Hyungi Ahn
6966be9cf6
LLM 이 article_ids 를 자율적으로 비워두는 케이스 (2026-05-12 첫 briefing 6 topics 모두 빈 list) 를 서버에서 보정. 후처리 정책 (_resolve_article_ids): 1. LLM 이 준 id ∩ cluster member id (엉뚱한 id 차단, hallucination 방어) 2. 비어있으면 같은 country cluster member top weight N 개 자동 주입 3. cluster 안 country 매칭 멤버 0 → [] per-country cap = MAX_ARTICLE_IDS_PER_COUNTRY = 5. weight 내림차순. API 계약 강화: country_perspectives 가 있는 topic 은 article_ids ≥ 1 보장 (같은 country cluster member 존재 시). frontend / 외부 채널 / archive UI 모두 신뢰 가능. tests 3 케이스 추가.
270 lines
9.1 KiB
Python
270 lines
9.1 KiB
Python
"""Briefing historical 분기 회귀 — Plan §"Verification 9".
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3 경로 검증:
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1. flag off → retrieve_historical 호출 안 함, prompt {historical_block} = "(과거 참고 자료 없음)"
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2. flag on + fixture top-K → similarity ≥0.70 docs 만 반환
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3. flag on + zero match → 빈 list (no fallback hallucination)
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"""
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import os
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import sys
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from datetime import datetime, timezone, timedelta
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from pathlib import Path
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import numpy as np
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import pytest
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# PYTHONPATH = /app (디렉토리 안에서 실행 가정 또는 sys.path 추가)
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APP_DIR = Path(__file__).resolve().parent.parent / "app"
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if str(APP_DIR) not in sys.path:
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sys.path.insert(0, str(APP_DIR))
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from services.briefing.comparator import (
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HISTORICAL_SIMILARITY_MIN,
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HISTORICAL_TOP_K,
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_build_historical_block,
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_make_fallback,
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_sanitize_envelope,
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build_prompt,
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historical_enabled,
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retrieve_historical,
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)
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from services.clustering_common import normalize_vector
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def _make_doc(doc_id: int, embedding: np.ndarray, hours_ago: int = 1) -> dict:
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return {
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"id": doc_id,
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"title": f"doc {doc_id}",
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"ai_summary": f"summary {doc_id}",
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"embedding": embedding,
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"created_at": datetime.now(timezone.utc) - timedelta(hours=hours_ago),
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}
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def _make_cluster_with_centroid(centroid_vec: np.ndarray) -> dict:
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return {
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"centroid": normalize_vector(centroid_vec),
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"members": [],
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}
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def test_flag_default_off():
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"""env 미설정 → historical disabled."""
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os.environ.pop("BRIEFING_HISTORICAL_ENABLED", None)
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assert historical_enabled() is False
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def test_flag_on():
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os.environ["BRIEFING_HISTORICAL_ENABLED"] = "true"
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try:
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assert historical_enabled() is True
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finally:
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os.environ.pop("BRIEFING_HISTORICAL_ENABLED", None)
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def test_historical_block_empty_when_no_docs():
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"""경로 1: flag off 또는 historical_docs=[] → 빈 라벨."""
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block = _build_historical_block([])
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assert block == "(과거 참고 자료 없음)"
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def test_historical_block_has_label_when_docs():
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docs = [_make_doc(1, np.ones(1024, dtype=np.float32))]
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block = _build_historical_block(docs)
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assert "이전 30일 흐름" in block
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assert "직접 인용 금지" in block
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assert "[H1]" in block
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def test_retrieve_historical_topk():
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"""경로 2: flag on + fixture top-K similarity ≥ threshold."""
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# cluster centroid = 모두 1 방향
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centroid = np.ones(8, dtype=np.float32)
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cluster = _make_cluster_with_centroid(centroid)
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# 후보 10개: 5개는 centroid 와 유사 (sim≈1.0), 5개는 직교 (sim≈0)
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similar_emb = np.ones(8, dtype=np.float32)
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orthogonal_emb = np.array([1, -1, 1, -1, 1, -1, 1, -1], dtype=np.float32)
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candidates = (
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[_make_doc(i, similar_emb + np.random.rand(8).astype(np.float32) * 0.01) for i in range(1, 6)]
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+ [_make_doc(10 + i, orthogonal_emb) for i in range(5)]
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)
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out = retrieve_historical(cluster, candidates, top_k=5, sim_min=0.70)
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assert len(out) == 5
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# 모두 similar 그룹 (id 1~5) 만 선택됨
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selected_ids = {d["id"] for d in out}
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assert selected_ids.issubset({1, 2, 3, 4, 5})
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def test_retrieve_historical_zero_match():
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"""경로 3: 모든 candidate similarity < threshold → 빈 list."""
