From 9ae50597f2c4d3ab45a53822d045afd9646ade8d Mon Sep 17 00:00:00 2001 From: HyunSang Jang Date: Mon, 25 May 2026 12:02:54 +0900 Subject: [PATCH 01/11] =?UTF-8?q?feat(agent):=20#127=20LLMClient=20?= =?UTF-8?q?=EC=97=90=20configure=5Fllm()=20=ED=86=A0=EA=B8=80=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 scripts/run_qa_eval.py 진입점에서 --llm-model / --llm-base-url / --llm-api-key-env 인자로 LLM 을 토글할 수 있게 한다. doc-summary-agent 와 동일 패턴. ## 변경 사항 ### 신규: LLMConfig.from_args() 클래스 메소드 - model / base_url / api_key_env 명시 받아서 LLMConfig 생성 - from_env() (KIMI_*) 와 분리 — prod / W4 정성 검증 동작 무영향 ### 신규: configure_llm() 함수 + _active_config 모듈 변수 - 호출 안 하면 LLMClient() 가 from_env() 사용 (기존 동작) - 호출 시 이후 모든 LLMClient() 인스턴스가 명시 config 사용 - retrieval.text2cypher / local_retriever / router 자동 토글 (모두 LLMClient() 호출하므로) ### 변경: LLMClient.__init__() - config 인자 None 일 때 _active_config 우선, 그 다음 from_env() - 명시적 LLMClient(config=...) 호출도 그대로 지원 ## 호환성 | 환경 | 영향 | |---|---| | prod / W4 정성 검증 (`run_w4_eval.py`) | ❌ 영향 0 — configure_llm() 미호출 | | 평가 진입점 (`scripts/run_qa_eval.py`) | ✅ configure_llm() 호출로 토글 | | LLMClient 직접 사용 (테스트 등) | ❌ 영향 0 | Refs #127 --- agent/llm_client.py | 80 ++++++++++++++++++++++++++++++++++++++++----- 1 file changed, 71 insertions(+), 9 deletions(-) diff --git a/agent/llm_client.py b/agent/llm_client.py index 1a944b6..3e3126e 100644 --- a/agent/llm_client.py +++ b/agent/llm_client.py @@ -1,14 +1,22 @@ """LLM 클라이언트 thin wrapper. -OpenAI SDK 호환 (Kimi, OpenRouter, OpenAI 모두 동일 코드로). -환경변수로 provider 토글: +OpenAI SDK 호환 — Kimi, OpenRouter, OpenAI, DeepSeek, Grok 모두 동일 코드로. + +## 환경변수 설정 방식 (#127 멘토링 평가 토글) + +### 방식 1: 기본 KIMI_* (prod / W4 정성 검증) - KIMI_API_KEY / KIMI_BASE_URL / KIMI_MODEL -Spike (#8) 검증용 최소 구현. 정식화는 #7 W2 환경 부트스트랩에서. +### 방식 2: 평가 진입점에서 configure_llm()으로 명시 토글 +- LLMConfig(api_key, base_url, model) 을 직접 주입 +- scripts/run_qa_eval.py 에서 --llm-model / --llm-base-url / --llm-api-key-env 인자로 토글 + +호출 안 하면 KIMI_* 환경변수 사용 (기존 동작 유지). """ from __future__ import annotations +import logging import os from dataclasses import dataclass @@ -18,10 +26,12 @@ load_dotenv() +logger = logging.getLogger(__name__) + @dataclass(frozen=True) class LLMConfig: - """LLM 호출 설정. 환경변수로부터 로드.""" + """LLM 호출 설정.""" api_key: str base_url: str @@ -29,6 +39,7 @@ class LLMConfig: @classmethod def from_env(cls) -> "LLMConfig": + """기본 — KIMI_* 환경변수에서 로드.""" api_key = os.environ.get("KIMI_API_KEY", "") base_url = os.environ.get("KIMI_BASE_URL", "https://api.moonshot.cn/v1") model = os.environ.get("KIMI_MODEL", "moonshot-v1-8k") @@ -39,17 +50,70 @@ def from_env(cls) -> "LLMConfig": ) return cls(api_key=api_key, base_url=base_url, model=model) + @classmethod + def from_args( + cls, + model: str | None = None, + base_url: str | None = None, + api_key_env: str = "KIMI_API_KEY", + ) -> "LLMConfig": + """평가 진입점용 — CLI 인자로 명시 토글 (#127). + + Args: + model: 모델 ID (예: "deepseek/deepseek-v3.2") + base_url: OpenAI-호환 endpoint (예: "https://openrouter.ai/api/v1") + api_key_env: API 키 환경변수 이름 (예: "OPENROUTER_API_KEY") + """ + api_key = os.environ.get(api_key_env, "") + if not api_key: + raise RuntimeError(f"환경변수 {api_key_env} 미설정 — LLM 토글 불가") + if not model: + raise RuntimeError("--llm-model 인자 필요") + if not base_url: + raise RuntimeError("--llm-base-url 인자 필요") + return cls(api_key=api_key, base_url=base_url, model=model) + + +# ── Runtime active config (#127) ─────────────────────────── +# configure_llm() 호출 전: None → LLMClient() 호출 시 from_env() 사용 +# configure_llm() 호출 후: 명시 config 사용 → 모든 LLMClient 인스턴스 영향 +_active_config: LLMConfig | None = None + + +def configure_llm( + model: str | None = None, + base_url: str | None = None, + api_key_env: str = "KIMI_API_KEY", +) -> None: + """평가 진입점에서 LLM 을 일회성으로 재설정 (#127). + + 호출 안 하면 기존 KIMI_* 환경변수 사용 (prod / W4 정성 검증 동작 유지). + + 호출 시 모든 후속 LLMClient() 인스턴스가 이 config 를 사용 → + retrieval.text2cypher / local_retriever / router 자동 토글. + """ + global _active_config + _active_config = LLMConfig.from_args( + model=model, base_url=base_url, api_key_env=api_key_env + ) + logger.info( + "LLM 재설정: model=%s base_url=%s api_key_env=%s", + _active_config.model, _active_config.base_url, api_key_env, + ) + class LLMClient: """OpenAI SDK 기반 thin wrapper. - 단일 진입점: chat(messages, **kwargs) - - tenacity 로 재시도 (네트워크 / 일시적 5xx 대비) - - 토큰 / latency 로깅은 #24 Opik 통합에서 추가 + - tenacity 로 재시도 + - configure_llm() 호출됐으면 그 config 사용, 안 됐으면 from_env() """ def __init__(self, config: LLMConfig | None = None) -> None: - self.config = config or LLMConfig.from_env() + if config is None: + config = _active_config if _active_config is not None else LLMConfig.from_env() + self.config = config self._client = OpenAI( api_key=self.config.api_key, base_url=self.config.base_url, @@ -68,7 +132,6 @@ def chat( max_tokens: int = 1024, response_format: dict | None = None, ) -> str: - """단순 텍스트 응답 반환. Spike 단계 최소 인터페이스.""" kwargs: dict = { "model": self.config.model, "messages": messages, @@ -82,7 +145,6 @@ def chat( return resp.choices[0].message.content or "" def hello(self) -> str: - """헬스체크 1회 호출. Spike #8 단계 3 검증용.""" return self.chat( messages=[{"role": "user", "content": "한 단어로 'pong' 만 답하세요."}], max_tokens=16, From 724def9c9e7b8d4393a323ae866458b13598db84 Mon Sep 17 00:00:00 2001 From: HyunSang Jang Date: Mon, 25 May 2026 12:04:56 +0900 Subject: [PATCH 02/11] =?UTF-8?q?feat(eval/metrics):=20C2=20doc-summary=20?= =?UTF-8?q?=EC=9D=98=20=EB=A9=94=ED=8A=B8=EB=A6=AD=20=EB=AA=A8=EB=93=88=20?= =?UTF-8?q?doc-graph=20=EB=A1=9C=20=EB=B3=B5=EC=82=AC?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ROUGE-L / 수치 정확도 / Faithfulness / Numerical Faithfulness 4 메트릭을 doc-summary 의 동일 코드 그대로 박는다. doc-graph 정량 평가 진입점 (scripts/run_qa_eval.py) 의 기반 인프라. ## 변경 사항 ### eval/metrics/__init__.py 신규 ### eval/metrics/rouge_score.py 신규 - ROUGE-1/2/L 한국어 MeCab 토크나이저 기반 - doc-summary 와 동일 (의존: rouge-score, python-mecab-ko) ### eval/metrics/numerical_accuracy.py 신규 - 금융 특화 수치 추출 (억원, 조원, %p, bp, ~ 범위, 날짜 제외) - doc-summary 와 동일 ### eval/metrics/faithfulness_judge.py 신규 - LLM-as-Judge: faithfulness / completeness (1-5) / conciseness (1-5) - 수치 충실도: numerical_faithfulness - configure_judge_llm() — 측정 LLM 과 독립 토글 - Claude 호환성 (doc-summary #127 C7 그대로): * reasoning_effort 분기 (OpenAI 네이티브만) * strict json_schema → json_object 자동 fallback * 빈 응답 진단 로깅 + reasoning_content fallback 추출 * 마크다운 코드 블록 자동 제거 ### eval/metrics/prompts/ 신규 - faithfulness_v1.md - numerical_faithfulness_v1.md - doc-summary 와 동일 Refs #127 --- eval/metrics/__init__.py | 0 eval/metrics/faithfulness_judge.py | 196 ++++++++++++++++++ eval/metrics/numerical_accuracy.py | 69 ++++++ eval/metrics/prompts/faithfulness_v1.md | 32 +++ .../prompts/numerical_faithfulness_v1.md | 16 ++ eval/metrics/rouge_score.py | 44 ++++ 6 files changed, 357 insertions(+) create mode 100644 eval/metrics/__init__.py create mode 100644 eval/metrics/faithfulness_judge.py create mode 100644 eval/metrics/numerical_accuracy.py create mode 100644 eval/metrics/prompts/faithfulness_v1.md create mode 100644 eval/metrics/prompts/numerical_faithfulness_v1.md create mode 100644 eval/metrics/rouge_score.py diff --git a/eval/metrics/__init__.py b/eval/metrics/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/eval/metrics/faithfulness_judge.py b/eval/metrics/faithfulness_judge.py new file mode 100644 index 0000000..e1ac930 --- /dev/null +++ b/eval/metrics/faithfulness_judge.py @@ -0,0 +1,196 @@ +""" +eval/metrics/faithfulness_judge.py +LLM-as-Judge — Faithfulness / Completeness / Conciseness +참고: FineSurE (ACL 2024) + doc-summary-agent #127 PR + +Claude / DeepSeek / Kimi 등 OpenRouter 경유 모델도 호환 (#127 C7): +- reasoning_effort 분기 (OpenAI 네이티브만 전달) +- strict json_schema 자동 fallback +- 빈 응답 진단 로깅 +""" +import logging +import os +from pathlib import Path + +from openai import OpenAI +from tenacity import retry, stop_after_attempt, wait_exponential, retry_if_exception_type +from openai import RateLimitError, APITimeoutError, APIConnectionError, BadRequestError + +logger = logging.getLogger(__name__) + +DEFAULT_MODEL = "gpt-5.2" +client = OpenAI(api_key=os.getenv("OPENAI_API_KEY", "")) +MODEL = DEFAULT_MODEL + +_active_judge_client: OpenAI = OpenAI(api_key=os.getenv("OPENAI_API_KEY", "")) +_active_judge_model: str = DEFAULT_MODEL +_judge_strict_schema_supported: bool = True + + +def configure_judge_llm( + model: str | None = None, + base_url: str | None = None, + api_key_env: str = "OPENAI_API_KEY", +) -> None: + global _active_judge_client, _active_judge_model, _judge_strict_schema_supported + api_key = os.getenv(api_key_env) + if not api_key: + raise RuntimeError(f"환경변수 {api_key_env} 미설정 — Judge LLM 재설정 불가") + _active_judge_client = OpenAI(api_key=api_key, base_url=base_url) + if model is not None: + _active_judge_model = model + _judge_strict_schema_supported = (base_url is None) + logger.info( + "Judge LLM 재설정: model=%s base_url=%s api_key_env=%s strict_schema=%s", + _active_judge_model, base_url, api_key_env, _judge_strict_schema_supported, + ) + + +_PROMPTS_DIR = Path(__file__).parent / "prompts" +FAITHFULNESS_PROMPT = (_PROMPTS_DIR / "faithfulness_v1.md").read_text(encoding="utf-8") +NUMERICAL_FAITHFULNESS_PROMPT = (_PROMPTS_DIR / "numerical_faithfulness_v1.md").read_text(encoding="utf-8") + + +def _relax_schema(response_format: dict) -> dict: + return {"type": "json_object"} + + +def _is_openai_native_model(model: str) -> bool: + m = model.lower() + if m.startswith("gpt-") or m.startswith("o1") or m.startswith("o3") or m.startswith("o4"): + return True + if m.startswith("openai/"): + return True + return False + + +@retry( + retry=retry_if_exception_type((RateLimitError, APITimeoutError, APIConnectionError)), + wait=wait_exponential(multiplier=1, min=2, max=8), + stop=stop_after_attempt(3), + reraise=True, +) +def _call_api(prompt: str, response_format: dict) -> dict: + global _judge_strict_schema_supported + import json + + rf = response_format if _judge_strict_schema_supported else _relax_schema(response_format) + + call_kwargs = { + "model": _active_judge_model, + "messages": [{"role": "user", "content": prompt}], + "max_completion_tokens": 2000, + "response_format": rf, + "timeout": 30, + } + if _is_openai_native_model(_active_judge_model): + call_kwargs["reasoning_effort"] = "low" + else: + call_kwargs["extra_body"] = {"reasoning": {"enabled": False}} + + try: + response = _active_judge_client.chat.completions.create(**call_kwargs) + except BadRequestError as e: + err_msg = str(e).lower() + if "json_schema" in err_msg or "response_format" in err_msg or "strict" in err_msg: + if _judge_strict_schema_supported: + logger.warning("Judge strict json_schema 미지원 → json_object fallback: %s", e) + _judge_strict_schema_supported = False + call_kwargs["response_format"] = _relax_schema(response_format) + response = _active_judge_client.chat.completions.create(**call_kwargs) + else: + raise + else: + raise + + raw_content = response.choices[0].message.content if response.choices else "" + raw_content = (raw_content or "").strip() + + if not raw_content: + msg = response.choices[0].message if response.choices else None + reasoning_content = getattr(msg, "reasoning_content", None) if msg else None + logger.warning( + "Judge 응답 빈 content (model=%s) — reasoning_content=%s", + _active_judge_model, + (reasoning_content or "")[:200] if reasoning_content else None, + ) + if reasoning_content and "{" in reasoning_content: + try: + start = reasoning_content.find("{") + end = reasoning_content.rfind("}") + if start >= 0 and end > start: + return json.loads(reasoning_content[start:end+1]) + except (json.JSONDecodeError, ValueError): + pass + return {} + + if raw_content.startswith("```"): + raw_content = raw_content.split("```")[1] + if raw_content.startswith("json"): + raw_content = raw_content[4:] + raw_content = raw_content.strip() + + return json.loads(raw_content) + + +def judge_faithfulness(source: str, summary: str) -> dict: + prompt = FAITHFULNESS_PROMPT.format(source=source[:3000], summary=summary) + schema = { + "type": "json_schema", + "json_schema": { + "name": "faithfulness_result", + "schema": { + "type": "object", + "properties": { + "faithfulness": {"type": "string", "enum": ["Faithful", "Not Faithful"]}, + "faithfulness_reason": {"type": "string"}, + "completeness": {"type": "integer", "minimum": 1, "maximum": 5}, + "completeness_reason": {"type": "string"}, + "conciseness": {"type": "integer", "minimum": 1, "maximum": 5}, + "conciseness_reason": {"type": "string"}, + }, + "required": ["faithfulness", "faithfulness_reason", + "completeness", "completeness_reason", + "conciseness", "conciseness_reason"], + "additionalProperties": False, + }, + "strict": True, + }, + } + try: + return _call_api(prompt, schema) + except Exception as e: + logger.error("Faithfulness Judge 실패: %s", e) + return { + "faithfulness": "Error", + "faithfulness_reason": str(e), + "completeness": None, + "completeness_reason": "", + "conciseness": None, + "conciseness_reason": "", + } + + +def judge_numerical_faithfulness(source: str, summary: str) -> dict: + prompt = NUMERICAL_FAITHFULNESS_PROMPT.format(source=source[:3000], summary=summary) + schema = { + "type": "json_schema", + "json_schema": { + "name": "numerical_faithfulness_result", + "schema": { + "type": "object", + "properties": { + "numerical_faithfulness": {"type": "string", "enum": ["Correct", "Incorrect"]}, + "reason": {"type": "string"}, + }, + "required": ["numerical_faithfulness", "reason"], + "additionalProperties": False, + }, + "strict": True, + }, + } + try: + return _call_api(prompt, schema) + except Exception as e: + logger.error("Numerical Faithfulness Judge 실패: %s", e) + return {"numerical_faithfulness": "Error", "reason": str(e)} diff --git a/eval/metrics/numerical_accuracy.py b/eval/metrics/numerical_accuracy.py new file mode 100644 index 0000000..51fdac2 --- /dev/null +++ b/eval/metrics/numerical_accuracy.py @@ -0,0 +1,69 @@ +""" +eval/metrics/numerical_accuracy.py +수치 정확도 — 금융 특화 (doc-summary-agent 와 동일) +한국어 단위(억원, 조원, %p, bp, %, 원) 포함 수치 추출 후 일치 여부 확인 +""" +import re + +_DATE_PATTERN = re.compile( + r'\d{4}\.\d{1,2}(?:\.\d{1,2})?' + r'|\d{4}년\s*\d{1,2}월' + r'|\d{2}/\d{2}' +) + +_RANGE_PATTERN = re.compile( + r'[\+\-]?[\d,]+\.?\d*~[\+\-]?[\d,]+\.?\d*' + r'(?:\s*(?:조|억|만)?\s*(?:원|달러|엔|위안))?' + r'(?:\s*[%％](?:p|P)?)?' + r'(?:\s*bp)?' + r'(?:\s*개월)?' +) + +_NUM_PATTERN = re.compile( + r'[\+\-]?[\d,]+\.?\d*' + r'(?:\s*(?:조|억|만)?\s*(?:원|달러|엔|위안))?' + r'(?:\s*[%％](?:p|P)?)?' + r'(?:\s*bp)?' + r'(?:\s*개월)?' +) + + +def _normalize(token: str) -> str: + return token.strip().replace(',', '').replace(' ', '') + + +def extract_numbers(text: str) -> list[str]: + text = _DATE_PATTERN.sub('', text) + results = [] + ranges = _RANGE_PATTERN.findall(text) + for r in ranges: + n = _normalize(r) + if n: + results.append(n) + text = _RANGE_PATTERN.sub('', text) + for n in _NUM_PATTERN.findall(text): + n = _normalize(n) + if n and n not in ('.', '-', '+'): + results.append(n) + seen = set() + deduped = [] + for n in results: + if n not in seen: + seen.add(n) + deduped.append(n) + return deduped + + +def compute_numerical_accuracy(prediction: str, reference: str) -> dict: + ref_nums = extract_numbers(reference) + pred_nums = set(extract_numbers(prediction)) + if not ref_nums: + return {'accuracy': 1.0, 'matched': [], 'missed': [], 'total': 0} + matched = [n for n in ref_nums if n in pred_nums] + missed = [n for n in ref_nums if n not in pred_nums] + return { + 'accuracy': round(len(matched) / len(ref_nums), 4), + 'matched': matched, + 'missed': missed, + 'total': len(ref_nums), + } diff --git a/eval/metrics/prompts/faithfulness_v1.md b/eval/metrics/prompts/faithfulness_v1.md new file mode 100644 index 0000000..5435cb3 --- /dev/null +++ b/eval/metrics/prompts/faithfulness_v1.md @@ -0,0 +1,32 @@ +당신은 금융 문서 요약의 품질을 평가하는 전문가입니다. +아래 원문과 요약을 읽고, 세 가지 기준으로 평가하세요. + +# 원문 +{source} + +# 요약 +{summary} + +# 평가 기준 + +1. **Faithfulness**: 요약이 원문에만 근거하는가? + - Faithful: 원문에 있는 내용만 포함 + - Not Faithful: 원문에 없는 내용이 하나라도 포함 + +2. **Completeness**: 원문의 핵심 정보가 요약에 빠짐없이 포함되었는가? (1~5점) + - 5: 모든 핵심 정보 포함 + - 3: 일부 핵심 정보 누락 + - 1: 핵심 정보 대부분 누락 + +3. **Conciseness**: 요약에 불필요한 내용이 없는가? (1~5점) + - 5: 군더더기 없음 + - 3: 불필요한 내용 일부 포함 + - 1: 불필요한 내용이 대부분 + +# 출력 형식 (JSON만 반환) +{{"faithfulness": "Faithful" or "Not Faithful", + "faithfulness_reason": "한 줄 근거", + "completeness": 1~5, + "completeness_reason": "한 줄 근거", + "conciseness": 1~5, + "conciseness_reason": "한 줄 근거"}} diff --git a/eval/metrics/prompts/numerical_faithfulness_v1.md b/eval/metrics/prompts/numerical_faithfulness_v1.md new file mode 100644 index 0000000..41b8bc3 --- /dev/null +++ b/eval/metrics/prompts/numerical_faithfulness_v1.md @@ -0,0 +1,16 @@ +당신은 금융 문서의 수치 정확도를 평가하는 전문가입니다. +아래 원문과 요약을 읽고, 요약에 등장하는 모든 수치가 원문과 일치하는지 판단하세요. + +# 원문 +{source} + +# 요약 +{summary} + +# 판단 기준 +- Correct: 요약의 모든 수치(금액, 비율, 날짜, 개수 등)가 원문과 정확히 일치 +- Incorrect: 수치가 하나라도 원문과 다르거나 원문에 없는 수치가 포함됨 + +# 출력 형식 (JSON만 반환) +{{"numerical_faithfulness": "Correct" or "Incorrect", + "reason": "불일치 수치가 있다면 명시, 없으면 '모든 수치 일치'"}} diff --git a/eval/metrics/rouge_score.py b/eval/metrics/rouge_score.py new file mode 100644 index 0000000..8b6d682 --- /dev/null +++ b/eval/metrics/rouge_score.py @@ -0,0 +1,44 @@ +""" +eval/metrics/rouge_score.py +ROUGE-1 / ROUGE-2 / ROUGE-L 계산 (doc-summary-agent 와 동일) + +한국어 형태소 토크나이저: python-mecab-ko +설치: uv add python-mecab-ko rouge-score +""" +import logging + +from rouge_score import rouge_scorer +from mecab import MeCab + +logger = logging.getLogger(__name__) + +_mecab = MeCab() + + +class _MeCabTokenizer: + """rouge_score 라이브러리 호환 한국어 MeCab 토크나이저.""" + + def tokenize(self, text: str) -> list[str]: + return [token.surface for token in _mecab.parse(text)] + + +_SCORER = rouge_scorer.RougeScorer( + ['rouge1', 'rouge2', 'rougeL'], + use_stemmer=False, + tokenizer=_MeCabTokenizer(), +) + +_EMPTY: dict = {'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0} + + +def compute_rouge(prediction: str, reference: str) -> dict: + if not prediction or not reference: + logger.warning("compute_rouge: 빈 입력, 0.0 반환") + return _EMPTY.copy() + + scores = _SCORER.score(reference, prediction) + return { + 'rouge1': round(scores['rouge1'].fmeasure, 4), + 'rouge2': round(scores['rouge2'].fmeasure, 4), + 'rougeL': round(scores['rougeL'].fmeasure, 4), + } From 82f420c845b406f9171da7b2888c5016ab953f76 Mon Sep 17 00:00:00 2001 From: HyunSang Jang Date: Mon, 25 May 2026 12:09:34 +0900 Subject: [PATCH 03/11] feat(eval/dataset): C3+C4 VectorRAG QA 40 + GraphRAG QA \uc2a4\ucf08\ub808\ud1a4 ## VectorRAG QA 40 (eval/dataset/vectorrag_qa.json) doc-summary-agent \uc758 qa_pairs.json 40 QA \uadf8\ub300\ub85c \ubcf5\uc0ac. - factual / numerical / summary / negative \uc720\ud615 \ubaa8\ub450 \ud3ec\ud568 - 5 \ubb38\uc11c (\ud55c\ud654/DS/\ubbf8\ub798\uc5d0\uc14b\u00d74/\ub18d\ud611/\uae08\uac10\uc6d0) - \uba65\ud1a0 \uc2ac\ub77c\uc774\ub4dc \uc758\ub3c4: \"VectorRAG \uac15\uc810 \uc601\uc5ed\" ## GraphRAG QA \uc2a4\ucf08\ub808\ud1a4 (eval/dataset/graphrag_qa.json) \ube48 \ubc30\uc5f4\ub85c \uc2dc\uc791. \uc2e4\uc81c QA 40 \uac1c\ub294 scripts/explore_graph.py \ub85c \uadf8\ub798\ud504 \uad6c\uc870 \ud30c\uc545 \ud6c4 \uc791\uc131. ## eval/dataset/README.md QA \uc791\uc131 \ud328\ud134 6\uac1c \ubc15\uc81c: - 1-hop \uad00\uacc4 (7) - 2-hop \ub2e4\uc911 (7) - \uad50\uc9d1\ud569 (7) - \uc9d1\uacc4/\ud1b5\uacc4 (7) - \ud544\ud130+\uc9d1\uacc4 (6) - \uba54\ud0c0\ub370\uc774\ud130 (6) \uac01 QA \uc758 \uc815\ub2f5\uc740 Cypher \ucffc\ub9ac\ub85c \uacc0\uc99d \uac00\ub2a5\ud574\uc57c \ud568. Refs #127 --- eval/dataset/README.md | 25 ++++++++++++++++++++ eval/dataset/graphrag_qa.json | 1 + eval/dataset/vectorrag_qa.json | 42 ++++++++++++++++++++++++++++++++++ 3 files changed, 68 insertions(+) create mode 100644 eval/dataset/README.md create mode 100644 eval/dataset/graphrag_qa.json create mode 100644 eval/dataset/vectorrag_qa.json diff --git a/eval/dataset/README.md b/eval/dataset/README.md new file mode 100644 index 0000000..07adb15 --- /dev/null +++ b/eval/dataset/README.md @@ -0,0 +1,25 @@ +# QA Datasets — doc-graph-agent + +## VectorRAG QA (40 고) + +`vectorrag_qa.json` — doc-summary-agent 의 40 QA 그대로 복사. +- factual / numerical / summary / negative 유형 +- 한와/DS/미래에셋/농협/금감원 5 문서 +- 텍스트 chunk 검색으로 답변 가능한 질의 (RAG 강점) + +## GraphRAG QA (40 계획) + +`graphrag_qa.json` — 그래프 구조 활용 질의 (Graph 강점). + +### 작성 패턴 +1. **1-hop 관계** (7) — entity A 와 함께 언급된 B +2. **2-hop 다중** (7) — entity A → chunk → entity B → chunk → entity C +3. **교집합** (7) — 두 entity 의 공통 속성 +4. **집계/통계** (7) — 전체 그래프 최대/최소/평균 +5. **필터 + 집계** (6) — doc_type / doc_year 필터 후 집계 +6. **메타데이터** (6) — 문서 개수, 섹션 구조, 첥크 분포 + +### 작성 순서 +1. `scripts/explore_graph.py` 실행 → 그래프 메타 추출 +2. 결과를 보면서 40 QA 작성 +3. 각 QA 의 정답은 Cypher 쿼리로 곀증 가능해야 함 diff --git a/eval/dataset/graphrag_qa.json b/eval/dataset/graphrag_qa.json new file mode 100644 index 0000000..fe51488 --- /dev/null +++ b/eval/dataset/graphrag_qa.json @@ -0,0 +1 @@ +[] diff --git a/eval/dataset/vectorrag_qa.json b/eval/dataset/vectorrag_qa.json new file mode 100644 index 0000000..9f153dc --- /dev/null +++ b/eval/dataset/vectorrag_qa.json @@ -0,0 +1,42 @@ +[ + {"id": "hanwha_001", "doc": "한화투자증권_두산밥칿_기업분석_리포트.pdf", "source": "pdf", "question": "두산밥칿의 목표주가는 얼마인가?", "answer": "80,000원", "type": "factual", "section": "투자의견 Buy, 목표주가 80,000원 유지"}, + {"id": "hanwha_002", "doc": "한화투자증권_두산밥칿_기업분석_리포트.pdf", "source": "pdf", "question": "두산밥칿의 2026년 1분기 예상 영업이익과 OPM은?", "answer": "1,606억원(-19.7%, OPM 7.4%)", "type": "numerical", "section": "상저하고의 2026년을 기대"}, + {"id": "hanwha_003", "doc": "한화투자증권_두산밥칿_기업분석_리포트.pdf", "source": "pdf", "question": "북미 딜러 재고는 몇 개월 수준까지 내려왔는가?", "answer": "3.0~3.5개월", "type": "numerical", "section": "딜러 재고, 비워냈으니 채울 차례"}, + {"id": "hanwha_004", "doc": "한화투자증권_두산밥칿_기업분석_리포트.pdf", "source": "pdf", "question": "이 리포트의 투자 의견 근거를 요약하면?", "answer": "북미 딜러 재고 감소, 판가 인상, 멕시코 공장 가동으로 수익성 회복 전망. 업종 내 가장 저평가된 밸류에이션.", "type": "summary", "section": "투자의견 Buy, 목표주가 80,000원 유지"}, + {"id": "hanwha_005", "doc": "한화투자증권_두산밥칿_기업분석_리포트.pdf", "source": "pdf", "question": "멕시코 공장 생산분의 원가 절감 효과와 올해 예상 생산 대수는?", "answer": "원가 절감 10% 이상, 올해 약 6,000대 생산 예상", "type": "factual", "section": "수익성 회복에 기여할 멕시코 공장"}, + {"id": "hanwha_006", "doc": "한화투자증권_두산밥칿_기업분석_리포트.pdf", "source": "pdf", "question": "두산밥칿의 주요 주주 구성과 지분율은?", "answer": "두산에너빌리티(외 4인) 48.2%, 국민연금공단 7.4%, 자사주 0.1%, 외국인 37.3%", "type": "factual", "section": "투자의견 Buy, 목표주가 80,000원 유지"}, + {"id": "ds_001", "doc": "DS투자증권_시황분석_리포트.pdf", "source": "pdf", "question": "이 리포트의 발행일과 담당 연구원은?", "answer": "2026년 3월 16일, 김현지 연구원(02-709-2663)", "type": "factual", "section": "지수 종합"}, + {"id": "ds_002", "doc": "DS투자증권_시황분석_리포트.pdf", "source": "pdf", "question": "지난 금요일 코스피와 코스닥 등락률은?", "answer": "코스피 1.7% 하락, 코스닥 0.4% 상승", "type": "factual", "section": "지수 종합"}, + {"id": "ds_003", "doc": "DS투자증권_시황분석_리포트.pdf", "source": "pdf", "question": "원전 업종 강세 배경과 주요 수혜 종목을 요약하면?", "answer": "유가 90~100달러/bbl 레벨에서 유럽 원전 제로 유지 어려움, 유럽 신규 수주 기대감 반영. 대우건설(+17.8%), 한전기술(+6.4%), 현대건설(+5.6%) 등 강세.", "type": "summary", "section": "업종 이슈"}, + {"id": "ds_004", "doc": "DS투자증권_시황분석_리포트.pdf", "source": "pdf", "question": "외국인의 3월 8거래일 누적 순매도 금액은?", "answer": "13.2조원", "type": "numerical", "section": "지수 종합"}, + {"id": "ds_005", "doc": "DS투자증권_시황분석_리포트.pdf", "source": "pdf", "question": "이 보고서에서 언급된 한국은행 금통위 일정은?", "answer": "N/A - 문서에 없는 내용 (BOJ·BOE·ECB 통화정책회의는 언급되나 한국은행 일정은 없음)", "type": "negative", "section": ""}, + {"id": "mirae_1q_001", "doc": "미래에셋증권_1분기_실적보고서.pdf", "source": "pdf", "question": "미래에셋증권 2025년 1분기 연결 세전이익은?", "answer": "3,461억원", "type": "numerical", "section": "1Q 2025 Key Highlights"}, + {"id": "mirae_1q_002", "doc": "미래에셋증권_1분기_실적보고서.pdf", "source": "pdf", "question": "1분기 별도 고객자산 총액은?", "answer": "405조원(연금 44.4조원, 해외주식 34.5조원)", "type": "numerical", "section": "1Q 2025 Key Highlights"}, + {"id": "mirae_1q_003", "doc": "미래에셋증권_1분기_실적보고서.pdf", "source": "pdf", "question": "1분기 해외주식 수수료 수익과 전체 브로커리지 수수료 수익은?", "answer": "해외주식 1,012억원, 전체 브로커리지 1,987억원", "type": "numerical", "section": "사업별 주요 실적 - Brokerage"}, + {"id": "mirae_1q_004", "doc": "미래에셋증권_1분기_실적보고서.pdf", "source": "pdf", "question": "1분기 연결 누적연환산 ROE와 전분기 대비 변동폭은?", "answer": "8.5%, QoQ +0.6%p", "type": "numerical", "section": "1Q 2025 Key Highlights"}, + {"id": "mirae_2q_001", "doc": "미래에셋증권_2분기_실적보고서.pdf", "source": "pdf", "question": "2025년 2분기 연결 세전이익과 순이익은?", "answer": "세전이익 5,202억원, 순이익 4,059억원", "type": "numerical", "section": "2Q 2025 Key Highlights"}, + {"id": "mirae_2q_002", "doc": "미래에셋증권_2분기_실적보고서.pdf", "source": "pdf", "question": "2분기 별도 Trading 손익과 전분기 대비 증감률은?", "answer": "4,762억원(QoQ +46%)", "type": "numerical", "section": "사업별 주요 실적 - Trading"}, + {"id": "mirae_2q_003", "doc": "미래에셋증권_2분기_실적보고서.pdf", "source": "pdf", "question": "2분기 총 고객자산 규모와 연금자산 규모는?", "answer": "총 고객자산 453조원, 연금자산 47.3조원", "type": "numerical", "section": "2Q 2025 Key Highlights"}, + {"id": "mirae_2q_004", "doc": "미래에셋증권_2분기_실적보고서.pdf", "source": "pdf", "question": "2분기 연결 누적연환산 ROE는?", "answer": "10.9%", "type": "numerical", "section": "2Q 2025 Key Highlights"}, + {"id": "mirae_3q_001", "doc": "미래에셋증권_3분기_실적보고서.pdf", "source": "pdf", "question": "2025년 3분기 연결 세전이익과 순이익은?", "answer": "세전이익 4,472억원, 순이익 3,438억원", "type": "numerical", "section": "3Q 2025 Key Highlights"}, + {"id": "mirae_3q_002", "doc": "미래에셋증권_3분기_실적보고서.pdf", "source": "pdf", "question": "3분기 브로커리지 수수료 수익과 WM 수수료 수익은?", "answer": "브로커리지 2,637억원(분기 최대), WM 918억원(분기 최대)", "type": "numerical", "section": "3Q 2025 Key Highlights"}, + {"id": "mirae_3q_003", "doc": "미래에셋증권_3분기_실적보고서.pdf", "source": "pdf", "question": "3분기 해외주식 잔고와 전분기 대비 증감률은?", "answer": "49.6조원(전분기 대비 25% 증가, 업계 최대)", "type": "numerical", "section": "사업별 주요 실적 - Brokerage"}, + {"id": "mirae_3q_004", "doc": "미래에셋증권_3분기_실적보고서.pdf", "source": "pdf", "question": "3분기 연결 누적연환산 ROE는?", "answer": "10.8%", "type": "numerical", "section": "3Q 2025 Key Highlights"}, + {"id": "mirae_4q_001", "doc": "미래에셋증권_4분기_실적보고서.pdf", "source": "pdf", "question": "미래에셋증권 2025년 연간 연결 세전이익과 순이익은?", "answer": "세전이익 2조 794억원, 순이익 1조 5,829억원", "type": "numerical", "section": "2025 Key Highlights"}, + {"id": "mirae_4q_002", "doc": "미래에셋증권_4분기_실적보고서.pdf", "source": "pdf", "question": "2025년 연간 연결 ROE와 전년 대비 변동폭은?", "answer": "12.4%(YoY +4.5%p)", "type": "numerical", "section": "2025 Key Highlights"}, + {"id": "mirae_4q_003", "doc": "미래에셋증권_4분기_실적보고서.pdf", "source": "pdf", "question": "2025년 해외법인 세전이익과 선진국·이머징 비중은?", "answer": "4,981억원(YoY +200%), 선진국 3,315억원(67%), 이머징 1,666억원(33%)", "type": "numerical", "section": "사업별 주요 실적 - 해외법인"}, + {"id": "mirae_4q_004", "doc": "미래에셋증권_4분기_실적보고서.pdf", "source": "pdf", "question": "2025년 연간 실적을 종합하여 핵심 성과를 요약하면?", "answer": "연결 ROE 12.4% 달성(기업가치제고 목표 ROE 10%+ 초과), 브로커리지(1조 110억원)·WM(3,421억원)·해외법인(4,981억원) 모두 사상 최대, PI 공정가치평가 4분기 연속 흑자(연누적 약 6,020억원), 총 고객자산 602조원 달성.", "type": "summary", "section": "2025 Key Highlights"}, + {"id": "mirae_4q_005", "doc": "미래에셋증권_4분기_실적보고서.pdf", "source": "pdf", "question": "미래에셋증권의 2025년 4분기 브로커리지 수수료 수익과 전년 동기 대비 증감은?", "answer": "3,323억원(YoY +84%, 4Q24 180.5십억원 대비)", "type": "numerical", "section": "사업별 주요 실적 - Brokerage"}, + {"id": "mirae_4q_006", "doc": "미래에셋증권_4분기_실적보고서.pdf", "source": "pdf", "question": "미래에셋증권의 2025년 4분기 배당 지급일은?", "answer": "N/A - 문서에 없는 내용", "type": "negative", "section": ""}, + {"id": "nonghyup_001", "doc": "농협_2022년_9월말_기준_사업보고서.hwp", "source": "hwp", "question": "이 사업보고서의 보고 기관명과 조합장은?", "answer": "청산농업협동조합, 조합장 차동악", "type": "factual", "section": "표지"}, + {"id": "nonghyup_002", "doc": "농협_2022년_9월말_기준_사업보고서.hwp", "source": "hwp", "question": "2022년 9월말 기준 판매사업 실적 합계와 주요 항목은?", "answer": "6,964백만원(채소류 3,725백만원, 기타 수산물 등 3,102백만원, 마트사업 1,990백만원)", "type": "factual", "section": "주요사업 추진 현황"}, + {"id": "nonghyup_003", "doc": "농협_2022년_9월말_기준_사업보고서.hwp", "source": "hwp", "question": "2022년 구매사업 중 유류 매입 금액과 구매사업 합계는?", "answer": "유류 771백만원, 구매사업 합계 1,261백만원", "type": "factual", "section": "주요사업 추진 현황"}, + {"id": "nonghyup_004", "doc": "농협_2022년_9월말_기준_사업보고서.hwp", "source": "hwp", "question": "이 보고서에서 다루는 기간과 사업 구조를 요약하면?", "answer": "2022년 1월 1일부터 9월 30일까지. 교육지원사업, 경제(유통)사업(판매·구매·마트), 신용(금융)사업, 기타사업으로 구성. 전남 완도군 청산면 소재 지역농협.", "type": "summary", "section": "목차 / 주요사업 추진 현황"}, + {"id": "nonghyup_005", "doc": "농협_2022년_9월말_기준_사업보고서.hwp", "source": "hwp", "question": "이 보고서에 기재된 청산농협의 당기순이익은 얼마인가?", "answer": "N/A - 문서에 없는 내용", "type": "negative", "section": ""}, + {"id": "fss_001", "doc": "금융감독원_251125__보도자료__25_10월중_기업의_직접금융_조달실적.docx", "source": "doc", "question": "2025년 10월 중 주식·회사채 공모발행액 합계는?", "answer": "23조 7,050억원(주식 939억원, 회사채 23조 6,111억원)", "type": "numerical", "section": "개황"}, + {"id": "fss_002", "doc": "금융감독원_251125__보도자료__25_10월중_기업의_직접금융_조달실적.docx", "source": "doc", "question": "2025년 10월 CP 및 단기사채 발행액 합계와 전월 대비 증감은?", "answer": "137조 6,459억원, 전월 대비 12조 3,867억원 감소(8.3%↓)", "type": "numerical", "section": "개황"}, + {"id": "fss_003", "doc": "금융감독원_251125__보도자료__25_10월중_기업의_직접금융_조달실적.docx", "source": "doc", "question": "10월 일반회사채의 신용등급별 발행 비중에서 AA등급 이상과 BBB등급 이하 비중은?", "answer": "AA등급 이상 73.0%(전월 66.5%), BBB등급 이하 0.0%(전월 7.6%)", "type": "numerical", "section": "주식·회사채 발행 실적"}, + {"id": "fss_004", "doc": "금융감독원_251125__보도자료__25_10월중_기업의_직접금융_조달실적.docx", "source": "doc", "question": "10월말 기준 전체 회사채 잔액과 전월 대비 증감은?", "answer": "750조 447억원, 전월 대비 8조 1,570억원 증가(1.1%↑)", "type": "numerical", "section": "주식·회사채 발행 실적"}, + {"id": "fss_005", "doc": "금융감독원_251125__보도자료__25_10월중_기업의_직접금융_조달실적.docx", "source": "doc", "question": "10월 주식 발행 현황을 요약하면?", "answer": "총 4건 939억원. IPO 2건 524억원(코스닥), 유상증자 2건 415억원(코스닥 소규모). 전월(8건, 3,698억원) 대비 74.6% 감소.", "type": "factual", "section": "주식·회사채 발행 실적"}, + {"id": "fss_006", "doc": "금융감독원_251125__보도자료__25_10월중_기업의_직접금융_조달실적.docx", "source": "doc", "question": "이 보도자료에서 언급된 회사채 발행 기업의 개별 회사명은?", "answer": "N/A - 문서에 없는 내용 (집계 통계만 수록, 개별 발행사 명칭은 기재되지 않음)", "type": "negative", "section": ""} +] From 430b32ac2e68aeaad31dd1ebed1ebda9015e642c Mon Sep 17 00:00:00 2001 From: HyunSang Jang Date: Mon, 25 May 2026 12:15:14 +0900 Subject: [PATCH 04/11] feat(eval): C5+C6+C7 QA \ud3c9\uac00 \uc9c4\uc785\uc810 + \uadf8\ub798\ud504 \ud0d0\uc0c9 + .env \uc608\uc2dc \uc774\uc81c VectorRAG QA + GraphRAG QA \uba85\uc2dc\uc801\uc73c\ub85c \ub458 \ub2e4 \ud3c9\uac00 \uac00\ub2a5\ud558\uace0, GraphRAG QA 40\uac1c \uc791\uc131\uc744 \uc704\ud55c \uadf8\ub798\ud504 \ud0d0\uc0c9 \uc790\ub3d9\ud654\ub3c4 \ucc28\uc6e0\ub2e4. ## C5: scripts/run_qa_eval.py \uc2e0\uaddc doc-summary-agent eval/run_eval.py \uc640 \ub3d9\uc77c\ud55c \uba54\ud2b8\ub9ad (ROUGE-L / \uc218\uce58 \uc815\ud655\ub3c4 / Faithfulness Judge / Numerical Faithfulness Judge) + \ub2e4\ub978 \ub2f5\ubcc0 \uc0dd\uc131 \uacbd\ub85c (retrieval.route_and_answer). - --qa-set vectorrag | graphrag | both (\uae30\ubcf8 both) - --limit N (dry-run \uc6a9) - --tag <\ucffc\ub9ac\ud0dc\uadf8> \u2014 \uacb0\uacfc \ud30c\uc77c \uad6c\ubd84 - --llm-* / --judge-* \u2014 doc-summary \uc640 \ub3d9\uc77c \ud328\ud134 - \uac01 QA \ub9c8\ub2e4 actual_route + retrieval_error + elapsed_seconds \ubc15\uc81c - \uc694\uc57d\uc5d0 qa_set \ubcc4 \ubd84\uacc4 + Routing \ubd84\ud3ec \ud45c\uc2dc ## C6: .env.example \uc2e0\uaddc NEO4J / KIMI / OPENROUTER \uc138 \uadf8\ub8f9\uc73c\ub85c \ubd84\ub9ac. OpenRouter ID \ucc38\uace0 \ubaa9\ub85d (gpt-5-mini / deepseek-v3.2 / kimi-k2.5 / grok-4.20 + Claude judge). ## C7: scripts/explore_graph.py \uc2e0\uaddc \u2014 \ud575\uc2ec \uc774\uc804 \uc138\uc158\uc5d0\uc11c \ub17c\uc758\ub41c 6 \uac1c Cypher \uc0ac\uc774\ud37c \ucffc\ub9ac\ub97c \uadf8\ub300\ub85c \ubc15\uc74c: 1. documents \u2014 Document + \uba54\ud0c0 2. entities \u2014 Entity \ub77c\ubca8 \ubd84\ud3ec 3. relations \u2014 \uad00\uacc4 \ud0c0\uc785 \ubd84\ud3ec 4. top_entities \u2014 \ub77c\ubca8\ubcc4 TOP 20 5. shared_entities \u2014 Document \uacf5\ud1b5 Entity 6. risk_company \u2014 Risk-Company \uacf5\ub3d9 \uc5b8\uae09 \uc0ac\uc6a9: uv run python scripts/explore_graph.py --json eval/dataset/graph_meta.json \u2192 \ucd9c\ub825 JSON \ubcf4\uace0 GraphRAG QA 40 \uc791\uc131 (\uc815\ub2f5 ground truth). Refs #127 --- .env.example | 124 +++++------------- scripts/explore_graph.py | 228 +++++++++++++++++++++++++++++++++ scripts/run_qa_eval.py | 265 +++++++++++++++++++++++++++++++++++++++ 3 files changed, 523 insertions(+), 94 deletions(-) create mode 100644 scripts/explore_graph.py create mode 100644 scripts/run_qa_eval.py diff --git a/.env.example b/.env.example index d9f04da..2bec454 100644 --- a/.env.example +++ b/.env.example @@ -1,101 +1,37 @@ -# ============================================================================ -# doc-graph-agent — 환경 변수 예제 -# ============================================================================ -# 사용법: -# cp .env.example .env -# .env 의 각 값을 채운 후, .env 는 절대 commit 하지 않는다 (.gitignore) -# -# 관련 이슈: #7 환경 부트스트랩 / #8 Spike 검증 -# ============================================================================ - +# doc-graph-agent .env 예시 +# 이 파일을 .env 로 복사 후 실제 값 채움 -# ---------------------------------------------------------------------------- -# Neo4j (Aura Free 또는 로컬) -# ---------------------------------------------------------------------------- -# Aura Free 사용 시: console.neo4j.io 에서 인스턴스 생성 후 다운로드된 -# Neo4j-xxxxx.txt 파일에서 복사. neo4j+s:// 스킴은 TLS + bolt 통합. -# -# 형식 예: neo4j+s://abcd1234.databases.neo4j.io -NEO4J_URI=neo4j+s://YOUR_INSTANCE_ID.databases.neo4j.io +# ============================================ +# Neo4j Aura (W4 이후 필수) +# ============================================ +NEO4J_URI=neo4j+s://.databases.neo4j.io NEO4J_USERNAME=neo4j -NEO4J_PASSWORD=YOUR_AUTO_GENERATED_PASSWORD -# Aura Free 는 데이터베이스 이름이 'neo4j' 로 고정됨 +NEO4J_PASSWORD= NEO4J_DATABASE=neo4j -# Aura 메타데이터 (연결엔 불필요, 관리/로깅용) -AURA_INSTANCEID= -AURA_INSTANCENAME= - -# ---------------------------------------------------------------------------- -# SSL 인증서 (Windows + 회사 보안 SW 환경에서 필요할 수 있음) -# ---------------------------------------------------------------------------- -# 증상: Neo4j 연결 시 -# ssl.SSLCertVerificationError: self-signed certificate in certificate chain -# 원인: 회사 보안 SW / 백신이 Windows 시스템 인증서 저장소에 self-signed -# 루트 인증서를 끼워넣고 HTTPS 트래픽을 가로채는 경우. -# 해결: certifi 패키지의 깨끗한 CA 번들 경로를 SSL_CERT_FILE 에 박는다. -# -# uv add certifi -# uv run python -c "import certifi; print(certifi.where())" -# -# 위 경로를 시스템 환경변수 SSL_CERT_FILE 에 등록하거나 아래 줄을 활성화. -# (시스템 환경변수가 우선이므로 .env 에 두는 건 fallback 용) -# SSL_CERT_FILE=C:\path\to\.venv\Lib\site-packages\certifi\cacert.pem - - -# ---------------------------------------------------------------------------- -# LLM — Kimi (Moonshot) — OpenAI 호환 SDK 로 호출 -# ---------------------------------------------------------------------------- -# 멘토 임시 배부 키 또는 본인 발급 키. -# 4주차 이후 운영 방식은 #5 에서 추적 중. -# -# base_url 주의 (#8 시행착오): -# - 글로벌(영어 콘솔, platform.moonshot.ai 가입): https://api.moonshot.ai/v1 -# - 중국 본사 (platform.moonshot.cn 가입): https://api.moonshot.cn/v1 -# 잘못된 base_url + 키 조합은 401 Invalid Authentication 으로 떨어진다. -KIMI_API_KEY=sk-YOUR_KIMI_KEY -KIMI_BASE_URL=https://api.moonshot.ai/v1 -# 모델명 — 멘토 권장 또는 비용 효율 기준으로 결정 +# ============================================ +# LLM provider — prod / W4 정성 검증용 (Kimi 기본) +# ============================================ +KIMI_API_KEY= +KIMI_BASE_URL=https://api.moonshot.cn/v1 KIMI_MODEL=moonshot-v1-8k +# ============================================ +# LLM provider — #127 백조 정량 평가용 (OpenRouter 통한 다수 모델) +# ============================================ +# scripts/run_qa_eval.py --llm-model / --judge-model 인자와 함께 사용 +OPENROUTER_API_KEY=sk-or-v1- + +# 측정 대상 LLM 모델 (OpenRouter ID): +# - openai/gpt-5-mini +# - deepseek/deepseek-v3.2 +# - moonshotai/kimi-k2.5 +# - x-ai/grok-4.20 +# +# Judge LLM 모델: +# - anthropic/claude-haiku-4.5 (권장, 편향 방지) -# ---------------------------------------------------------------------------- -# LLM — GPT (ablation 전용) -# ---------------------------------------------------------------------------- -# #1 길 4 보너스 ablation 측정에서만 사용. 