critical_issues 섹션 정보 출력
parent
35e5e98524
commit
484ee41810
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@ -1,7 +1,12 @@
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import os
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import os
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from pydantic import BaseModel
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from pydantic import BaseModel
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from common.utils import get_env
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from common.utils import get_env
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from integrations.llm.schemas.report import ReportInput, ReportOutput, YouTubeDiagnosisInput, YouTubeDiagnosisOutput, BrandConsistencyInput, BrandConsistencyOutput
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from integrations.llm.schemas.report import (
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ReportInput, ReportOutput,
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CriticalIssuesInput, CriticalIssuesOutput,
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YouTubeDiagnosisInput, YouTubeDiagnosisOutput,
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BrandConsistencyInput, BrandConsistencyOutput,
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)
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from integrations.llm.schemas.plan import PlanInput, PlanOutput
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from integrations.llm.schemas.plan import PlanInput, PlanOutput
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from integrations.llm.schemas.market import (
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from integrations.llm.schemas.market import (
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MarketCompetitorsInput, MarketCompetitorsOutput,
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MarketCompetitorsInput, MarketCompetitorsOutput,
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@ -94,3 +99,10 @@ brand_consistency_prompt = Prompt(
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input_class=BrandConsistencyInput,
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input_class=BrandConsistencyInput,
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output_class=BrandConsistencyOutput,
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output_class=BrandConsistencyOutput,
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)
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)
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critical_issues_prompt = Prompt(
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file_name="critical_issues_prompt.txt",
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prompt_model="REPORT_MODEL",
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input_class=CriticalIssuesInput,
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output_class=CriticalIssuesOutput,
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)
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@ -357,6 +357,17 @@ class YouTubeDiagnosisOutput(BaseModel):
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diagnosis: list[DiagnosisItem]
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diagnosis: list[DiagnosisItem]
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# --- Diagnosis ---
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class CriticalIssuesInput(BaseModel):
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clinic_name: str | None = None
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data: str | None = None
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class CriticalIssuesOutput(BaseModel):
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diagnosis: list[DiagnosisItem]
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# --- BrandConsistency ---
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# --- BrandConsistency ---
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class BrandConsistencyInput(BaseModel):
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class BrandConsistencyInput(BaseModel):
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@ -0,0 +1,13 @@
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다음은 성형외과/피부과 {clinic_name} 의 전 채널 수집 데이터입니다.
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{data}
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위 데이터를 바탕으로 이 병원의 마케팅 전반에 걸친 핵심 문제점과 개선사항을 진단해줘.
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각 항목은 category(진단 카테고리), detail(상세 설명), severity(critical/warning/info) 형식의 JSON 배열로 출력해줘.
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현재 주요 진단 카테고리는 3개야.
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브랜드 아이덴티티 파편화
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콘텐츠 전략 부재
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플랫폼 간 유입 단절
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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
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from common.db.source import select_run_raw_data, select_run_mainpage_url
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from common.db.source import select_run_raw_data, select_run_mainpage_url
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from common.db.market import select_market
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from common.db.market import select_market
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from integrations.llm.llm_service import LLMService
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from integrations.llm.llm_service import LLMService
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from integrations.llm.prompt import report_prompt, plan_prompt, youtube_diagnosis_prompt, brand_consistency_prompt
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from integrations.llm.prompt import report_prompt, plan_prompt, youtube_diagnosis_prompt, brand_consistency_prompt, critical_issues_prompt
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from integrations.llm.schemas.report import ReportOutput, ClinicSnapshot, YouTubeAudit, BrandConsistencyOutput
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from integrations.llm.schemas.report import ReportOutput, ClinicSnapshot, YouTubeAudit, BrandConsistencyOutput, CriticalIssuesOutput, DiagnosisItem
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from integrations.llm.schemas.plan import PlanOutput
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from integrations.llm.schemas.plan import PlanOutput
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logger = logging.getLogger(__name__)
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logger = logging.getLogger(__name__)
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@ -208,6 +208,17 @@ async def _build_youtube_audit(youtube: dict) -> dict:
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return YouTubeAudit.model_validate(yt_patch).model_dump()
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return YouTubeAudit.model_validate(yt_patch).model_dump()
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async def _build_critical_issues(analysis_run_id: str, raw: dict) -> list[dict]:
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result: CriticalIssuesOutput = await LLMService(provider="perplexity").generate(
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critical_issues_prompt,
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{
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"clinic_name": (raw.get("mainpage") or {}).get("clinicName"),
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"data": json.dumps(raw, ensure_ascii=False),
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},
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)
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return [DiagnosisItem.model_validate(item).model_dump() for item in result.diagnosis]
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async def _build_overrides(analysis_run_id: str) -> dict:
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async def _build_overrides(analysis_run_id: str) -> dict:
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raw = await select_run_raw_data(analysis_run_id)
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raw = await select_run_raw_data(analysis_run_id)
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if not raw:
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if not raw:
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@ -243,6 +254,7 @@ async def _build_overrides(analysis_run_id: str) -> dict:
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brand = await generate_brand_consistency(analysis_run_id)
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brand = await generate_brand_consistency(analysis_run_id)
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brand_patch = brand.model_dump()["brand_inconsistencies"]
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brand_patch = brand.model_dump()["brand_inconsistencies"]
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critical_issues = await _build_critical_issues(analysis_run_id, raw)
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fb_patch: dict = {}
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fb_patch: dict = {}
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if fb_pages:
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if fb_pages:
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@ -259,6 +271,8 @@ async def _build_overrides(analysis_run_id: str) -> dict:
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overrides["facebook_audit"] = fb_patch
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overrides["facebook_audit"] = fb_patch
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if yt_patch:
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if yt_patch:
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overrides["youtube_audit"] = yt_patch
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overrides["youtube_audit"] = yt_patch
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if critical_issues:
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overrides["problem_diagnosis"] = critical_issues
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return overrides
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return overrides
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