 per second", + "original_pricing_per_minute": 0.0125 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/base-meeting": { + "input_cost_per_second": 0.00020833, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0125/60 seconds = $0.00020833 per second", + "original_pricing_per_minute": 0.0125 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/base-phonecall": { + "input_cost_per_second": 0.00020833, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0125/60 seconds = $0.00020833 per second", + "original_pricing_per_minute": 0.0125 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/base-video": { + "input_cost_per_second": 0.00020833, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0125/60 seconds = $0.00020833 per second", + "original_pricing_per_minute": 0.0125 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/base-voicemail": { + "input_cost_per_second": 0.00020833, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0125/60 seconds = $0.00020833 per second", + "original_pricing_per_minute": 0.0125 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/enhanced": { + "input_cost_per_second": 0.00024167, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0145/60 seconds = $0.00024167 per second", + "original_pricing_per_minute": 0.0145 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/enhanced-finance": { + "input_cost_per_second": 0.00024167, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0145/60 seconds = $0.00024167 per second", + "original_pricing_per_minute": 0.0145 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/enhanced-general": { + "input_cost_per_second": 0.00024167, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0145/60 seconds = $0.00024167 per second", + "original_pricing_per_minute": 0.0145 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/enhanced-meeting": { + "input_cost_per_second": 0.00024167, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0145/60 seconds = $0.00024167 per second", + "original_pricing_per_minute": 0.0145 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/enhanced-phonecall": { + "input_cost_per_second": 0.00024167, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0145/60 seconds = $0.00024167 per second", + "original_pricing_per_minute": 0.0145 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-2": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-2-atc": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-2-automotive": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-2-conversationalai": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-2-drivethru": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-2-finance": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-2-general": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-2-meeting": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-2-phonecall": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-2-video": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-2-voicemail": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + "original_pricing_per_minute": 0.0043 + }, + "mode": "audio_transcription", + "output_cost_per_second": 0.0, + "source": "https://deepgram.com/pricing", + "supported_endpoints": [ + "/v1/audio/transcriptions" + ] + }, + "deepgram/nova-3": { + "input_cost_per_second": 7.167e-05, + "litellm_provider": "deepgram", + "metadata": { + "calculation": "$0.0043/60 seconds = $0.00007167 per second", + 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true + } +} From 25e1ff585afe19566605517ce75bbba359d004ef Mon Sep 17 00:00:00 2001 From: youngsu5582 <98307410+youngsu5582@users.noreply.github.com> Date: Thu, 21 May 2026 23:09:11 +0900 Subject: [PATCH 06/17] =?UTF-8?q?feat:=20ChatClient=20=EC=9D=98=20?= =?UTF-8?q?=EC=82=AC=EC=A0=84=20=ED=94=84=EB=A1=AC=ED=94=84=ED=8A=B8=20?= =?UTF-8?q?=EC=84=B8=ED=8C=85?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - 시스템 프롬프트 + layer1, layer2 데이터 주입 - 진행 결과 progress md 로 정리 --- .gitignore | 6 +- progress.md | 49 ++++++++++++ .../bootcamp/config/ChatClientConfig.java | 75 ++++++++++++++++++- src/main/resources/prompts/faq-system.st | 21 ++++++ 4 files changed, 148 insertions(+), 3 deletions(-) create mode 100644 progress.md create mode 100644 src/main/resources/prompts/faq-system.st diff --git a/.gitignore b/.gitignore index 1c4fe0c..aca6b8d 100644 --- a/.gitignore +++ b/.gitignore @@ -41,4 +41,8 @@ __pycache__/ # Matrix metadata **/.omc/ -src/main/resources/application-*.yml \ No newline at end of file +src/main/resources/application-*.yml + +src/main/resources/layer1_faq/ +src/main/resources/layer2_policies/ +src/main/resources/layer3_examples/ \ No newline at end of file diff --git a/progress.md b/progress.md new file mode 100644 index 0000000..a306817 --- /dev/null +++ b/progress.md @@ -0,0 +1,49 @@ +## 진행도 + +### 단순 사용자 프롬프트만 주입 + +``` +=== 평가 결과 === +전체: 14/150 (9.3%) + +난이도별: + easy : 2/30 (7%) + hard : 2/26 (8%) + medium : 10/94 (11%) +``` + +### 시스템 프롬프트 주입 + +``` +=== 평가 결과 === +전체: 6/150 (4.0%) + +난이도별: + easy : 0/30 (0%) + hard : 0/26 (0%) + medium : 6/94 (6%) +``` + +### 시스템 프롬프트 + 파라미터 주입 + +``` +=== 평가 결과 === +전체: 39/150 (26.0%) + +난이도별: + easy : 12/30 (40%) + hard : 2/26 (8%) + medium : 25/94 (27%) +``` + +``` +=== 평가 결과 === +전체: 40/150 (26.7%) + +난이도별: + easy : 11/30 (37%) + hard : 2/26 (8%) + medium : 27/94 (29%) +``` + +적당히 한계가 존재한다.. diff --git a/src/main/java/com/cholog/bootcamp/config/ChatClientConfig.java b/src/main/java/com/cholog/bootcamp/config/ChatClientConfig.java index b3a4ce2..9bbc540 100644 --- a/src/main/java/com/cholog/bootcamp/config/ChatClientConfig.java +++ b/src/main/java/com/cholog/bootcamp/config/ChatClientConfig.java @@ -1,16 +1,87 @@ package com.cholog.bootcamp.config; import lombok.NoArgsConstructor; +import lombok.extern.slf4j.Slf4j; import org.springframework.ai.chat.client.ChatClient; +import org.springframework.beans.factory.annotation.Value; import org.springframework.context.annotation.Bean; import org.springframework.context.annotation.Configuration; +import org.springframework.core.io.Resource; +import java.nio.charset.StandardCharsets; +import java.util.Map; + +/** + * FAQ 챗봇용 {@link ChatClient} 구성. + * + *

3계층 지식 베이스를 system prompt에 임베드한다: + *

+ * + *

의도적으로 제외된 자료: + *

+ */ +@Slf4j @Configuration @NoArgsConstructor public class ChatClientConfig { @Bean - public ChatClient chatClient(ChatClient.Builder builder) { - return builder.build(); + public ChatClient chatClient(ChatClient.Builder builder, + @Value("classpath:prompts/faq-system.st") Resource systemTemplateResource, + @Value("classpath:layer1_faq/*.md") Resource[] faqResources, + @Value("classpath:layer2_policies/current/*.md") Resource[] policyResources, + @Value("classpath:layer3_examples/*.md") Resource[] exampleResources) { + + var systemTemplate = loadSystemTemplate(systemTemplateResource); + var faq = concatResources(faqResources, "faq"); + var policies = concatResources(policyResources, "policies"); + var examples = concatResources(exampleResources, "examples"); + + return builder + .defaultSystem(spec -> spec + .text(systemTemplate) + .params(Map.of( + "faq", faq, + "policies", policies, + "examples", examples))) + .build(); + } + + private String loadSystemTemplate(Resource systemTemplate) { + try { + String content = systemTemplate.getContentAsString(StandardCharsets.UTF_8); + log.info("faq 시스템 프롬프트를 로드합니다. 길이: {}", content.length()); + return content; + } catch (Exception e) { + log.warn("faq 시스템 프롬프트 로드중 문제가 발생했습니다.", e); + return ""; + } + } + + private String concatResources(Resource[] resources, String label) { + try { + log.info("{} layer 파일을 로드합니다. 개수: {}", label, resources.length); + + var sb = new StringBuilder(); + for (Resource r : resources) { + sb.append("## ").append(r.getFilename()).append(System.lineSeparator()); + sb.append(r.getContentAsString(StandardCharsets.UTF_8)); + sb.append(System.lineSeparator()).append(System.lineSeparator()); + sb.append("---"); + sb.append(System.lineSeparator()).append(System.lineSeparator()); + } + return sb.toString(); + } catch (Exception e) { + log.warn("{} layer 로드중 문제가 발생했습니다.", label, e); + return ""; + } } } diff --git a/src/main/resources/prompts/faq-system.st b/src/main/resources/prompts/faq-system.st new file mode 100644 index 0000000..4a6ebc7 --- /dev/null +++ b/src/main/resources/prompts/faq-system.st @@ -0,0 +1,21 @@ +You are a customer support assistant for an e-commerce platform. +You answer questions based on the knowledge base provided below. + +답변 규칙: +1. 사용자가 한국어로 질문하면 한국어로, 영어로 질문하면 영어로 답하세요. +2. 자료의 우선순위: **Policies > FAQ > Examples**. + - Policies(현행 정책)와 FAQ가 충돌하면 Policies를 따르세요. + - Examples(과거 상담 예시)는 답변 톤/구조 참고용일 뿐, 정책의 근거로 삼지 마세요. +3. 자료에 명시적으로 있는 정보만 사용하세요. 추측하거나 일반 상식으로 보충하지 마세요. +4. 자료에 없는 질문이면 "해당 정보는 자료에서 확인되지 않습니다. 고객지원으로 문의해주세요."라고만 답하세요. +5. 답변은 핵심만 2-4문장 이내로 간결하게. 불필요한 인사말, 사과, 부연 설명 금지. +6. 정책/숫자/기간 같은 구체값은 원문 그대로 인용하세요. 임의로 바꾸지 마세요. + +# 1. Public FAQ +{faq} + +# 2. Current Policies (authoritative) +{policies} + +# 3. Past Conversation Examples (style reference only) +{examples} From 9b236914cea105b297feab3d503b84dceed78599 Mon Sep 17 00:00:00 2001 From: youngsu5582 <98307410+youngsu5582@users.noreply.github.com> Date: Wed, 13 May 2026 21:05:33 +0900 Subject: [PATCH 07/17] =?UTF-8?q?feat:=20=ED=94=84=EB=A1=AC=ED=94=84?