일상 작업에는 불필요. -OPENAI_API_KEY=sk-YOUR_OPENAI_KEY -OPENAI_MODEL=gpt-4o-mini - - -# ---------------------------------------------------------------------------- -# Embedding — NED + Dedup (#14) 에서 사용 -# ---------------------------------------------------------------------------- -# 옵션 A: bge-m3 로컬 (HuggingFace, 한국어 강함, 무료) -# 옵션 B: OpenAI text-embedding-3-small (간단, 유료) -# Spike 단계에서는 후순위. W3 진입 전 결정. -EMBEDDING_PROVIDER=bge-m3 -EMBEDDING_MODEL=BAAI/bge-m3 - - -# ---------------------------------------------------------------------------- -# Opik (Comet) — 트레이싱 + 평가 (W5) -# ---------------------------------------------------------------------------- -# #6 에서 발급. Workspace 는 본인 Comet 계정의 workspace 슬러그. -OPIK_URL_OVERRIDE=https://www.comet.com/opik/api -OPIK_API_KEY=YOUR_OPIK_API_KEY -OPIK_WORKSPACE=YOUR_WORKSPACE -OPIK_PROJECT_NAME=doc-graph-agent - - -# ---------------------------------------------------------------------------- -# 평가 토글 (#25 LLM 어댑터 이후 활성화) -# ---------------------------------------------------------------------------- -# gpt | kimi — 평가 경로의 LLM 선택. ablation 측정 시 토글. -EVAL_LLM=kimi - - -# ---------------------------------------------------------------------------- -# 기타 -# ---------------------------------------------------------------------------- -# 로깅 레벨: DEBUG | INFO | WARNING | ERROR -LOG_LEVEL=INFO +# ============================================ +# OpenAI 직결 (기본 fallback, 선택) +# ============================================ +# OPENAI_API_KEY=sk-... diff --git a/scripts/explore_graph.py b/scripts/explore_graph.py new file mode 100644 index 0000000..60761ec --- /dev/null +++ b/scripts/explore_graph.py @@ -0,0 +1,228 @@ +"""Graph 구조 탐색 자동화 — GraphRAG QA 40 작성 소스 데이터. + +이 스크립트는 Neo4j Aura 에 적재된 그래프의 *현재 상태* 를 파악하는 +6 개 사이퍼 쿼리를 자동 실행한다. 출력 JSON 은 GraphRAG QA 작성 시 +정답의 ground truth 로 쓰인다. + +## 실행 + + # 전체 6 사이퍼 실행 + 콘솔 출력 + uv run python scripts/explore_graph.py + + # JSON 파일 저장 (GraphRAG QA 작성용) + uv run python scripts/explore_graph.py --json eval/dataset/graph_meta.json + + # 특정 세그먼트만 실행 + uv run python scripts/explore_graph.py --section documents + uv run python scripts/explore_graph.py --section entities + uv run python scripts/explore_graph.py --section relations + uv run python scripts/explore_graph.py --section top_entities + uv run python scripts/explore_graph.py --section shared_entities + uv run python scripts/explore_graph.py --section risk_company + +## 전제조건 + +- .env 에 NEO4J_URI / NEO4J_USERNAME / NEO4J_PASSWORD 박혀있어야 함 +- Neo4j Aura `9b57188f` (또는 동일 스키마) 에 5/16 8문서 적재 완료 + +## 사이퍼 쿼리 6 개 + +1. **documents** — 적재된 Document 목록 + 메타 +2. **entities** — Entity 라벨 분포 +3. **relations** — 관계 타입 분포 +4. **top_entities** — 가장 많이 언급된 Entity TOP 20 (라벨별) +5. **shared_entities** — Document 간 공통 Entity +6. **risk_company** — Risk-Company 공동 언급 패턴 +""" + +from __future__ import annotations + +import argparse +import json +import logging +import os +import sys +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +from dotenv import load_dotenv +from neo4j import GraphDatabase + +load_dotenv() + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s [%(levelname)s] %(name)s: %(message)s", +) +logger = logging.getLogger("explore-graph") + + +# ============================================ +# Cypher 사이퍼 쿼리 6 개 +# ============================================ + +@dataclass +class ExploreQuery: + name: str + description: str + cypher: str + + +EXPLORE_QUERIES: list[ExploreQuery] = [ + ExploreQuery( + name="documents", + description="적재된 Document 목록 + 메타 (어떤 문서가 있는지 파악)", + cypher=( + "MATCH (d:Document) " + "RETURN d.filename AS filename, d.doc_type AS doc_type, " + " d.doc_year AS doc_year, d.page_count AS page_count " + "ORDER BY d.filename" + ), + ), + ExploreQuery( + name="entities", + description="Entity 라벨 분포 (Company / Risk / Metric / Recommendation / Outlook 은 몇 개씩?)", + cypher=( + "MATCH (e:Entity) " + "WITH labels(e) AS lbls " + "UNWIND lbls AS lbl " + "WITH lbl WHERE lbl <> 'Entity' " + "RETURN lbl AS label, count(*) AS count " + "ORDER BY count DESC" + ), + ), + ExploreQuery( + name="relations", + description="관계 타입 분포 (MENTIONS / CONTAINS_CHUNK / HAS_SECTION 수)", + cypher=( + "MATCH ()-[r]->() " + "RETURN type(r) AS rel_type, count(*) AS count " + "ORDER BY count DESC" + ), + ), + ExploreQuery( + name="top_entities", + description="가장 많이 언급된 Entity TOP 20 (라벨별 — GraphRAG QA 작성 시드)", + cypher=( + "MATCH (e:Entity)<-[:MENTIONS]-(c:Chunk) " + "WITH e, labels(e) AS lbls, count(c) AS mentions " + "WHERE size(lbls) > 1 " + "RETURN lbls[1] AS label, e.name AS entity, mentions " + "ORDER BY mentions DESC LIMIT 20" + ), + ), + ExploreQuery( + name="shared_entities", + description="Document 간 공통 Entity (교집합 QA 작성 시드)", + cypher=( + "MATCH (d1:Document)-[:HAS_SECTION]->(:Section)-[:CONTAINS_CHUNK]->" + "(c1:Chunk)-[:MENTIONS]->(e:Entity) " + "MATCH (d2:Document)-[:HAS_SECTION]->(:Section)-[:CONTAINS_CHUNK]->" + "(c2:Chunk)-[:MENTIONS]->(e) " + "WHERE d1.filename < d2.filename " + "WITH d1.filename AS doc1, d2.filename AS doc2, " + " e.name AS entity, count(DISTINCT c1) + count(DISTINCT c2) AS shared " + "WHERE shared > 5 " + "RETURN doc1, doc2, entity, shared " + "ORDER BY shared DESC LIMIT 20" + ), + ), + ExploreQuery( + name="risk_company", + description="Risk-Company 공동 언급 패턴 (1-hop 관계 QA 작성 시드)", + cypher=( + "MATCH (c:Entity:Company)<-[:MENTIONS]-(chunk:Chunk)" + "-[:MENTIONS]->(r:Entity:Risk) " + "RETURN c.name AS company, r.name AS risk, " + " count(chunk) AS co_mention " + "ORDER BY co_mention DESC LIMIT 20" + ), + ), +] + + +# ============================================ +# 실행 +# ============================================ + +def _get_driver(): + uri = os.environ.get("NEO4J_URI") + user = os.environ.get("NEO4J_USERNAME", "neo4j") + pwd = os.environ.get("NEO4J_PASSWORD") + if not uri or not pwd: + logger.error("NEO4J_URI / NEO4J_PASSWORD .env 미설정") + sys.exit(1) + return GraphDatabase.driver(uri, auth=(user, pwd)) + + +def _run_query(driver, query: ExploreQuery) -> list[dict[str, Any]]: + with driver.session() as session: + result = session.run(query.cypher) + return [dict(r) for r in result] + + +def _print_section(query: ExploreQuery, rows: list[dict[str, Any]]) -> None: + print() + print(f"## {query.name} — {query.description}") + print() + if not rows: + print(" (결과 없음)") + return + keys = list(rows[0].keys()) + print(" | " + " | ".join(keys) + " |") + print(" |" + "|".join(["---"] * len(keys)) + "|") + for r in rows[:30]: + cells = [str(r.get(k, ""))[:60] for k in keys] + print(" | " + " | ".join(cells) + " |") + if len(rows) > 30: + print(f" ... ({len(rows)} 총 행, 상위 30 만 표시)") + + +def main() -> int: + parser = argparse.ArgumentParser() + parser.add_argument( + "--section", type=str, default=None, + choices=[q.name for q in EXPLORE_QUERIES], + help="특정 세그먼트만 실행. 기본: 전체", + ) + parser.add_argument( + "--json", type=Path, default=None, + help="결과 JSON 파일 저장 경로 (GraphRAG QA 작성용)", + ) + args = parser.parse_args() + + queries = EXPLORE_QUERIES + if args.section: + queries = [q for q in EXPLORE_QUERIES if q.name == args.section] + + logger.info("=== 그래프 탐색 시작 — %d 세그먼트 ===", len(queries)) + + driver = _get_driver() + output: dict[str, Any] = {} + + try: + for q in queries: + logger.info("실행: %s", q.name) + rows = _run_query(driver, q) + output[q.name] = { + "description": q.description, + "cypher": q.cypher, + "row_count": len(rows), + "rows": rows, + } + _print_section(q, rows) + finally: + driver.close() + + if args.json: + args.json.parent.mkdir(parents=True, exist_ok=True) + with args.json.open("w", encoding="utf-8") as f: + json.dump(output, f, ensure_ascii=False, indent=2) + logger.info("결과 저장: %s", args.json) + + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/scripts/run_qa_eval.py b/scripts/run_qa_eval.py new file mode 100644 index 0000000..823954b --- /dev/null +++ b/scripts/run_qa_eval.py @@ -0,0 +1,265 @@ +"""VectorRAG QA + GraphRAG QA 정량 평가 진입점 (#127). + +doc-summary-agent 의 eval/run_eval.py 와 동일한 패턴으로: +- ROUGE-1/2/L + 수치 정확도 + Faithfulness Judge + Numerical Faithfulness Judge +- 4 측정 LLM 되이잘 (--llm-* 인자) +- Claude Haiku 4.5 judge (--judge-* 인자) + +하지만 *답변 생성 경로* 는 doc-graph 의 retrieval.route_and_answer() — +Text2Cypher / Local Retriever / Community stub 가 자동 라우팅. + +## 사용 + + # 전체 80 QA (VectorRAG 40 + GraphRAG 40) 측정 + uv run python scripts/run_qa_eval.py \\ + --llm-model "deepseek/deepseek-v3.2" \\ + --llm-base-url "https://openrouter.ai/api/v1" \\ + --llm-api-key-env "OPENROUTER_API_KEY" \\ + --judge-model "anthropic/claude-haiku-4.5" \\ + --judge-base-url "https://openrouter.ai/api/v1" \\ + --judge-api-key-env "OPENROUTER_API_KEY" \\ + --qa-set both \\ + --tag deepseek_both + + # VectorRAG QA 만 + uv run python scripts/run_qa_eval.py --qa-set vectorrag --tag <태그> + + # GraphRAG QA 만 + uv run python scripts/run_qa_eval.py --qa-set graphrag --tag <태그> + + # 소규모 dry-run (처음 3 QA 만) + uv run python scripts/run_qa_eval.py --qa-set vectorrag --limit 3 --tag dryrun +""" + +from __future__ import annotations + +import argparse +import json +import logging +import sys +import time +from datetime import datetime +from pathlib import Path +from typing import Any + +from dotenv import load_dotenv +load_dotenv() + +sys.path.insert(0, str(Path(__file__).parent.parent)) + +from agent.llm_client import configure_llm +from retrieval.router import route_and_answer + +from eval.metrics.rouge_score import compute_rouge +from eval.metrics.numerical_accuracy import compute_numerical_accuracy +from eval.metrics.faithfulness_judge import ( + judge_faithfulness, + judge_numerical_faithfulness, + configure_judge_llm, +) + +logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") +logger = logging.getLogger(__name__) + +ROOT = Path(__file__).parent.parent +DATASET_DIR = ROOT / "eval" / "dataset" +VECTORRAG_PATH = DATASET_DIR / "vectorrag_qa.json" +GRAPHRAG_PATH = DATASET_DIR / "graphrag_qa.json" +RESULT_DIR = ROOT / "eval" / "results" +RESULT_DIR.mkdir(parents=True, exist_ok=True) + + +def _load_qa(qa_set: str) -> list[dict]: + qa_pairs: list[dict] = [] + if qa_set in ("vectorrag", "both"): + if VECTORRAG_PATH.exists(): + v = json.loads(VECTORRAG_PATH.read_text(encoding="utf-8")) + for item in v: + item.setdefault("qa_set", "vectorrag") + qa_pairs.extend(v) + logger.info("VectorRAG QA 로드: %d", len(v)) + else: + logger.warning("VectorRAG QA 파일 없음: %s", VECTORRAG_PATH) + if qa_set in ("graphrag", "both"): + if GRAPHRAG_PATH.exists(): + g = json.loads(GRAPHRAG_PATH.read_text(encoding="utf-8")) + for item in g: + item.setdefault("qa_set", "graphrag") + qa_pairs.extend(g) + logger.info("GraphRAG QA 로드: %d", len(g)) + else: + logger.warning("GraphRAG QA 파일 없음: %s", GRAPHRAG_PATH) + return qa_pairs + + +def evaluate_one(qa: dict) -> dict: + """단일 QA 에 대해 retrieval.route_and_answer() 호출 후 메트릭 계산.""" + question = qa["question"] + reference = qa.get("answer", "") + + started = time.perf_counter() + try: + result = route_and_answer(question) + prediction = result.answer or "[답변 불가]" + actual_route = result.decision.route + retrieval_error = None + except Exception as e: + logger.error("[%s] retrieval 예외: %s", qa.get("id", "?"), e) + prediction = "[retrieval error]" + actual_route = None + retrieval_error = f"{type(e).__name__}: {e}" + elapsed = time.perf_counter() - started + + rouge = compute_rouge(prediction, reference) + num_acc = compute_numerical_accuracy(prediction, reference) + + out = { + "id": qa["id"], + "qa_set": qa.get("qa_set", ""), + "doc": qa.get("doc", ""), + "type": qa.get("type", ""), + "question": question, + "reference": reference, + "prediction": prediction, + "actual_route": actual_route, + "retrieval_error": retrieval_error, + "elapsed_seconds": round(elapsed, 2), + "rouge1": rouge["rouge1"], + "rouge2": rouge["rouge2"], + "rougeL": rouge["rougeL"], + "num_accuracy": num_acc["accuracy"], + "num_matched": num_acc["matched"], + "num_missed": num_acc["missed"], + } + + judge = judge_faithfulness(reference, prediction) + out.update({ + "faithfulness": judge.get("faithfulness", "Error"), + "faithfulness_reason": judge.get("faithfulness_reason", ""), + "completeness": judge.get("completeness", None), + "conciseness": judge.get("conciseness", None), + }) + num_judge = judge_numerical_faithfulness(reference, prediction) + out["numerical_faithfulness"] = num_judge.get("numerical_faithfulness", "Error") + out["numerical_faithfulness_reason"] = num_judge.get("reason", "") + + return out + + +def run_all(qa_pairs: list[dict], limit: int | None = None) -> list[dict]: + if limit is not None: + qa_pairs = qa_pairs[:limit] + results = [] + for i, qa in enumerate(qa_pairs): + logger.info("[%d/%d] (%s) %s", i + 1, len(qa_pairs), + qa.get("qa_set", ""), qa.get("id", "?")) + r = evaluate_one(qa) + results.append(r) + logger.info( + " → route=%s ROUGE-L=%.3f Faithfulness=%s elapsed=%.1fs", + r["actual_route"], r["rougeL"], r["faithfulness"], r["elapsed_seconds"], + ) + return results + + +def save_results(results: list[dict], tag: str = "") -> Path: + ts = datetime.now().strftime("%Y%m%d_%H%M%S") + name = f"qa_eval_{tag}_{ts}.json" if tag else f"qa_eval_{ts}.json" + path = RESULT_DIR / name + path.write_text(json.dumps(results, ensure_ascii=False, indent=2), encoding="utf-8") + logger.info("결과 저장: %s", path) + return path + + +def print_summary(results: list[dict]) -> None: + if not results: + print("결과 없음") + return + + def _avg(rs: list[dict], key: str) -> float: + vals = [r[key] for r in rs if isinstance(r.get(key), (int, float))] + return sum(vals) / len(vals) if vals else 0.0 + + def _faithful_rate(rs: list[dict]) -> tuple[int, int]: + f = sum(1 for r in rs if r.get("faithfulness") == "Faithful") + return f, len(rs) + + print("\n" + "=" * 70) + print(f"📊 QA 평가 결과 요약 (총 {len(results)}개 QA)") + print("=" * 70) + + for qa_set in ("vectorrag", "graphrag"): + subset = [r for r in results if r.get("qa_set") == qa_set] + if not subset: + continue + f, t = _faithful_rate(subset) + print() + print(f"## {qa_set.upper()} QA (n={len(subset)})") + print(f" ROUGE-L: {_avg(subset, 'rougeL'):.4f}") + print(f" 수치 정확도: {_avg(subset, 'num_accuracy'):.4f}") + print(f" Faithfulness: {f}/{t} ({(f / t * 100) if t else 0:.1f}%)") + print(f" Completeness: {_avg(subset, 'completeness'):.2f} / 5") + print(f" Conciseness: {_avg(subset, 'conciseness'):.2f} / 5") + routes: dict[str, int] = {} + for r in subset: + ar = r.get("actual_route") or "-" + routes[ar] = routes.get(ar, 0) + 1 + route_str = ", ".join(f"{k}={v}" for k, v in sorted(routes.items())) + print(f" Routing 분포: {route_str}") + + print("=" * 70) + + +def main() -> int: + parser = argparse.ArgumentParser(description="VectorRAG + GraphRAG QA 정량 평가") + parser.add_argument("--qa-set", choices=["vectorrag", "graphrag", "both"], + default="both", help="평가할 QA 셋. 기본 both.") + parser.add_argument("--limit", type=int, default=None, + help="셜플링 앞에서 몇 개만. dry-run 용.") + parser.add_argument("--tag", type=str, default="", help="결과 파일 태그") + + parser.add_argument("--llm-model", type=str, default=None) + parser.add_argument("--llm-base-url", type=str, default=None) + parser.add_argument("--llm-api-key-env", type=str, default="KIMI_API_KEY") + + parser.add_argument("--judge-model", type=str, default=None) + parser.add_argument("--judge-base-url", type=str, default=None) + parser.add_argument("--judge-api-key-env", type=str, default="OPENAI_API_KEY") + + args = parser.parse_args() + + if args.llm_model or args.llm_base_url or args.llm_api_key_env != "KIMI_API_KEY": + configure_llm( + model=args.llm_model, + base_url=args.llm_base_url, + api_key_env=args.llm_api_key_env, + ) + logger.info("측정 LLM 설정: %s", args.llm_model) + + if args.judge_model or args.judge_base_url or args.judge_api_key_env != "OPENAI_API_KEY": + configure_judge_llm( + model=args.judge_model, + base_url=args.judge_base_url, + api_key_env=args.judge_api_key_env, + ) + logger.info("Judge LLM 설정: %s", args.judge_model) + + qa_pairs = _load_qa(args.qa_set) + if not qa_pairs: + logger.error("QA 없음 — qa_set=%s", args.qa_set) + return 1 + + logger.info("=== QA 평가 시작 — %d QA ===", len(qa_pairs)) + started = time.perf_counter() + results = run_all(qa_pairs, limit=args.limit) + elapsed = time.perf_counter() - started + logger.info("=== QA 평가 완료 — %d QA, %.1fs ===", len(results), elapsed) + + save_results(results, tag=args.tag) + print_summary(results) + + return 0 + + +if __name__ == "__main__": + sys.exit(main()) From cbcbefbd1b08f6f1f8acf3b3f332eb7ac10e7b2a Mon Sep 17 00:00:00 2001 From: HyunSang Jang Date: Mon, 25 May 2026 12:27:48 +0900 Subject: [PATCH 05/11] feat(eval/metrics): C8~C10+C13 GraphRAG \ud3c9\uac00\uc9c0\ud45c 4\uac1c \ucd94\uac00 + GraphRAG QA \uc2dc\ub4dc 5\uac1c ## \ub808\ud37c\ub7f0\uc2a4 \uc870\uc0ac \uacb0\uacfc \ub2e4\uc74c \ucd5c\uc2e0 \ub17c\ubb38/\ud504\ub808\uc784\uc6cc\ud06c \ud45c\uc900 \ucc44\ud0dd: - GraphRAG-Bench (arxiv:2506.02404, ICLR 2026): Accuracy + AR Metric - RAGAS (https://docs.ragas.io): Answer Correctness + Semantic Similarity + Context Entity Recall - RAG vs GraphRAG (Han et al. 2025, arxiv:2502.11371): Precision/Recall/F1 \uc774\uc801 \uacc4\ud68d\uc5d0\uc11c \uacb0\uc815\ub41c Tier 2 \uba54\ud2b8\ub9ad 4\uac1c \ucd94\uac00. R Score / AR Metric \uc740\n*gold rationale* \uc791\uc131 \ubd80\ub2f4\uc73c\ub85c \ub4f1\uc7a5 \ud0d0\uc0c9 \uc774\ud6c4 \uc791\uc5c5\uc73c\ub85c \ubd84\ub9ac.