Food Photo Analysis to Restaurant & Book Recommendations with GPT Vision and Google APIs — n8n 워크플로
개요
AI Recommender: From Food Photo to Restaurant and Book (Google Books Integrated) What it does
Analyzes a food photo with an AI vision model to extract dish name + category
Searches nearby restaurants with Google Places and selects the single best (rating → reviews tie-break)
Finds a matching book via Google Books and posts a tidy summary to Slack
Who it’s for
Foodies, bloggers, and teams who want a plug-and-play flow that turns a single food photo into a dining pick + themed reading.
How i
사용된 노드
워크플로 미리보기
작동 원리
- 1
트리거
워크플로는 트리거 트리거로 시작합니다.
- 2
처리
데이터가 18개의 노드를 통해 흐릅니다, connecting agent, code, googlebooks.
- 3
출력
워크플로가 자동화를 완료하고 구성된 대상에 결과를 전달합니다.
노드 세부 정보 (18)
HTTP Request
httpRequest
Slack
slack
Google Books
googleBooks
Code
code
AI Agent
n8n-nodes-langchain.agent
OpenRouter Chat Model
n8n-nodes-langchain.lmChatOpenRouter
이 워크플로 가져오는 방법
- 1오른쪽의 JSON 다운로드 버튼을 클릭하여 워크플로 파일을 저장합니다.
- 2n8n 인스턴스를 열고 워크플로 → 새로 만들기 → 파일에서 가져오기로 이동합니다.
- 3다운로드된
food-photo-analysis-to-restaurant-book-recommendations-with-gpt-vision-and-google-apis파일을 선택하고 가져오기를 클릭합니다. - 4각 서비스 노드에 대한 자격 증명（API 키, OAuth 등）을 설정합니다.
- 5워크플로 테스트를 클릭하여 모든 것이 작동하는지 확인한 후 활성화합니다.
또는 n8n → JSON에서 가져오기에 직접 붙여넣기:
통합
제작자
noda
@shusaku
태그
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