= =?UTF-8?q?=ED=8A=B8=EB=82=B4=20=ED=9A=8C=ED=94=BC=20=EA=B7=9C=EC=B9=99=20?= =?UTF-8?q?=EC=A0=9C=EA=B1=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - 정확도 다소 상승(Easy 는 일정치 도달) --- progress.md | 32 ++++++++++++++++++++++++ src/main/resources/prompts/faq-system.st | 9 +++++-- 2 files changed, 39 insertions(+), 2 deletions(-) diff --git a/progress.md b/progress.md index a306817..145e581 100644 --- a/progress.md +++ b/progress.md @@ -47,3 +47,35 @@ ``` 적당히 한계가 존재한다.. + +--- + +### 프롬프트 튜닝 + +``` +=== 평가 결과 === +전체: 69/150 (46.0%) + +난이도별: + easy : 13/30 (43%) + hard : 10/26 (38%) + medium : 46/94 (49%) +``` + +``` +=== 평가 결과 === +전체: 69/150 (46.0%) + +난이도별: + easy : 16/30 (53%) + hard : 11/26 (42%) + medium : 42/94 (45%) +``` + +회피 규칙 제거 +(정보가 있으면, 단순 라우팅이 아니라 정보 응답하게 명시) + +Easy 는 한계 도달 + +--- + diff --git a/src/main/resources/prompts/faq-system.st b/src/main/resources/prompts/faq-system.st index 4a6ebc7..9936619 100644 --- a/src/main/resources/prompts/faq-system.st +++ b/src/main/resources/prompts/faq-system.st @@ -7,8 +7,13 @@ You answer questions based on the knowledge base provided below. - Policies(현행 정책)와 FAQ가 충돌하면 Policies를 따르세요. - Examples(과거 상담 예시)는 답변 톤/구조 참고용일 뿐, 정책의 근거로 삼지 마세요. 3. 자료에 명시적으로 있는 정보만 사용하세요. 추측하거나 일반 상식으로 보충하지 마세요. -4. 자료에 없는 질문이면 "해당 정보는 자료에서 확인되지 않습니다. 고객지원으로 문의해주세요."라고만 답하세요. -5. 답변은 핵심만 2-4문장 이내로 간결하게. 불필요한 인사말, 사과, 부연 설명 금지. +4. 자료에 명확한 답이 있으면 **자신감 있게 답하세요**. + 회피 응답("고객지원으로 문의해주세요")은 자료의 FAQ·Policies 모든 섹션을 검토한 뒤에도 관련 정보를 전혀 찾을 수 없을 때만 사용하세요. + 비슷하거나 부분적인 정보가 있으면 "정확한 X는 자료에 없지만, 관련하여 Y는 다음과 같습니다"처럼 부분 정보라도 제공하세요. +5. 답변은 자료에 있는 **모든 관련 조건과 예외를 포함**하여 작성하세요. + - 등급별(standard/Plus/VIP)/케이스별/예외 조건이 있으면 빠짐없이 명시 + - 정책에 명시된 추가 조건(예: 미개봉, 시한, 제외 지역, 영업일 기준)을 함께 답변 + - 길이는 정확성을 위해 필요한 만큼. 불필요한 인사말/사과만 금지. 6. 정책/숫자/기간 같은 구체값은 원문 그대로 인용하세요. 임의로 바꾸지 마세요. # 1. Public FAQ From 9573335fbabcc624144fa220f06384ca04990969 Mon Sep 17 00:00:00 2001 From: youngsu5582 <98307410+youngsu5582@users.noreply.github.com> Date: Sun, 17 May 2026 15:39:36 +0900 Subject: [PATCH 08/17] =?UTF-8?q?feat:=20RAG=20=EC=9C=84=ED=95=9C=20Vector?= =?UTF-8?q?=20Store=20=EC=B6=94=EA=B0=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - SimpleVectorStore 통한 로컬 세팅 --- build.gradle | 1 + docs/RAG.MD | 25 ++++++++ .../bootcamp/config/VectorStoreConfig.java | 59 +++++++++++++++++++ .../bootcamp/controller/VectorController.java | 28 +++++++++ .../bootcamp/dto/VectorResponseDto.java | 20 +++++++ .../bootcamp/service/VectorService.java | 23 ++++++++ 6 files changed, 156 insertions(+) create mode 100644 docs/RAG.MD create mode 100644 src/main/java/com/cholog/bootcamp/config/VectorStoreConfig.java create mode 100644 src/main/java/com/cholog/bootcamp/controller/VectorController.java create mode 100644 src/main/java/com/cholog/bootcamp/dto/VectorResponseDto.java create mode 100644 src/main/java/com/cholog/bootcamp/service/VectorService.java diff --git a/build.gradle b/build.gradle index 71883d3..8a405d3 100644 --- a/build.gradle +++ b/build.gradle @@ -26,6 +26,7 @@ dependencyManagement { dependencies { implementation 'org.springframework.boot:spring-boot-starter-web' implementation 'org.springframework.ai:spring-ai-starter-model-openai' + implementation 'org.springframework.ai:spring-ai-rag' compileOnly 'org.projectlombok:lombok' testImplementation 'org.springframework.boot:spring-boot-starter-test' diff --git a/docs/RAG.MD b/docs/RAG.MD new file mode 100644 index 0000000..6bdff52 --- /dev/null +++ b/docs/RAG.MD @@ -0,0 +1,25 @@ +## RAG + +Retrieval-Augmented Generation + +질문과 관련된 문서 조각만 검색을 해, LLM 컨텍스트에 주입하는 패턴 + +### 전체 문서 전달 + +문서를 전체 LIST 로 전달하면, 컨텍스트가 폭발하게 된다. +노이즈가 많기 때문에 방향을 잃게 된다. - lost in the middle + +> lost in the middle +> 컨텍스트는 앞/뒤는 잘 기억하지만, 중간은 흐릿하게 기억한다. + +일정 수치를 넘어가면, 한계에 도달한다.(토큰 비용 역시 증가) + +이러한 문제를 해결하기 위해 RAG 를 사용한다. + +- 검색 단계에서 무관한 문서 제거, 컨텍스트에 관련성 높은 요소들만 전달 + (LLM 도 관련 요소만 받기에, 정확도 상승) + +- 입력 토큰이 일정하다 + +질문당, top-n 개의 요소만 선택해서 주입한다. +문서가 늘어나더라도 검색 후에는 동일하다. \ No newline at end of file diff --git a/src/main/java/com/cholog/bootcamp/config/VectorStoreConfig.java b/src/main/java/com/cholog/bootcamp/config/VectorStoreConfig.java new file mode 100644 index 0000000..8c397af --- /dev/null +++ b/src/main/java/com/cholog/bootcamp/config/VectorStoreConfig.java @@ -0,0 +1,59 @@ +package com.cholog.bootcamp.config; + +import jakarta.annotation.PostConstruct; +import lombok.extern.slf4j.Slf4j; +import org.springframework.ai.document.Document; +import org.springframework.ai.embedding.EmbeddingModel; +import org.springframework.ai.transformer.splitter.TokenTextSplitter; +import org.springframework.ai.vectorstore.SimpleVectorStore; +import org.springframework.ai.vectorstore.VectorStore; +import org.springframework.beans.factory.annotation.Value; +import org.springframework.context.annotation.Bean; +import org.springframework.context.annotation.Configuration; +import org.springframework.core.io.Resource; + +import java.io.IOException; +import java.nio.charset.StandardCharsets; +import java.util.ArrayList; +import java.util.Arrays; +import java.util.List; +import java.util.Map; + +@Slf4j +@Configuration +public class VectorStoreConfig { + + @Bean + public VectorStore vectorStore(EmbeddingModel model, + @Value("classpath:prompts/faq-system.st") Resource systemTemplateResource, + @Value("classpath:layer1_faq/*.md") Resource[] faqResources, + @Value("classpath:layer2_policies/current/*.md") Resource[] policyResources, + @Value("classpath:layer3_examples/*.md") Resource[] exampleResources) { + var store = SimpleVectorStore.builder(model).build(); + + var documents = new ArrayList(); + documents.addAll(toDocuments(faqResources, "faq")); + documents.addAll(toDocuments(policyResources, "policy")); + documents.addAll(toDocuments(exampleResources, "example")); + + var chunks = new TokenTextSplitter().apply(documents); + + store.add(chunks); + log.info("vector store 적재 완료. 원본 {} -> 청크 {}", documents.size(), chunks.size()); + return store; + } + + private List toDocuments(Resource[] resources, String layer) { + return Arrays.stream(resources) + .map(r -> { + try { + return new Document( + r.getContentAsString(StandardCharsets.UTF_8), + Map.of("source", r.getFilename(), "layer", layer)); + } catch (IOException e) { + throw new IllegalStateException("리소스 로드 실패: " + r.getFilename(), e); + } + }) + .toList(); + } +} diff --git a/src/main/java/com/cholog/bootcamp/controller/VectorController.java b/src/main/java/com/cholog/bootcamp/controller/VectorController.java new file mode 100644 index 0000000..a356785 --- /dev/null +++ b/src/main/java/com/cholog/bootcamp/controller/VectorController.java @@ -0,0 +1,28 @@ +package com.cholog.bootcamp.controller; + +import com.cholog.bootcamp.dto.VectorResponseDto; +import com.cholog.bootcamp.service.VectorService; +import lombok.RequiredArgsConstructor; +import lombok.extern.slf4j.Slf4j; +import org.springframework.http.ResponseEntity; +import org.springframework.web.bind.annotation.*; + +import java.util.List; + +@RestController +@RequiredArgsConstructor +@RequestMapping("/api/vector") +@Slf4j +public class VectorController { + + private final VectorService vectorService; + + @GetMapping + public ResponseEntity> question( + @RequestParam String keyword + ) { + log.info("Vector 요청이 들어왔습니다. {}", keyword); + var response = vectorService.request(keyword); + return ResponseEntity.ok(response); + } +} diff --git a/src/main/java/com/cholog/bootcamp/dto/VectorResponseDto.java b/src/main/java/com/cholog/bootcamp/dto/VectorResponseDto.java new file mode 100644 index 0000000..58f0944 --- /dev/null +++ b/src/main/java/com/cholog/bootcamp/dto/VectorResponseDto.java @@ -0,0 +1,20 @@ +package com.cholog.bootcamp.dto; + +import org.springframework.ai.document.Document; + +import java.util.Map; + +public record VectorResponseDto( + Double score, + String text, + Map metadata +) { + + public static VectorResponseDto from(Document document) { + return new VectorResponseDto( + document.getScore(), + document.getText(), + document.getMetadata() + ); + } +} diff --git a/src/main/java/com/cholog/bootcamp/service/VectorService.java b/src/main/java/com/cholog/bootcamp/service/VectorService.java new file mode 100644 index 0000000..718f226 --- /dev/null +++ b/src/main/java/com/cholog/bootcamp/service/VectorService.java @@ -0,0 +1,23 @@ +package com.cholog.bootcamp.service; + +import com.cholog.bootcamp.dto.VectorResponseDto; +import lombok.RequiredArgsConstructor; +import lombok.extern.slf4j.Slf4j; +import org.springframework.ai.vectorstore.VectorStore; +import org.springframework.stereotype.Service; + +import java.util.List; + +@Service +@RequiredArgsConstructor +@Slf4j +public class VectorService { + + private final VectorStore vectorStore; + + public List request(String keyword) { + var hits = vectorStore.similaritySearch(keyword); + log.info("{} 에 대한 결과: {}개", keyword, hits.size()); + return hits.stream().map(VectorResponseDto::from).toList(); + } +} From eddc0dc230acb58589bd6a337b02b14aae4269a9 Mon Sep 17 00:00:00 2001 From: youngsu5582 <98307410+youngsu5582@users.noreply.github.com> Date: Tue, 19 May 2026 22:16:37 +0900 Subject: [PATCH 09/17] =?UTF-8?q?feat:=20=EB=AC=B8=EC=84=9C=20RAG=20?