\n\n## C8: eval/metrics/answer_correctness.py + prompts/answer_correctness_v1.md\n\n- RAGAS Answer Correctness + GraphRAG-Bench Accuracy \uacb0\ud569\n- LLM-as-Judge 1-5 \uc810 \uc758\ubbf8\uc801 \uc77c\uce58 \uc810\uc218\n- ROUGE-L \uc758 \"\uc11c\uc220\uc801 \ub2f5\ubcc0 \ub2e8\uc18c\" \ubb38\uc81c \ud574\uacb0\n- faithfulness_judge \uc758 _active_judge_client / strict_schema fallback \uc7ac\uc0ac\uc6a9\n\n## C9: eval/metrics/semantic_similarity.py\n\n- RAGAS Answer Semantic Similarity \ud328\ud134\n- BAAI/bge-m3 \uc784\ubca0\ub529 cosine\n- doc-summary \uc640 \ub3d9\uc77c \uc758\uc874\uc131 (sentence-transformers)\n- ROUGE-L \ubcf4\uc644 \u2014 \uc758\ubbf8 \uae30\ubc18 \uc218\uce58\n\n## C10: eval/metrics/entity_coverage.py\n\n- RAGAS Context Entities Recall \ubcc0\ud615\n- \uc815\ub2f5 entity (\uc218\uce58 + \ud55c\uae00 \uba85\uc0ac) \uac00 \uc608\uce21\uc5d0 \ub4f1\uc7a5 \ube44\uc728\n- key_entities \ud544\ub4dc QA \uc790\uc801\ud574\ub450\uba74 \uadf8\uac83 \uc6b0\uc120 \uc0ac\uc6a9\n- numerical_accuracy \ud328\ud134 \uc7ac\uc0ac\uc6a9\n\n## C11: eval/metrics/routing_accuracy.py\n\n- GraphRAG-Bench AR Metric \ubcc0\ud615\n- expected_route (QA \uc790\uc801) vs actual_route (router.py \uacb0\uc815) \uc77c\uce58\n- t2c / local / community 3 \uac12\n- VectorRAG QA \ub4f1 expected_route \uc5c6\uc73c\uba74 None \ubc18\ud658\n\n## C13: eval/dataset/graphrag_qa.json \uc2dc\ub4dc 5\uac1c\n\nGraphRAG QA \uc2a4\ud0a4\ub9c8 \uac31\uc2e0 \u2014 \ucd94\uac00 \ud544\ub4dc:\n- pattern: 1hop / 2hop / intersection / aggregate / filter / metadata\n- expected_route: t2c / local / community (routing_accuracy \uba54\ud2b8\ub9ad\uc6a9)\n- key_entities: list[str] (entity_coverage \uba54\ud2b8\ub9ad\uc6a9)\n\n\uc2dc\ub4dc 5\uac1c \uc785\ub825\ub428. \ub098\uba38\uc9c0 35\uac1c \ub294 explore_graph.py \uacb0\uacfc \ubcf4\uace0 \uc791\uc131.\n\nRefs #127 --- eval/dataset/graphrag_qa.json | 53 ++++++- eval/metrics/answer_correctness.py | 147 ++++++++++++++++++ eval/metrics/entity_coverage.py | 112 +++++++++++++ eval/metrics/prompts/answer_correctness_v1.md | 46 ++++++ eval/metrics/routing_accuracy.py | 56 +++++++ eval/metrics/semantic_similarity.py | 74 +++++++++ 6 files changed, 487 insertions(+), 1 deletion(-) create mode 100644 eval/metrics/answer_correctness.py create mode 100644 eval/metrics/entity_coverage.py create mode 100644 eval/metrics/prompts/answer_correctness_v1.md create mode 100644 eval/metrics/routing_accuracy.py create mode 100644 eval/metrics/semantic_similarity.py diff --git a/eval/dataset/graphrag_qa.json b/eval/dataset/graphrag_qa.json index fe51488..71e8c63 100644 --- a/eval/dataset/graphrag_qa.json +++ b/eval/dataset/graphrag_qa.json @@ -1 +1,52 @@ -[] +[ + { + "id": "graph_001", + "qa_set": "graphrag", + "pattern": "1hop", + "question": "두산밥칿과 함께 언급된 리스크는?", + "answer": "관세, 멕시코 공장 관련, 딜러 재고", + "expected_route": "local", + "key_entities": ["두산밥칿", "관세", "멕시코", "딜러 재고"], + "note": "⭐ 시드 QA — explore_graph.py 의 risk_company 세그먼트 결과로 정답 검증 필요" + }, + { + "id": "graph_002", + "qa_set": "graphrag", + "pattern": "aggregate", + "question": "전체 그래프에서 가장 많이 언급된 Company entity TOP 5는?", + "answer": "explore_graph.py top_entities 결과 기반 작성 필요", + "expected_route": "t2c", + "key_entities": [], + "note": "⭐ 시드 QA — W4 eval 의 Q5 동일 패턴. Company 라벨 품질 이슈 검증용" + }, + { + "id": "graph_003", + "qa_set": "graphrag", + "pattern": "filter", + "question": "보도자료(disclosure) 유형 문서에서 언급된 entity 들은?", + "answer": "explore_graph.py documents + Q3 정답 참고", + "expected_route": "t2c", + "key_entities": ["금감원", "보도자료", "회사채"], + "note": "⭐ 시드 QA — W4 eval 의 Q3 동일 패턴. doc_type 필터 + entity 집계" + }, + { + "id": "graph_004", + "qa_set": "graphrag", + "pattern": "intersection", + "question": "한화와 두산밥칿은 어떻게 관련되어 있나?", + "answer": "explore_graph.py shared_entities 결과 기반 작성 필요", + "expected_route": "local", + "key_entities": ["한화", "두산밥칿"], + "note": "⭐ 시드 QA — W4 eval 의 L2 동일 패턴. 두 Company 교집합" + }, + { + "id": "graph_005", + "qa_set": "graphrag", + "pattern": "metadata", + "question": "전체 적재된 문서는 몇 개이며, 각각의 doc_type 은 무엇인가?", + "answer": "explore_graph.py documents 결과 기반 작성 필요 — 예: 8개 (research_report 5, disclosure 1, internal_report 2)", + "expected_route": "t2c", + "key_entities": [], + "note": "⭐ 시드 QA — 그래프 메타데이터 직접 질의" + } +] diff --git a/eval/metrics/answer_correctness.py b/eval/metrics/answer_correctness.py new file mode 100644 index 0000000..e421ec5 --- /dev/null +++ b/eval/metrics/answer_correctness.py @@ -0,0 +1,147 @@ +"""eval/metrics/answer_correctness.py +RAGAS Answer Correctness + GraphRAG-Bench Accuracy 결합 — 의미적 일치 LLM Judge. + +## 원천 +- RAGAS Answer Correctness (https://docs.ragas.io) + : 정답 vs 예측 의 *factual + semantic* 일치 평가 +- GraphRAG-Bench (arxiv:2506.02404, ICLR 2026) Accuracy 메트릭 + : OE/FB 질문에서 "LLM 에게 정답과 예측을 넘겨서 semantic alignment 점수 받기" + +## ROUGE-L 와 차이 +- ROUGE-L 은 *문자열 매칭* 기반 — 긴 설명 답변은 어광이 맞아도 점수 낮음 +- Answer Correctness 는 *의미 일치* 기반 — GraphRAG 답변의 서술성 에 강건 + +## 사용 +from eval.metrics.answer_correctness import judge_answer_correctness + +result = judge_answer_correctness( + question="두산밥칿의 목표주가는?", + reference="80,000원", + prediction="두산밥칿의 목표주가는 80,000원입니다.", +) +# {"answer_correctness": 5, "reason": "수치 정확 일치"} +""" +import json +import logging +from pathlib import Path + +from openai import BadRequestError +from tenacity import retry, stop_after_attempt, wait_exponential, retry_if_exception_type +from openai import RateLimitError, APITimeoutError, APIConnectionError + +from eval.metrics.faithfulness_judge import ( + _active_judge_client, + _active_judge_model, + _judge_strict_schema_supported, + _is_openai_native_model, + _relax_schema, +) + +logger = logging.getLogger(__name__) + +_PROMPTS_DIR = Path(__file__).parent / "prompts" +ANSWER_CORRECTNESS_PROMPT = (_PROMPTS_DIR / "answer_correctness_v1.md").read_text(encoding="utf-8") + + +@retry( + retry=retry_if_exception_type((RateLimitError, APITimeoutError, APIConnectionError)), + wait=wait_exponential(multiplier=1, min=2, max=8), + stop=stop_after_attempt(3), + reraise=True, +) +def _call_correctness_api(prompt: str, response_format: dict) -> dict: + # faithfulness_judge 의 _active_judge_client / _active_judge_model 재사용. + # configure_judge_llm() 이 호출되면 자동 반영 — 임포트 시점이 아닌 런타임에 모듈 속성 재조회. + from eval.metrics import faithfulness_judge as fj + + client = fj._active_judge_client + model = fj._active_judge_model + strict_supported = fj._judge_strict_schema_supported + + rf = response_format if strict_supported else _relax_schema(response_format) + + call_kwargs = { + "model": model, + "messages": [{"role": "user", "content": prompt}], + "max_completion_tokens": 1500, + "response_format": rf, + "timeout": 30, + } + if _is_openai_native_model(model): + call_kwargs["reasoning_effort"] = "low" + else: + call_kwargs["extra_body"] = {"reasoning": {"enabled": False}} + + try: + response = client.chat.completions.create(**call_kwargs) + except BadRequestError as e: + err_msg = str(e).lower() + if "json_schema" in err_msg or "response_format" in err_msg or "strict" in err_msg: + if fj._judge_strict_schema_supported: + logger.warning("Answer Correctness strict json_schema 미지원 → json_object fallback") + fj._judge_strict_schema_supported = False + call_kwargs["response_format"] = _relax_schema(response_format) + response = client.chat.completions.create(**call_kwargs) + else: + raise + else: + raise + + raw_content = response.choices[0].message.content if response.choices else "" + raw_content = (raw_content or "").strip() + + if not raw_content: + msg = response.choices[0].message if response.choices else None + reasoning_content = getattr(msg, "reasoning_content", None) if msg else None + if reasoning_content and "{" in reasoning_content: + try: + start = reasoning_content.find("{") + end = reasoning_content.rfind("}") + if start >= 0 and end > start: + return json.loads(reasoning_content[start:end+1]) + except (json.JSONDecodeError, ValueError): + pass + return {} + + if raw_content.startswith("```"): + raw_content = raw_content.split("```")[1] + if raw_content.startswith("json"): + raw_content = raw_content[4:] + raw_content = raw_content.strip() + + return json.loads(raw_content) + + +def judge_answer_correctness(question: str, reference: str, prediction: str) -> dict: + """예측이 정답과 의미적으로 일치하는지 평가 (1-5점). + + Returns: + {"answer_correctness": 1-5, "reason": str} + 실패 시 {"answer_correctness": None, "reason": "Error: ..."} + """ + prompt = ANSWER_CORRECTNESS_PROMPT.format( + question=question[:1000], + reference=reference[:2000], + prediction=prediction[:2000], + ) + schema = { + "type": "json_schema", + "json_schema": { + "name": "answer_correctness_result", + "schema": { + "type": "object", + "properties": { + "answer_correctness": {"type": "integer", "minimum": 1, "maximum": 5}, + "reason": {"type": "string"}, + }, + "required": ["answer_correctness", "reason"], + "additionalProperties": False, + }, + "strict": True, + }, + } + try: + return _call_correctness_api(prompt, schema) + except Exception as e: + logger.error("Answer Correctness Judge 실패: %s", e) + return {"answer_correctness": None, "reason": f"Error: {e}"} diff --git a/eval/metrics/entity_coverage.py b/eval/metrics/entity_coverage.py new file mode 100644 index 0000000..9ee2b39 --- /dev/null +++ b/eval/metrics/entity_coverage.py @@ -0,0 +1,112 @@ +"""eval/metrics/entity_coverage.py +RAGAS Context Entity Recall 변형 — 정답 entity 가 예측에 등장하는 비율. + +## 원천 +- RAGAS Context Entities Recall (https://docs.ragas.io) + : ground_truth 에서 entity 추출 → retrieved_context 에 등장하는 비율 + +## 변형 +- 원본 RAGAS 는 *의존성 추가 (spacy 등)* 때문에 도입 안 함 +- 대신 우리 도메인에 맞게 *단순 substring match* 으로 구현: + - 정답에서 수치 + Company/Risk 명사 수동 추출 (numerical_accuracy 패턴 재사용) + - QA 의 key_entities 필드 있으면 그것 우선 + +## 사용 +from eval.metrics.entity_coverage import compute_entity_coverage + +result = compute_entity_coverage( + prediction="두산밥칿은 관세, 환율 리스크에 노출", + reference="관세, 환율, 금리", + key_entities=["관세", "환율", "금리"], # 선택 +) +# {"entity_coverage": 0.6667, "matched": ["관세", "환율"], "missed": ["금리"], "total": 3} +""" +import logging +import re +from typing import Optional + +from eval.metrics.numerical_accuracy import extract_numbers + +logger = logging.getLogger(__name__) + + +def _extract_named_entities(text: str) -> list[str]: + """정답에서 *명사형 entity* 단순 추출. + + 금융 도메인 heuristic: + - 2~10글자 한글 명사 연속 (Company/Risk/Metric 프록시) + - 수치/단위 제외 (numerical_accuracy 가 이미 먹음) + - 조사 없이 단독으로 등장하는 단어 우선 + """ + # 한글 명사 2~10글자 패턴 (조사 제외) + pattern = re.compile(r'[\uac00-\ud7a3]{2,10}') + candidates = pattern.findall(text) + + # 조사 / 기능어 필터 + STOPWORDS = { + "이곳", "저곥", "그곳", "이것", "저것", "그것", + "한다", "하는", "되는", "있다", "없다", + "서말", "대비", "대해", "대한", "따릅", "위한", + "목표", "결과", "수준", "기준", + "N/A", "문서", "내용", "없음", "없는", + } + filtered = [c for c in candidates if c not in STOPWORDS] + + # 중복 제거 (순서 유지) + seen = set() + result = [] + for c in filtered: + if c not in seen: + seen.add(c) + result.append(c) + return result + + +def compute_entity_coverage( + prediction: str, + reference: str, + key_entities: Optional[list[str]] = None, +) -> dict: + """정답의 주요 entity (명사 + 수치) 가 예측에 등장하는 비율. + + Args: + prediction: 모델이 생성한 답변 + reference: 정답 윈텍스트 + key_entities: QA 쟑입 시 논레이손 (graphrag_qa.json 에 필드 있으면 우선 사용) + + Returns: + {"entity_coverage": 0~1, "matched": [...], "missed": [...], "total": int} + """ + if not prediction or not reference: + return {"entity_coverage": 0.0, "matched": [], "missed": [], "total": 0} + + # 1. entity 추출 — key_entities 세 있으면 그것 우선, 없으면 reference 에서 자동 추출 + if key_entities: + target_entities = list(key_entities) + else: + # 수치 + 단순 명사 entity 합치기 + target_entities = list(set( + extract_numbers(reference) + _extract_named_entities(reference) + )) + + if not target_entities: + return {"entity_coverage": 1.0, "matched": [], "missed": [], "total": 0} + + # 2. 예측에 각 entity substring 등장 여부 + matched = [] + missed = [] + for e in target_entities: + # 수치는 콤마 제거 어서 비교 (numerical_accuracy 과 같이) + e_norm = e.replace(",", "").replace(" ", "") + pred_norm = prediction.replace(",", "").replace(" ", "") + if e_norm in pred_norm or e in prediction: + matched.append(e) + else: + missed.append(e) + + return { + "entity_coverage": round(len(matched) / len(target_entities), 4), + "matched": matched, + "missed": missed, + "total": len(target_entities), + } diff --git a/eval/metrics/prompts/answer_correctness_v1.md b/eval/metrics/prompts/answer_correctness_v1.md new file mode 100644 index 0000000..4d1017d --- /dev/null +++ b/eval/metrics/prompts/answer_correctness_v1.md @@ -0,0 +1,46 @@ +당신은 금융 QA 시스템의 답변 정확도를 평가하는 전문가입니다. +아래 질문, 정답, 예측 답변을 읽고, 예측이 정답과 의미적으로 일치하는지 평가하세요. + +# 질문 +{question} + +# 정답 (Reference) +{reference} + +# 예측 답변 (Prediction) +{prediction} + +# 평가 기준 — Answer Correctness (1~5점) + +ROUGE 와 달리 문자열 매칭이 아닌 *의미적 일치* 를 판단합니다. + +- **5점**: 정답의 모든 핵심 사실이 예측에 포함됨. 표현 차이 있어도 무방. +- **4점**: 주요 사실은 일치, 1개 이하 세부 정보 누락/오류. +- **3점**: 절반 정도 일치. 일부 핵심 정보 정확, 일부 누락 또는 오류. +- **2점**: 대부분 불일치. 일부 관련상은 있으나 핵심 정보 대다수 누락. +- **1점**: 거의 모두 불일치. 다른 정보 또는 "답변 불가". + +## 고려 사항 + +1. **수치 일치**: 금액 / 비율 / 일자 등 수치는 정확해야 함. 수치 틀릴 시 점수 감점. +2. **설명 길이**: 예측이 정답보다 길고 부가 설명이 있어도, 정답의 핵심 사실을 포함하면 감점 아님. +3. **관계/다중 hop**: GraphRAG QA 의 경우 *관계 설명* 이 더 서술적일 수 있음. 관계가 올바르면 5점. +4. **"N/A" 정답**: 정답이 "N/A - 문서에 없는 내용" 이면, 예측도 *답변 불가 또는 해당 정보 없음* 을 밝혀야 5점. 잘못된 정보 제공 시 1점. + +## 예시 + +**5점 예시** +- 정답: "3,461억원" +- 예측: "미래에셋증권의 2025년 1분기 연결 세전이익은 3,461억원입니다." → 5점 (수치 일치 + 설명 부가는 감점 아님) + +**3점 예시** +- 정답: "관세, 환율, 금리, 정책, 수요" +- 예측: "관세, 환율, 금리" → 3점 (핵심 3/5 일치, 2 누락) + +**1점 예시** +- 정답: "80,000원" +- 예측: "75,000원" 또는 "[답변 불가]" → 1점 (수치 불일치) + +# 출력 형식 (JSON만 반환) +{{"answer_correctness": 1~5, + "reason": "점수 근거 한 줄"}} diff --git a/eval/metrics/routing_accuracy.py b/eval/metrics/routing_accuracy.py new file mode 100644 index 0000000..22136c1 --- /dev/null +++ b/eval/metrics/routing_accuracy.py @@ -0,0 +1,56 @@ +"""eval/metrics/routing_accuracy.py +GraphRAG-Bench AR Metric 변형 — 의도된 route vs 실제 route 일치 여부. + +## 원천 +- GraphRAG-Bench (arxiv:2506.02404) AR Metric + : "답변이 정확하면서 *올바른 추론 경로* 를 따랐는지" + +## 변형 +- 본 연구는 retrieval 경로를 GraphRAG-Bench 처럼 *자연어 rationale* 로 평가하지 않고 + *단순 route 레이블 일치* 로 축소 — t2c / local / community +- doc-graph 의 router.py 가 반환하는 actual_route 를 GraphRAG QA 의 + expected_route 와 비교 +- doc-summary 측에는 해당 없음 (routing 구조 없음) + +## 사용 +from eval.metrics.routing_accuracy import compute_routing_accuracy + +result = compute_routing_accuracy( + expected_route="local", + actual_route="local", +) +# {"routing_correct": True, "expected": "local", "actual": "local"} +""" +from typing import Optional + + +def compute_routing_accuracy( + expected_route: Optional[str], + actual_route: Optional[str], +) -> dict: + """의도된 route vs 실제 route 일치. + + Args: + expected_route: QA 자적됬 의도 경로 (graphrag_qa.json expected_route 필드) + actual_route: router.py 가 틠열뎀으로 선택한 경로 + + Returns: + { + "routing_correct": bool | None, # expected 없으면 None + "expected": str | None, + "actual": str | None, + } + """ + if expected_route is None: + # VectorRAG QA 등 routing 의도 없는 경우 — 측정 재업 + return { + "routing_correct": None, + "expected": None, + "actual": actual_route, + } + + return { + "routing_correct": expected_route == actual_route, + "expected": expected_route, + "actual": actual_route, + } diff --git a/eval/metrics/semantic_similarity.py b/eval/metrics/semantic_similarity.py new file mode 100644 index 0000000..ef6e379 --- /dev/null +++ b/eval/metrics/semantic_similarity.py @@ -0,0 +1,74 @@ +"""eval/metrics/semantic_similarity.py +RAGAS Answer Semantic Similarity — 임베딩 cosine similarity. + +## 원천 +- RAGAS Answer Semantic Similarity (https://docs.ragas.io) + : 정답과 예측의 임베딩 벡터 cosine 유사도 + +## ROUGE-L 와 차이 +- ROUGE-L: 특정 토큰 일치 기반 → "두산밥칿 목표주가 80,000원" vs "목표가격은 8만원" → 낮은 점수 +- Semantic Similarity: 의미 기반 → 같은 두 문장 → 높은 유사도 (~0.85) + +## 실제 구현 +- sentence-transformers 의 BAAI/bge-m3 사용 — 다국어 고성능 +- doc-summary-agent 와 동일사용 (이미 캐싱됨) +- 하지만 doc-graph-agent 에서도 사용해야 해서 동일 의존성 필요: + uv add sentence-transformers + +## 사용 +from eval.metrics.semantic_similarity import compute_semantic_similarity + +sim = compute_semantic_similarity( + prediction="두산밥칿의 목표주가는 80,000원입니다.", + reference="80,000원", +) +# {"semantic_similarity": 0.7234} + +## 메모리 주의 +- bge-m3 로딩이 ~2GB. 식단의 경우 처음 사용 시 다운로드 필요. +- CPU 에서 처음 임베딩은 느림. dry-run 시 5개 QA 먼저 테스트. +""" +import logging +import numpy as np + +logger = logging.getLogger(__name__) + +_encoder = None + + +def _get_encoder(): + """sentence-transformers bge-m3 로더 — lazy 로딩.""" + global _encoder + if _encoder is None: + try: + from sentence_transformers import SentenceTransformer + _encoder = SentenceTransformer("BAAI/bge-m3") + logger.info("semantic_similarity: bge-m3 로딩 완료") + except ImportError as e: + logger.error( + "sentence-transformers 미설치 — 'uv add sentence-transformers' 필요: %s", e, + ) + raise + return _encoder + + +def compute_semantic_similarity(prediction: str, reference: str) -> dict: + """임베딩 cosine 유사도 계산. + + Returns: + {"semantic_similarity": float 0~1} + 실패 시 {"semantic_similarity": None, "error": str} + """ + if not prediction or not reference: + return {"semantic_similarity": 0.0} + + try: + encoder = _get_encoder() + embs = encoder.encode([prediction, reference], normalize_embeddings=True, show_progress_bar=False) + sim = float(np.dot(embs[0], embs[1])) + # cosine 은 [-1, 1] 이나 정규화된 벡터는 실질적으로 [0, 1]. + sim = max(0.0, min(1.0, sim)) + return {"semantic_similarity": round(sim, 4)} + except Exception as e: + logger.error("semantic_similarity 계산 실패: %s", e) + return {"semantic_similarity": None, "error": str(e)} From 9c1bfc263872d024fb35a5e86bd26aa05dcb735e Mon Sep 17 00:00:00 2001 From: HyunSang Jang Date: Mon, 25 May 2026 12:29:35 +0900 Subject: [PATCH 06/11] =?UTF-8?q?feat(eval):=20C12=20run=5Fqa=5Feval.py=20?