Community Insights using Qdrant, Python and Information Extractor — n8n 워크플로
개요
This n8n template is one of a 3-part series exploring use-cases for clustering vector embeddings: Survey Insights Customer Insights Community Insights
This template demonstrates the Community Insights scenario where HN commments can be quickly grouped by similarity and an AI agent can generate insights on those groupings.
With this workflow, Researchers or HN users can quickly breakdown community consensus on a particular topic and identify frequently mentioned positives and negatives.
Sample
사용된 노드
워크플로 미리보기
작동 원리
- 1
트리거
워크플로는 트리거 트리거로 시작합니다.
- 2
처리
데이터가 23개의 노드를 통해 흐릅니다, connecting code, documentdefaultdataloader, embeddingsopenai.
- 3
출력
워크플로가 자동화를 완료하고 구성된 대상에 결과를 전달합니다.
노드 세부 정보 (23)
Google Sheets
googleSheets
HTTP Request
httpRequest
Hacker News
hackerNews
Code
code
Embeddings OpenAI
n8n-nodes-langchain.embeddingsOpenAi
OpenAI Chat Model
n8n-nodes-langchain.lmChatOpenAi
Recursive Character Text Splitter
n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter
Default Data Loader
n8n-nodes-langchain.documentDefaultDataLoader
Qdrant Vector Store
n8n-nodes-langchain.vectorStoreQdrant
이 워크플로 가져오는 방법
- 1오른쪽의 JSON 다운로드 버튼을 클릭하여 워크플로 파일을 저장합니다.
- 2n8n 인스턴스를 열고 워크플로 → 새로 만들기 → 파일에서 가져오기로 이동합니다.
- 3다운로드된
community-insights-using-qdrant-python-and-information-extractor파일을 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
Jimleuk
@jimleuk
태그
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