feat(ai_news): include summary + pub_date in LLM prompt

stock-lab/app/screener/ai_news/analyzer.py

@@ -27,6 +27,25 @@ def _clamp(x: float, lo: float = -10.0, hi: float = 10.0) -> float:
     return max(lo, min(hi, x))
 
 
+def _format_news_block(news: List[Dict[str, Any]]) -> str:
+    """news dict 리스트 → prompt 에 들어가는 텍스트 블록.
+
+    summary 가 있으면 title 다음 줄에 indent 해서 포함 (최대 200자).
+    pub_date 가 있으면 title 앞에 표시.
+    """
+    lines: List[str] = []
+    for n in news:
+        date = (n.get("pub_date") or "").strip()
+        title = (n.get("title") or "").strip()
+        summary = (n.get("summary") or "").strip()
+        prefix = f"[{date}] " if date else ""
+        if summary:
+            lines.append(f"- {prefix}{title}\n  {summary[:200]}")
+        else:
+            lines.append(f"- {prefix}{title}")
+    return "\n".join(lines)
+
+
 async def score_sentiment(
     llm,
     ticker: str,
@@ -36,7 +55,7 @@ async def score_sentiment(
     model: str = DEFAULT_MODEL,
 ) -> Dict[str, Any]:
     """Returns {ticker, score_raw, reason, news_count, tokens_input, tokens_output, model}."""
-    news_block = "\n".join(f"- {n['title']}" for n in news)
+    news_block = _format_news_block(news)
     prompt = PROMPT_TEMPLATE.format(
         name=name or ticker, ticker=ticker,
         n=len(news), news_block=news_block,

stock-lab/tests/test_ai_news_analyzer.py

@@ -17,7 +17,10 @@ def _mk_llm(content_text: str, in_tokens: int = 100, out_tokens: int = 20):
     return llm
 
 
-NEWS = [{"title": "삼성전자, HBM 양산"}, {"title": "메모리 가격 반등"}]
+NEWS = [
+    {"title": "삼성전자, HBM 양산", "summary": "1분기 영업이익 사상 최대", "pub_date": "2026-05-14"},
+    {"title": "메모리 가격 반등", "summary": "", "pub_date": "2026-05-14"},
+]
 
 
 @pytest.mark.asyncio
@@ -53,3 +56,15 @@ async def test_score_sentiment_clamps_negative_out_of_range():
     llm = _mk_llm(json.dumps({"score": -42.0, "reason": "초악재"}))
     out = await analyzer.score_sentiment(llm, "005930", NEWS)
     assert out["score_raw"] == -10.0
+
+
+@pytest.mark.asyncio
+async def test_score_sentiment_includes_summary_in_prompt():
+    """summary 가 있으면 prompt 에 포함, 없으면 title 만."""
+    llm = _mk_llm(json.dumps({"score": 5.0, "reason": "ok"}))
+    await analyzer.score_sentiment(llm, "005930", NEWS, name="삼성전자")
+    call = llm.messages.create.call_args
+    user_msg = call.kwargs["messages"][0]["content"]
+    assert "1분기 영업이익 사상 최대" in user_msg  # summary 포함
+    assert "삼성전자, HBM 양산" in user_msg  # title 포함
+    assert "2026-05-14" in user_msg  # pub_date 포함