= =?UTF-8?q?=EB=A1=9C=20=EB=B6=84=EC=84=9D=20=EB=B0=8F=20RAG=20=EB=A5=BC=20?= =?UTF-8?q?=ED=86=B5=ED=95=9C=20=EA=B2=80=EC=83=89?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - chunk 400 + temperature 0.2 로 설정 --- progress.md | 24 ++++++++++ .../bootcamp/config/ChatClientConfig.java | 6 +-- .../bootcamp/config/VectorStoreConfig.java | 17 +++++-- .../FrequentlyQuestionChatApiController.java | 8 ++-- .../FrequentlyQuestionChatApiService.java | 47 +++++++++++++++++++ src/main/resources/application.yml | 2 +- src/main/resources/prompts/faq-system.st | 42 ++++++++--------- 7 files changed, 110 insertions(+), 36 deletions(-) diff --git a/progress.md b/progress.md index 145e581..178f426 100644 --- a/progress.md +++ b/progress.md @@ -79,3 +79,27 @@ Easy 는 한계 도달 --- +### RAG 도입 + +``` +=== 평가 결과 === +전체: 82/150 (54.7%) + +난이도별: + easy : 16/30 (53%) + hard : 15/26 (58%) + medium : 51/94 (54%) +``` + +``` +=== 평가 결과 === +전체: 78/150 (52.0%) + +난이도별: + easy : 17/30 (57%) + hard : 10/26 (38%) + medium : 51/94 (54%) +``` + +chunk size 400 + temperature 0.2 로 설정 +아직 RAG 의 도입의 효과를 잘못 느끼겠다.. diff --git a/src/main/java/com/cholog/bootcamp/config/ChatClientConfig.java b/src/main/java/com/cholog/bootcamp/config/ChatClientConfig.java index 9bbc540..3a65af5 100644 --- a/src/main/java/com/cholog/bootcamp/config/ChatClientConfig.java +++ b/src/main/java/com/cholog/bootcamp/config/ChatClientConfig.java @@ -47,11 +47,7 @@ public ChatClient chatClient(ChatClient.Builder builder, return builder .defaultSystem(spec -> spec - .text(systemTemplate) - .params(Map.of( - "faq", faq, - "policies", policies, - "examples", examples))) + .text(systemTemplate)) .build(); } diff --git a/src/main/java/com/cholog/bootcamp/config/VectorStoreConfig.java b/src/main/java/com/cholog/bootcamp/config/VectorStoreConfig.java index 8c397af..bc74a99 100644 --- a/src/main/java/com/cholog/bootcamp/config/VectorStoreConfig.java +++ b/src/main/java/com/cholog/bootcamp/config/VectorStoreConfig.java @@ -1,6 +1,5 @@ package com.cholog.bootcamp.config; -import jakarta.annotation.PostConstruct; import lombok.extern.slf4j.Slf4j; import org.springframework.ai.document.Document; import org.springframework.ai.embedding.EmbeddingModel; @@ -25,7 +24,6 @@ public class VectorStoreConfig { @Bean public VectorStore vectorStore(EmbeddingModel model, - @Value("classpath:prompts/faq-system.st") Resource systemTemplateResource, @Value("classpath:layer1_faq/*.md") Resource[] faqResources, @Value("classpath:layer2_policies/current/*.md") Resource[] policyResources, @Value("classpath:layer3_examples/*.md") Resource[] exampleResources) { @@ -36,7 +34,20 @@ public VectorStore vectorStore(EmbeddingModel model, documents.addAll(toDocuments(policyResources, "policy")); documents.addAll(toDocuments(exampleResources, "example")); - var chunks = new TokenTextSplitter().apply(documents); + + TokenTextSplitter splitter = TokenTextSplitter.builder() + .withChunkSize(400) + .withMinChunkSizeChars(200) + .withMaxNumChunks(10000) + .withKeepSeparator(true) + .build(); + var chunks = splitter.apply(documents); + chunks.forEach(chunk -> { + if (chunk.getText() != null) { + log.info("chunk ID: {}, TEXT: {}", chunk.getId(), + chunk.getText().substring(0, Math.min(80, chunk.getText().length())).replace("\n", " ")); + } + }); store.add(chunks); log.info("vector store 적재 완료. 원본 {} -> 청크 {}", documents.size(), chunks.size()); diff --git a/src/main/java/com/cholog/bootcamp/controller/FrequentlyQuestionChatApiController.java b/src/main/java/com/cholog/bootcamp/controller/FrequentlyQuestionChatApiController.java index a3ab541..fd73932 100644 --- a/src/main/java/com/cholog/bootcamp/controller/FrequentlyQuestionChatApiController.java +++ b/src/main/java/com/cholog/bootcamp/controller/FrequentlyQuestionChatApiController.java @@ -4,8 +4,7 @@ import com.cholog.bootcamp.dto.FrequentlyQuestionChatResponseDto; import com.cholog.bootcamp.service.FrequentlyQuestionChatApiService; import lombok.RequiredArgsConstructor; -import org.slf4j.Logger; -import org.slf4j.LoggerFactory; +import lombok.extern.slf4j.Slf4j; import org.springframework.http.ResponseEntity; import org.springframework.web.bind.annotation.PostMapping; import org.springframework.web.bind.annotation.RequestBody; @@ -15,10 +14,9 @@ @RestController @RequestMapping("/api/chat") @RequiredArgsConstructor +@Slf4j public class FrequentlyQuestionChatApiController { - private static final Logger log = LoggerFactory.getLogger(FrequentlyQuestionChatApiController.class); - private final FrequentlyQuestionChatApiService frequentlyQuestionChatApiService; @PostMapping @@ -27,7 +25,7 @@ public ResponseEntity question( ) { log.info("FAQ 요청이 들어왔습니다. {}", dto.question()); // TODO 서비스 레이어 및 응답 구현 - var response = frequentlyQuestionChatApiService.chat(dto); + var response = frequentlyQuestionChatApiService.chatWithRag(dto); return ResponseEntity.ok(response); } } diff --git a/src/main/java/com/cholog/bootcamp/service/FrequentlyQuestionChatApiService.java b/src/main/java/com/cholog/bootcamp/service/FrequentlyQuestionChatApiService.java index 85acae4..ce26206 100644 --- a/src/main/java/com/cholog/bootcamp/service/FrequentlyQuestionChatApiService.java +++ b/src/main/java/com/cholog/bootcamp/service/FrequentlyQuestionChatApiService.java @@ -7,9 +7,13 @@ import lombok.extern.slf4j.Slf4j; import org.springframework.ai.chat.client.ChatClient; import org.springframework.ai.chat.prompt.Prompt; +import org.springframework.ai.document.Document; +import org.springframework.ai.vectorstore.SearchRequest; +import org.springframework.ai.vectorstore.VectorStore; import org.springframework.stereotype.Service; import java.math.BigDecimal; +import java.util.stream.Collectors; @Slf4j @Service @@ -18,6 +22,7 @@ public class FrequentlyQuestionChatApiService { private final ChatClient chatClient; private final PricingCalculator pricingCalculator; + private final VectorStore vectorStore; public FrequentlyQuestionChatResponseDto chat(FrequentlyQuestionChatRequestDto requestDto) { var prompt = Prompt.builder() @@ -44,6 +49,48 @@ public FrequentlyQuestionChatResponseDto chat(FrequentlyQuestionChatRequestDto r return new FrequentlyQuestionChatResponseDto(generation.getText(), usage); } + public FrequentlyQuestionChatResponseDto chatWithRag(FrequentlyQuestionChatRequestDto requestDto) { + + String question = requestDto.question(); + + var hits = vectorStore.similaritySearch(SearchRequest.builder() + .query(question) + .topK(8) + .build()); + log.info("hits 결과: {}", hits.stream().map(Document::getId).toList()); + + var context = hits.stream() + .map(d -> "## " + d.getMetadata().get("source") + "\n" + d.getText()) + .collect(Collectors.joining("\n\n---\n\n")); + + var response = chatClient.prompt(context) + .user(u -> u.text(""" + 참고 문서: + {context} + + 질문: {question} + """) + .param("context", context) + .param("question", question)) + .call() + .chatResponse(); + + if (response == null) { + return new FrequentlyQuestionChatResponseDto( + "응답이 없습니다.", TokenUsage.EMPTY + ); + } + + var generation = response.getResult().getOutput(); + var metadata = response.getMetadata(); + + var usage = TokenUsage.from(metadata.getUsage()); + var price = calculateModelPrice(metadata.getModel(), usage); + + log.info("[{}] 토큰 사용량: {}, 토큰 비용: {}$\n결과: {}", metadata.getModel(), usage, price, generation.getText()); + return new FrequentlyQuestionChatResponseDto(generation.getText(), usage); + } + private BigDecimal calculateModelPrice(String model, TokenUsage usage) { try { return pricingCalculator.calculatePrice(model, usage); diff --git a/src/main/resources/application.yml b/src/main/resources/application.yml index 6233b35..dcec8fe 100644 --- a/src/main/resources/application.yml +++ b/src/main/resources/application.yml @@ -7,7 +7,7 @@ spring: chat: options: model: gpt-4.1-nano - temperature: 0.1 + temperature: 0.2 embedding: options: model: text-embedding-3-small diff --git a/src/main/resources/prompts/faq-system.st b/src/main/resources/prompts/faq-system.st index 9936619..1dfd417 100644 --- a/src/main/resources/prompts/faq-system.st +++ b/src/main/resources/prompts/faq-system.st @@ -1,26 +1,24 @@ -You are a customer support assistant for an e-commerce platform. -You answer questions based on the knowledge base provided below. +당신은 Cholog Corporation의 FAQ 챗봇입니다. -답변 규칙: -1. 사용자가 한국어로 질문하면 한국어로, 영어로 질문하면 영어로 답하세요. -2. 자료의 우선순위: **Policies > FAQ > Examples**. - - Policies(현행 정책)와 FAQ가 충돌하면 Policies를 따르세요. - - Examples(과거 상담 예시)는 답변 톤/구조 참고용일 뿐, 정책의 근거로 삼지 마세요. -3. 자료에 명시적으로 있는 정보만 사용하세요. 추측하거나 일반 상식으로 보충하지 마세요. -4. 자료에 명확한 답이 있으면 **자신감 있게 답하세요**. - 회피 응답("고객지원으로 문의해주세요")은 자료의 FAQ·Policies 모든 섹션을 검토한 뒤에도 관련 정보를 전혀 찾을 수 없을 때만 사용하세요. - 비슷하거나 부분적인 정보가 있으면 "정확한 X는 자료에 없지만, 관련하여 Y는 다음과 같습니다"처럼 부분 정보라도 제공하세요. -5. 답변은 자료에 있는 **모든 관련 조건과 예외를 포함**하여 작성하세요. - - 등급별(standard/Plus/VIP)/케이스별/예외 조건이 있으면 빠짐없이 명시 - - 정책에 명시된 추가 조건(예: 미개봉, 시한, 제외 지역, 영업일 기준)을 함께 답변 - - 길이는 정확성을 위해 필요한 만큼. 불필요한 인사말/사과만 금지. -6. 정책/숫자/기간 같은 구체값은 원문 그대로 인용하세요. 임의로 바꾸지 마세요. +[답변 원칙] +1. 참고 문서에 있는 내용만 근거로 답하세요. 추측 금지. +2. 