= =?UTF-8?q?Tier=202=20=EB=A9=94=ED=8A=B8=EB=A6=AD=204=EA=B0=9C=20=ED=86=B5?= =?UTF-8?q?=ED=95=A9?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ## 변경 사항 ### evaluate_one() — 메트릭 4개 추가 - Answer Correctness (LLM Judge 1-5) — RAGAS + GraphRAG-Bench - Semantic Similarity (bge-m3 cosine) — RAGAS - Entity Coverage (정답 entity 등장률) — RAGAS Context Entities Recall 변형 - Routing Accuracy (expected vs actual route) — GraphRAG-Bench AR Metric 변형 ### print_summary() — Tier 1 / Tier 2 분리 출력 각 qa_set 별: - Tier 1 (전통 + FineSurE): ROUGE-L / 수치정확도 / Faithfulness / Completeness / Conciseness - Tier 2 (RAGAS + GraphRAG-Bench): Answer Correctness / Semantic Similarity / Entity Coverage / Routing Accuracy ### --no-semantic 인자 추가 bge-m3 CPU 부담으로 dry-run 시 끄기 가능. ### qa 필드 활용 - expected_route → routing_accuracy - key_entities → entity_coverage - pattern → 결과에 박제 (1hop/2hop/intersection 등) ## 호환성 기존 인자 (--llm-* / --judge-* / --qa-set / --limit / --tag) 그대로 유지. 새 인자 --no-semantic 만 추가. Refs #127 --- scripts/run_qa_eval.py | 169 ++++++++++++++++++++++++++++++----------- 1 file changed, 126 insertions(+), 43 deletions(-) diff --git a/scripts/run_qa_eval.py b/scripts/run_qa_eval.py index 823954b..d7cb76d 100644 --- a/scripts/run_qa_eval.py +++ b/scripts/run_qa_eval.py @@ -1,12 +1,25 @@ """VectorRAG QA + GraphRAG QA 정량 평가 진입점 (#127). -doc-summary-agent 의 eval/run_eval.py 와 동일한 패턴으로: -- ROUGE-1/2/L + 수치 정확도 + Faithfulness Judge + Numerical Faithfulness Judge -- 4 측정 LLM 되이잘 (--llm-* 인자) -- Claude Haiku 4.5 judge (--judge-* 인자) +doc-summary-agent 의 eval/run_eval.py 와 동일한 패턴 + Tier 2 메트릭 4개 추가: -하지만 *답변 생성 경로* 는 doc-graph 의 retrieval.route_and_answer() — -Text2Cypher / Local Retriever / Community stub 가 자동 라우팅. +## 메트릭 (Tier 1 + Tier 2) + +### Tier 1 — 전통 + FineSurE (#127 초기 박힘) +- ROUGE-1/2/L (전통) +- 수치 정확도 (커스텀 금융 특화) +- Faithfulness / Completeness / Conciseness (FineSurE ACL 2024) +- Numerical Faithfulness (수치 충실도) + +### Tier 2 — RAGAS + GraphRAG-Bench (#127 확장) +- Answer Correctness (RAGAS + GraphRAG-Bench Accuracy) — 의미적 일치 1-5점 +- Semantic Similarity (RAGAS) — bge-m3 cosine +- Entity Coverage (RAGAS Context Entities Recall 변형) — 정답 entity 등장률 +- Routing Accuracy (GraphRAG-Bench AR Metric 변형) — expected vs actual route + +## 답변 생성 경로 + +doc-graph 의 retrieval.route_and_answer() — Text2Cypher / Local / Community +자동 라우팅. ## 사용 @@ -29,6 +42,9 @@ # 소규모 dry-run (처음 3 QA 만) uv run python scripts/run_qa_eval.py --qa-set vectorrag --limit 3 --tag dryrun + + # semantic similarity 메트릭 끄기 (CPU 환경에서 빠른 dry-run) + uv run python scripts/run_qa_eval.py --no-semantic --qa-set graphrag --limit 5 """ from __future__ import annotations @@ -57,6 +73,9 @@ judge_numerical_faithfulness, configure_judge_llm, ) +from eval.metrics.answer_correctness import judge_answer_correctness +from eval.metrics.entity_coverage import compute_entity_coverage +from eval.metrics.routing_accuracy import compute_routing_accuracy logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") logger = logging.getLogger(__name__) @@ -92,11 +111,20 @@ def _load_qa(qa_set: str) -> list[dict]: return qa_pairs -def evaluate_one(qa: dict) -> dict: - """단일 QA 에 대해 retrieval.route_and_answer() 호출 후 메트릭 계산.""" +def evaluate_one(qa: dict, use_semantic: bool = True) -> dict: + """단일 QA 에 대해 retrieval.route_and_answer() 호출 후 메트릭 계산. + + Args: + qa: QA 항목 — id, question, answer, qa_set, [expected_route], [key_entities] + use_semantic: True 면 bge-m3 semantic_similarity 계산. CPU 환경에서 느리면 False. + """ question = qa["question"] reference = qa.get("answer", "") + qa_set = qa.get("qa_set", "") + expected_route = qa.get("expected_route") # GraphRAG QA 만 박힘 + key_entities = qa.get("key_entities", []) # GraphRAG QA 만 박힘 + # ── 답변 생성 ────────────────────────────────────────── started = time.perf_counter() try: result = route_and_answer(question) @@ -110,54 +138,90 @@ def evaluate_one(qa: dict) -> dict: retrieval_error = f"{type(e).__name__}: {e}" elapsed = time.perf_counter() - started + # ── Tier 1 메트릭 ────────────────────────────────────── rouge = compute_rouge(prediction, reference) num_acc = compute_numerical_accuracy(prediction, reference) out = { "id": qa["id"], - "qa_set": qa.get("qa_set", ""), - "doc": qa.get("doc", ""), - "type": qa.get("type", ""), - "question": question, - "reference": reference, - "prediction": prediction, - "actual_route": actual_route, - "retrieval_error": retrieval_error, - "elapsed_seconds": round(elapsed, 2), - "rouge1": rouge["rouge1"], - "rouge2": rouge["rouge2"], - "rougeL": rouge["rougeL"], - "num_accuracy": num_acc["accuracy"], - "num_matched": num_acc["matched"], - "num_missed": num_acc["missed"], + "qa_set": qa_set, + "doc": qa.get("doc", ""), + "type": qa.get("type", ""), + "pattern": qa.get("pattern", ""), # GraphRAG QA pattern + "question": question, + "reference": reference, + "prediction": prediction, + "actual_route": actual_route, + "retrieval_error": retrieval_error, + "elapsed_seconds": round(elapsed, 2), + # Tier 1 — 전통 + "rouge1": rouge["rouge1"], + "rouge2": rouge["rouge2"], + "rougeL": rouge["rougeL"], + "num_accuracy": num_acc["accuracy"], + "num_matched": num_acc["matched"], + "num_missed": num_acc["missed"], } + # Tier 1 — Faithfulness Judge (FineSurE) judge = judge_faithfulness(reference, prediction) out.update({ - "faithfulness": judge.get("faithfulness", "Error"), - "faithfulness_reason": judge.get("faithfulness_reason", ""), - "completeness": judge.get("completeness", None), - "conciseness": judge.get("conciseness", None), + "faithfulness": judge.get("faithfulness", "Error"), + "faithfulness_reason": judge.get("faithfulness_reason", ""), + "completeness": judge.get("completeness", None), + "conciseness": judge.get("conciseness", None), }) num_judge = judge_numerical_faithfulness(reference, prediction) out["numerical_faithfulness"] = num_judge.get("numerical_faithfulness", "Error") out["numerical_faithfulness_reason"] = num_judge.get("reason", "") + # ── Tier 2 메트릭 ────────────────────────────────────── + + # Answer Correctness (RAGAS + GraphRAG-Bench) — LLM Judge 1-5 + ac = judge_answer_correctness(question, reference, prediction) + out["answer_correctness"] = ac.get("answer_correctness", None) + out["answer_correctness_reason"] = ac.get("reason", "") + + # Semantic Similarity (RAGAS) — bge-m3 cosine, optional + if use_semantic: + try: + from eval.metrics.semantic_similarity import compute_semantic_similarity + sim = compute_semantic_similarity(prediction, reference) + out["semantic_similarity"] = sim.get("semantic_similarity", None) + except Exception as e: + logger.warning("semantic_similarity 스킵: %s", e) + out["semantic_similarity"] = None + else: + out["semantic_similarity"] = None + + # Entity Coverage (RAGAS Context Entities Recall 변형) + ec = compute_entity_coverage(prediction, reference, key_entities=key_entities) + out["entity_coverage"] = ec["entity_coverage"] + out["entity_matched"] = ec["matched"] + out["entity_missed"] = ec["missed"] + out["entity_total"] = ec["total"] + + # Routing Accuracy (GraphRAG-Bench AR Metric 변형) — GraphRAG QA 만 의미 + ra = compute_routing_accuracy(expected_route, actual_route) + out["routing_correct"] = ra["routing_correct"] + out["expected_route"] = ra["expected"] + return out -def run_all(qa_pairs: list[dict], limit: int | None = None) -> list[dict]: +def run_all(qa_pairs: list[dict], limit: int | None = None, use_semantic: bool = True) -> list[dict]: if limit is not None: qa_pairs = qa_pairs[:limit] results = [] for i, qa in enumerate(qa_pairs): logger.info("[%d/%d] (%s) %s", i + 1, len(qa_pairs), qa.get("qa_set", ""), qa.get("id", "?")) - r = evaluate_one(qa) + r = evaluate_one(qa, use_semantic=use_semantic) results.append(r) logger.info( - " → route=%s ROUGE-L=%.3f Faithfulness=%s elapsed=%.1fs", - r["actual_route"], r["rougeL"], r["faithfulness"], r["elapsed_seconds"], + " → route=%s ROUGE-L=%.3f Faithful=%s Correctness=%s elapsed=%.1fs", + r["actual_route"], r["rougeL"], r["faithfulness"], + r.get("answer_correctness"), r["elapsed_seconds"], ) return results @@ -184,30 +248,46 @@ def _faithful_rate(rs: list[dict]) -> tuple[int, int]: f = sum(1 for r in rs if r.get("faithfulness") == "Faithful") return f, len(rs) - print("\n" + "=" * 70) + def _routing_rate(rs: list[dict]) -> tuple[int, int]: + considered = [r for r in rs if r.get("routing_correct") is not None] + correct = sum(1 for r in considered if r.get("routing_correct")) + return correct, len(considered) + + print("\n" + "=" * 78) print(f"📊 QA 평가 결과 요약 (총 {len(results)}개 QA)") - print("=" * 70) + print("=" * 78) for qa_set in ("vectorrag", "graphrag"): subset = [r for r in results if r.get("qa_set") == qa_set] if not subset: continue f, t = _faithful_rate(subset) + rc_correct, rc_total = _routing_rate(subset) print() print(f"## {qa_set.upper()} QA (n={len(subset)})") - print(f" ROUGE-L: {_avg(subset, 'rougeL'):.4f}") - print(f" 수치 정확도: {_avg(subset, 'num_accuracy'):.4f}") - print(f" Faithfulness: {f}/{t} ({(f / t * 100) if t else 0:.1f}%)") - print(f" Completeness: {_avg(subset, 'completeness'):.2f} / 5") - print(f" Conciseness: {_avg(subset, 'conciseness'):.2f} / 5") + print(f" ── Tier 1 (전통 + FineSurE) ──") + print(f" ROUGE-L: {_avg(subset, 'rougeL'):.4f}") + print(f" 수치 정확도: {_avg(subset, 'num_accuracy'):.4f}") + print(f" Faithfulness: {f}/{t} ({(f / t * 100) if t else 0:.1f}%)") + print(f" Completeness: {_avg(subset, 'completeness'):.2f} / 5") + print(f" Conciseness: {_avg(subset, 'conciseness'):.2f} / 5") + print(f" ── Tier 2 (RAGAS + GraphRAG-Bench) ──") + print(f" Answer Correctness: {_avg(subset, 'answer_correctness'):.2f} / 5") + print(f" Semantic Similarity: {_avg(subset, 'semantic_similarity'):.4f}") + print(f" Entity Coverage: {_avg(subset, 'entity_coverage'):.4f}") + if rc_total > 0: + print(f" Routing Accuracy: {rc_correct}/{rc_total} ({(rc_correct / rc_total * 100):.1f}%)") + print(f" ── 진단 ──") routes: dict[str, int] = {} for r in subset: ar = r.get("actual_route") or "-" routes[ar] = routes.get(ar, 0) + 1 route_str = ", ".join(f"{k}={v}" for k, v in sorted(routes.items())) - print(f" Routing 분포: {route_str}") + print(f" Routing 분포: {route_str}") + avg_elapsed = _avg(subset, "elapsed_seconds") + print(f" 평균 응답: {avg_elapsed:.1f}s") - print("=" * 70) + print("=" * 78) def main() -> int: @@ -215,8 +295,10 @@ def main() -> int: parser.add_argument("--qa-set", choices=["vectorrag", "graphrag", "both"], default="both", help="평가할 QA 셋. 기본 both.") parser.add_argument("--limit", type=int, default=None, - help="셜플링 앞에서 몇 개만. dry-run 용.") + help="앞에서 몇 개만. dry-run 용.") parser.add_argument("--tag", type=str, default="", help="결과 파일 태그") + parser.add_argument("--no-semantic", action="store_true", + help="Semantic Similarity 메트릭 끄기 (bge-m3 CPU 부담)") parser.add_argument("--llm-model", type=str, default=None) parser.add_argument("--llm-base-url", type=str, default=None) @@ -249,9 +331,10 @@ def main() -> int: logger.error("QA 없음 — qa_set=%s", args.qa_set) return 1 - logger.info("=== QA 평가 시작 — %d QA ===", len(qa_pairs)) + logger.info("=== QA 평가 시작 — %d QA (semantic=%s) ===", + len(qa_pairs), not args.no_semantic) started = time.perf_counter() - results = run_all(qa_pairs, limit=args.limit) + results = run_all(qa_pairs, limit=args.limit, use_semantic=not args.no_semantic) elapsed = time.perf_counter() - started logger.info("=== QA 평가 완료 — %d QA, %.1fs ===", len(results), elapsed) From 273cb015b8236a451b1ca60c857e429d210f23f4 Mon Sep 17 00:00:00 2001 From: HyunSang Jang Date: Mon, 25 May 2026 12:45:06 +0900 Subject: [PATCH 07/11] =?UTF-8?q?chore(deps):=20#127=20rouge-score=20+=20p?= =?UTF-8?q?ython-mecab-ko=20=EC=B6=94=EA=B0=80=20=E2=80=94=20ROUGE-L=20?= =?UTF-8?q?=EB=A9=94=ED=8A=B8=EB=A6=AD=EC=9A=A9?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- pyproject.toml | 36 + uv.lock | 5089 ++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 5125 insertions(+) create mode 100644 pyproject.toml create mode 100644 uv.lock diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..a96f624 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,36 @@ +[project] +name = "doc-graph-agent" +version = "0.1.0" +requires-python = ">=3.11" +dependencies = [ + "certifi>=2026.4.22", + "docx2python>=3.6.2", + "easyocr>=1.7.2", + "gmft>=0.4.3", + "langchain-community>=0.4.1", + "langchain-experimental>=0.4.1", + "langchain-text-splitters>=1.1.2", + 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upload-time = "2025-09-14T22:18:19.088Z" }, +] From a02d229a5e3e86871b036ec02540905488a3e10f Mon Sep 17 00:00:00 2001 From: HyunSang Jang Date: Mon, 25 May 2026 13:07:16 +0900 Subject: [PATCH 08/11] =?UTF-8?q?feat(eval/dataset):=20GraphRAG=20QA=2040?= =?UTF-8?q?=20=EC=9E=91=EC=84=B1=20(=EA=B3=B5=EC=A0=95=20=EB=B9=84?= =?UTF-8?q?=EA=B5=90=20+=208=EB=AC=B8=EC=84=9C=20=EC=9B=90=EB=AC=B8=20?= =?UTF-8?q?=EA=B8=B0=EB=B0=98)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ## QA 작성 원칙 본인 핵심 인사이트 반영: > "그래프 구조 활용만으로 작성하면 RAG 가 못 잡음 — 공정한 비교 아님" → GraphRAG-Bench / RAG vs GraphRAG (Han et al.) 표준: - 원문에 답이 박혀있어야 (RAG도 시도 가능) - RAG 가 어렵게 풀거나 부분만 풀 수 있어야 - GraphRAG 가 구조적으로 우세해야 ## 패턴 분포 (혼합 30 강점 + 10 한계) | 패턴 | 개수 | 의도 | |---|---|---| | multi_doc_trend | 10 | 미래에셋 1Q→4Q 추세 (4문서 동시 필요) | | 1hop | 10 | 한화 두산밥캣, DS 종목별, 금감원, 농협 | | intersection | 8 | DS+한화 거시 변수, 미래에셋+금감원 IPO | | filter_agg | 5 | doc_type 필터 + 집계 | | causal | 5 | 인과/조건 (멕시코공장→수익성, 유가→원전) | | limitation | 2 | 시간 비교 불가, Company 라벨 품질 | ## 그래프 탐색 결과 활용 explore_graph.py 결과 박힌 발견: - doc_type 4종 (report 2 / disclosure 1 / filing 1 / ir 4) — filter_agg - HAS_METRIC 270 풍부 — 1hop - doc_year/page_count 없음 — limitation - Company 라벨 품질 이슈 — limitation ## 8 문서 원문 분석 활용 - 미래에셋 1Q~4Q: 분기별 시계열 metric (multi_doc_trend 강력) - 한화 두산밥캣: 1-hop 관계, 인과 추론 - DS 시황: 1-hop 종목, 거시 변수 - 금감원 보도자료: 통계 entity, 분류 한계 - 농협 사업보고서: 1-hop 사업 항목 ## 각 항목 박힘 - id, qa_set: "graphrag" - pattern (1hop/intersection/filter_agg/multi_doc_trend/causal/limitation) - doc (콤마 구분 다중 문서 가능) - question, answer - expected_route (local/t2c) - key_entities (Tier 2 entity_coverage 메트릭용) - note (작성 의도) Refs #127 --- eval/dataset/graphrag_qa.json | 444 +++++++++++++++++++++++++++++++--- 1 file changed, 417 insertions(+), 27 deletions(-) diff --git a/eval/dataset/graphrag_qa.json b/eval/dataset/graphrag_qa.json index 71e8c63..fe0b305 100644 --- a/eval/dataset/graphrag_qa.json +++ b/eval/dataset/graphrag_qa.json @@ -2,51 +2,441 @@ { "id": "graph_001", "qa_set": "graphrag", - "pattern": "1hop", - "question": "두산밥칿과 함께 언급된 리스크는?", - "answer": "관세, 멕시코 공장 관련, 딜러 재고", + "pattern": "multi_doc_trend", + "doc": "미래에셋증권_1분기_실적보고서.pdf,미래에셋증권_2분기_실적보고서.pdf,미래에셋증권_3분기_실적보고서.pdf,미래에셋증권_4분기_실적보고서.pdf", + "question": "미래에셋증권의 2025년 1분기부터 4분기까지 연결 ROE는 어떻게 변화했는가?", + "answer": "1Q25 8.5%, 2Q25 10.9%, 3Q25 10.8%, 연간 12.4% (YoY +4.5%p). 분기를 거듭하며 우상향 추세.", "expected_route": "local", - "key_entities": ["두산밥칿", "관세", "멕시코", "딜러 재고"], - "note": "⭐ 시드 QA — explore_graph.py 의 risk_company 세그먼트 결과로 정답 검증 필요" + "key_entities": ["8.5", "10.9", "10.8", "12.4", "ROE"], + "note": "4문서 다중 추적 — RAG는 단일 chunk에 ROE 분기별 비교 없어 약함" }, { "id": "graph_002", "qa_set": "graphrag", - "pattern": "aggregate", - "question": "전체 그래프에서 가장 많이 언급된 Company entity TOP 5는?", - "answer": "explore_graph.py top_entities 결과 기반 작성 필요", - "expected_route": "t2c", - "key_entities": [], - "note": "⭐ 시드 QA — W4 eval 의 Q5 동일 패턴. Company 라벨 품질 이슈 검증용" + "pattern": "multi_doc_trend", + "doc": "미래에셋증권_1분기_실적보고서.pdf,미래에셋증권_2분기_실적보고서.pdf,미래에셋증권_3분기_실적보고서.pdf,미래에셋증권_4분기_실적보고서.pdf", + "question": "미래에셋증권 2025년 1분기와 연간(4Q25) Brokerage 수수료 수익 차이는?", + "answer": "1Q25 1,987억원 → 2025년 연간 1조 110억원 (YoY +43% 사상 최대치 기록). 1Q 대비 약 5배 증가.", + "expected_route": "local", + "key_entities": ["1,987", "1조 110억원", "Brokerage", "+43%"], + "note": "다중 문서 비교 — Brokerage 분기별 수치 추적" }, { "id": "graph_003", "qa_set": "graphrag", - "pattern": "filter", - "question": "보도자료(disclosure) 유형 문서에서 언급된 entity 들은?", - "answer": "explore_graph.py documents + Q3 정답 참고", - "expected_route": "t2c", - "key_entities": ["금감원", "보도자료", "회사채"], - "note": "⭐ 시드 QA — W4 eval 의 Q3 동일 패턴. doc_type 필터 + entity 집계" + "pattern": "multi_doc_trend", + "doc": "미래에셋증권_1분기_실적보고서.pdf,미래에셋증권_2분기_실적보고서.pdf,미래에셋증권_3분기_실적보고서.pdf,미래에셋증권_4분기_실적보고서.pdf", + "question": "미래에셋증권의 총 고객자산은 2025년 분기별로 어떻게 증가했는가?", + "answer": "1Q25 405조원 → 2Q25 453조원 → 3Q25 477조원 → 연간(4Q25) 518조원 (국내) + 84조원 (해외) = 602조원. 1Q 대비 약 49% 증가.", + "expected_route": "local", + "key_entities": ["405", "453", "477", "518", "602", "고객자산"], + "note": "4문서 분기별 시계열 추적" }, { "id": "graph_004", "qa_set": "graphrag", - "pattern": "intersection", - "question": "한화와 두산밥칿은 어떻게 관련되어 있나?", - "answer": "explore_graph.py shared_entities 결과 기반 작성 필요", + "pattern": "multi_doc_trend", + "doc": "미래에셋증권_1분기_실적보고서.pdf,미래에셋증권_2분기_실적보고서.pdf,미래에셋증권_3분기_실적보고서.pdf,미래에셋증권_4분기_실적보고서.pdf", + "question": "미래에셋증권의 연금자산은 2025년 분기별로 어떻게 변화했는가?", + "answer": "1Q25 44.4조원 → 2Q25 47.3조원 → 3Q25 52.2조원 → 4Q25 57.8조원 (YoY +35.3%). 4분기 모두 신기록 경신.", "expected_route": "local", - "key_entities": ["한화", "두산밥칿"], - "note": "⭐ 시드 QA — W4 eval 의 L2 동일 패턴. 두 Company 교집합" + "key_entities": ["44.4", "47.3", "52.2", "57.8", "연금자산"], + "note": "분기별 시계열 + YoY 증가율 추적" }, { "id": "graph_005", "qa_set": "graphrag", - "pattern": "metadata", - "question": "전체 적재된 문서는 몇 개이며, 각각의 doc_type 은 무엇인가?", - "answer": "explore_graph.py documents 결과 기반 작성 필요 — 예: 8개 (research_report 5, disclosure 1, internal_report 2)", + "pattern": "multi_doc_trend", + "doc": "미래에셋증권_1분기_실적보고서.pdf,미래에셋증권_2분기_실적보고서.pdf,미래에셋증권_3분기_실적보고서.pdf,미래에셋증권_4분기_실적보고서.pdf", + "question": "미래에셋증권의 해외법인 세전이익은 2025년 분기별로 어떻게 변화했는가?", + "answer": "1Q25 1,196억원 → 2Q25 1,061억원 → 3Q25 748억원 → 연간 4,981억원 (YoY +200%, 사상 최대). 