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centroid = np.ones(8, dtype=np.float32)
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cluster = _make_cluster_with_centroid(centroid)
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orthogonal_emb = np.array([1, -1, 1, -1, 1, -1, 1, -1], dtype=np.float32)
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candidates = [_make_doc(i, orthogonal_emb) for i in range(5)]
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out = retrieve_historical(cluster, candidates, top_k=5, sim_min=0.70)
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assert out == []
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def test_retrieve_historical_empty_candidates():
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centroid = np.ones(8, dtype=np.float32)
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cluster = _make_cluster_with_centroid(centroid)
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assert retrieve_historical(cluster, [], top_k=5) == []
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def test_sanitize_envelope_valid():
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cluster = {"members": [{"id": 1}, {"id": 2}]}
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parsed = {
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"topic_label": "이란 충돌",
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"headline": "긴장 격화",
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"country_perspectives": [
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{"country": "kr", "summary": "유가 충격", "article_ids": [1]},
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{"country": "us", "summary": "외교 압박", "article_ids": [2]},
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],
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"divergences": ["KR=경제 / US=외교"],
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"convergences": ["민간 사상 우려 공통"],
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"key_quotes": [{"country": "US", "source": "NYT", "quote": "Tehran ..."}],
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"historical_context": "지난 3주 6회 공방",
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}
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sanitized = _sanitize_envelope(parsed, cluster)
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assert sanitized is not None
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assert sanitized["topic_label"] == "이란 충돌"
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# country 대문자 변환
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assert sanitized["country_perspectives"][0]["country"] == "KR"
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assert sanitized["historical_context"] == "지난 3주 6회 공방"
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assert sanitized["llm_fallback_used"] is False
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def test_sanitize_envelope_empty_perspectives_to_fallback():
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"""country_perspectives 비어 있으면 None (caller 가 fallback 발동)."""
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cluster = {"members": []}
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parsed = {
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"topic_label": "X",
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"headline": "Y",
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"country_perspectives": [],
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}
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assert _sanitize_envelope(parsed, cluster) is None
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def test_fallback_row_fixed_form():
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"""Plan §"Fallback Topic Row 고정 형태"."""
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cluster = {"members": [{"id": 1}]}
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fb = _make_fallback(cluster)
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assert fb["topic_label"] == "주요 뉴스 묶음"
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assert fb["country_perspectives"] == []
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assert fb["divergences"] == []
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assert fb["convergences"] == []
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assert fb["key_quotes"] == []
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assert fb["historical_context"] is None
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assert fb["llm_fallback_used"] is True
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def test_prompt_includes_both_blocks():
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selected = [_make_doc(1, np.ones(8, dtype=np.float32))]
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selected[0]["country"] = "KR"
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selected[0]["ai_sub_group"] = "경향신문"
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selected[0]["ai_summary_truncated"] = "오늘 한국 뉴스"
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prompt = build_prompt(selected, historical_docs=[])
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assert "{articles_block}" not in prompt # 치환됨
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assert "{historical_block}" not in prompt
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assert "(KR · 경향신문)" in prompt
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assert "(과거 참고 자료 없음)" in prompt
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def test_perspective_summary_cap_enforced():
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"""sanitize 가 길이 cap 강제."""
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cluster = {"members": []}
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long_summary = "가" * 500 # 500자, cap=240
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parsed = {
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"topic_label": "T",
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"headline": "H",
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"country_perspectives": [{"country": "KR", "summary": long_summary, "article_ids": []}],
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}
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s = _sanitize_envelope(parsed, cluster)
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assert s is not None
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assert len(s["country_perspectives"][0]["summary"]) <= 241 # 240 + "…"
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def test_article_ids_fallback_when_llm_empty():
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"""LLM 이 article_ids 를 비워두면 같은 country cluster member top-N 자동 주입."""
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cluster = {
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"members": [
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{"id": 101, "country": "KR", "weight": 0.9},
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{"id": 102, "country": "KR", "weight": 0.8},
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{"id": 103, "country": "KR", "weight": 0.7},
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{"id": 201, "country": "US", "weight": 0.5},
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]
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}
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parsed = {
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"topic_label": "T",
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"headline": "H",
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"country_perspectives": [
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{"country": "KR", "summary": "한국 시각", "article_ids": []},
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{"country": "US", "summary": "미국 시각", "article_ids": []},
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],
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}
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s = _sanitize_envelope(parsed, cluster)
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assert s is not None
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kr = next(p for p in s["country_perspectives"] if p["country"] == "KR")
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us = next(p for p in s["country_perspectives"] if p["country"] == "US")
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assert kr["article_ids"] == [101, 102, 103] # weight desc
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assert us["article_ids"] == [201]
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def test_article_ids_intersect_with_cluster():
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"""LLM 이 엉뚱한 id 를 넣으면 cluster member 와 교집합만."""
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cluster = {
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"members": [
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{"id": 101, "country": "KR", "weight": 0.9},
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{"id": 102, "country": "KR", "weight": 0.8},
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]
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}
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parsed = {
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"topic_label": "T",
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"headline": "H",
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"country_perspectives": [
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{"country": "KR", "summary": "한국 시각", "article_ids": [101, 999, 888]},
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],
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}
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s = _sanitize_envelope(parsed, cluster)
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assert s is not None
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assert s["country_perspectives"][0]["article_ids"] == [101]
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def test_article_ids_capped_to_max():
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"""후처리 후에도 country 당 MAX_ARTICLE_IDS_PER_COUNTRY cap."""
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cluster = {
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"members": [
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{"id": i, "country": "KR", "weight": 1.0 / i} for i in range(1, 15)
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]
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}
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parsed = {
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"topic_label": "T",
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"headline": "H",
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"country_perspectives": [
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{"country": "KR", "summary": "한국 시각", "article_ids": []},
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],
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}
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s = _sanitize_envelope(parsed, cluster)
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assert s is not None
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from services.briefing.comparator import MAX_ARTICLE_IDS_PER_COUNTRY
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assert len(s["country_perspectives"][0]["article_ids"]) == MAX_ARTICLE_IDS_PER_COUNTRY
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def test_max_perspectives_cap():
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cluster = {"members": []}
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parsed = {
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"topic_label": "T",
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"headline": "H",
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"country_perspectives": [
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{"country": f"C{i}", "summary": "s", "article_ids": []} for i in range(20)
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],
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}
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s = _sanitize_envelope(parsed, cluster)
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assert s is not None
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assert len(s["country_perspectives"]) <= 10
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