관련 정보는 빠짐없이 포함하세요: + - 회원 등급별 차이 (Standard / Plus / VIP / 구독자) — 질문이 특정 등급만 다뤄도 다른 등급과의 차이가 있으면 명시 + - 상품 카테고리별 차이 (일반/냉장/마켓플레이스) + - 수치 정보 (금액, 기간, 한도, 영업일, %) + - 조건/예외/제약 +3. 사용자가 잘못된 전제로 질문하면("X 맞나요?", "Y라고 들었는데") 먼저 정정한 뒤 정확한 정책을 안내하세요. +4. 마켓플레이스 관련은 판매자 정책이 우선이며, 그 사실을 항상 명시하세요. -# 1. Public FAQ -{faq} +[혼동 주의 — 자주 틀리는 부분] +- 냉장 배송료는 회원 등급/무료배송과 무관한 고정 요금입니다. VIP·구독자도 냉장 배송료는 부담합니다. +- 등급 기준·환불 기간 같은 수치는 문서 값을 정확히 인용하세요. 임의 추정 금지. -# 2. Current Policies (authoritative) -{policies} +[정보 부족 시] +- 개별 주문 상태/이력 질문(예: "내 배송 언제 와요", "저번에 물어본 거")은 주문번호를 먼저 요청하세요. +- "다른 고객 사례", "보상받은 사람 있나요" 같은 일화성 질문은 사례 공유 불가를 알리고, 문서에 있는 공식 정책/절차로 안내하세요. -# 3. Past Conversation Examples (style reference only) -{examples} +[형식] +- 한국어로 답변 +- 핵심 수치는 굵게 또는 명확히 분리 +- 조건이 여럿이면 항목별로 나열 \ No newline at end of file From 405d115d4963fa80a0eb914955620d4ad507ba47 Mon Sep 17 00:00:00 2001 From: youngsu5582 <98307410+youngsu5582@users.noreply.github.com> Date: Thu, 21 May 2026 22:47:25 +0900 Subject: [PATCH 10/17] =?UTF-8?q?feat:=20=ED=8F=89=EA=B0=80=EB=B0=A9?= =?UTF-8?q?=EC=8B=9D=20=EB=B3=80=EA=B2=BD?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - 도메인 숫자 및 정책에 대해 검증하는 스크립트 추가 - 이를 통한 관련 내용 progress.md 에 작성 --- data/strict_evaluate.py | 243 ++++++++++++++++ data/strict_questions.json | 561 +++++++++++++++++++++++++++++++++++++ progress.md | 70 +++++ 3 files changed, 874 insertions(+) create mode 100644 data/strict_evaluate.py create mode 100644 data/strict_questions.json diff --git a/data/strict_evaluate.py b/data/strict_evaluate.py new file mode 100644 index 0000000..daa8600 --- /dev/null +++ b/data/strict_evaluate.py @@ -0,0 +1,243 @@ +""" +도메인 정책 엄격 평가 스크립트 + +evaluate.py 와 달리 LLM judge 를 쓰지 않습니다. +정답에 반드시 포함되어야 하는 정규식 패턴(must_contain) 과 +절대 등장해서는 안 되는 패턴(must_not_contain) 으로 결정적으로 판정합니다. + +같은 답변에 대해 항상 같은 결과를 내므로 프롬프트/RAG 변경의 효과를 +노이즈 없이 측정할 수 있습니다. + +사전 준비: + python -m venv .venv + .venv/bin/pip install requests + +실행: + .venv/bin/python strict_evaluate.py + .venv/bin/python strict_evaluate.py --verbose + .venv/bin/python strict_evaluate.py --parallel 10 + .venv/bin/python strict_evaluate.py --category cold_chain + .venv/bin/python strict_evaluate.py --category adversarial --verbose +""" + +import json +import re +import argparse +import time +from concurrent.futures import ThreadPoolExecutor, as_completed +from pathlib import Path + +import requests + +DATA_DIR = Path(__file__).parent +SERVER_URL = "http://localhost:11240/api/chat" + + +def ask_server(question: str) -> dict | None: + try: + resp = requests.post(SERVER_URL, json={"question": question}, timeout=60) + if resp.status_code == 200: + return resp.json() + print(f" [ERROR] HTTP {resp.status_code}: {resp.text[:100]}") + return None + except requests.exceptions.ConnectionError: + print(f" [ERROR] 서버에 연결할 수 없습니다: {SERVER_URL}") + return None + except requests.exceptions.Timeout: + print(f" [ERROR] 타임아웃 (60초)") + return None + + +def check_answer(answer: str, q: dict) -> dict: + """정규식 패턴으로 결정적으로 검증합니다. + + must_contain: 모든 패턴이 매칭되어야 정답 + must_not_contain: 하나라도 매칭되면 오답 + """ + failures = [] + + for pattern in q.get("must_contain", []): + if not re.search(pattern, answer, re.IGNORECASE | re.DOTALL): + failures.append(f"누락 must_contain: /{pattern}/") + + for pattern in q.get("must_not_contain", []): + if re.search(pattern, answer, re.IGNORECASE | re.DOTALL): + failures.append(f"금지 패턴 등장 must_not_contain: /{pattern}/") + + return { + "score": 1 if not failures else 0, + "failures": failures, + } + + +def process_question(q: dict) -> dict: + start = time.time() + response = ask_server(q["question"]) + + if response is None: + return { + "qid": q["id"], + "category": q["category"], + "status": "error", + "question": q["question"], + "duration": time.time() - start, + } + + actual = response.get("answer", "") + result = check_answer(actual, q) + + return { + "qid": q["id"], + "category": q["category"], + "status": "ok", + "score": result["score"], + "failures": result["failures"], + "question": q["question"], + "expected": q.get("expected", ""), + "answer": actual, + "reference": q.get("reference", {}), + "duration": time.time() - start, + } + + +def main(): + parser = argparse.ArgumentParser(description="도메인 정책 엄격 평가") + parser.add_argument("--verbose", action="store_true", help="질문별 상세 출력") + parser.add_argument("--limit", type=int, default=0, help="평가할 질문 수 제한") + parser.add_argument("--parallel", type=int, default=1, help="병렬 워커 수") + parser.add_argument("--category", type=str, default="", help="특정 카테고리만 실행") + parser.add_argument("--show-answer", action="store_true", help="실패 시 실제 답변 출력") + args = parser.parse_args() + + questions_path = DATA_DIR / "strict_questions.json" + with open(questions_path) as f: + questions = json.load(f) + + if args.category: + questions = [q for q in questions if q["category"] == args.category] + + if args.limit > 0: + questions = questions[: args.limit] + + print("=== 도메인 정책 엄격 평가 ===") + print(f"서버: {SERVER_URL}") + print(f"질문 수: {len(questions)}") + if args.parallel > 1: + print(f"병렬 워커: {args.parallel}") + print() + + # 서버 연결 확인 + test_resp = ask_server("test") + if test_resp is None: + print("서버에 연결할 수 없습니다. 서버가 실행 중인지 확인하세요:") + print(f" ./gradlew bootRun") + return + + cat_results: dict = {} + total_correct = 0 + total_count = 0 + error_count = 0 + durations: list = [] + detail_results: list = [] + start_time = time.time() + + def collect(r: dict, completed_idx: int, total: int): + nonlocal total_correct, total_count, error_count + durations.append(r["duration"]) + cat = r["category"] + cat_results.setdefault(cat, {"correct": 0, "total": 0}) + + if r["status"] == "error": + error_count += 1 + if args.verbose: + print(f"[{r['qid']}] ERROR — 서버 응답 없음") + return + + cat_results[cat]["total"] += 1 + total_count += 1 + if r["score"] == 1: + cat_results[cat]["correct"] += 1 + total_correct += 1 + marker = "✓" + else: + marker = "✗" + + if args.verbose: + print(f"[{r['qid']}] {marker} ({cat:12s}) {r['question'][:50]}") + if r["score"] == 0: + for f in r["failures"]: + print(f" {f}") + ref = r["reference"] + if isinstance(ref, dict): + print(f" 근거: {ref.get('file', '')}:{ref.get('line', '')}") + if ref.get("quote"): + print(f" 원문: {ref['quote'][:100]}") + if args.show_answer: + print(f" 답변: {r['answer'][:200]}") + + detail_results.append(r) + + if not args.verbose and completed_idx % 10 == 0: + print(f" 진행: {completed_idx}/{total}") + + if args.parallel > 1: + with ThreadPoolExecutor(max_workers=args.parallel) as executor: + futures = [executor.submit(process_question, q) for q in questions] + completed = 0 + for fut in as_completed(futures): + r = fut.result() + completed += 1 + collect(r, completed, len(questions)) + else: + for i, q in enumerate(questions): + r = process_question(q) + collect(r, i + 1, len(questions)) + + elapsed = time.time() - start_time + + print() + print("=== 평가 결과 ===") + pct = total_correct / max(total_count, 1) * 100 + print(f"전체: {total_correct}/{total_count} ({pct:.1f}%)") + print() + print("카테고리별:") + for cat in sorted(cat_results.keys()): + c = cat_results[cat] + cp = c["correct"] / max(c["total"], 1) * 100 + print(f" {cat:14s}: {c['correct']:2d}/{c['total']:2d} ({cp:3.0f}%)") + + if error_count > 0: + print(f"\n 에러: {error_count}건") + + print(f"\n벽시계 시간: {elapsed:.1f}초") + if durations: + print(f"평균 응답: {sum(durations)/len(durations):.1f}초/질문") + + result_file = DATA_DIR / "strict_eval_result.json" + with open(result_file, "w") as f: + json.dump( + { + "total": total_count, + "correct": total_correct, + "error": error_count, + "accuracy": total_correct / max(total_count, 1), + "by_category": cat_results, + "elapsed_seconds": elapsed, + "details": detail_results, + }, + f, + indent=2, + ensure_ascii=False, + ) + print(f"\n결과 저장: {result_file}") + + # 실패 케이스 요약 + failures = [d for d in detail_results if d.get("score") == 0] + if failures and not args.verbose: + print(f"\n실패 케이스 {len(failures)}건 — --verbose 로 상세 확인") + for d in failures[:5]: + print(f" [{d['qid']}] ({d['category']}) {d['question'][:60]}") + + +if __name__ == "__main__": + main() diff --git a/data/strict_questions.json b/data/strict_questions.json new file mode 100644 index 0000000..1ad4c66 --- /dev/null +++ b/data/strict_questions.json @@ -0,0 +1,561 @@ +[ + { + "id": "S001", + "category": "account", + "question": "회원가입 시 받는 웰컴 쿠폰은 얼마인가요?", + "expected": "3,000원", + "must_contain": ["3,?000\\s*원"], + "must_not_contain": [], + "reference": { + "file": "layer1_faq/account.md", + "line": "24-26", + "quote": "All new accounts start at Standard tier and receive a one-time 3,000 won welcome coupon valid for 30 days." + } + }, + { + "id": "S002", + "category": "account", + "question": "웰컴 쿠폰은 며칠 동안 사용할 수 있나요?", + "expected": "30일", + "must_contain": ["30\\s*일"], + "must_not_contain": [], + "reference": { + "file": "layer1_faq/account.md", + "line": "26", + "quote": "3,000 won welcome coupon valid for 30 days." + } + }, + { + "id": "S003", + "category": "account", + "question": "플러스 등급이 되려면 연간 얼마를 써야 하나요?", + "expected": "200,000원 (20만원)", + "must_contain": ["(200,?000\\s*원|20\\s*만\\s*원)"], + "must_not_contain": ["150,?000", "15\\s*만\\s*원"], + "reference": { + "file": "layer2_policies/current/membership-tiers.md", + "line": "18", + "quote": "Plus | ≥ 200,000 won | January 1st each year" + } + }, + { + "id": "S004", + "category": "account", + "question": "VIP 등급이 되려면 연간 얼마를 써야 하나요?", + "expected": "800,000원 (80만원)", + "must_contain": ["(800,?000\\s*원|80\\s*만\\s*원)"], + "must_not_contain": ["600,?000", "60\\s*만\\s*원"], + "reference": { + "file": "layer2_policies/current/membership-tiers.md", + "line": "19", + "quote": "VIP | ≥ 800,000 won | January 1st each year" + } + }, + { + "id": "S005", + "category": "account", + "question": "VIP 전용 고객센터 전화번호가 뭔가요?", + "expected": "1588-0002", + "must_contain": ["1588[-\\s]?0002"], + "must_not_contain": [], + "reference": { + "file": "layer1_faq/support.md", + "line": "11", + "quote": "1588-0000 (general); 1588-0002 (VIP priority line)" + } + }, + { + "id": "S006", + "category": "account", + "question": "일반 고객센터 전화번호가 뭔가요?", + "expected": "1588-0000", + "must_contain": ["1588[-\\s]?0000"], + "must_not_contain": [], + "reference": { + "file": "layer1_faq/support.md", + "line": "11", + "quote": "1588-0000 (general); 1588-0002 (VIP priority line)" + } + }, + { + "id": "S007", + "category": "account", + "question": "회원 등급은 언제 평가되나요?", + "expected": "매년 1월 1일", + "must_contain": ["1\\s*월\\s*1\\s*일|매년\\s*1\\s*월"], + "must_not_contain": [], + "reference": { + "file": "layer2_policies/current/membership-tiers.md", + "line": "42-44", + "quote": "Tier status is reviewed annually on January 1st." + } + }, + { + "id": "S008", + "category": "points", + "question": "1포인트는 얼마의 가치인가요?", + "expected": "1원", + "must_contain": ["1\\s*(점|포인트|point).{0,30}1\\s*원|1\\s*원\\s*=\\s*1\\s*(점|포인트)|1\\s*point\\s*=\\s*1\\s*won"], + "must_not_contain": [], + "reference": { + "file": "layer2_policies/current/point-earning-rules.md", + "line": "37", + "quote": "1 point = 1 won" + } + }, + { + "id": "S009", + "category": "points", + "question": "포인트는 최소 몇 점부터 사용할 수 있나요?", + "expected": "1,000점", + "must_contain": ["1,?000\\s*(점|포인트|points?)"], + "must_not_contain": [], + "reference": { + "file": "layer2_policies/current/point-earning-rules.md", + "line": "36", + "quote": "Minimum redemption: 1,000 points per order" + } + }, + { + "id": "S010", + "category": "points", + "question": "포인트 유효기간은 얼마인가요?", + "expected": "12개월", + "must_contain": ["12\\s*개월|12\\s*months|1\\s*년"], + "must_not_contain": [], + "reference": { + "file": "layer2_policies/current/point-earning-rules.md", + "line": "43", + "quote": "All points expire 12 months from the date earned." + } + }, + { + "id": "S011", + "category": "points", + "question": "Standard 회원의 포인트 적립률은 몇 퍼센트인가요?", + "expected": "1%", + "must_contain": ["Standard.{0,30}1\\s*%|1\\s*%.{0,30}Standard|스탠다드.{0,30}1\\s*%"], + "must_not_contain": [], + "reference": { + "file": "layer2_policies/current/point-earning-rules.md", + "line": "17", + "quote": "Standard | 1% of order subtotal" + } + }, + { + "id": "S012", + "category": "points", + "question": "Plus 회원의 포인트 적립률은 몇 퍼센트인가요?", + "expected": "3%", + "must_contain": ["Plus.{0,30}3\\s*%|3\\s*%.{0,30}Plus|플러스.{0,30}3\\s*%"], + "must_not_contain": [], + "reference": { + "file": "layer2_policies/current/point-earning-rules.md", + "line": "18", + "quote": "Plus | 3% of order subtotal" + } + }, + { + "id": "S013", + "category": "points", + "question": "VIP 회원의 포인트 적립률은 몇 퍼센트인가요?", + "expected": "5%", + "must_contain": ["VIP.{0,30}5\\s*%|5\\s*%.{0,30}VIP"], + "must_not_contain": [], + "reference": { + "file": "layer2_policies/current/point-earning-rules.md", + "line": "19", + "quote": "VIP | 5% of order subtotal" + } + }, + { + "id": "S014", + "category": "points", + "question": "친환경 포장(Green Packaging)을 선택하면 포인트가 얼마나 적립되나요?", + "expected": "200 포인트", + "must_contain": ["200\\s*(점|포인트|points?)"], + "must_not_contain": [], + "reference": { + "file": "layer1_faq/eco_green.md", + "line": "15-17", + "quote": "Each order using Green Packaging earns a 200-point eco bonus credited 7 days after delivery confirmation." + } + }, + { + "id": "S015", + "category": "returns", + "question": "표준 반품 기간은 며칠인가요?", + "expected": "14일", + "must_contain": ["14\\s*(일|days|calendar\\s*days)"], + "must_not_contain": ["(^|[^0-9])7\\s*일\\s*(이내|안에|내에|입니다)", "(^|[^0-9])10\\s*일\\s*(이내|안에|내에|입니다)"], + "reference": { + "file": "layer2_policies/current/return-policy-v3.md", + "line": "16", + "quote": "may be returned within 14 calendar days of delivery" + } + }, + { + "id": "S016", + "category": "returns", + "question": "단순 변심으로 반품할 때 수수료가 얼마인가요?", + "expected": "3,000원", + "must_contain": ["3,?000\\s*원"], + "must_not_contain": ["5,?000\\s*원\\s*(수수료|handling)"], + "reference": { + "file": "layer2_policies/current/return-policy-v3.md", + "line": "36", + "quote": "a handling fee of 3,000 won is deducted from the refund" + } + }, + { + "id": "S017", + "category": "returns", + "question": "신용카드로 결제했을 때 환불은 며칠 걸리나요?", + "expected": "3~5 영업일", + "must_contain": ["3\\s*[~∼\\-–]\\s*5\\s*영업일|3\\s*[~∼\\-–]\\s*5\\s*business"], + "must_not_contain": [], + "reference": { + "file": "layer1_faq/returns.md", + "line": "26", + "quote": "Credit card: 3–5 business days" + } + }, + { + "id": "S018", + "category": "returns", + "question": "잘못된 상품이 배송됐을 때 사과 쿠폰은 얼마 받나요?", + "expected": "5,000원", + "must_contain": ["5,?000\\s*원"], + "must_not_contain": [], + "reference": { + "file": "layer1_faq/returns.md", + "line": "51-52", + "quote": "A 5,000 won apology coupon is also issued automatically." + } + }, + { + "id": "S019", + "category": "returns", + "question": "계좌이체로 결제했을 때 환불은 최대 며칠 걸리나요?", + "expected": "최대 7 영업일", + "must_contain": ["7\\s*영업일|7\\s*business"], + "must_not_contain": [], + "reference": { + "file": "layer1_faq/returns.md", + "line": "28", + "quote": "Bank transfer: up to 7 business days" + } + }, + { + "id": "S020", + "category": "shipping", + "question": "Economy 배송비는 얼마인가요?", + "expected": "2,500원", + "must_contain": ["2,?500\\s*원"], + "must_not_contain": [], + "reference": { + "file": "layer2_policies/current/shipping-standard.md", + "line": "17", + "quote": "Economy | 2,500 won | 2–4 business days" + } + }, + { + "id": "S021", + "category": "shipping", + "question": "Priority 배송비는 얼마인가요?", + "expected": "5,000원", + "must_contain": ["5,?000\\s*원"], + "must_not_contain": [], + "reference": { + "file": "layer2_policies/current/shipping-standard.md", + "line": "18", + "quote": "Priority | 5,000 won | Next business day" + } + }, + { + "id": "S022", + "category": "shipping", + "question": "Same-Day 당일 배송 요금은 얼마인가요?", + "expected": "8,000원", + "must_contain": ["8,?000\\s*원"], + "must_not_contain": [], + "reference": { + "file": "layer2_policies/current/shipping-standard.md", + "line": "19", + "quote": "Same-Day | 8,000 won | Same day" + } + }, + { + "id": "S023", + "category": "shipping", + "question": "Standard 회원이 무료배송을 받으려면 주문 금액이 얼마 이상이어야 하나요?", + "expected": "20,000원 이상", + "must_contain": ["(20,?000\\s*원|2\\s*만\\s*원)"], + "must_not_contain": ["30,?000\\s*원", "3\\s*만\\s*원"], + "reference": { + "file": "layer2_policies/current/shipping-standard.md", + "line": "23", + "quote": "Standard members: Economy shipping free on orders ≥ 20,000 won" + } + }, + { + "id": "S024", + "category": "shipping", + "question": "당일 발송 마감 시간은 언제인가요?", + "expected": "오후 1시 (13시) KST", + "must_contain": ["(오후\\s*1\\s*시|1\\s*PM|13\\s*시|13:00)"], + "must_not_contain": [], + "reference": { + "file": "layer2_policies/current/shipping-standard.md", + "line": "30", + "quote": "Orders confirmed before 1 PM KST on business days are dispatched the same afternoon." + } + }, + { + "id": "S025", + "category": "cold_chain", + "question": "냉장 상품 배송료는 얼마인가요?", + "expected": "4,000원 (등급 무관 고정)", + "must_contain": ["4,?000\\s*원"], + "must_not_contain": [], + "reference": { + "file": "layer2_policies/current/shipping-cold-chain.md", + "line": "35-39", + "quote": "Cold-Chain Fee | Any amount | Flat 4,000 won. Cold-chain fee applies regardless of membership tier or free-shipping eligibility." + } + }, + { + "id": "S026", + "category": "cold_chain", + "question": "냉장 배송은 무슨 요일에 가능한가요?", + "expected": "월요일~목요일", + "must_contain": ["(월요일|월\\s*[~∼\\-–]|월부터)", "(목요일|[~∼\\-–]\\s*목|목까지)"], + "must_not_contain": [], + "reference": { + "file": "layer2_policies/current/shipping-cold-chain.md", + "line": "25-31", + "quote": "Cold-chain delivery is available Monday through Thursday only." + } + }, + { + "id": "S027", + "category": "cold_chain", + "question": "냉장 상품은 어떤 택배사로 배송되나요?", + "expected": "CJ Logistics Fresh / CJ대한통운 Fresh", + "must_contain": ["CJ"], + "must_not_contain": [], + "reference": { + "file": "layer2_policies/current/shipping-cold-chain.md", + "line": "21", + "quote": "All cold-chain orders are shipped exclusively via CJ Logistics Fresh." + } + }, + { + "id": "S028", + "category": "subscription", + "question": "구독 회원의 무료배송 조건은 어떻게 되나요?", + "expected": "모든 주문 무료배송 (금액 무관)", + "must_contain": ["(모든\\s*주문|all\\s*orders|금액\\s*(무관|상관없|에?\\s*관계없)|regardless\\s*of\\s*(order\\s*)?amount)"], + "must_not_contain": ["(20,?000\\s*원|2\\s*만\\s*원).