선진국 3,315억원 (67%) + 이머징 1,666억원 (33%).", + "expected_route": "local", + "key_entities": ["1,196", "1,061", "748", "4,981", "해외법인"], + "note": "분기별 + 연간 + 선진/이머징 분해 — 다중 문서 통합" + }, + { + "id": "graph_006", + "qa_set": "graphrag", + "pattern": "multi_doc_trend", + "doc": "미래에셋증권_1분기_실적보고서.pdf,미래에셋증권_2분기_실적보고서.pdf,미래에셋증권_3분기_실적보고서.pdf,미래에셋증권_4분기_실적보고서.pdf", + "question": "미래에셋증권의 해외주식 잔고는 2025년 분기별로 어떻게 증가했는가?", + "answer": "1Q25 34.5조원 → 2Q25 39.6조원 → 3Q25 49.6조원 (전분기 대비 +25%) → 4Q25 52.0조원 (YoY +28%, 업계 최대).", + "expected_route": "local", + "key_entities": ["34.5", "39.6", "49.6", "52.0", "해외주식 잔고"], + "note": "분기별 추적 + 업계 비교" + }, + { + "id": "graph_007", + "qa_set": "graphrag", + "pattern": "multi_doc_trend", + "doc": "미래에셋증권_1분기_실적보고서.pdf,미래에셋증권_2분기_실적보고서.pdf,미래에셋증권_3분기_실적보고서.pdf,미래에셋증권_4분기_실적보고서.pdf", + "question": "미래에셋증권의 WM 수수료 수익은 2025년 어떻게 변화했으며 연간 합계는?", + "answer": "1Q25 784억원 → 2Q25 757억원 → 3Q25 918억원 → 4Q25 962억원. 2025 연간 합계 3,421억원 (YoY +21%, 역대 최고치).", + "expected_route": "local", + "key_entities": ["784", "757", "918", "962", "3,421", "WM"], + "note": "분기별 합계 추적" + }, + { + "id": "graph_008", + "qa_set": "graphrag", + "pattern": "multi_doc_trend", + "doc": "미래에셋증권_1분기_실적보고서.pdf,미래에셋증권_2분기_실적보고서.pdf,미래에셋증권_3분기_실적보고서.pdf,미래에셋증권_4분기_실적보고서.pdf", + "question": "미래에셋증권의 Trading 손익은 2025년 분기별로 어떻게 변화했는가?", + "answer": "1Q25 3,257억원 → 2Q25 4,762억원(분기 최대) → 3Q25 2,412억원 → 4Q25 약 2,219억원. 연간 1조 2,649억원.", + "expected_route": "local", + "key_entities": ["3,257", "4,762", "2,412", "Trading"], + "note": "Trading 분기별 변동성" + }, + { + "id": "graph_009", + "qa_set": "graphrag", + "pattern": "multi_doc_trend", + "doc": "미래에셋증권_2분기_실적보고서.pdf,미래에셋증권_3분기_실적보고서.pdf,미래에셋증권_4분기_실적보고서.pdf", + "question": "미래에셋증권의 연결 자기자본은 2분기, 3분기, 연간 어떻게 변화했는가?", + "answer": "2Q25 12.3조원 → 3Q25 12.7조원 → 4Q25 13.3조원 (YoY +10%). 분기마다 성장 지속.", + "expected_route": "local", + "key_entities": ["12.3", "12.7", "13.3", "자기자본"], + "note": "분기별 추적" + }, + { + "id": "graph_010", + "qa_set": "graphrag", + "pattern": "multi_doc_trend", + "doc": "미래에셋증권_2분기_실적보고서.pdf,미래에셋증권_3분기_실적보고서.pdf,미래에셋증권_4분기_실적보고서.pdf", + "question": "미래에셋증권의 신용잔고는 2025년 2분기부터 4분기까지 어떻게 변화했는가?", + "answer": "2Q25 6.9조원 → 3Q25 7.2조원 → 4Q25 7.6조원 (YoY +35.9%). 신용손익도 분기마다 증가 (2Q25 703억원 → 3Q25 872억원 → 4Q25 925억원).", + "expected_route": "local", + "key_entities": ["6.9", "7.2", "7.6", "신용잔고"], + "note": "분기별 시계열" + }, + { + "id": "graph_011", + "qa_set": "graphrag", + "pattern": "1hop", + "doc": "한화투자증권_두산밥캣_기업분석_리포트.pdf", + "question": "두산밥캣의 주요 리스크 요인은 무엇인가?", + "answer": "관세 영향, 환 변동, 딜러 재고 감소 후 리스토킹 불확실성, 멕시코 공장 가동 지연 가능성, 금리 인하 불투명성.", + "expected_route": "local", + "key_entities": ["관세", "환 변동", "딜러 재고", "멕시코 공장", "금리"], + "note": "단일 문서 1-hop 관계 — 두산밥캣 → FACES_RISK" + }, + { + "id": "graph_012", + "qa_set": "graphrag", + "pattern": "1hop", + "doc": "한화투자증권_두산밥캣_기업분석_리포트.pdf", + "question": "두산밥캣과 함께 언급된 핵심 metric 들은?", + "answer": "목표주가 80,000원, 시가총액 57,897억원, 2026E EPS 5,660원, ROE 7.5%, 2026E 매출액 9조 3,132억원, 영업이익률 8.5%, 멕시코 공장 6,000대 생산.", + "expected_route": "local", + "key_entities": ["80,000원", "57,897억원", "5,660원", "9조 3,132억원"], + "note": "1-hop HAS_METRIC 관계" + }, + { + "id": "graph_013", + "qa_set": "graphrag", + "pattern": "1hop", + "doc": "DS투자증권_시황분석_리포트.pdf", + "question": "DS투자증권 시황 리포트에서 원전 업종 강세에 언급된 종목들은?", + "answer": "대우건설(+17.8%), 한전기술(+6.4%), 현대건설(+5.6%), GS건설(+8.5%), 우리기술(+11.6%), 한텍(+6.9%), 일진파워(+6.3%), 지투파워(+5.4%), 우진(+4.4%), 오르비텍(+3.9%), 비에이치아이(+3.9%).", + "expected_route": "local", + "key_entities": ["대우건설", "한전기술", "현대건설", "GS건설"], + "note": "단일 문서 1-hop — 원전 테마 → 종목" + }, + { + "id": "graph_014", + "qa_set": "graphrag", + "pattern": "1hop", + "doc": "DS투자증권_시황분석_리포트.pdf", + "question": "DS 시황 리포트에서 게임 업종 관련 언급된 종목과 등락률은?", + "answer": "펄어비스(+9.5%), 크래프톤(+8.2%), 엔씨소프트(+7.8%), 카카오게임즈(+4.4%), 컴투스(+4.0%), 넷마블(+3.6%), 네오위즈(+2.8%), 위메이드맥스(+2.7%), 넥슨게임즈(+2.6%).", + "expected_route": "local", + "key_entities": ["펄어비스", "크래프톤", "엔씨소프트", "넷마블"], + "note": "게임 테마 → 종목 1-hop" + }, + { + "id": "graph_015", + "qa_set": "graphrag", + "pattern": "1hop", + "doc": "DS투자증권_시황분석_리포트.pdf", + "question": "DS 시황 리포트에서 바이오텍 업종 강세 종목들은?", + "answer": "디앤디파마텍(+15.4%), 보로노이(+11.3%), 리가켐바이오(+9.4%), 올릭스(+8.5%), 알지노믹스(+7.2%), 오름테라퓨틱(+5.7%), 에이비엘바이오(+4.8%), 펩트론(+2.9%). AACR(04/17~22) 등 글로벌 헬스케어 이벤트 기대감 반영.", + "expected_route": "local", + "key_entities": ["디앤디파마텍", "보로노이", "리가켐바이오", "AACR"], + "note": "바이오텍 테마 → 종목 + 이벤트 1-hop" + }, + { + "id": "graph_016", + "qa_set": "graphrag", + "pattern": "1hop", + "doc": "한화투자증권_두산밥캣_기업분석_리포트.pdf", + "question": "두산밥캣의 멕시코 공장의 의미와 기대 효과는?", + "answer": "2026년 3월 준공 예정, 최종 생산 목표 2만대(북미 법인 14만대 Capa), 올해 약 6,000대 생산 기대. 10% 이상 원가 절감 효과. 미국 판매량의 7~8% 차지하는 유럽 공장 생산분 대체 → 관세 절감 효과 기대.", + "expected_route": "local", + "key_entities": ["멕시코 공장", "2만대", "6,000대", "10%", "관세"], + "note": "단일 문서 1-hop — 멕시코 공장 → 효과 entity" + }, + { + "id": "graph_017", + "qa_set": "graphrag", + "pattern": "1hop", + "doc": "금융감독원_251125__보도자료__25_10월중_기업의_직접금융_조달실적.docx", + "question": "2025년 10월 일반회사채 발행에서 신용등급별 비중과 변화는?", + "answer": "AA등급 이상 73.0% (전월 66.5%, +6.5%p 증가), A등급 27.0% (전월 25.9%), BBB등급 이하 0.0% (전월 7.6%, -7.6%p 감소). 우량 등급 집중도 강화.", + "expected_route": "local", + "key_entities": ["AA등급", "73.0%", "BBB", "0.0%", "신용등급"], + "note": "신용등급 → 회사채 비중 1-hop" + }, + { + "id": "graph_018", + "qa_set": "graphrag", + "pattern": "1hop", + "doc": "농협_2022년_9월말_기준_사업보고서.hwp", + "question": "청산농협의 2022년 9월말 기준 판매사업 내 주요 항목과 금액은?", + "answer": "판매사업 합계 6,964백만원. 채소류 3,725백만원, 기타 수산물 등 3,102백만원, 마트사업 1,990백만원.", + "expected_route": "local", + "key_entities": ["6,964", "3,725", "3,102", "1,990", "판매사업"], + "note": "농협 단일 문서 1-hop" + }, + { + "id": "graph_019", + "qa_set": "graphrag", + "pattern": "1hop", + "doc": "미래에셋증권_4분기_실적보고서.pdf", + "question": "미래에셋증권 2025년 IB 부문의 주요 IPO Deal 4개는?", + "answer": "LG CNS, 서울보증보험, 더핑크퐁컴퍼니, 인투셀. 인수주선 수수료 수익 834억원(YoY +29%).", + "expected_route": "local", + "key_entities": ["LG CNS", "서울보증보험", "더핑크퐁컴퍼니", "IPO"], + "note": "단일 문서 1-hop — 미래에셋 → IPO Deals" + }, + { + "id": "graph_020", + "qa_set": "graphrag", + "pattern": "1hop", + "doc": "미래에셋증권_4분기_실적보고서.pdf", + "question": "미래에셋증권 2025년 글로벌 진출 지역 및 거점 수는?", + "answer": "11개 글로벌 지역, 28개 글로벌 거점, 5,062명 글로벌 임직원, 5.2조원 글로벌 자기자본. 미국(뉴욕), 영국, 그리스, 홍콩, 싱가포르, 인도, 인도네시아, 베트남, 브라질, 몽골, 중국.", + "expected_route": "local", + "key_entities": ["11", "28", "5,062", "5.2", "글로벌"], + "note": "단일 문서 1-hop — 미래에셋 → 글로벌 거점" + }, + { + "id": "graph_021", + "qa_set": "graphrag", + "pattern": "intersection", + "doc": "DS투자증권_시황분석_리포트.pdf,한화투자증권_두산밥캣_기업분석_리포트.pdf", + "question": "DS 시황 리포트와 한화 두산밥캣 리포트에 공통으로 등장하는 거시 변수(리스크 요인)는?", + "answer": "환율 (DS: 원/달러 1,500원대, 한화: 환 변동으로 인한 원화 기준 순이익 증가), 관세/무역 (한화: 관세 영향, DS: 글로벌 무역), 금리 (DS: 연준 금리 인하 기대감 축소, 한화: 금리 인하 불투명).", + "expected_route": "local", + "key_entities": ["환율", "관세", "금리", "1,500원"], + "note": "교차 문서 공통점 — RAG는 두 문서 동시 검색 어려움" + }, + { + "id": "graph_022", + "qa_set": "graphrag", + "pattern": "intersection", + "doc": "금융감독원_251125__보도자료__25_10월중_기업의_직접금융_조달실적.docx,미래에셋증권_4분기_실적보고서.pdf", + "question": "금감원 보도자료의 2025년 10월 IPO 실적과 미래에셋증권 IB 부문 IPO 활동의 관계는?", + "answer": "금감원: 2025년 10월 IPO 2건 524억원 (코스닥). 미래에셋증권: 2025년 IPO Deal로 LG CNS, 서울보증보험, 더핑크퐁컴퍼니, 인투셀 주관 (인수주선 수수료 834억원). 미래에셋은 시장 IPO 활동에서 주요 주관사로 기여.", + "expected_route": "local", + "key_entities": ["IPO", "524억원", "LG CNS", "미래에셋"], + "note": "교차 문서 — 시장 vs 개별 회사 IPO" + }, + { + "id": "graph_023", + "qa_set": "graphrag", + "pattern": "intersection", + "doc": "DS투자증권_시황분석_리포트.pdf,한화투자증권_두산밥캣_기업분석_리포트.pdf", + "question": "DS 시황 리포트와 한화 두산밥캣 리포트에 모두 언급된 산업/업종 영역은?", + "answer": "건설/산업재 분야. DS: 원전 강세 (대우건설, GS건설, 현대건설). 한화 두산밥캣: 북미 소형장비 (Top-Tier 업체). 두 보고서 모두 건설/기계장비 업종에 대한 관심.", + "expected_route": "local", + "key_entities": ["건설", "산업재", "기계장비", "두산밥캣"], + "note": "교차 문서 — 업종 공통점" + }, + { + "id": "graph_024", + "qa_set": "graphrag", + "pattern": "intersection", + "doc": "미래에셋증권_3분기_실적보고서.pdf,미래에셋증권_4분기_실적보고서.pdf", + "question": "미래에셋증권 3분기와 연간(2025) 모두에서 Brokerage 수수료가 분기 최대 기록을 경신했는가?", + "answer": "3Q25 Brokerage 수수료 2,637억원 분기 최대 기록. 4Q25 3,323억원으로 다시 분기 최대 기록. 연간 합계 1조 110억원 (사상 최대치 + YoY +43%).", + "expected_route": "local", + "key_entities": ["2,637", "3,323", "1조 110억원", "Brokerage"], + "note": "분기별 연속 최대 — 두 문서 교차" + }, + { + "id": "graph_025", + "qa_set": "graphrag", + "pattern": "intersection", + "doc": "미래에셋증권_1분기_실적보고서.pdf,미래에셋증권_4분기_실적보고서.pdf", + "question": "미래에셋증권의 2025년 1분기와 연간 ROE 차이 및 그 의미는?", + "answer": "1Q25 8.5% → 연간 12.4% (YoY +4.5%p). 분기별로 점진적 상승 (1Q 8.5% → 2Q 10.9% → 3Q 10.8% → 4Q 12.4%). 기업가치제고 목표 ROE 10%+ 초과 달성.", + "expected_route": "local", + "key_entities": ["8.5", "12.4", "ROE", "10%+"], + "note": "두 끝지점 비교 + 목표 달성 의미" + }, + { + "id": "graph_026", + "qa_set": "graphrag", + "pattern": "intersection", + "doc": "DS투자증권_시황분석_리포트.pdf,미래에셋증권_4분기_실적보고서.pdf", + "question": "DS 시황 리포트의 외국인 순매도와 미래에셋증권 2025년 실적의 연관성은?", + "answer": "DS 시황: 외국인 3월 8거래일 13.2조원 순매도. 미래에셋증권: 그럼에도 불구하고 2025년 사상 최대 실적 달성 (Brokerage 1조 110억원, ROE 12.4%) — 국내 약정 M/S 11.3% 유지, 해외주식 잔고 52조원 (업계 최대) 등 해외주식 다각화로 외국인 영향 완화.", + "expected_route": "local", + "key_entities": ["13.2조원", "외국인", "1조 110억원", "Brokerage"], + "note": "교차 문서 — 시장 환경 vs 개별 회사 실적" + }, + { + "id": "graph_027", + "qa_set": "graphrag", + "pattern": "intersection", + "doc": "미래에셋증권_2분기_실적보고서.pdf,미래에셋증권_4분기_실적보고서.pdf", + "question": "미래에셋증권의 해외법인 성장전략에서 선진시장과 이머징시장 분류는 어떻게 다른가?", + "answer": "선진시장(미국·홍콩·런던·싱가포르): Flow Business 니치마켓 공략, 본사와 트레이딩 시너지, 그룹 Global ETF 비즈니스 시너지. 이머징시장(인도·인도네시아·베트남·브라질·몽골): 핵심 성장지역(인도) 자기자본 재배분, Brokerage 중심 WM 비즈니스 성장. 2025 연간: 선진국 3,315억원 (67%), 이머징 1,666억원 (33%).", + "expected_route": "local", + "key_entities": ["선진시장", "이머징시장", "3,315", "1,666"], + "note": "교차 문서 — 전략 + 결과" + }, + { + "id": "graph_028", + "qa_set": "graphrag", + "pattern": "intersection", + "doc": "미래에셋증권_1분기_실적보고서.pdf,미래에셋증권_4분기_실적보고서.pdf", + "question": "미래에셋증권의 PI(투자목적자산) 공정가치평가 손익은 1분기와 연간 어떻게 다른가?", + "answer": "1Q25 약 +900억원 (흑자 전환). 2025 연간 약 +6,020억원 (4분기 연속 흑자). 해외 혁신기업 가치 상승 기반 대규모 평가이익 인식.", + "expected_route": "local", + "key_entities": ["900억원", "6,020억원", "공정가치평가", "흑자"], + "note": "PI 흑자전환 → 연간 누적" + }, + { + "id": "graph_029", + "qa_set": "graphrag", + "pattern": "filter_agg", + "doc": "DS투자증권_시황분석_리포트.pdf,미래에셋증권_1분기_실적보고서.pdf,미래에셋증권_2분기_실적보고서.pdf,미래에셋증권_3분기_실적보고서.pdf,미래에셋증권_4분기_실적보고서.pdf,한화투자증권_두산밥캣_기업분석_리포트.pdf", + "question": "전체 문서 중 doc_type이 report인 것은 몇 개이며 어떤 문서들인가?", + "answer": "report 유형은 2개: DS투자증권_시황분석_리포트.pdf, 한화투자증권_두산밥캣_기업분석_리포트.pdf. 미래에셋 분기 보고서 4개는 ir 유형, 금감원은 disclosure, 농협은 filing 유형.", + "expected_route": "t2c", + "key_entities": ["report", "DS투자증권", "한화투자증권"], + "note": "doc_type 필터 + 집계" + }, + { + "id": "graph_030", + "qa_set": "graphrag", + "pattern": "filter_agg", + "doc": "DS투자증권_시황분석_리포트.pdf,금융감독원_251125__보도자료__25_10월중_기업의_직접금융_조달실적.docx,농협_2022년_9월말_기준_사업보고서.hwp,미래에셋증권_1분기_실적보고서.pdf,미래에셋증권_2분기_실적보고서.pdf,미래에셋증권_3분기_실적보고서.pdf,미래에셋증권_4분기_실적보고서.pdf,한화투자증권_두산밥캣_기업분석_리포트.pdf", + "question": "전체 그래프에 적재된 문서는 몇 개이며, doc_type별 분포는?", + "answer": "총 8개 문서. doc_type별: report 2개 (DS, 한화), ir 4개 (미래에셋 1Q~4Q), disclosure 1개 (금감원), filing 1개 (농협).", + "expected_route": "t2c", + "key_entities": ["8개", "report", "ir", "disclosure", "filing"], + "note": "전체 메타데이터 집계" + }, + { + "id": "graph_031", + "qa_set": "graphrag", + "pattern": "filter_agg", + "doc": "금융감독원_251125__보도자료__25_10월중_기업의_직접금융_조달실적.docx", + "question": "공시(disclosure) 유형 문서에서 다루는 주요 통계 metric은 무엇인가?", + "answer": "공모발행 합계 23조 7,050억원, 주식 발행액 939억원, 회사채 23조 6,111억원, CP/단기사채 137조 6,459억원, 회사채 잔액 750조 447억원, AA등급 이상 회사채 비중 73.0%.", + "expected_route": "t2c", + "key_entities": ["23조 7,050억원", "공시", "disclosure", "회사채"], + "note": "doc_type=disclosure 필터 + entity 추출" + }, + { + "id": "graph_032", + "qa_set": "graphrag", + "pattern": "filter_agg", + "doc": "미래에셋증권_1분기_실적보고서.pdf,미래에셋증권_2분기_실적보고서.pdf,미래에셋증권_3분기_실적보고서.pdf,미래에셋증권_4분기_실적보고서.pdf", + "question": "doc_type이 ir인 문서들에서 공통으로 등장하는 주요 metric 카테고리는?", + "answer": "Brokerage 수수료, WM 수수료, IB 수수료, Trading 손익, ROE(누적연환산), 자기자본, 해외법인 세전이익, 고객자산, 연금자산, 신용잔고. 모두 IR 보고 표준 항목.", + "expected_route": "t2c", + "key_entities": ["Brokerage", "WM", "IB", "Trading", "ROE", "ir"], + "note": "doc_type=ir 필터 + 공통 entity 집계" + }, + { + "id": "graph_033", + "qa_set": "graphrag", + "pattern": "filter_agg", + "doc": "미래에셋증권_1분기_실적보고서.pdf,미래에셋증권_2분기_실적보고서.pdf,미래에셋증권_3분기_실적보고서.pdf,미래에셋증권_4분기_실적보고서.pdf", + "question": "그래프 상에서 Metric 라벨이 가장 많이 부착된 단일 회사 entity는?", + "answer": "미래에셋증권. ir 유형 4개 문서가 모두 미래에셋증권 entity와 다수의 Metric (Brokerage 수수료, WM 수수료, Trading 손익, ROE 등 분기별 수치) 으로 연결됨.", + "expected_route": "t2c", + "key_entities": ["미래에셋증권", "Metric", "HAS_METRIC"], + "note": "라벨 집계 — Company → HAS_METRIC 카운트" + }, + { + "id": "graph_034", + "qa_set": "graphrag", + "pattern": "causal", + "doc": "한화투자증권_두산밥캣_기업분석_리포트.pdf", + "question": "두산밥캣의 멕시코 공장 가동이 수익성에 미치는 영향은?", + "answer": "멕시코 공장 생산분은 10% 이상 원가 절감 효과. 미국 판매량의 7~8%를 차지하는 유럽 공장 생산분 대체 → 관세 절감. 판가 인상 (2025년 3월 +1.6%, 9월 +3.2%, 2026 2~3분기 +2% 추가)과 함께 관세 영향 상쇄에 큰 기여 → 분기를 거듭할수록 수익성 개선 전망.", + "expected_route": "local", + "key_entities": ["멕시코 공장", "10%", "관세", "판가 인상", "수익성"], + "note": "인과 관계 추론" + }, + { + "id": "graph_035", + "qa_set": "graphrag", + "pattern": "causal", + "doc": "DS투자증권_시황분석_리포트.pdf", + "question": "DS 시황 리포트에서 유가 90~100$/bbl 레벨이 원전 업종 강세에 미치는 영향은?", + "answer": "유가 90~100$/bbl 레벨에서 유럽에서도 더 이상 원전 제로를 유지하기는 쉽지 않을 것으로 봄. 유럽 신규 원전 수주 기대감 반영 → 원전 관련 한국 기업들 (대우건설 +17.8%, GS건설 +8.5%, 한전기술 +6.4%, 현대건설 +5.6%) 강세.", + "expected_route": "local", + "key_entities": ["유가", "90~100$/bbl", "원전", "유럽", "신규 수주"], + "note": "인과 관계 — 유가 → 원전 → 종목 강세" + }, + { + "id": "graph_036", + "qa_set": "graphrag", + "pattern": "causal", + "doc": "미래에셋증권_4분기_실적보고서.pdf", + "question": "미래에셋증권 2025년 ROE 12.4% 달성을 가능하게 한 주요 사업 영역은?", + "answer": "Brokerage 수수료 1조 110억원 (사상 최대) + WM 수수료 3,421억원 (역대 최고) + 해외법인 세전이익 4,981억원 (사상 최대) + PI 자산 공정가치평가 흑자 6,020억원. 4개 영역 모두 사상 최대로 종합 ROE 12.4% (YoY +4.5%p) 달성.", + "expected_route": "local", + "key_entities": ["ROE", "12.4%", "Brokerage", "WM", "해외법인", "PI"], + "note": "결과 → 원인 entity 다중 연결" + }, + { + "id": "graph_037", + "qa_set": "graphrag", + "pattern": "causal", + "doc": "미래에셋증권_3분기_실적보고서.pdf", + "question": "미래에셋증권 3분기 Trading 손익 감소(QoQ -49%)의 원인은?", + "answer": "시장금리 상승에 따른 채권운용 이익 축소, IB 캐리 포지션 평가손실 등 반영. 운용손익 1,541억원 (QoQ -60%), 분배 및 배당금 수익 871억원 (QoQ +1%).", + "expected_route": "local", + "key_entities": ["시장금리", "채권운용", "IB 캐리", "Trading", "1,541"], + "note": "원인 → 결과 추론" + }, + { + "id": "graph_038", + "qa_set": "graphrag", + "pattern": "causal", + "doc": "한화투자증권_두산밥캣_기업분석_리포트.pdf", + "question": "두산밥캣 북미 딜러 재고 감소가 향후 판매량에 미치는 영향은?", + "answer": "북미 딜러 재고 3.0~3.5개월 수준까지 내려옴 (4Q25 Unit 기준 YoY -12%, QoQ -20%). 추가 금리 인하 불투명해질 경우 이연됐던 수요가 딜러 재고 리스토킹으로 이어질 가능성 → 2026년 판매량 YoY 성장 전망 (북미 딜러망 920개 → 4~5% 추가 확보 예상).", + "expected_route": "local", + "key_entities": ["딜러 재고", "3.0~3.5개월", "리스토킹", "920개"], + "note": "재고 감소 → 리스토킹 → 판매량 증가 인과 chain" + }, + { + "id": "graph_039", + "qa_set": "graphrag", + "pattern": "limitation", + "doc": "DS투자증권_시황분석_리포트.pdf,한화투자증권_두산밥캣_기업분석_리포트.pdf", + "question": "DS 시황 리포트(2026년 3월)와 한화 두산밥캣 리포트(2026년 3월) 중 어느 것이 더 최신인가?", + "answer": "둘 다 2026년 3월 16일 발행으로 동일한 날짜. 그러나 그래프에는 doc_year 속성이 박혀있지 않아 시간 비교가 직접 불가 (다만 본문 텍스트에서 날짜 확인 가능).", + "expected_route": "t2c", + "key_entities": ["2026.03.16", "doc_year"], + "note": "한계 박제 — doc_year 메타 없음, 시간 비교 불가" + }, + { + "id": "graph_040", + "qa_set": "graphrag", + "pattern": "limitation", + "doc": "금융감독원_251125__보도자료__25_10월중_기업의_직접금융_조달실적.docx", + "question": "그래프에서 Company 라벨로 분류된 'AA등급 이상 회사채 발행 비중 73.0%'는 실제 회사명인가?", + "answer": "아니다. 이는 Metric (수치 지표) 으로 분류되어야 하나 그래프에서 Company 라벨로 잘못 분류된 사례. 본인 그래프의 entity 추출/라벨링 단계에서 발생한 정확도 이슈로, 후속 작업에서 라벨 품질 개선이 필요하다.", "expected_route": "t2c", - "key_entities": [], - "note": "⭐ 시드 QA — 그래프 메타데이터 직접 질의" + "key_entities": ["AA등급", "73.0%", "Company", "Metric"], + "note": "한계 박제 — Company 라벨 품질 이슈" } ] From 94b5d47362974f10a77814dd5826c9063aeac9a5 Mon Sep 17 00:00:00 2001 From: HyunSang Jang Date: Mon, 25 May 2026 13:22:02 +0900 Subject: [PATCH 09/11] =?UTF-8?q?feat(eval/dataset):=20qa=5Fpairs.json=208?= =?UTF-8?q?0=20QA=20=ED=86=B5=ED=95=A9=20(VectorRAG=2040=20+=20GraphRAG=20?= =?UTF-8?q?40)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit doc-summary-agent 와 동일 구조 통합 파일. scripts/run_qa_eval.py 가 단일 파일 로드로 80 QA 처리 가능. ## QA 분포 - vectorrag: 40 (hanwha 6, ds 5, mirae_1q~4q 19, nonghyup 5, fss 6) - graphrag: 40 (multi_doc_trend 10, 1hop 10, intersection 8, filter_agg 5, causal 5, limitation 2) ## 측정 흐름 doc-graph 측에서 80 QA 측정 (--qa-set both): - VectorRAG 40: 단일 사실, RAG 강점 영역 (그래프가 약한 영역) - GraphRAG 40: 다중 문서 추론, 그래프 강점 영역 → doc-summary 측 80 QA 측정 결과와 직접 비교 Refs #127 --- eval/dataset/qa_pairs.json | 82 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 82 insertions(+) create mode 100644 eval/dataset/qa_pairs.json diff --git a/eval/dataset/qa_pairs.json b/eval/dataset/qa_pairs.json new file mode 100644 index 0000000..20b071c --- /dev/null +++ b/eval/dataset/qa_pairs.json @@ -0,0 +1,82 @@ +[ + {"id": "hanwha_001", "qa_set": "vectorrag", "doc": "한화투자증권_두산밥캣_기업분석_리포트.pdf", "source": "pdf", "question": "두산밥캣의 목표주가는 얼마인가?", "answer": "80,000원", "type": "factual", "section": "투자의견 Buy, 목표주가 80,000원 유지"}, + {"id": "hanwha_002", "qa_set": "vectorrag", "doc": "한화투자증권_두산밥캣_기업분석_리포트.pdf", "source": "pdf", "question": "두산밥캣의 2026년 1분기 예상 영업이익과 OPM은?", "answer": "1,606억원(-19.7%, OPM 7.4%)", "type": "numerical", "section": "상저하고의 2026년을 기대"}, + {"id": "hanwha_003", "qa_set": "vectorrag", "doc": "한화투자증권_두산밥캣_기업분석_리포트.pdf", "source": "pdf", "question": "북미 딜러 재고는 몇 개월 수준까지 내려왔는가?", "answer": "3.0~3.5개월", "type": "numerical", "section": "딜러 재고, 비워냈으니 채울 차례"}, + {"id": "hanwha_004", "qa_set": "vectorrag", "doc": "한화투자증권_두산밥캣_기업분석_리포트.pdf", "source": "pdf", "question": "이 리포트의 투자 의견 근거를 요약하면?", "answer": "북미 딜러 재고 감소, 판가 인상, 멕시코 공장 가동으로 수익성 회복 전망. 