diff --git a/app/integrations/firecrawl.py b/app/integrations/firecrawl.py
index f3313de..12b1137 100644
--- a/app/integrations/firecrawl.py
+++ b/app/integrations/firecrawl.py
@@ -16,7 +16,7 @@ class FirecrawlClient:
HTTPMethod.POST,
url=f"{FIRECRAWL_BASE}/scrape",
headers=self._headers(),
- json_body={"url": url, "formats": ["json", "links"], "jsonOptions": json_options, "waitFor": wait_for},
+ json_body={"url": url, "formats": ["json", "links"], "jsonOptions": json_options, "waitFor": wait_for, "maxAge": 0},
label="firecrawl-scrape",
)
if not resp or not resp.is_success:
@@ -76,7 +76,7 @@ class FirecrawlClient:
"url": url,
"formats": ["json", "links", "rawHtml"],
"jsonOptions": {
- "prompt": "Extract: clinic name (Korean), clinic name (English), address, phone with dash format, business hours, slogan, services offered, doctors with name/title/specialty, brand identity (primary/accent/background/text colors in hex, heading/body fonts, logo URL from the actual header/main ![]()
src, og:image from content, favicon URL)",
+ "prompt": "Extract: 클리닉 이름 - clinicName (Korean), clinic name (English), address, phone with dash format, business hours, slogan, services offered, doctors with name/title/specialty, brand identity (primary/accent/background/text colors in hex, heading/body fonts, logo URL from the actual header/main ![]()
src, og:image from content, favicon URL)",
"schema": {
"type": "object",
"properties": {
@@ -127,6 +127,7 @@ class FirecrawlClient:
},
},
"waitFor": 5000,
+ "maxAge": 0,
},
timeout=60,
label="firecrawl-clinic-info",
@@ -187,6 +188,7 @@ class FirecrawlClient:
},
},
"waitFor": 5000,
+ "maxAge": 0,
},
timeout=60,
label="firecrawl-gangnamunni",
diff --git a/app/integrations/llm/prompt.py b/app/integrations/llm/prompt.py
index 6f9c646..79cbb14 100644
--- a/app/integrations/llm/prompt.py
+++ b/app/integrations/llm/prompt.py
@@ -13,7 +13,7 @@ from integrations.llm.schemas.report import (
ScoresInput, ScoresOutput,
OtherChannelsInput, OtherChannelsOutput
)
-from integrations.llm.schemas.plan import PlanInput, PlanOutput
+from integrations.llm.schemas.plan import PlanInput, PlanOutput, SummarizeInput, SummarizeOutput
from integrations.llm.schemas.market import (
MarketCompetitorsInput, MarketCompetitorsOutput,
MarketKeywordsInput, MarketKeywordsOutput,
@@ -64,6 +64,13 @@ plan_prompt = Prompt(
output_class=PlanOutput,
)
+summarize_prompt = Prompt(
+ file_name="summarize_prompt.txt",
+ prompt_model="PLAN_MODEL",
+ input_class=SummarizeInput,
+ output_class=SummarizeOutput,
+)
+
market_competitors_prompt = Prompt(
file_name="market_competitors_prompt.txt",
prompt_model="MARKET_MODEL",
diff --git a/app/integrations/llm/schemas/plan.py b/app/integrations/llm/schemas/plan.py
index fc2c8cd..24e787c 100644
--- a/app/integrations/llm/schemas/plan.py
+++ b/app/integrations/llm/schemas/plan.py
@@ -2,6 +2,15 @@ from typing import Literal
from pydantic import BaseModel
+class SummarizeInput(BaseModel):
+ label: str
+ data: str
+
+
+class SummarizeOutput(BaseModel):
+ summary: str
+
+
class PlanInput(BaseModel):
clinic_name: str | None = None
clinic_name_en: str | None = None
diff --git a/app/integrations/llm/temp-prompt/summarize_prompt.txt b/app/integrations/llm/temp-prompt/summarize_prompt.txt
new file mode 100644
index 0000000..6e143ea
--- /dev/null
+++ b/app/integrations/llm/temp-prompt/summarize_prompt.txt
@@ -0,0 +1,7 @@
+다음은 "{label}" 원본 데이터입니다.
+
+{data}
+
+위 데이터를 마케팅 플랜 생성에 필요한 핵심 정보만 남기고 간결하게 요약하세요.
+구체적인 수치·날짜·고유명사(채널명, 게시물 제목 등)는 그대로 보존하고, 중복되거나 플랜 작성에 불필요한 메타데이터는 제거하세요.
+요약문 하나의 문자열로만 출력하세요.
diff --git a/app/services/analysis.py b/app/services/analysis.py
index e640658..bd9a8f0 100644
--- a/app/services/analysis.py
+++ b/app/services/analysis.py
@@ -1,3 +1,4 @@
+import asyncio
import json
import logging
from urllib.parse import urlparse
@@ -7,7 +8,7 @@ from common.db.run import update_run_report, update_run_plan, select_run_report_
from common.db.source import select_run_raw_data, select_mainpage_logo_url
from common.db.market import select_market
from integrations.llm.llm_service import LLMService
-from integrations.llm.prompt import report_prompt, plan_prompt, youtube_diagnosis_prompt, brand_consistency_prompt, critical_issues_prompt, transformation_prompt, roadmap_prompt, scores_prompt, other_channels_prompt
+from integrations.llm.prompt import report_prompt, plan_prompt, summarize_prompt, youtube_diagnosis_prompt, brand_consistency_prompt, critical_issues_prompt, transformation_prompt, roadmap_prompt, scores_prompt, other_channels_prompt
from integrations.llm.schemas.report import ReportOutput, ClinicSnapshot, YouTubeAudit, BrandConsistencyOutput, CriticalIssuesOutput, DiagnosisItem, TransformationProposal, RoadmapOutput, RoadmapMonth, ScoresOutput, ChannelScore, WebsiteAudit, OtherChannelsOutput, OtherChannel
from services.branding import analyze_branding
from services.instagram_audit import build_instagram_audit
@@ -49,32 +50,52 @@ async def generate_plan(analysis_run_id: str) -> PlanOutput:
def _json(v) -> str | None:
return json.dumps(v, ensure_ascii=False) if v else None
- input_data = {
- "clinic_name": clinic.get("clinicName"),
- "clinic_name_en": clinic.get("clinicNameEn"),
- "address": clinic.get("address"),
- "phone": clinic.get("phone"),
- "slogan": clinic.get("slogan"),
- "services": json.dumps(clinic.get("services", []), ensure_ascii=False),
- "doctors": json.dumps(clinic.get("doctors", []), ensure_ascii=False),
+ # map: 큰 입력은 LLM으로 압축 요약해 100KB 초과 에러를 방지하고, 작은 입력은 그대로 둔다.
+ large_fields = {
"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")),
"tiktok": _json(tiktok),
- "instagram": _json(instagram),
- "facebook": _json(facebook),
- "naver_blog": _json(_naver_blog_summary(naver_blog)),
+ "instagram": _json(instagram),
+ "facebook": _json(facebook),
"naver_cafe": _json(naver_cafe),
- "kakao_talk": _json(kakaotalk),
"channel_logos": _json(branding.get("channelLogos")),
"brand_assets": _json(branding.get("brandAssets")),
}
+ summarized = dict(zip(
+ large_fields.keys(),
+ await asyncio.gather(*(_summarize(label, data) for label, data in large_fields.items())),
+ ))
+
+ # reduce: 요약된 입력을 모아 최종 플랜 생성.
+ input_data = {
+ "clinic_name": clinic.get("clinicName"),
+ "clinic_name_en": clinic.get("clinicNameEn"),
+ "address": clinic.get("address"),
+ "phone": clinic.get("phone"),
+ "slogan": clinic.get("slogan"),
+ "services": json.dumps(clinic.get("services", []), ensure_ascii=False),
+ "doctors": json.dumps(clinic.get("doctors", []), ensure_ascii=False),
+ "naver_blog": _json(_naver_blog_summary(naver_blog)),
+ "kakao_talk": _json(kakaotalk),
+ **summarized,
+ }
return await LLMService(provider="perplexity").generate(plan_prompt, input_data)
+_SUMMARIZE_THRESHOLD = 4000 # 이 길이(문자 수)를 넘는 입력만 요약 LLM 호출 (불필요한 호출 방지)
+
+
+async def _summarize(label: str, data: str | None) -> str | None:
+ if not data or len(data) <= _SUMMARIZE_THRESHOLD:
+ return data
+ result = await LLMService(provider="perplexity").generate(summarize_prompt, {"label": label, "data": data})
+ return result.summary
+
+
def _build_clinic_snapshot(mainpage: dict, gangnam_unni: dict, brand_assets: dict, logo_url: str | None) -> dict:
snapshot: dict = {}
doctors = gangnam_unni.get("doctors", [])