{0,15}(이상|넘|초과|부터)"], + "reference": { + "file": "layer2_policies/current/subscription-terms.md", + "line": "23-25", + "quote": "Free shipping on all orders (including non-subscription purchases), regardless of order amount." + } + }, + { + "id": "S029", + "category": "subscription", + "question": "12개월 연속 구독하면 어떤 혜택이 있나요?", + "expected": "30,000원 쿠폰", + "must_contain": ["(30,?000\\s*원|3\\s*만\\s*원)\\s*(쿠폰|coupon)?"], + "must_not_contain": [], + "reference": { + "file": "layer2_policies/current/subscription-terms.md", + "line": "47-48", + "quote": "12 months | 30,000 won coupon" + } + }, + { + "id": "S030", + "category": "subscription", + "question": "6개월 연속 구독하면 어떤 혜택이 있나요?", + "expected": "10,000 포인트", + "must_contain": ["10,?000\\s*(점|포인트|points?)"], + "must_not_contain": [], + "reference": { + "file": "layer2_policies/current/subscription-terms.md", + "line": "47", + "quote": "6 months | 10,000 loyalty points" + } + }, + { + "id": "S031", + "category": "subscription", + "question": "구독 일시정지는 최대 몇 개월까지 가능한가요?", + "expected": "3개월", + "must_contain": ["3\\s*개월|3\\s*months"], + "must_not_contain": [], + "reference": { + "file": "layer2_policies/current/subscription-terms.md", + "line": "39", + "quote": "Pause (up to 3 months): No charge during pause; resume anytime" + } + }, + { + "id": "S032", + "category": "marketplace", + "question": "마켓플레이스 구매자 보호는 얼마까지 보장되나요?", + "expected": "500,000원", + "must_contain": ["(500,?000\\s*원|50\\s*만\\s*원)"], + "must_not_contain": [], + "reference": { + "file": "layer1_faq/marketplace.md", + "line": "9-10", + "quote": "Cholog guarantees buyer protection on all Marketplace transactions up to 500,000 won." + } + }, + { + "id": "S033", + "category": "marketplace", + "question": "마켓플레이스 셀러의 반품 기간 범위는 얼마인가요?", + "expected": "3일 ~ 30일", + "must_contain": ["3\\s*(일|days?)", "30\\s*(일|days?)"], + "must_not_contain": [], + "reference": { + "file": "layer1_faq/marketplace.md", + "line": "15-16", + "quote": "Return windows for Marketplace sellers range from 3 to 30 days depending on the seller." + } + }, + { + "id": "S034", + "category": "marketplace", + "question": "셀러와 분쟁이 있을 때 며칠 안에 해결 안 되면 Cholog에 에스컬레이션 할 수 있나요?", + "expected": "3 영업일", + "must_contain": ["3\\s*영업일|3\\s*business\\s*days?"], + "must_not_contain": [], + "reference": { + "file": "layer1_faq/marketplace.md", + "line": "30-31", + "quote": "If unresolved within 3 business days, you can escalate to Cholog Buyer Protection" + } + }, + { + "id": "S035", + "category": "complaint", + "question": "배송이 3 영업일 넘게 지연됐을 때 보상은 얼마인가요?", + "expected": "2,000원 쿠폰", + "must_contain": ["2,?000\\s*원"], + "must_not_contain": [], + "reference": { + "file": "layer2_policies/current/complaint-handling.md", + "line": "37", + "quote": "Delivery delay > 3 business days (non-island) | 2,000 won coupon" + } + }, + { + "id": "S036", + "category": "complaint", + "question": "위조 상품으로 확인되면 보상이 어떻게 되나요?", + "expected": "10,000원 쿠폰 + 환불", + "must_contain": ["10,?000\\s*원"], + "must_not_contain": [], + "reference": { + "file": "layer2_policies/current/complaint-handling.md", + "line": "39", + "quote": "Confirmed counterfeit product | Full refund + 10,000 won coupon" + } + }, + { + "id": "S037", + "category": "payment", + "question": "무이자 할부는 최소 얼마부터 가능한가요?", + "expected": "30,000원 이상", + "must_contain": ["(30,?000\\s*원|3\\s*만\\s*원)"], + "must_not_contain": [], + "reference": { + "file": "layer1_faq/payment.md", + "line": "22", + "quote": "Orders totaling 30,000 won or more are eligible for 2- or 3-month interest-free installments" + } + }, + { + "id": "S038", + "category": "payment", + "question": "6개월 무이자 할부는 얼마 이상 주문해야 하나요?", + "expected": "100,000원 이상", + "must_contain": ["(100,?000\\s*원|10\\s*만\\s*원)"], + "must_not_contain": [], + "reference": { + "file": "layer1_faq/payment.md", + "line": "23", + "quote": "Orders over 100,000 won unlock a 6-month interest-free plan." + } + }, + { + "id": "S039", + "category": "payment", + "question": "Cholog Pay에 충전하면 보너스가 몇 퍼센트인가요?", + "expected": "2%", + "must_contain": ["2\\s*%"], + "must_not_contain": [], + "reference": { + "file": "layer1_faq/payment.md", + "line": "41-43", + "quote": "Load funds via bank transfer and earn a 2% top-up bonus" + } + }, + { + "id": "S040", + "category": "adversarial", + "question": "반품 기간이 7일이라고 들었는데 맞나요?", + "expected": "아니요, 14일이 현재 정책입니다", + "must_contain": ["14\\s*일", "(아니|틀린|변경|14일이|14일로|14일\\s*입니다|14일이\\s*맞)"], + "must_not_contain": [], + "reference": { + "file": "layer2_policies/current/return-policy-v3.md vs deprecated/return-policy-v1.md", + "line": "16", + "quote": "v1: 7 calendar days (deprecated 2023-03-01). Current v3: 14 calendar days." + } + }, + { + "id": "S041", + "category": "adversarial", + "question": "Plus 등급 기준이 연 15만원 맞나요?", + "expected": "아니요, 현재는 200,000원입니다", + "must_contain": ["(200,?000\\s*원|20\\s*만\\s*원)", "(아니|틀린|변경|현재|아닙니다|20만원이\\s*맞|200,000원이\\s*맞)"], + "must_not_contain": [], + "reference": { + "file": "layer2_policies/current/membership-tiers.md vs deprecated/membership-tiers-final-v2.md", + "line": "18", + "quote": "v2 (2023): Plus ≥ 150,000 won. v3 (2024 current): Plus ≥ 200,000 won." + } + }, + { + "id": "S042", + "category": "adversarial", + "question": "무료배송 기준이 3만원 맞나요?", + "expected": "아니요, 현재는 20,000원입니다 (Standard 기준)", + "must_contain": ["(20,?000\\s*원|2\\s*만\\s*원)", "(아니|틀린|변경|현재|아닙니다|20,000원이\\s*맞|2만원이\\s*맞)"], + "must_not_contain": [], + "reference": { + "file": "layer2_policies/current/shipping-standard.md vs deprecated/shipping-standard-2023.md", + "line": "17, 23", + "quote": "2023: ≥ 30,000 won. 2024 current: ≥ 20,000 won." + } + }, + { + "id": "S043", + "category": "adversarial", + "question": "VIP인데 냉장 상품 배송비도 무료인가요?", + "expected": "아니요, 냉장 배송료 4,000원은 등급 무관 고정입니다", + "must_contain": ["4,?000\\s*원"], + "must_not_contain": ["VIP.{0,30}냉장.{0,30}무료", "냉장.{0,20}무료.{0,20}배송"], + "reference": { + "file": "layer2_policies/current/shipping-cold-chain.md", + "line": "38-39", + "quote": "Cold-chain fee applies regardless of membership tier or free-shipping eligibility." + } + } +] diff --git a/progress.md b/progress.md index 178f426..3981f61 100644 --- a/progress.md +++ b/progress.md @@ -103,3 +103,73 @@ Easy 는 한계 도달 chunk size 400 + temperature 0.2 로 설정 아직 RAG 의 도입의 효과를 잘못 느끼겠다.. + +### 평가방식 변경 + +기존 평가방식은, LLM 에게 돌린 결과를 LLM 에게 다시 평가받는 방식이였다. +이 과정에서, 평가의 의미가 희석된다고 생각했다. + +- `1,000 포인트에 대한 정보를 포함하지 않아 핵심 사실` 와 같이 핵심 사실이라는 모호한 평가 +- `실제 답변은 탈퇴 방법에 대한 구체적인 지침` 와 같이 완벽한 튜닝이 아니면 어려운 응답과 평가 + +=> 이런 점들을 기반으로, 정책적인 요소는 틀리지 않는 챗봇을 만들기로 결정했다. + +내가 생각하는 고객지원 챗봇은, 상담원이 받을 때 곤란하지 않은 응답을 제공해야 한다고 생각했다. +그러기 위해선 + +- 모호하거나, 모르면 차라리 모른다고 대답한다. +- 절대, 거짓말을 하지 않는다. (필요한 포인트가 5,000 인데 3,000 이라고 응답) + +을 목적으로 했다. +그래서, 새로운 평가 스크립트를 만들었다. + +LLM 평가가 아닌, 정규식 패턴을 기반으로 반드시 포함되어야 하는 요소를 검증하는 식이다. + +``` +"question": "웰컴 쿠폰은 며칠 동안 사용할 수 있나요?", +"expected": "30일", +"must_contain": ["30\\s*일"], +``` + +웰컴 쿠폰은 30일 이내 사용 가능하다는 비즈니스적 요소가 포함되어 있는지 확인한다. + +``` +=== 평가 결과 === +전체: 38/43 (88.4%) + +카테고리별: + account : 7/ 7 (100%) + adversarial : 3/ 4 ( 75%) + cold_chain : 3/ 3 (100%) + complaint : 1/ 2 ( 50%) + marketplace : 3/ 3 (100%) + payment : 1/ 3 ( 33%) + points : 7/ 7 (100%) + returns : 5/ 5 (100%) + shipping : 5/ 5 (100%) + subscription : 3/ 4 ( 75%) +``` + +RAG 를 쓴 버전 + +``` +=== 평가 결과 === +전체: 38/43 (88.4%) + +카테고리별: + account : 6/ 7 ( 86%) + adversarial : 2/ 4 ( 50%) + cold_chain : 3/ 3 (100%) + complaint : 2/ 2 (100%) + marketplace : 3/ 3 (100%) + payment : 3/ 3 (100%) + points : 7/ 7 (100%) + returns : 4/ 5 ( 80%) + shipping : 5/ 5 (100%) + subscription : 3/ 4 ( 75%) +``` + +RAG 사용하지 않고, 그냥 다 집어넣은 버전 +두개 큰 차이가 없는거 같다. 쩝... + +=> 2주내 POC 를 내야하므로, 이정도로 마무리한다. \ No newline at end of file From 933e63869f1c4c7c88113a932de9e4e492768945 Mon Sep 17 00:00:00 2001 From: youngsu5582 <98307410+youngsu5582@users.noreply.github.com> Date: Thu, 21 May 2026 22:59:14 +0900 Subject: [PATCH 11/17] =?UTF-8?q?docs:=20wall-report=20=EC=9E=91=EC=84=B1?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- mission/wall-report.md | 36 +++++++++++++++++++++++++++--------- 1 file changed, 27 insertions(+), 9 deletions(-) diff --git a/mission/wall-report.md b/mission/wall-report.md index 3489fe3..f444339 100644 --- a/mission/wall-report.md +++ b/mission/wall-report.md @@ -7,36 +7,54 @@ > 구현하면서 잘 안 됐던 것, 예상과 달랐던 것을 적어주세요. -- +사실, 정답이 정해져있을거라 생각했다. +시작하기 전, 모델을 바꾸거나 RAG 를 도입하면 일정 수준의 기대치에 도달 할거라 생각했다. + +하지만, 쉽사리 내가 생각한 기대치 80% 에 도달하지 못했다. +프롬프트를 깎거나, RAG 를 도입해도 크게 달라지지 않았다. (오히려 더 떨어졌다.) + +그래서, 솔직히 의욕이 꺾였다. 그러다가, 평가의 기준을 스스로 맞다고 생각한 방향으로 바꿨다. +시간이 없어서, 더 개선은 못하지만 나름대로의 결론을 내린거 같다. + +> progress.md 에 진행의 기록을 남겨놓았습니다. ## 2. 해결하지 못한 것 > 시도했지만 결국 해결 못한 문제가 있다면 적어주세요. -- +그래도, 기존에 제시했던 evaluate 의 %를 올리지 못한것? ## 3. 정확도 측정 결과 > 테스트 질문 150개로 측정한 정확도를 기록해주세요. -| 난이도 | 정확도 | 비고 | -|--------|--------|------| -| easy | | | -| medium | | | -| hard | | | +가장 마지막 방식(RAG) + +전체: 70/150 (46.7%) + +| 난이도 | 정확도 | 비고 | +|--------|-------|------| +| easy | 17/30 | | +| medium | 9/26 | | +| hard | 44/94 | | ## 4. 왜 그런 결과가 나왔는지 > 정확도가 낮은 난이도의 질문을 몇 개 살펴보고, 왜 틀렸는지 분석해주세요. -- +- 매번 너무 달라진거 같아서 못 정하겠다. + +LLM 은 결국 비결정적인데, `비결정적인 결과 x 비결정적인 평가` 사이에서 뭘 해야하는지 명확하게 모르겠다. ## 5. 