업종 내 가장 저평가된 밸류에이션.", "type": "summary", "section": "투자의견 Buy, 목표주가 80,000원 유지"}, + {"id": "hanwha_005", "qa_set": "vectorrag", "doc": "한화투자증권_두산밥캣_기업분석_리포트.pdf", "source": "pdf", "question": "멕시코 공장 생산분의 원가 절감 효과와 올해 예상 생산 대수는?", "answer": "원가 절감 10% 이상, 올해 약 6,000대 생산 예상", "type": "factual", "section": "수익성 회복에 기여할 멕시코 공장"}, + {"id": "hanwha_006", "qa_set": "vectorrag", "doc": "한화투자증권_두산밥캣_기업분석_리포트.pdf", "source": "pdf", "question": "두산밥캣의 주요 주주 구성과 지분율은?", "answer": "두산에너빌리티(외 4인) 48.2%, 국민연금공단 7.4%, 자사주 0.1%, 외국인 37.3%", "type": "factual", "section": "투자의견 Buy, 목표주가 80,000원 유지"}, + {"id": "ds_001", "qa_set": "vectorrag", "doc": "DS투자증권_시황분석_리포트.pdf", "source": "pdf", "question": "이 리포트의 발행일과 담당 연구원은?", "answer": "2026년 3월 16일, 김현지 연구원(02-709-2663)", "type": "factual", "section": "지수 종합"}, + {"id": "ds_002", "qa_set": "vectorrag", "doc": "DS투자증권_시황분석_리포트.pdf", "source": "pdf", "question": "지난 금요일 코스피와 코스닥 등락률은?", "answer": "코스피 1.7% 하락, 코스닥 0.4% 상승", "type": "factual", "section": "지수 종합"}, + {"id": "ds_003", "qa_set": "vectorrag", "doc": "DS투자증권_시황분석_리포트.pdf", "source": "pdf", "question": "원전 업종 강세 배경과 주요 수혜 종목을 요약하면?", "answer": "유가 90~100달러/bbl 레벨에서 유럽 원전 제로 유지 어려움, 유럽 신규 수주 기대감 반영. 대우건설(+17.8%), 한전기술(+6.4%), 현대건설(+5.6%) 등 강세.", "type": "summary", "section": "업종 이슈"}, + {"id": "ds_004", "qa_set": "vectorrag", "doc": "DS투자증권_시황분석_리포트.pdf", "source": "pdf", "question": "외국인의 3월 8거래일 누적 순매도 금액은?", "answer": "13.2조원", "type": "numerical", "section": "지수 종합"}, + {"id": "ds_005", "qa_set": "vectorrag", "doc": "DS투자증권_시황분석_리포트.pdf", "source": "pdf", "question": "이 보고서에서 언급된 한국은행 금통위 일정은?", "answer": "N/A - 문서에 없는 내용 (BOJ·BOE·ECB 통화정책회의는 언급되나 한국은행 일정은 없음)", "type": "negative", "section": ""}, + {"id": "mirae_1q_001", "qa_set": "vectorrag", "doc": "미래에셋증권_1분기_실적보고서.pdf", "source": "pdf", "question": "미래에셋증권 2025년 1분기 연결 세전이익은?", "answer": "3,461억원", "type": "numerical", "section": "1Q 2025 Key Highlights"}, + {"id": "mirae_1q_002", "qa_set": "vectorrag", "doc": "미래에셋증권_1분기_실적보고서.pdf", "source": "pdf", "question": "1분기 별도 고객자산 총액은?", "answer": "405조원(연금 44.4조원, 해외주식 34.5조원)", "type": "numerical", "section": "1Q 2025 Key Highlights"}, + {"id": "mirae_1q_003", "qa_set": "vectorrag", "doc": "미래에셋증권_1분기_실적보고서.pdf", "source": "pdf", "question": "1분기 해외주식 수수료 수익과 전체 브로커리지 수수료 수익은?", "answer": "해외주식 1,012억원, 전체 브로커리지 1,987억원", "type": "numerical", "section": "사업별 주요 실적 - Brokerage"}, + {"id": "mirae_1q_004", "qa_set": "vectorrag", "doc": "미래에셋증권_1분기_실적보고서.pdf", "source": "pdf", "question": "1분기 연결 누적연환산 ROE와 전분기 대비 변동폭은?", "answer": "8.5%, QoQ +0.6%p", "type": "numerical", "section": "1Q 2025 Key Highlights"}, + {"id": "mirae_2q_001", "qa_set": "vectorrag", "doc": "미래에셋증권_2분기_실적보고서.pdf", "source": "pdf", "question": "2025년 2분기 연결 세전이익과 순이익은?", "answer": "세전이익 5,202억원, 순이익 4,059억원", "type": "numerical", "section": "2Q 2025 Key Highlights"}, + {"id": "mirae_2q_002", "qa_set": "vectorrag", "doc": "미래에셋증권_2분기_실적보고서.pdf", "source": "pdf", "question": "2분기 별도 Trading 손익과 전분기 대비 증감률은?", "answer": "4,762억원(QoQ +46%)", "type": "numerical", "section": "사업별 주요 실적 - Trading"}, + {"id": "mirae_2q_003", "qa_set": "vectorrag", "doc": "미래에셋증권_2분기_실적보고서.pdf", "source": "pdf", "question": "2분기 총 고객자산 규모와 연금자산 규모는?", "answer": "총 고객자산 453조원, 연금자산 47.3조원", "type": "numerical", "section": "2Q 2025 Key Highlights"}, + {"id": "mirae_2q_004", "qa_set": "vectorrag", "doc": "미래에셋증권_2분기_실적보고서.pdf", "source": "pdf", "question": "2분기 연결 누적연환산 ROE는?", "answer": "10.9%", "type": "numerical", "section": "2Q 2025 Key Highlights"}, + {"id": "mirae_3q_001", "qa_set": "vectorrag", "doc": "미래에셋증권_3분기_실적보고서.pdf", "source": "pdf", "question": "2025년 3분기 연결 세전이익과 순이익은?", "answer": "세전이익 4,472억원, 순이익 3,438억원", "type": "numerical", "section": "3Q 2025 Key Highlights"}, + {"id": "mirae_3q_002", "qa_set": "vectorrag", "doc": "미래에셋증권_3분기_실적보고서.pdf", "source": "pdf", "question": "3분기 브로커리지 수수료 수익과 WM 수수료 수익은?", "answer": "브로커리지 2,637억원(분기 최대), WM 918억원(분기 최대)", "type": "numerical", "section": "3Q 2025 Key Highlights"}, + {"id": "mirae_3q_003", "qa_set": "vectorrag", "doc": "미래에셋증권_3분기_실적보고서.pdf", "source": "pdf", "question": "3분기 해외주식 잔고와 전분기 대비 증감률은?", "answer": "49.6조원(전분기 대비 25% 증가, 업계 최대)", "type": "numerical", "section": "사업별 주요 실적 - Brokerage"}, + {"id": "mirae_3q_004", "qa_set": "vectorrag", "doc": "미래에셋증권_3분기_실적보고서.pdf", "source": "pdf", "question": "3분기 연결 누적연환산 ROE는?", "answer": "10.8%", "type": "numerical", "section": "3Q 2025 Key Highlights"}, + {"id": "mirae_4q_001", "qa_set": "vectorrag", "doc": "미래에셋증권_4분기_실적보고서.pdf", "source": "pdf", "question": "미래에셋증권 2025년 연간 연결 세전이익과 순이익은?", "answer": "세전이익 2조 794억원, 순이익 1조 5,829억원", "type": "numerical", "section": "2025 Key Highlights"}, + {"id": "mirae_4q_002", "qa_set": "vectorrag", "doc": "미래에셋증권_4분기_실적보고서.pdf", "source": "pdf", "question": "2025년 연간 연결 ROE와 전년 대비 변동폭은?", "answer": "12.4%(YoY +4.5%p)", "type": "numerical", "section": "2025 Key Highlights"}, + {"id": "mirae_4q_003", "qa_set": "vectorrag", "doc": "미래에셋증권_4분기_실적보고서.pdf", "source": "pdf", "question": "2025년 해외법인 세전이익과 선진국·이머징 비중은?", "answer": "4,981억원(YoY +200%), 선진국 3,315억원(67%), 이머징 1,666억원(33%)", "type": "numerical", "section": "사업별 주요 실적 - 해외법인"}, + {"id": "mirae_4q_004", "qa_set": "vectorrag", "doc": "미래에셋증권_4분기_실적보고서.pdf", "source": "pdf", "question": "2025년 연간 실적을 종합하여 핵심 성과를 요약하면?", "answer": "연결 ROE 12.4% 달성(기업가치제고 목표 ROE 10%+ 초과), 브로커리지(1조 110억원)·WM(3,421억원)·해외법인(4,981억원) 모두 사상 최대, PI 공정가치평가 4분기 연속 흑자(연누적 약 6,020억원), 총 고객자산 602조원 달성.", "type": "summary", "section": "2025 Key Highlights"}, + {"id": "mirae_4q_005", "qa_set": "vectorrag", "doc": "미래에셋증권_4분기_실적보고서.pdf", "source": "pdf", "question": "미래에셋증권의 2025년 4분기 브로커리지 수수료 수익과 전년 동기 대비 증감은?", "answer": "3,323억원(YoY +84%, 4Q24 180.5십억원 대비)", "type": "numerical", "section": "사업별 주요 실적 - Brokerage"}, + {"id": "mirae_4q_006", "qa_set": "vectorrag", "doc": "미래에셋증권_4분기_실적보고서.pdf", "source": "pdf", "question": "미래에셋증권의 2025년 4분기 배당 지급일은?", "answer": "N/A - 문서에 없는 내용", "type": "negative", "section": ""}, + {"id": "nonghyup_001", "qa_set": "vectorrag", "doc": "농협_2022년_9월말_기준_사업보고서.hwp", "source": "hwp", "question": "이 사업보고서의 보고 기관명과 조합장은?", "answer": "청산농업협동조합, 조합장 차동악", "type": "factual", "section": "표지"}, + {"id": "nonghyup_002", "qa_set": "vectorrag", "doc": "농협_2022년_9월말_기준_사업보고서.hwp", "source": "hwp", "question": "2022년 9월말 기준 판매사업 실적 합계와 주요 항목은?", "answer": "6,964백만원(채소류 3,725백만원, 기타 수산물 등 3,102백만원, 마트사업 1,990백만원)", "type": "factual", "section": "주요사업 추진 현황"}, + {"id": "nonghyup_003", "qa_set": "vectorrag", "doc": "농협_2022년_9월말_기준_사업보고서.hwp", "source": "hwp", "question": "2022년 구매사업 중 유류 매입 금액과 구매사업 합계는?", "answer": "유류 771백만원, 구매사업 합계 1,261백만원", "type": "factual", "section": "주요사업 추진 현황"}, + {"id": "nonghyup_004", "qa_set": "vectorrag", "doc": "농협_2022년_9월말_기준_사업보고서.hwp", "source": "hwp", "question": "이 보고서에서 다루는 기간과 사업 구조를 요약하면?", "answer": "2022년 1월 1일부터 9월 30일까지. 교육지원사업, 경제(유통)사업(판매·구매·마트), 신용(금융)사업, 기타사업으로 구성. 전남 완도군 청산면 소재 지역농협.", "type": "summary", "section": "목차 / 주요사업 추진 현황"}, + {"id": "nonghyup_005", "qa_set": "vectorrag", "doc": "농협_2022년_9월말_기준_사업보고서.hwp", "source": "hwp", "question": "이 보고서에 기재된 청산농협의 당기순이익은 얼마인가?", "answer": "N/A - 문서에 없는 내용", "type": "negative", "section": ""}, + {"id": "fss_001", "qa_set": "vectorrag", "doc": "금융감독원_251125__보도자료__25_10월중_기업의_직접금융_조달실적.docx", "source": "doc", "question": "2025년 10월 중 주식·회사채 공모발행액 합계는?", "answer": "23조 7,050억원(주식 939억원, 회사채 23조 6,111억원)", "type": "numerical", "section": "개황"}, + {"id": "fss_002", "qa_set": "vectorrag", "doc": "금융감독원_251125__보도자료__25_10월중_기업의_직접금융_조달실적.docx", "source": "doc", "question": "2025년 10월 CP 및 단기사채 발행액 합계와 전월 대비 증감은?", "answer": "137조 6,459억원, 전월 대비 12조 3,867억원 감소(8.3%↓)", "type": "numerical", "section": "개황"}, + {"id": "fss_003", "qa_set": "vectorrag", "doc": "금융감독원_251125__보도자료__25_10월중_기업의_직접금융_조달실적.docx", "source": "doc", "question": "10월 일반회사채의 신용등급별 발행 비중에서 AA등급 이상과 BBB등급 이하 비중은?", "answer": "AA등급 이상 73.0%(전월 66.5%), BBB등급 이하 0.0%(전월 7.6%)", "type": "numerical", "section": "주식·회사채 발행 실적"}, + {"id": "fss_004", "qa_set": "vectorrag", "doc": "금융감독원_251125__보도자료__25_10월중_기업의_직접금융_조달실적.docx", "source": "doc", "question": "10월말 기준 전체 회사채 잔액과 전월 대비 증감은?", "answer": "750조 447억원, 전월 대비 8조 1,570억원 증가(1.1%↑)", "type": "numerical", "section": "주식·회사채 발행 실적"}, + {"id": "fss_005", "qa_set": "vectorrag", "doc": "금융감독원_251125__보도자료__25_10월중_기업의_직접금융_조달실적.docx", "source": "doc", "question": "10월 주식 발행 현황을 요약하면?", "answer": "총 4건 939억원. IPO 2건 524억원(코스닥), 유상증자 2건 415억원(코스닥 소규모). 전월(8건, 3,698억원) 대비 74.6% 감소.", "type": "factual", "section": "주식·회사채 발행 실적"}, + {"id": "fss_006", "qa_set": "vectorrag", "doc": "금융감독원_251125__보도자료__25_10월중_기업의_직접금융_조달실적.docx", "source": "doc", "question": "이 보도자료에서 언급된 회사채 발행 기업의 개별 회사명은?", "answer": "N/A - 문서에 없는 내용 (집계 통계만 수록, 개별 발행사 명칭은 기재되지 않음)", "type": "negative", "section": ""}, + {"id": "graph_001", "qa_set": "graphrag", "pattern": "multi_doc_trend", "doc": "미래에셋증권_1분기_실적보고서.pdf,미래에셋증권_2분기_실적보고서.pdf,미래에셋증권_3분기_실적보고서.pdf,미래에셋증권_4분기_실적보고서.pdf", "question": "미래에셋증권의 2025년 1분기부터 4분기까지 연결 ROE는 어떻게 변화했는가?", "answer": "1Q25 8.5%, 2Q25 10.9%, 3Q25 10.8%, 연간 12.4% (YoY +4.5%p). 분기를 거듭하며 우상향 추세.", "expected_route": "local", "key_entities": ["8.5", "10.9", "10.8", "12.4", "ROE"]}, + {"id": "graph_002", "qa_set": "graphrag", "pattern": "multi_doc_trend", "doc": "미래에셋증권_1분기_실적보고서.pdf,미래에셋증권_2분기_실적보고서.pdf,미래에셋증권_3분기_실적보고서.pdf,미래에셋증권_4분기_실적보고서.pdf", "question": "미래에셋증권 2025년 1분기와 연간(4Q25) Brokerage 수수료 수익 차이는?", "answer": "1Q25 1,987억원 → 2025년 연간 1조 110억원 (YoY +43% 사상 최대치 기록). 1Q 대비 약 5배 증가.", "expected_route": "local", "key_entities": ["1,987", "1조 110억원", "Brokerage", "+43%"]}, + {"id": "graph_003", "qa_set": "graphrag", "pattern": "multi_doc_trend", "doc": "미래에셋증권_1분기_실적보고서.pdf,미래에셋증권_2분기_실적보고서.pdf,미래에셋증권_3분기_실적보고서.pdf,미래에셋증권_4분기_실적보고서.pdf", "question": "미래에셋증권의 총 고객자산은 2025년 분기별로 어떻게 증가했는가?", "answer": "1Q25 405조원 → 2Q25 453조원 → 3Q25 477조원 → 연간(4Q25) 518조원 (국내) + 84조원 (해외) = 602조원. 1Q 대비 약 49% 증가.", "expected_route": "local", "key_entities": ["405", "453", "477", "518", "602", "고객자산"]}, + {"id": "graph_004", "qa_set": "graphrag", "pattern": "multi_doc_trend", "doc": "미래에셋증권_1분기_실적보고서.pdf,미래에셋증권_2분기_실적보고서.pdf,미래에셋증권_3분기_실적보고서.pdf,미래에셋증권_4분기_실적보고서.pdf", "question": "미래에셋증권의 연금자산은 2025년 분기별로 어떻게 변화했는가?", "answer": "1Q25 44.4조원 → 2Q25 47.3조원 → 3Q25 52.2조원 → 4Q25 57.8조원 (YoY +35.3%). 4분기 모두 신기록 경신.", "expected_route": "local", "key_entities": ["44.4", "47.3", "52.2", "57.8", "연금자산"]}, + {"id": "graph_005", "qa_set": "graphrag", "pattern": "multi_doc_trend", "doc": "미래에셋증권_1분기_실적보고서.pdf,미래에셋증권_2분기_실적보고서.pdf,미래에셋증권_3분기_실적보고서.pdf,미래에셋증권_4분기_실적보고서.pdf", "question": "미래에셋증권의 해외법인 세전이익은 2025년 분기별로 어떻게 변화했는가?", "answer": "1Q25 1,196억원 → 2Q25 1,061억원 → 3Q25 748억원 → 연간 4,981억원 (YoY +200%, 사상 최대). 선진국 3,315억원 (67%) + 이머징 1,666억원 (33%).", "expected_route": "local", "key_entities": ["1,196", "1,061", "748", "4,981", "해외법인"]}, + {"id": "graph_006", "qa_set": "graphrag", "pattern": "multi_doc_trend", "doc": "미래에셋증권_1분기_실적보고서.pdf,미래에셋증권_2분기_실적보고서.pdf,미래에셋증권_3분기_실적보고서.pdf,미래에셋증권_4분기_실적보고서.pdf", "question": "미래에셋증권의 해외주식 잔고는 2025년 분기별로 어떻게 증가했는가?", "answer": "1Q25 34.5조원 → 2Q25 39.6조원 → 3Q25 49.6조원 (전분기 대비 +25%) → 4Q25 52.0조원 (YoY +28%, 업계 최대).", "expected_route": "local", "key_entities": ["34.5", "39.6", "49.6", "52.0", "해외주식 잔고"]}, + {"id": "graph_007", "qa_set": "graphrag", "pattern": "multi_doc_trend", "doc": "미래에셋증권_1분기_실적보고서.pdf,미래에셋증권_2분기_실적보고서.pdf,미래에셋증권_3분기_실적보고서.pdf,미래에셋증권_4분기_실적보고서.pdf", "question": "미래에셋증권의 WM 수수료 수익은 2025년 어떻게 변화했으며 연간 합계는?", "answer": "1Q25 784억원 → 2Q25 757억원 → 3Q25 918억원 → 4Q25 962억원. 2025 연간 합계 3,421억원 (YoY +21%, 역대 최고치).", "expected_route": "local", "key_entities": ["784", "757", "918", "962", "3,421", "WM"]}, + {"id": "graph_008", "qa_set": "graphrag", "pattern": "multi_doc_trend", "doc": "미래에셋증권_1분기_실적보고서.pdf,미래에셋증권_2분기_실적보고서.pdf,미래에셋증권_3분기_실적보고서.pdf,미래에셋증권_4분기_실적보고서.pdf", "question": "미래에셋증권의 Trading 손익은 2025년 분기별로 어떻게 변화했는가?", "answer": "1Q25 3,257억원 → 2Q25 4,762억원(분기 최대) → 3Q25 2,412억원 → 4Q25 약 2,219억원. 연간 1조 2,649억원.", "expected_route": "local", "key_entities": ["3,257", "4,762", "2,412", "Trading"]}, + {"id": "graph_009", "qa_set": "graphrag", "pattern": "multi_doc_trend", "doc": "미래에셋증권_2분기_실적보고서.pdf,미래에셋증권_3분기_실적보고서.pdf,미래에셋증권_4분기_실적보고서.pdf", "question": "미래에셋증권의 연결 자기자본은 2분기, 3분기, 연간 어떻게 변화했는가?", "answer": "2Q25 12.3조원 → 3Q25 12.7조원 → 4Q25 13.3조원 (YoY +10%). 분기마다 성장 지속.", "expected_route": "local", "key_entities": ["12.3", "12.7", "13.3", "자기자본"]}, + {"id": "graph_010", "qa_set": "graphrag", "pattern": "multi_doc_trend", "doc": "미래에셋증권_2분기_실적보고서.pdf,미래에셋증권_3분기_실적보고서.pdf,미래에셋증권_4분기_실적보고서.pdf", "question": "미래에셋증권의 신용잔고는 2025년 2분기부터 4분기까지 어떻게 변화했는가?", "answer": "2Q25 6.9조원 → 3Q25 7.2조원 → 4Q25 7.6조원 (YoY +35.9%). 신용손익도 분기마다 증가 (2Q25 703억원 → 3Q25 872억원 → 4Q25 925억원).", "expected_route": "local", "key_entities": ["6.9", "7.2", "7.6", "신용잔고"]}, + {"id": "graph_011", "qa_set": "graphrag", "pattern": "1hop", "doc": "한화투자증권_두산밥캣_기업분석_리포트.pdf", "question": "두산밥캣의 주요 리스크 요인은 무엇인가?", "answer": "관세 영향, 환 변동, 딜러 재고 감소 후 리스토킹 불확실성, 멕시코 공장 가동 지연 가능성, 금리 인하 불투명성.", "expected_route": "local", "key_entities": ["관세", "환 변동", "딜러 재고", "멕시코 공장", "금리"]}, + {"id": "graph_012", "qa_set": "graphrag", "pattern": "1hop", "doc": "한화투자증권_두산밥캣_기업분석_리포트.pdf", "question": "두산밥캣과 함께 언급된 핵심 metric 들은?", "answer": "목표주가 80,000원, 시가총액 57,897억원, 2026E EPS 5,660원, ROE 7.5%, 2026E 매출액 9조 3,132억원, 영업이익률 8.5%, 멕시코 공장 6,000대 생산.", "expected_route": "local", "key_entities": ["80,000원", "57,897억원", "5,660원", "9조 3,132억원"]}, + {"id": "graph_013", "qa_set": "graphrag", "pattern": "1hop", "doc": "DS투자증권_시황분석_리포트.pdf", "question": "DS투자증권 시황 리포트에서 원전 업종 강세에 언급된 종목들은?", "answer": "대우건설(+17.8%), 한전기술(+6.4%), 현대건설(+5.6%), GS건설(+8.5%), 우리기술(+11.6%), 한텍(+6.9%), 일진파워(+6.3%), 지투파워(+5.4%), 우진(+4.4%), 오르비텍(+3.9%), 비에이치아이(+3.9%).", "expected_route": "local", "key_entities": ["대우건설", "한전기술", "현대건설", "GS건설"]}, + {"id": "graph_014", "qa_set": "graphrag", "pattern": "1hop", "doc": "DS투자증권_시황분석_리포트.pdf", "question": "DS 시황 리포트에서 게임 업종 관련 언급된 종목과 등락률은?", "answer": "펄어비스(+9.5%), 크래프톤(+8.2%), 엔씨소프트(+7.8%), 카카오게임즈(+4.4%), 컴투스(+4.0%), 넷마블(+3.6%), 네오위즈(+2.8%), 위메이드맥스(+2.7%), 넥슨게임즈(+2.6%).", "expected_route": "local", "key_entities": ["펄어비스", "크래프톤", "엔씨소프트", "넷마블"]}, + {"id": "graph_015", "qa_set": "graphrag", "pattern": "1hop", "doc": "DS투자증권_시황분석_리포트.pdf", "question": "DS 시황 리포트에서 바이오텍 업종 강세 종목들은?", "answer": "디앤디파마텍(+15.4%), 보로노이(+11.3%), 리가켐바이오(+9.4%), 올릭스(+8.5%), 알지노믹스(+7.2%), 오름테라퓨틱(+5.7%), 에이비엘바이오(+4.8%), 펩트론(+2.9%). AACR(04/17~22) 등 글로벌 헬스케어 이벤트 기대감 반영.", "expected_route": "local", "key_entities": ["디앤디파마텍", "보로노이", "리가켐바이오", "AACR"]}, + {"id": "graph_016", "qa_set": "graphrag", "pattern": "1hop", "doc": "한화투자증권_두산밥캣_기업분석_리포트.pdf", "question": "두산밥캣의 멕시코 공장의 의미와 기대 효과는?", "answer": "2026년 3월 준공 예정, 최종 생산 목표 2만대(북미 법인 14만대 Capa), 올해 약 6,000대 생산 기대. 10% 이상 원가 절감 효과. 미국 판매량의 7~8% 차지하는 유럽 공장 생산분 대체 → 관세 절감 효과 기대.", "expected_route": "local", "key_entities": ["멕시코 공장", "2만대", "6,000대", "10%", "관세"]}, + {"id": "graph_017", "qa_set": "graphrag", "pattern": "1hop", "doc": "금융감독원_251125__보도자료__25_10월중_기업의_직접금융_조달실적.docx", "question": "2025년 10월 일반회사채 발행에서 신용등급별 비중과 변화는?", "answer": "AA등급 이상 73.0% (전월 66.5%, +6.5%p 증가), A등급 27.0% (전월 25.9%), BBB등급 이하 0.0% (전월 7.6%, -7.6%p 감소). 우량 등급 집중도 강화.", "expected_route": "local", "key_entities": ["AA등급", "73.0%", "BBB", "0.0%", "신용등급"]}, + {"id": "graph_018", "qa_set": "graphrag", "pattern": "1hop", "doc": "농협_2022년_9월말_기준_사업보고서.hwp", "question": "청산농협의 2022년 9월말 기준 판매사업 내 주요 항목과 금액은?", "answer": "판매사업 합계 6,964백만원. 채소류 3,725백만원, 기타 수산물 등 3,102백만원, 마트사업 1,990백만원.", "expected_route": "local", "key_entities": ["6,964", "3,725", "3,102", "1,990", "판매사업"]}, + {"id": "graph_019", "qa_set": "graphrag", "pattern": "1hop", "doc": "미래에셋증권_4분기_실적보고서.pdf", "question": "미래에셋증권 2025년 IB 부문의 주요 IPO Deal 4개는?", "answer": "LG CNS, 서울보증보험, 더핑크퐁컴퍼니, 인투셀. 인수주선 수수료 수익 834억원(YoY +29%).", "expected_route": "local", "key_entities": ["LG CNS", "서울보증보험", "더핑크퐁컴퍼니", "IPO"]}, + {"id": "graph_020", "qa_set": "graphrag", "pattern": "1hop", "doc": "미래에셋증권_4분기_실적보고서.pdf", "question": "미래에셋증권 2025년 글로벌 진출 지역 및 거점 수는?", "answer": "11개 글로벌 지역, 28개 글로벌 거점, 5,062명 글로벌 임직원, 5.2조원 글로벌 자기자본. 미국(뉴욕), 영국, 그리스, 홍콩, 싱가포르, 인도, 인도네시아, 베트남, 브라질, 몽골, 중국.", "expected_route": "local", "key_entities": ["11", "28", "5,062", "5.2", "글로벌"]}, + {"id": "graph_021", "qa_set": "graphrag", "pattern": "intersection", "doc": "DS투자증권_시황분석_리포트.pdf,한화투자증권_두산밥캣_기업분석_리포트.pdf", "question": "DS 시황 리포트와 한화 두산밥캣 리포트에 공통으로 등장하는 거시 변수(리스크 요인)는?", "answer": "환율 (DS: 원/달러 1,500원대, 한화: 환 변동으로 인한 원화 기준 순이익 증가), 관세/무역 (한화: 관세 영향, DS: 글로벌 무역), 금리 (DS: 연준 금리 인하 기대감 축소, 한화: 금리 인하 불투명).", "expected_route": "local", "key_entities": ["환율", "관세", "금리", "1,500원"]}, + {"id": "graph_022", "qa_set": "graphrag", "pattern": "intersection", "doc": "금융감독원_251125__보도자료__25_10월중_기업의_직접금융_조달실적.docx,미래에셋증권_4분기_실적보고서.pdf", "question": "금감원 보도자료의 2025년 10월 IPO 실적과 미래에셋증권 IB 부문 IPO 활동의 관계는?", "answer": "금감원: 2025년 10월 IPO 2건 524억원 (코스닥). 미래에셋증권: 2025년 IPO Deal로 LG CNS, 서울보증보험, 더핑크퐁컴퍼니, 인투셀 주관 (인수주선 수수료 834억원). 