마켓 분석 데이터 추가

app/common/db.py

@@ -59,6 +59,17 @@ async def fetchone(sql: str, args: tuple = ()) -> dict | None:
         finally:
             conn.close()
 
+
+async def fetchall(sql: str, args: tuple = ()) -> list[dict]:
+    pool = await get_pool()
+    async with pool.acquire() as conn:
+        try:
+            async with conn.cursor(aiomysql.DictCursor) as cur:
+                await cur.execute(sql, args)
+                return await cur.fetchall()
+        finally:
+            conn.close()
+
 async def insert_instagram_row(hospital_id: str, url: str) -> int:
     return await execute("INSERT INTO instagram_data (hospital_id, url) VALUES (%s, %s)", (hospital_id, url))
 
@@ -247,3 +258,14 @@ async def save_hospital_raw_data(hospital_id: str, data: dict, analysis_run_id:
         ),
     )
     await _insert_hospital_history(hospital_id, analysis_run_id)
+
+
+async def get_market_analysis(analysis_run_id: str) -> dict:
+    rows = await fetchall(
+        "SELECT analysis_type, data FROM market_analysis WHERE analysis_run_id = %s AND status = 'done'",
+        (analysis_run_id,),
+    )
+    return {
+        row["analysis_type"]: json.loads(row["data"]) if isinstance(row["data"], str) else row["data"]
+        for row in rows
+    }

app/integrations/llm/schemas/plan.py

@@ -11,6 +11,10 @@ class PlanInput(BaseModel):
     services: str | None = None
     doctors: str | None = None
     report: str | None = None
+    market_competitors: str | None = None
+    market_keywords: str | None = None
+    market_trend: str | None = None
+    market_target_audience: str | None = None
 
 
 # --- BrandGuide ---

app/integrations/llm/schemas/report.py

@@ -317,6 +317,10 @@ class ReportInput(BaseModel):
     naver_blog: str | None = None
     youtube: str | None = None
     gangnam_unni: str | None = None
+    market_competitors: str | None = None
+    market_keywords: str | None = None
+    market_trend: str | None = None
+    market_target_audience: str | None = None
 
 
 # --- MarketingReport ---

app/integrations/llm/temp-prompt/plan_prompt.txt

@@ -15,6 +15,20 @@
 - 시술: {services}
 - 의료진: {doctors}
 
+## 시장 분석 데이터
+
+### 경쟁 병원
+{market_competitors}
+
+### 검색 키워드 트렌드
+{market_keywords}
+
+### 시장 트렌드
+{market_trend}
+
+### 잠재 고객 분석
+{market_target_audience}
+
 ## 분석 리포트
 {report}
 

app/integrations/llm/temp-prompt/report_prompt.txt

@@ -12,6 +12,20 @@
 - 시술: {services}
 - 의료진: {doctors}
 
+## 시장 분석 데이터
+
+### 경쟁 병원
+{market_competitors}
+
+### 검색 키워드 트렌드
+{market_keywords}
+
+### 시장 트렌드
+{market_trend}
+
+### 잠재 고객 분석
+{market_target_audience}
+
 ## 채널 데이터
 
 ### 인스타그램

app/services/analysis.py

@@ -1,6 +1,6 @@
 import json
 import logging
-from common.db import fetchone, execute, get_analysis_raw_data, save_analysis_report
+from common.db import fetchone, execute, get_analysis_raw_data, save_analysis_report, get_market_analysis
 from integrations.llm.llm_service import LLMService
 from integrations.llm.prompt import report_prompt, plan_prompt
 from integrations.llm.schemas.report import ReportOutput
@@ -22,6 +22,10 @@ async def generate_report(analysis_run_id: str) -> ReportOutput:
     raw_data = clinic_row["raw_data"] if clinic_row else None
     clinic = json.loads(raw_data) if isinstance(raw_data, str) else (raw_data or {})
     raw = await get_analysis_raw_data(analysis_run_id)
+    market = await get_market_analysis(analysis_run_id)
+
+    def _json(v) -> str | None:
+        return json.dumps(v, ensure_ascii=False) if v else None
 
     input_data = {
         "clinic_name":            clinic.get("clinicName"),
@@ -31,8 +35,12 @@ async def generate_report(analysis_run_id: str) -> ReportOutput:
         "slogan":                 clinic.get("slogan"),
         "services":               json.dumps(clinic.get("services", []), ensure_ascii=False),
         "doctors":                json.dumps(clinic.get("doctors", []), ensure_ascii=False),
+        "market_competitors":     _json(market.get("competitors")),
+        "market_keywords":        _json(market.get("keywords")),
+        "market_trend":           _json(market.get("trend")),
+        "market_target_audience": _json(market.get("target_audience")),
         **{
-            channel: json.dumps(data, ensure_ascii=False) if data else None
+            channel: _json(data)
             for channel, data in raw.items()
         },
     }
@@ -53,6 +61,10 @@ async def generate_plan(analysis_run_id: str) -> PlanOutput:
     clinic = json.loads(raw_data) if isinstance(raw_data, str) else (raw_data or {})
     report_data = run["report_data"]
     report = json.loads(report_data) if isinstance(report_data, str) else report_data
+    market = await get_market_analysis(analysis_run_id)
+
+    def _json(v) -> str | None:
+        return json.dumps(v, ensure_ascii=False) if v else None
 
     input_data = {
         "clinic_name":            clinic.get("clinicName"),
@@ -62,7 +74,11 @@ async def generate_plan(analysis_run_id: str) -> PlanOutput:
         "slogan":                 clinic.get("slogan"),
         "services":               json.dumps(clinic.get("services", []), ensure_ascii=False),
         "doctors":                json.dumps(clinic.get("doctors", []), ensure_ascii=False),
-        "report":         json.dumps(report, ensure_ascii=False) if report else None,
+        "report":                 _json(report),
+        "market_competitors":     _json(market.get("competitors")),
+        "market_keywords":        _json(market.get("keywords")),
+        "market_trend":           _json(market.get("trend")),
+        "market_target_audience": _json(market.get("target_audience")),
     }
 
     return await LLMService(provider="perplexity").generate(plan_prompt, input_data)