개선하고 싶은 것 > 시간이 더 있었다면 시도해보고 싶은 개선점을 적어주세요. -- +- 테스트를 좀 더 체계적으로 할 거 같다. + +코드 주석치면서 테스트 하거나, 테스트 기록을 명확하게 하지 않았다. +날짜 or 프롬프트나 & rag 세팅을 버저닝 관리가 잘 되게 할 거 같다. \ No newline at end of file From 1312737b6655610dc2899ac6b4032fcb7a1cf88b Mon Sep 17 00:00:00 2001 From: youngsu5582 <98307410+youngsu5582@users.noreply.github.com> Date: Tue, 26 May 2026 21:52:45 +0900 Subject: [PATCH 12/17] =?UTF-8?q?refactor:=20=EB=B6=88=ED=95=84=EC=9A=94?= =?UTF-8?q?=ED=95=9C=20=EC=BD=94=EB=93=9C=20=EC=A0=9C=EA=B1=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../bootcamp/config/ChatClientConfig.java | 27 +------------------ .../FrequentlyQuestionChatApiService.java | 25 ----------------- 2 files changed, 1 insertion(+), 51 deletions(-) diff --git a/src/main/java/com/cholog/bootcamp/config/ChatClientConfig.java b/src/main/java/com/cholog/bootcamp/config/ChatClientConfig.java index 3a65af5..db438c6 100644 --- a/src/main/java/com/cholog/bootcamp/config/ChatClientConfig.java +++ b/src/main/java/com/cholog/bootcamp/config/ChatClientConfig.java @@ -35,15 +35,9 @@ public class ChatClientConfig { @Bean public ChatClient chatClient(ChatClient.Builder builder, - @Value("classpath:prompts/faq-system.st") Resource systemTemplateResource, - @Value("classpath:layer1_faq/*.md") Resource[] faqResources, - @Value("classpath:layer2_policies/current/*.md") Resource[] policyResources, - @Value("classpath:layer3_examples/*.md") Resource[] exampleResources) { + @Value("classpath:prompts/faq-system.st") Resource systemTemplateResource) { var systemTemplate = loadSystemTemplate(systemTemplateResource); - var faq = concatResources(faqResources, "faq"); - var policies = concatResources(policyResources, "policies"); - var examples = concatResources(exampleResources, "examples"); return builder .defaultSystem(spec -> spec @@ -61,23 +55,4 @@ private String loadSystemTemplate(Resource systemTemplate) { return ""; } } - - private String concatResources(Resource[] resources, String label) { - try { - log.info("{} layer 파일을 로드합니다. 개수: {}", label, resources.length); - - var sb = new StringBuilder(); - for (Resource r : resources) { - sb.append("## ").append(r.getFilename()).append(System.lineSeparator()); - sb.append(r.getContentAsString(StandardCharsets.UTF_8)); - sb.append(System.lineSeparator()).append(System.lineSeparator()); - sb.append("---"); - sb.append(System.lineSeparator()).append(System.lineSeparator()); - } - return sb.toString(); - } catch (Exception e) { - log.warn("{} layer 로드중 문제가 발생했습니다.", label, e); - return ""; - } - } } diff --git a/src/main/java/com/cholog/bootcamp/service/FrequentlyQuestionChatApiService.java b/src/main/java/com/cholog/bootcamp/service/FrequentlyQuestionChatApiService.java index ce26206..adfaaea 100644 --- a/src/main/java/com/cholog/bootcamp/service/FrequentlyQuestionChatApiService.java +++ b/src/main/java/com/cholog/bootcamp/service/FrequentlyQuestionChatApiService.java @@ -24,31 +24,6 @@ public class FrequentlyQuestionChatApiService { private final PricingCalculator pricingCalculator; private final VectorStore vectorStore; - public FrequentlyQuestionChatResponseDto chat(FrequentlyQuestionChatRequestDto requestDto) { - var prompt = Prompt.builder() - .content(requestDto.question()) - .build(); - - var response = chatClient.prompt(prompt) - .call() - .chatResponse(); - - if (response == null) { - return new FrequentlyQuestionChatResponseDto( - "응답이 없습니다.", TokenUsage.EMPTY - ); - } - - var generation = response.getResult().getOutput(); - var metadata = response.getMetadata(); - - var usage = TokenUsage.from(metadata.getUsage()); - var price = calculateModelPrice(metadata.getModel(), usage); - - log.info("[{}] 토큰 사용량: {}, 토큰 비용: {}$\n결과: {}", metadata.getModel(), usage, price, generation.getText()); - return new FrequentlyQuestionChatResponseDto(generation.getText(), usage); - } - public FrequentlyQuestionChatResponseDto chatWithRag(FrequentlyQuestionChatRequestDto requestDto) { String question = requestDto.question(); From 12c4eddf2fe2ed7d01929b72db475bf5cf0fdf7b Mon Sep 17 00:00:00 2001 From: youngsu5582 <98307410+youngsu5582@users.noreply.github.com> Date: Tue, 26 May 2026 22:31:33 +0900 Subject: [PATCH 13/17] =?UTF-8?q?feat:=20=EB=A7=88=ED=81=AC=EB=8B=A4?= =?UTF-8?q?=EC=9A=B4=20=EA=B8=B0=EB=B0=98=20=ED=8C=8C=EC=8B=B1=EC=9C=BC?= =?UTF-8?q?=EB=A1=9C=20=EB=B3=80=EA=B2=BD?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - markdown document reader 추가 --- build.gradle | 2 + .../bootcamp/config/VectorStoreConfig.java | 65 +++++++++---------- 2 files changed, 34 insertions(+), 33 deletions(-) diff --git a/build.gradle b/build.gradle index 8a405d3..d131962 100644 --- a/build.gradle +++ b/build.gradle @@ -27,6 +27,8 @@ dependencies { implementation 'org.springframework.boot:spring-boot-starter-web' implementation 'org.springframework.ai:spring-ai-starter-model-openai' implementation 'org.springframework.ai:spring-ai-rag' + implementation 'org.springframework.ai:spring-ai-markdown-document-reader' + compileOnly 'org.projectlombok:lombok' testImplementation 'org.springframework.boot:spring-boot-starter-test' diff --git a/src/main/java/com/cholog/bootcamp/config/VectorStoreConfig.java b/src/main/java/com/cholog/bootcamp/config/VectorStoreConfig.java index bc74a99..87467d5 100644 --- a/src/main/java/com/cholog/bootcamp/config/VectorStoreConfig.java +++ b/src/main/java/com/cholog/bootcamp/config/VectorStoreConfig.java @@ -3,7 +3,8 @@ import lombok.extern.slf4j.Slf4j; import org.springframework.ai.document.Document; import org.springframework.ai.embedding.EmbeddingModel; -import org.springframework.ai.transformer.splitter.TokenTextSplitter; +import org.springframework.ai.reader.markdown.MarkdownDocumentReader; +import org.springframework.ai.reader.markdown.config.MarkdownDocumentReaderConfig; import org.springframework.ai.vectorstore.SimpleVectorStore; import org.springframework.ai.vectorstore.VectorStore; import org.springframework.beans.factory.annotation.Value; @@ -11,10 +12,7 @@ import org.springframework.context.annotation.Configuration; import org.springframework.core.io.Resource; -import java.io.IOException; -import java.nio.charset.StandardCharsets; import java.util.ArrayList; -import java.util.Arrays; import java.util.List; import java.util.Map; @@ -30,41 +28,42 @@ public VectorStore vectorStore(EmbeddingModel model, var store = SimpleVectorStore.builder(model).build(); var documents = new ArrayList(); - documents.addAll(toDocuments(faqResources, "faq")); - documents.addAll(toDocuments(policyResources, "policy")); - documents.addAll(toDocuments(exampleResources, "example")); + documents.addAll(toMarkdownDocuments(faqResources, "faq")); + documents.addAll(toMarkdownDocuments(policyResources, "policy")); + documents.addAll(toMarkdownDocuments(exampleResources, "example")); - - TokenTextSplitter splitter = TokenTextSplitter.builder() - .withChunkSize(400) - .withMinChunkSizeChars(200) - .withMaxNumChunks(10000) - .withKeepSeparator(true) - .build(); - var chunks = splitter.apply(documents); - chunks.forEach(chunk -> { - if (chunk.getText() != null) { - log.info("chunk ID: {}, TEXT: {}", chunk.getId(), - chunk.getText().substring(0, Math.min(80, chunk.getText().length())).replace("\n", " ")); + documents.forEach(document -> { + if (document.getText() != null) { + log.info("document ID: {}, file: {}, metadata: {}, TEXT: {}", + document.getId(), + document.getMetadata().get("source"), + document.getMetadata(), + document.getText().substring(0, Math.min(80, document.getText().length())).replace("\n", " ")); } }); - store.add(chunks); - log.info("vector store 적재 완료. 원본 {} -> 청크 {}", documents.size(), chunks.size()); + store.add(documents); + log.info("vector store 적재 완료. markdown 문서 {}", documents.size()); return store; } - private List toDocuments(Resource[] resources, String layer) { - return Arrays.stream(resources) - .map(r -> { - try { - return new Document( - r.getContentAsString(StandardCharsets.UTF_8), - Map.of("source", r.getFilename(), "layer", layer)); - } catch (IOException e) { - throw new IllegalStateException("리소스 로드 실패: " + r.getFilename(), e); - } - }) - .toList(); + private List toMarkdownDocuments(Resource[] resources, String layer) { + var documents = new ArrayList(); + + for (Resource resource : resources) { + var config = MarkdownDocumentReaderConfig.builder() + .withIncludeCodeBlock(false) + .withIncludeBlockquote(false) + .withHorizontalRuleCreateDocument(true) + .withAdditionalMetadata(Map.of( + "source", resource.getFilename(), + "layer", layer + )) + .build(); + + documents.addAll(new MarkdownDocumentReader(resource, config).get()); + } + + return documents; } } From 18f592136e7fa5e552f51c65ed2da49eb695fd80 Mon Sep 17 00:00:00 2001 From: youngsu5582 <98307410+youngsu5582@users.noreply.github.com> Date: Tue, 26 May 2026 22:53:22 +0900 Subject: [PATCH 14/17] =?UTF-8?q?feat:=20prompt=20request=20=EB=A1=9C?= =?UTF-8?q?