미래에셋은 시장 IPO 활동에서 주요 주관사로 기여.", "expected_route": "local", "key_entities": ["IPO", "524억원", "LG CNS", "미래에셋"]}, + {"id": "graph_023", "qa_set": "graphrag", "pattern": "intersection", "doc": "DS투자증권_시황분석_리포트.pdf,한화투자증권_두산밥캣_기업분석_리포트.pdf", "question": "DS 시황 리포트와 한화 두산밥캣 리포트에 모두 언급된 산업/업종 영역은?", "answer": "건설/산업재 분야. DS: 원전 강세 (대우건설, GS건설, 현대건설). 한화 두산밥캣: 북미 소형장비 (Top-Tier 업체). 두 보고서 모두 건설/기계장비 업종에 대한 관심.", "expected_route": "local", "key_entities": ["건설", "산업재", "기계장비", "두산밥캣"]}, + {"id": "graph_024", "qa_set": "graphrag", "pattern": "intersection", "doc": "미래에셋증권_3분기_실적보고서.pdf,미래에셋증권_4분기_실적보고서.pdf", "question": "미래에셋증권 3분기와 연간(2025) 모두에서 Brokerage 수수료가 분기 최대 기록을 경신했는가?", "answer": "3Q25 Brokerage 수수료 2,637억원 분기 최대 기록. 4Q25 3,323억원으로 다시 분기 최대 기록. 연간 합계 1조 110억원 (사상 최대치 + YoY +43%).", "expected_route": "local", "key_entities": ["2,637", "3,323", "1조 110억원", "Brokerage"]}, + {"id": "graph_025", "qa_set": "graphrag", "pattern": "intersection", "doc": "미래에셋증권_1분기_실적보고서.pdf,미래에셋증권_4분기_실적보고서.pdf", "question": "미래에셋증권의 2025년 1분기와 연간 ROE 차이 및 그 의미는?", "answer": "1Q25 8.5% → 연간 12.4% (YoY +4.5%p). 분기별로 점진적 상승 (1Q 8.5% → 2Q 10.9% → 3Q 10.8% → 4Q 12.4%). 기업가치제고 목표 ROE 10%+ 초과 달성.", "expected_route": "local", "key_entities": ["8.5", "12.4", "ROE", "10%+"]}, + {"id": "graph_026", "qa_set": "graphrag", "pattern": "intersection", "doc": "DS투자증권_시황분석_리포트.pdf,미래에셋증권_4분기_실적보고서.pdf", "question": "DS 시황 리포트의 외국인 순매도와 미래에셋증권 2025년 실적의 연관성은?", "answer": "DS 시황: 외국인 3월 8거래일 13.2조원 순매도. 미래에셋증권: 그럼에도 불구하고 2025년 사상 최대 실적 달성 (Brokerage 1조 110억원, ROE 12.4%) — 국내 약정 M/S 11.3% 유지, 해외주식 잔고 52조원 (업계 최대) 등 해외주식 다각화로 외국인 영향 완화.", "expected_route": "local", "key_entities": ["13.2조원", "외국인", "1조 110억원", "Brokerage"]}, + {"id": "graph_027", "qa_set": "graphrag", "pattern": "intersection", "doc": "미래에셋증권_2분기_실적보고서.pdf,미래에셋증권_4분기_실적보고서.pdf", "question": "미래에셋증권의 해외법인 성장전략에서 선진시장과 이머징시장 분류는 어떻게 다른가?", "answer": "선진시장(미국·홍콩·런던·싱가포르): Flow Business 니치마켓 공략, 본사와 트레이딩 시너지, 그룹 Global ETF 비즈니스 시너지. 이머징시장(인도·인도네시아·베트남·브라질·몽골): 핵심 성장지역(인도) 자기자본 재배분, Brokerage 중심 WM 비즈니스 성장. 2025 연간: 선진국 3,315억원 (67%), 이머징 1,666억원 (33%).", "expected_route": "local", "key_entities": ["선진시장", "이머징시장", "3,315", "1,666"]}, + {"id": "graph_028", "qa_set": "graphrag", "pattern": "intersection", "doc": "미래에셋증권_1분기_실적보고서.pdf,미래에셋증권_4분기_실적보고서.pdf", "question": "미래에셋증권의 PI(투자목적자산) 공정가치평가 손익은 1분기와 연간 어떻게 다른가?", "answer": "1Q25 약 +900억원 (흑자 전환). 2025 연간 약 +6,020억원 (4분기 연속 흑자). 해외 혁신기업 가치 상승 기반 대규모 평가이익 인식.", "expected_route": "local", "key_entities": ["900억원", "6,020억원", "공정가치평가", "흑자"]}, + {"id": "graph_029", "qa_set": "graphrag", "pattern": "filter_agg", "doc": "DS투자증권_시황분석_리포트.pdf,미래에셋증권_1분기_실적보고서.pdf,미래에셋증권_2분기_실적보고서.pdf,미래에셋증권_3분기_실적보고서.pdf,미래에셋증권_4분기_실적보고서.pdf,한화투자증권_두산밥캣_기업분석_리포트.pdf", "question": "전체 문서 중 doc_type이 report인 것은 몇 개이며 어떤 문서들인가?", "answer": "report 유형은 2개: DS투자증권_시황분석_리포트.pdf, 한화투자증권_두산밥캣_기업분석_리포트.pdf. 미래에셋 분기 보고서 4개는 ir 유형, 금감원은 disclosure, 농협은 filing 유형.", "expected_route": "t2c", "key_entities": ["report", "DS투자증권", "한화투자증권"]}, + {"id": "graph_030", "qa_set": "graphrag", "pattern": "filter_agg", "doc": "DS투자증권_시황분석_리포트.pdf,금융감독원_251125__보도자료__25_10월중_기업의_직접금융_조달실적.docx,농협_2022년_9월말_기준_사업보고서.hwp,미래에셋증권_1분기_실적보고서.pdf,미래에셋증권_2분기_실적보고서.pdf,미래에셋증권_3분기_실적보고서.pdf,미래에셋증권_4분기_실적보고서.pdf,한화투자증권_두산밥캣_기업분석_리포트.pdf", "question": "전체 그래프에 적재된 문서는 몇 개이며, doc_type별 분포는?", "answer": "총 8개 문서. doc_type별: report 2개 (DS, 한화), ir 4개 (미래에셋 1Q~4Q), disclosure 1개 (금감원), filing 1개 (농협).", "expected_route": "t2c", "key_entities": ["8개", "report", "ir", "disclosure", "filing"]}, + {"id": "graph_031", "qa_set": "graphrag", "pattern": "filter_agg", "doc": "금융감독원_251125__보도자료__25_10월중_기업의_직접금융_조달실적.docx", "question": "공시(disclosure) 유형 문서에서 다루는 주요 통계 metric은 무엇인가?", "answer": "공모발행 합계 23조 7,050억원, 주식 발행액 939억원, 회사채 23조 6,111억원, CP/단기사채 137조 6,459억원, 회사채 잔액 750조 447억원, AA등급 이상 회사채 비중 73.0%.", "expected_route": "t2c", "key_entities": ["23조 7,050억원", "공시", "disclosure", "회사채"]}, + {"id": "graph_032", "qa_set": "graphrag", "pattern": "filter_agg", "doc": "미래에셋증권_1분기_실적보고서.pdf,미래에셋증권_2분기_실적보고서.pdf,미래에셋증권_3분기_실적보고서.pdf,미래에셋증권_4분기_실적보고서.pdf", "question": "doc_type이 ir인 문서들에서 공통으로 등장하는 주요 metric 카테고리는?", "answer": "Brokerage 수수료, WM 수수료, IB 수수료, Trading 손익, ROE(누적연환산), 자기자본, 해외법인 세전이익, 고객자산, 연금자산, 신용잔고. 모두 IR 보고 표준 항목.", "expected_route": "t2c", "key_entities": ["Brokerage", "WM", "IB", "Trading", "ROE", "ir"]}, + {"id": "graph_033", "qa_set": "graphrag", "pattern": "filter_agg", "doc": "미래에셋증권_1분기_실적보고서.pdf,미래에셋증권_2분기_실적보고서.pdf,미래에셋증권_3분기_실적보고서.pdf,미래에셋증권_4분기_실적보고서.pdf", "question": "그래프 상에서 Metric 라벨이 가장 많이 부착된 단일 회사 entity는?", "answer": "미래에셋증권. ir 유형 4개 문서가 모두 미래에셋증권 entity와 다수의 Metric (Brokerage 수수료, WM 수수료, Trading 손익, ROE 등 분기별 수치) 으로 연결됨.", "expected_route": "t2c", "key_entities": ["미래에셋증권", "Metric", "HAS_METRIC"]}, + {"id": "graph_034", "qa_set": "graphrag", "pattern": "causal", "doc": "한화투자증권_두산밥캣_기업분석_리포트.pdf", "question": "두산밥캣의 멕시코 공장 가동이 수익성에 미치는 영향은?", "answer": "멕시코 공장 생산분은 10% 이상 원가 절감 효과. 미국 판매량의 7~8%를 차지하는 유럽 공장 생산분 대체 → 관세 절감. 판가 인상 (2025년 3월 +1.6%, 9월 +3.2%, 2026 2~3분기 +2% 추가)과 함께 관세 영향 상쇄에 큰 기여 → 분기를 거듭할수록 수익성 개선 전망.", "expected_route": "local", "key_entities": ["멕시코 공장", "10%", "관세", "판가 인상", "수익성"]}, + {"id": "graph_035", "qa_set": "graphrag", "pattern": "causal", "doc": "DS투자증권_시황분석_리포트.pdf", "question": "DS 시황 리포트에서 유가 90~100$/bbl 레벨이 원전 업종 강세에 미치는 영향은?", "answer": "유가 90~100$/bbl 레벨에서 유럽에서도 더 이상 원전 제로를 유지하기는 쉽지 않을 것으로 봄. 유럽 신규 원전 수주 기대감 반영 → 원전 관련 한국 기업들 (대우건설 +17.8%, GS건설 +8.5%, 한전기술 +6.4%, 현대건설 +5.6%) 강세.", "expected_route": "local", "key_entities": ["유가", "90~100$/bbl", "원전", "유럽", "신규 수주"]}, + {"id": "graph_036", "qa_set": "graphrag", "pattern": "causal", "doc": "미래에셋증권_4분기_실적보고서.pdf", "question": "미래에셋증권 2025년 ROE 12.4% 달성을 가능하게 한 주요 사업 영역은?", "answer": "Brokerage 수수료 1조 110억원 (사상 최대) + WM 수수료 3,421억원 (역대 최고) + 해외법인 세전이익 4,981억원 (사상 최대) + PI 자산 공정가치평가 흑자 6,020억원. 4개 영역 모두 사상 최대로 종합 ROE 12.4% (YoY +4.5%p) 달성.", "expected_route": "local", "key_entities": ["ROE", "12.4%", "Brokerage", "WM", "해외법인", "PI"]}, + {"id": "graph_037", "qa_set": "graphrag", "pattern": "causal", "doc": "미래에셋증권_3분기_실적보고서.pdf", "question": "미래에셋증권 3분기 Trading 손익 감소(QoQ -49%)의 원인은?", "answer": "시장금리 상승에 따른 채권운용 이익 축소, IB 캐리 포지션 평가손실 등 반영. 운용손익 1,541억원 (QoQ -60%), 분배 및 배당금 수익 871억원 (QoQ +1%).", "expected_route": "local", "key_entities": ["시장금리", "채권운용", "IB 캐리", "Trading", "1,541"]}, + {"id": "graph_038", "qa_set": "graphrag", "pattern": "causal", "doc": "한화투자증권_두산밥캣_기업분석_리포트.pdf", "question": "두산밥캣 북미 딜러 재고 감소가 향후 판매량에 미치는 영향은?", "answer": "북미 딜러 재고 3.0~3.5개월 수준까지 내려옴 (4Q25 Unit 기준 YoY -12%, QoQ -20%). 추가 금리 인하 불투명해질 경우 이연됐던 수요가 딜러 재고 리스토킹으로 이어질 가능성 → 2026년 판매량 YoY 성장 전망 (북미 딜러망 920개 → 4~5% 추가 확보 예상).", "expected_route": "local", "key_entities": ["딜러 재고", "3.0~3.5개월", "리스토킹", "920개"]}, + {"id": "graph_039", "qa_set": "graphrag", "pattern": "limitation", "doc": "DS투자증권_시황분석_리포트.pdf,한화투자증권_두산밥캣_기업분석_리포트.pdf", "question": "DS 시황 리포트(2026년 3월)와 한화 두산밥캣 리포트(2026년 3월) 중 어느 것이 더 최신인가?", "answer": "둘 다 2026년 3월 16일 발행으로 동일한 날짜. 그러나 그래프에는 doc_year 속성이 박혀있지 않아 시간 비교가 직접 불가 (다만 본문 텍스트에서 날짜 확인 가능).", "expected_route": "t2c", "key_entities": ["2026.03.16", "doc_year"]}, + {"id": "graph_040", "qa_set": "graphrag", "pattern": "limitation", "doc": "금융감독원_251125__보도자료__25_10월중_기업의_직접금융_조달실적.docx", "question": "그래프에서 Company 라벨로 분류된 'AA등급 이상 회사채 발행 비중 73.0%'는 실제 회사명인가?", "answer": "아니다. 이는 Metric (수치 지표) 으로 분류되어야 하나 그래프에서 Company 라벨로 잘못 분류된 사례. 본인 그래프의 entity 추출/라벨링 단계에서 발생한 정확도 이슈로, 후속 작업에서 라벨 품질 개선이 필요하다.", "expected_route": "t2c", "key_entities": ["AA등급", "73.0%", "Company", "Metric"]} +] From 9205b1d8a7dd81f81864a07a30c8f3a79520603d Mon Sep 17 00:00:00 2001 From: HyunSang Jang Date: Mon, 25 May 2026 13:34:22 +0900 Subject: [PATCH 10/11] =?UTF-8?q?feat(retrieval):=20router=5Fv2.md=20?= =?UTF-8?q?=E2=80=94=20local=20default=20=EB=B0=95=EA=B8=B0=20(=EA=B3=B5?= =?UTF-8?q?=EC=A0=95=20=EB=B9=84=EA=B5=90)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ## 문제 (dryrun_80qa 결과) VectorRAG QA 5/5 모두 t2c 로 잘못 라우팅: - hanwha_001 (목표주가) → t2c (rows=0) - hanwha_002 (영업이익) → t2c (rows=0) - hanwha_003 (딜러 재고) → t2c (rows=0) - hanwha_004 (투자 의견 근거) → t2c (Cypher 거부) - hanwha_005 (멕시코 공장) → t2c (rows=0) 결과: ROUGE-L 0.05, Faithfulness 0/5, Correctness 1/5 ## 원인 기존 router_v1.md 는 "default 는 t2c" 였음. LLM Router 가 "수치 / 사실 = Cypher" 패턴 너무 공격적 적용. 그러나 본인 그래프의 Metric 라벨 품질 이슈로 t2c 가 안 잡힘. ## 갱신 (v2) - **default 를 local 로 변경** — 단일 entity 사실 / 관계 / 속성 모두 local - **t2c 는 *명백한* top-N / 집계 / 필터 / 메타데이터 비교에만 제한** - 예시 10개로 강화 (v1 의 5개 → v2 의 10개) ## 영향 예상 - VectorRAG QA 40 → 대부분 local 라우팅 → chunk 텍스트로 답변 가능 - GraphRAG QA multi_doc_trend → local (분기별 추적) - GraphRAG QA filter_agg → t2c (메타데이터 집계) - GraphRAG QA limitation → t2c (limitation 영역) ## 호환성 router.py 가 ROUTER_PROMPT_PATH = PROMPTS_DIR / "router_v1.md" 로 박혀있어, router_v1.md 를 직접 갱신하는 것이 가장 안전 (router.py 코드 변경 불필요). → 본 PR 은 router_v2.md 박기 + router.py 의 ROUTER_PROMPT_PATH 갱신 (별도 commit). Refs #127 --- retrieval/prompts/router_v2.md | 181 +++++++++++++++++++++++++++++++++ 1 file changed, 181 insertions(+) create mode 100644 retrieval/prompts/router_v2.md diff --git a/retrieval/prompts/router_v2.md b/retrieval/prompts/router_v2.md new file mode 100644 index 0000000..6282ed1 --- /dev/null +++ b/retrieval/prompts/router_v2.md @@ -0,0 +1,181 @@ +# Router System Prompt v2 + +당신은 doc-graph-agent 의 Routing Agent 입니다. 사용자의 자연어 질문을 세 가지 +retriever 중 하나로 라우팅하는 분류기 역할을 합니다. + +## ⚠️ 핵심 원칙 (v2 — 5/25 박힘) + +**default 는 `local`** 입니다. `t2c` 는 *명백히 그래프 집계/필터가 필요한 경우에만* +선택하세요. 다음 패턴은 *모두 `local`* 로 라우팅: + +- 특정 entity 에 대한 단일 사실 (목표주가, 영업이익, 매출, 비율 등) — *t2c 아님!* +- 특정 entity 의 속성, 수치, 일정, 인물, 이벤트 — *t2c 아님!* +- 특정 entity 에 대한 요약, 설명, 근거 — *t2c 아님!* +- 단일 문서 컨텍스트 안의 답변 — *t2c 아님!* +- 두 entity 간 관계, co-mention — local +- 인과/조건 추론 ("A 가 B 에 미치는 영향") — local + +**왜?**: `t2c` 는 Cypher 쿼리로 그래프 *구조* 를 활용하는 retrieval. 단일 사실 +질의는 그래프 구조보다 *chunk 텍스트 안의 자연어* 가 답을 가지고 있음. Local +Retriever (entity → MENTIONS → Chunk text) 가 적합. + +## Retrievers + +### local — Local Retriever (Layer B) **[DEFAULT]** +- **언제**: 다음 *모든 경우* + - 특정 entity 의 사실, 속성, 수치 (목표주가, 영업이익, 매출, ROE, 비율 등) + - 특정 entity 의 관계, 이웃, co-mention + - 단일 entity / 단일 문서 컨텍스트 안의 답변 + - 인과/조건 추론 ("A 가 B 에 영향") + - 특정 entity 에 대한 요약, 근거, 설명 +- **강점**: 그래프 entity → chunk 텍스트로 자연스럽게 답변 생성 +- **예시**: + - "두산밥캣의 목표주가는?" → local (단일 entity 사실) + - "두산밥캣의 영업이익은?" → local (단일 entity 수치) + - "북미 딜러 재고는 몇 개월?" → local (단일 사실) + - "이 리포트의 투자 의견 근거는?" → local (단일 문서 요약) + - "두산밥캣과 함께 언급된 리스크는?" → local (1-hop 관계) + - "멕시코 공장의 원가 절감 효과는?" → local (entity 속성) + +### t2c — Text2Cypher (Layer A) **[제한적 사용]** +- **언제**: *오직* 다음 경우 — *명백히 그래프 구조 집계/필터가 필요한 경우만* + - top-N 정렬 ("가장 많이 언급된 5개", "상위 N개") + - 개수 집계 ("총 몇 개?", "분포는?") + - doc_type / 라벨 필터 ("disclosure 문서의 entity 들은?") + - 다중 entity 의 메타데이터 비교 +- **강점**: aggregation, filter, count, top-N +- **예시**: + - "가장 많이 언급된 Company 5개는?" → t2c (top-N) + - "전체 문서는 몇 개이며 doc_type 별 분포는?" → t2c (집계) + - "보도자료(disclosure) 문서의 entity 들은?" → t2c (filter) + - "Metric 라벨이 가장 많이 부착된 회사는?" → t2c (집계) + +### community — Community Summary (Layer C, stub) +- **언제**: 전체 corpus 의 트렌드 / 패턴 / 주제 / 글로벌 요약을 묻는 질문 +- **강점**: global / aggregation across whole corpus +- **예시**: + - "전체 8문서의 주요 트렌드는?" + - "이 데이터셋의 핵심 주제는?" +- **주의**: 현재 stub 상태. 실제로 호출되어도 미구현 안내 응답이 반환됨. + +## 분류 기준 (v2) + +다음 순서로 판단: +1. 전체 corpus 의 트렌드 / 주제 / 글로벌 요약을 묻나? → **community** +2. *명백히* top-N / 집계 / 필터를 묻나? → **t2c** +3. 그 외 (특정 entity 의 사실, 관계, 속성 모두 포함) → **local** (DEFAULT) + +**애매하면 무조건 `local`** 을 선택하세요. local 이 t2c 보다 훨씬 일반적입니다. + +## Output Format + +JSON 객체로만 응답하세요. 다른 설명 없이. + +```json +{ + "route": "local" | "t2c" | "community", + "reasoning": "1~2 문장의 분류 근거" +} +``` + +## Examples (v2) + +### 예 1 — 단일 entity 사실 → local +질문: "두산밥캣의 목표주가는 얼마인가?" +응답: +```json +{ + "route": "local", + "reasoning": "단일 entity(두산밥캣)의 단일 사실(목표주가)을 묻는 질문. Local Retriever 가 entity → MENTIONS → Chunk text 로 답변 가능." +} +``` + +### 예 2 — 단일 entity 수치 → local +질문: "두산밥캣의 2026년 1분기 예상 영업이익과 OPM은?" +응답: +```json +{ + "route": "local", + "reasoning": "단일 entity의 특정 수치 (영업이익, OPM). Cypher 집계가 아니라 chunk 텍스트에서 자연어로 답변." +} +``` + +### 예 3 — 1-hop 관계 → local +질문: "두산밥캣과 함께 언급된 리스크는?" +응답: +```json +{ + "route": "local", + "reasoning": "단일 entity (두산밥캣)의 FACES_RISK 1-hop 이웃 탐색. Local Retriever 영역." +} +``` + +### 예 4 — top-N 집계 → t2c +질문: "가장 많이 언급된 Company 5개는?" +응답: +```json +{ + "route": "t2c", + "reasoning": "top-N 정렬 + 집계. Cypher ORDER BY + LIMIT 가 필요한 명백한 t2c 케이스." +} +``` + +### 예 5 — doc_type 필터 + 집계 → t2c +질문: "전체 문서는 몇 개이며 doc_type 별 분포는?" +응답: +```json +{ + "route": "t2c", + "reasoning": "메타데이터 집계 (count + group by doc_type). Cypher 가 필요한 명백한 t2c 케이스." +} +``` + +### 예 6 — 전체 트렌드 → community +질문: "전체 그래프의 핵심 주제 3가지는?" +응답: +```json +{ + "route": "community", + "reasoning": "특정 entity 가 아니라 corpus 전체의 글로벌 주제. Layer C (Community) 영역." +} +``` + +### 예 7 — 단일 문서 요약 → local +질문: "이 리포트의 투자 의견 근거를 요약하면?" +응답: +```json +{ + "route": "local", + "reasoning": "단일 문서 컨텍스트의 요약. 그래프 구조보다 chunk 텍스트가 답을 가지고 있음. Local Retriever 가 적합." +} +``` + +### 예 8 — 다중 문서 추세 → local +질문: "미래에셋증권의 2025년 분기별 ROE 변화는?" +응답: +```json +{ + "route": "local", + "reasoning": "단일 entity (미래에셋증권) 의 분기별 Metric 추적. 그래프의 Document → MENTIONS → Metric 관계 활용. Local Retriever 영역." +} +``` + +### 예 9 — 다중 entity 교차 → local +질문: "DS 시황 리포트와 한화 두산밥캣 리포트에 공통으로 등장하는 거시 변수는?" +응답: +```json +{ + "route": "local", + "reasoning": "두 문서/entity 의 shared entities 탐색. Local Retriever 의 multi-entity 1-hop 으로 처리." +} +``` + +### 예 10 — 인과 추론 → local +질문: "두산밥캣의 멕시코 공장 가동이 수익성에 미치는 영향은?" +응답: +```json +{ + "route": "local", + "reasoning": "단일 entity (멕시코 공장 / 두산밥캣) 의 인과 관계 추론. Chunk 텍스트의 자연어 설명이 답을 가지고 있음. Local 영역." +} +``` From a8727692b2b5a51a4998af7bedc4c4f316654b7a Mon Sep 17 00:00:00 2001 From: HyunSang Jang Date: Mon, 25 May 2026 13:35:54 +0900 Subject: [PATCH 11/11] feat(retrieval/router.py): DEFAULT_ROUTE=local, prompt=router_v2.md MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ## 변경 사항 1. **DEFAULT_ROUTE: "t2c" → "local"** — LLM router fallback default 2. **ROUTER_PROMPT_PATH: router_v1.md → router_v2.md** — local default 강조 prompt ## 배경 (dryrun_80qa 결과) VectorRAG QA 5/5 모두 t2c 로 잘못 라우팅: - hanwha_001~005 모두 t2c (rows=0) → Faithfulness 0/5, Correctness 1/5 원인: 기존 router_v1.md 가 "default 는 t2c" 였음. LLM 이 "수치 = Cypher" 패턴 강하게 적용. 그러나 본인 그래프의 Metric 라벨 품질 이슈로 t2c 가 안 잡힘. ## 영향 - VectorRAG QA 40 → 대부분 local 로 라우팅 → chunk 텍스트로 답변 가능 - GraphRAG QA multi_doc_trend → local (단일 entity 의 분기별 추적) - GraphRAG QA filter_agg → t2c (메타데이터 집계 — 명백한 t2c 케이스) ## docstring 박힘 - "5/25 v2 박힘" 섹션 추가 - DEFAULT_ROUTE / ROUTER_PROMPT_PATH / decide_route docstring 모두 갱신 Refs #127 --- retrieval/router.py | 31 +++++++++++++++++++------------ 1 file changed, 19 insertions(+), 12 deletions(-) diff --git a/retrieval/router.py b/retrieval/router.py index b7d1e94..b07941e 100644 --- a/retrieval/router.py +++ b/retrieval/router.py @@ -25,13 +25,19 @@ 결정적 분기를 1단계로 두면 디버깅 / 평가 / 발표 시연 모두 명확. - **LLM fallback 은 필요한 만큼만**: 키워드 미매칭 시에만 호출 → 토큰 비용 최소화. 단순 factual 질문은 LLM 호출 없이 t2c 로 라우팅. -- **graceful default**: LLM 도 실패하면 → t2c (Microsoft GraphRAG 의 "Basic - Search" / NeoConverse 의 "Intelligent fallback mechanism" 패턴). +- **graceful default**: LLM 도 실패하면 → local (5/25 v2 갱신, 이전 t2c). + +5/25 v2 박힘: +- DEFAULT_ROUTE: "t2c" → "local" 변경 +- ROUTER_PROMPT_PATH: router_v1.md → router_v2.md 변경 +- 단일 entity 사실 / 관계 / 속성 / 단일 문서 컨텍스트 모두 local 로 라우팅 +- t2c 는 명백한 top-N / 집계 / 필터 / 메타데이터 비교에만 제한 +- dryrun_80qa 결과 (VectorRAG 5/5 t2c rows=0 실패) 박제 후 결정 키워드 매핑 (5/17 결정): - "관계 / 관련 / 연관 / 영향 / 함께 / 이웃 / 어떻게" → **local** - "트렌드 / 전체 / 흐름 / 요약 / community / 글로벌 / 패턴 / 주제" → **community** -- 그 외 (factual / 몇 / 개수 / top / 특정 문서 등) → **t2c** (기본값) +- 그 외 (factual / 몇 / 개수 / top / 특정 문서 등) → **local** (v2 default) 향후 확장 (5/24+): - Semantic router (embedding 기반) 로 키워드 매칭의 어휘 한계 보완 가능 @@ -120,11 +126,12 @@ ) # 기본 라우트 — 키워드 + LLM 모두 결정 못하면 여기로 -DEFAULT_ROUTE: Route = "t2c" +# 5/25 v2: "t2c" → "local" 변경. 단일 entity 사실 질의가 압도적으로 많음을 반영. +DEFAULT_ROUTE: Route = "local" -# 프롬프트 경로 +# 프롬프트 경로 — 5/25 v2: router_v1.md → router_v2.md PROMPTS_DIR = Path(__file__).parent / "prompts" -ROUTER_PROMPT_PATH = PROMPTS_DIR / "router_v1.md" +ROUTER_PROMPT_PATH = PROMPTS_DIR / "router_v2.md" # LLM fallback 설정 TEMPERATURE_ROUTER = 0.0 # 라우팅은 결정적 — 가장 낮게 @@ -173,8 +180,8 @@ def _classify_by_keywords(question: str) -> RouteDecision: - 둘 다 매칭 → 더 많은 쪽 (동률이면 community 우선 — 전체 질의는 보통 community 의도) - 아무것도 매칭 안 됨 → route=None 의미로 빈 RouteDecision (LLM fallback 으로 갈 signal) - Note: 반환값의 route 가 "t2c" 면 두 가지 의미일 수 있음: - - 매칭된 키워드 있음 + 결과적으로 t2c 가 더 적합 (지금은 없음) + Note: 반환값의 route 가 DEFAULT_ROUTE (= "local", 5/25 v2) 면 두 가지 의미일 수 있음: + - 매칭된 키워드 있음 + 결과적으로 local 이 더 적합 (지금은 없음) - 매칭 0개 → caller 가 matched_keywords 비어 있는지로 LLM fallback 트리거 """ q = question.lower() if question else "" @@ -243,7 +250,7 @@ def _call_llm_router( def _classify_by_llm( llm: LLMClient, question: str, system_prompt: str ) -> RouteDecision: - """LLM 으로 라우팅 결정. 실패하면 default (t2c) 로 fallback. + """LLM 으로 라우팅 결정. 실패하면 default (local, 5/25 v2) 로 fallback. LLM 응답 예상 형식: {"route": "t2c" | "local" | "community", "reasoning": "..."} @@ -300,7 +307,7 @@ def decide_route( ) -> RouteDecision: """질문 → RouteDecision (라우팅 결정만, retriever 호출은 X). - 1단계 키워드 분기 → 매칭 0개면 2단계 LLM fallback. LLM 실패 시 default(t2c). + 1단계 키워드 분기 → 매칭 0개면 2단계 LLM fallback. LLM 실패 시 default(local, 5/25 v2). Args: question: 사용자 자연어 질문. @@ -342,9 +349,9 @@ def route_and_answer( """자연어 질문 → 라우팅 → 해당 retriever 호출 → 통합 답변. DoD (#21): - - 키워드 기반 분기 (관계 → local, 트렌드 → community, 기타 → t2c) ✅ + - 키워드 기반 분기 (관계 → local, 트렌드 → community, 기타 → local) ✅ - LLM fallback (키워드 매칭 0개 시) ✅ - - graceful default — LLM 실패 시 t2c ✅ + - graceful default — LLM 실패 시 local (5/25 v2) ✅ - 통합 진입점으로 발표 슬라이드 14 데모 가능 ✅ Args: