roadmap 섹션 정보 출력

db-migration
jaehwang 2026-06-02 16:34:18 +09:00
parent 2232273200
commit 45a74ab970
4 changed files with 49 additions and 2 deletions

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@ -7,6 +7,7 @@ from integrations.llm.schemas.report import (
YouTubeDiagnosisInput, YouTubeDiagnosisOutput,
BrandConsistencyInput, BrandConsistencyOutput,
TransformationInput, TransformationProposal,
RoadmapInput, RoadmapOutput,
)
from integrations.llm.schemas.plan import PlanInput, PlanOutput
from integrations.llm.schemas.market import (
@ -114,3 +115,10 @@ transformation_prompt = Prompt(
input_class=TransformationInput,
output_class=TransformationProposal,
)
roadmap_prompt = Prompt(
file_name="roadmap_prompt.txt",
prompt_model="REPORT_MODEL",
input_class=RoadmapInput,
output_class=RoadmapOutput,
)

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@ -368,6 +368,17 @@ class CriticalIssuesOutput(BaseModel):
diagnosis: list[DiagnosisItem]
# --- Roadmap ---
class RoadmapInput(BaseModel):
clinic_name: str | None = None
data: str | None = None
class RoadmapOutput(BaseModel):
roadmap: list[RoadmapMonth]
# --- Transformation ---
class TransformationInput(BaseModel):

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@ -0,0 +1,14 @@
다음은 성형외과/피부과 {clinic_name} 의 전 채널 수집 데이터입니다.
{data}
위 데이터를 바탕으로 이 병원의 3개월 마케팅 실행 로드맵을 수립해줘.
month 1, 2, 3 각각 하나씩, 총 3개 항목을 포함한 roadmap JSON 배열로 출력해줘.
각 항목은 아래 형식을 따라줘:
- month: 월 번호 (1, 2, 3)
- title: 해당 월의 핵심 테마 (예: "브랜드 정비")
- subtitle: 한 줄 부제 (예: "기반 구축 — 로고·계정 통일")
- tasks: 실행 과제 목록, 각 과제는 task(string)와 completed(false)로 구성
출처 번호([1], [2] 등)는 포함하지 마.

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@ -8,8 +8,8 @@ from common.db.run import select_run, update_run_report, update_run_plan
from common.db.source import select_run_raw_data, select_run_mainpage_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
from integrations.llm.schemas.report import ReportOutput, ClinicSnapshot, YouTubeAudit, BrandConsistencyOutput, CriticalIssuesOutput, DiagnosisItem, TransformationProposal
from integrations.llm.prompt import report_prompt, plan_prompt, youtube_diagnosis_prompt, brand_consistency_prompt, critical_issues_prompt, transformation_prompt, roadmap_prompt
from integrations.llm.schemas.report import ReportOutput, ClinicSnapshot, YouTubeAudit, BrandConsistencyOutput, CriticalIssuesOutput, DiagnosisItem, TransformationProposal, RoadmapOutput, RoadmapMonth
from integrations.llm.schemas.plan import PlanOutput
logger = logging.getLogger(__name__)
@ -208,6 +208,17 @@ async def _build_youtube_audit(youtube: dict) -> dict:
return YouTubeAudit.model_validate(yt_patch).model_dump()
async def _build_roadmap(analysis_run_id: str, raw: dict) -> list[dict]:
result: RoadmapOutput = await LLMService(provider="perplexity").generate(
roadmap_prompt,
{
"clinic_name": (raw.get("mainpage") or {}).get("clinicName"),
"data": json.dumps(raw, ensure_ascii=False),
},
)
return [RoadmapMonth.model_validate(item).model_dump() for item in result.roadmap]
async def _build_transformation(analysis_run_id: str, raw: dict) -> dict:
result: TransformationProposal = await LLMService(provider="perplexity").generate(
transformation_prompt,
@ -267,6 +278,7 @@ async def _build_overrides(analysis_run_id: str) -> dict:
brand_patch = brand.model_dump()["brand_inconsistencies"]
critical_issues = await _build_critical_issues(analysis_run_id, raw)
transformation = await _build_transformation(analysis_run_id, raw)
roadmap = await _build_roadmap(analysis_run_id, raw)
fb_patch: dict = {}
if fb_pages:
@ -287,6 +299,8 @@ async def _build_overrides(analysis_run_id: str) -> dict:
overrides["problem_diagnosis"] = critical_issues
if transformation:
overrides["transformation"] = transformation
if roadmap:
overrides["roadmap"] = roadmap
return overrides