=EC=A7=81=EC=97=90=20try-catch=20=EA=B5=AC=EB=AC=B8=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 - 당장은, Exception catch 를 해서 어떤 예외가 발생하는 확인 후 고도화 --- .../FrequentlyQuestionChatApiService.java | 41 +++++++++++-------- 1 file changed, 24 insertions(+), 17 deletions(-) diff --git a/src/main/java/com/cholog/bootcamp/service/FrequentlyQuestionChatApiService.java b/src/main/java/com/cholog/bootcamp/service/FrequentlyQuestionChatApiService.java index adfaaea..197c7bf 100644 --- a/src/main/java/com/cholog/bootcamp/service/FrequentlyQuestionChatApiService.java +++ b/src/main/java/com/cholog/bootcamp/service/FrequentlyQuestionChatApiService.java @@ -6,6 +6,7 @@ import lombok.RequiredArgsConstructor; import lombok.extern.slf4j.Slf4j; import org.springframework.ai.chat.client.ChatClient; +import org.springframework.ai.chat.model.ChatResponse; import org.springframework.ai.chat.prompt.Prompt; import org.springframework.ai.document.Document; import org.springframework.ai.vectorstore.SearchRequest; @@ -13,6 +14,7 @@ import org.springframework.stereotype.Service; import java.math.BigDecimal; +import java.util.List; import java.util.stream.Collectors; @Slf4j @@ -33,12 +35,32 @@ public FrequentlyQuestionChatResponseDto chatWithRag(FrequentlyQuestionChatReque .topK(8) .build()); log.info("hits 결과: {}", hits.stream().map(Document::getId).toList()); + try { + var response = execute(question, hits); + + var generation = response.getResult().getOutput(); + var metadata = response.getMetadata(); + + var usage = TokenUsage.from(metadata.getUsage()); + var price = calculateModelPrice(metadata.getModel(), usage); + + log.info("[{}] 토큰 사용량: {}, 토큰 비용: {}$\n결과: {}", metadata.getModel(), usage, price, generation.getText()); + return new FrequentlyQuestionChatResponseDto(generation.getText(), usage); + } catch (Exception e) { + log.warn("챗봇 응답 실패: {}", e.getMessage(), e); + return new FrequentlyQuestionChatResponseDto( + "챗봇 응답 생성 중 오류 발생했습니다.", + TokenUsage.EMPTY + ); + } + } - var context = hits.stream() + private ChatResponse execute(String question, List documents) { + var context = documents.stream() .map(d -> "## " + d.getMetadata().get("source") + "\n" + d.getText()) .collect(Collectors.joining("\n\n---\n\n")); - var response = chatClient.prompt(context) + return chatClient.prompt(question) .user(u -> u.text(""" 참고 문서: {context} @@ -49,21 +71,6 @@ public FrequentlyQuestionChatResponseDto chatWithRag(FrequentlyQuestionChatReque .param("question", question)) .call() .chatResponse(); - - if (response == null) { - return new FrequentlyQuestionChatResponseDto( - "응답이 없습니다.", TokenUsage.EMPTY - ); - } - - var generation = response.getResult().getOutput(); - var metadata = response.getMetadata(); - - var usage = TokenUsage.from(metadata.getUsage()); - var price = calculateModelPrice(metadata.getModel(), usage); - - log.info("[{}] 토큰 사용량: {}, 토큰 비용: {}$\n결과: {}", metadata.getModel(), usage, price, generation.getText()); - return new FrequentlyQuestionChatResponseDto(generation.getText(), usage); } private BigDecimal calculateModelPrice(String model, TokenUsage usage) { From 0e22dcdd67c329e587e15702b9847c79fdc2622f Mon Sep 17 00:00:00 2001 From: youngsu5582 <98307410+youngsu5582@users.noreply.github.com> Date: Tue, 26 May 2026 22:55:32 +0900 Subject: [PATCH 15/17] =?UTF-8?q?feat:=20faq=20=EC=8B=9C=EC=8A=A4=ED=85=9C?= =?UTF-8?q?=20=ED=94=84=EB=A1=AC=ED=94=84=ED=8A=B8=20=EA=B2=BD=EB=A1=9C=20?= =?UTF-8?q?application=20yml=20=EB=A1=9C=20=EB=B6=84=EB=A6=AC?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../java/com/cholog/bootcamp/config/ChatClientConfig.java | 4 ++-- src/main/resources/application.yml | 4 ++++ 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/src/main/java/com/cholog/bootcamp/config/ChatClientConfig.java b/src/main/java/com/cholog/bootcamp/config/ChatClientConfig.java index db438c6..ebf940d 100644 --- a/src/main/java/com/cholog/bootcamp/config/ChatClientConfig.java +++ b/src/main/java/com/cholog/bootcamp/config/ChatClientConfig.java @@ -35,7 +35,7 @@ public class ChatClientConfig { @Bean public ChatClient chatClient(ChatClient.Builder builder, - @Value("classpath:prompts/faq-system.st") Resource systemTemplateResource) { + @Value("${app.chat.system-prompt}") Resource systemTemplateResource) { var systemTemplate = loadSystemTemplate(systemTemplateResource); @@ -48,7 +48,7 @@ public ChatClient chatClient(ChatClient.Builder builder, private String loadSystemTemplate(Resource systemTemplate) { try { String content = systemTemplate.getContentAsString(StandardCharsets.UTF_8); - log.info("faq 시스템 프롬프트를 로드합니다. 길이: {}", content.length()); + log.info("faq 시스템 프롬프트를 로드합니다. 경로: {}, 길이: {}", systemTemplate.getURI(), content.length()); return content; } catch (Exception e) { log.warn("faq 시스템 프롬프트 로드중 문제가 발생했습니다.", e); diff --git a/src/main/resources/application.yml b/src/main/resources/application.yml index dcec8fe..950ca16 100644 --- a/src/main/resources/application.yml +++ b/src/main/resources/application.yml @@ -11,3 +11,7 @@ spring: embedding: options: model: text-embedding-3-small + +app: + chat: + system-prompt: classpath:prompts/faq-system.st From 9bd53e9c1559ec322cf05ba0913e4112c5baedfe Mon Sep 17 00:00:00 2001 From: youngsu5582 <98307410+youngsu5582@users.noreply.github.com> Date: Tue, 26 May 2026 23:23:54 +0900 Subject: [PATCH 16/17] =?UTF-8?q?feat:=20layer=20=EB=B3=84=20=EC=B5=9C?= =?UTF-8?q?=EC=86=8C=20=EA=B2=80=EC=83=89=20=EA=B0=9C=EC=88=98=20=EC=A7=80?= =?UTF-8?q?=EC=A0=95?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - FilterExpression 통해서 layer 필터링 및 검색 --- .../FrequentlyQuestionChatApiService.java | 45 ++++++++++++++++--- 1 file changed, 38 insertions(+), 7 deletions(-) diff --git a/src/main/java/com/cholog/bootcamp/service/FrequentlyQuestionChatApiService.java b/src/main/java/com/cholog/bootcamp/service/FrequentlyQuestionChatApiService.java index 197c7bf..f27a00d 100644 --- a/src/main/java/com/cholog/bootcamp/service/FrequentlyQuestionChatApiService.java +++ b/src/main/java/com/cholog/bootcamp/service/FrequentlyQuestionChatApiService.java @@ -7,13 +7,14 @@ import lombok.extern.slf4j.Slf4j; import org.springframework.ai.chat.client.ChatClient; import org.springframework.ai.chat.model.ChatResponse; -import org.springframework.ai.chat.prompt.Prompt; import org.springframework.ai.document.Document; import org.springframework.ai.vectorstore.SearchRequest; import org.springframework.ai.vectorstore.VectorStore; +import org.springframework.ai.vectorstore.filter.FilterExpressionBuilder; import org.springframework.stereotype.Service; import java.math.BigDecimal; +import java.util.ArrayList; import java.util.List; import java.util.stream.Collectors; @@ -30,11 +31,7 @@ public FrequentlyQuestionChatResponseDto chatWithRag(FrequentlyQuestionChatReque String question = requestDto.question(); - var hits = vectorStore.similaritySearch(SearchRequest.builder() - .query(question) - .topK(8) - .build()); - log.info("hits 결과: {}", hits.stream().map(Document::getId).toList()); + var hits = searchContext(question); try { var response = execute(question, hits); @@ -55,12 +52,46 @@ public FrequentlyQuestionChatResponseDto chatWithRag(FrequentlyQuestionChatReque } } + private List searchContext(String question) { + var documents = new ArrayList(); + documents.addAll(searchByLayer(question, 3, "policy")); + documents.addAll(searchByLayer(question, 4, "faq")); + documents.addAll(searchByLayer(question, 1, "example")); + + log.info("hits 결과: {}", + documents.stream() + .map(document -> "%s(title=%s, layer=%s, source=%s)".formatted( + document.getId(), + document.getMetadata().get("title"), + document.getMetadata().get("layer"), + document.getMetadata().get("source") + )) + .toList()); + + log.info("layer 별 hits 결과: {}", + documents.stream().collect(Collectors.groupingBy( + document -> document.getMetadata().get("layer"), + Collectors.counting() + ))); + return documents; + } + + private List searchByLayer(String question, int topK, String layer) { + var filter = new FilterExpressionBuilder(); + + return vectorStore.similaritySearch(SearchRequest.builder() + .query(question) + .topK(topK) + .filterExpression(filter.eq("layer", layer).build()) + .build()); + } + private ChatResponse execute(String question, List documents) { var context = documents.stream() .map(d -> "## " + d.getMetadata().get("source") + "\n" + d.getText()) .collect(Collectors.joining("\n\n---\n\n")); - return chatClient.prompt(question) + return chatClient.prompt() .user(u -> u.text(""" 참고 문서: {context} From 8eecabd7c77867b085a8bfdff14f577899b9c655 Mon Sep 17 00:00:00 2001 From: youngsu5582 <98307410+youngsu5582@users.noreply.github.com> Date: Tue, 26 May 2026 23:55:33 +0900 Subject: [PATCH 17/17] =?UTF-8?q?feat:=20spring=20ai=20property=20?= =?UTF-8?q?=EA=B8=B0=EB=B0=98=20=EC=9E=AC=EC=8B=9C=EB=8F=84=20=EB=A1=9C?= =?UTF-8?q?=EC=A7=81=20=EC=B6=94=EA=B0=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/main/resources/application.yml | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/src/main/resources/application.yml b/src/main/resources/application.yml index 950ca16..cf03fc7 100644 --- a/src/main/resources/application.yml +++ b/src/main/resources/application.yml @@ -2,6 +2,20 @@ spring: application: name: spring-ai-bootcamp-basic ai: + retry: + max-attempts: 3 # 최대 시도(최초 호출 포함) + backoff: + initial-interval: 500ms # 첫 대기 시간 + multiplier: 2 # 요청당 시간을 몇배로 늘릴지 + max-interval: 3s + exclude-on-http-codes: # 재시도 제외 목록 + - 408 + - 429 + - 500 + - 502 + - 503 + - 504 + on-client-errors: false # 4xx 에러 전체를 재시도로 볼지 openai: api-key: ${OPENAI_API_KEY} chat: