Recipe Recommendations with Qdrant and Mistral — n8n 워크플로

높음 복잡도⚡ 트리거24개의 노드🏷️ Miscellaneous👁 6,207회 조회작성자: Jimleuk

개요

This n8n workflow demonstrates creating a recipe recommendation chatbot using the Qdrant vector store recommendation API.

Use this example to build recommendation features in your AI Agents for your users.

How it works

For our recipes, we'll use HelloFresh's weekly course and recipes for data. We'll scrape the website for this data. Each recipe is split, vectorised and inserted into a Qdrant Collection using Mistral Embeddings Additionally the whole recipe is stored in a SQLite database for l

사용된 노드

HTTP RequestCodeHTMLAI AgentRecursive Character Text SplitterCall n8n Workflow ToolDefault Data LoaderEmbeddings Mistral CloudMistral Cloud Chat ModelQdrant Vector Store

워크플로 미리보기

Step 1. Fetch Available Courses For the Curre
To populate our vectorstore, we'll scrape the weekly me
Step 2. Create Recipe Documents For VectorSto
To populate our vectorstore, we'll scrape the weekly me
Step 3. Vectorise Recipes For Recommendation
Read more about Qdrant node
We'll sto
Step 4. Save Original Document to Database
Read more about Code Node
Finally, let's have the original document stored in
5. Chat with Our HelloFresh Recommendation AI
Read more about AI Agents
This agent is designed
5. Using Qdrant's Recommend API & Grouping Fu
Read more about Qdrant's Recommend API
Unlike basic similarity search, Q
Try it out!
This workflow does the following:
* Fetches and stores this week's HelloFresh's menu
* Builds the foundation of a recommendation engine by s
🚨Ensure Qdrant collection exists!
You'll need to run the following command in Qdrant:
```
PUT collections/hello_fresh
{
"vectors": {
🚨Configure Your Qdrant Connection
* Be sure to enter your endpoint address
doctoolembedmodel
⚡
W
When clicking "Test work…
Get This Week's Menu
Extract Available Courses
Extract Server Data
G
Get Course Metadata
Get Recipe
Embeddings Mistral Cloud
Default Data Loader
M
Merge Course & Recipe
P
Prepare Documents
Recursive Character Text…
⚡
C
Chat Trigger
Extract Recipe Details
Qdrant Recommend API
⚡
E
Execute Workflow Trigger
Mistral Cloud Chat Model
G
Get Tool Response
W
Wait for Rate Limits
Get Mistral Embeddings
Use Qdrant Recommend API
Get Recipes From DB
Save Recipes to DB
AI Agent
Qdrant Vector Store
24 nodes22 edges

작동 원리

  1. 1

    트리거

    워크플로는 트리거 트리거로 시작합니다.

  2. 2

    처리

    데이터가 24개의 노드를 통해 흐릅니다, connecting agent, chattrigger, code.

  3. 3

    출력

    워크플로가 자동화를 완료하고 구성된 대상에 결과를 전달합니다.

노드 세부 정보 (24)

HT

HTTP Request

httpRequest

#1
CO

Code

code

#2
HT

HTML

html

#3
AI

AI Agent

n8n-nodes-langchain.agent

#4
RE

Recursive Character Text Splitter

n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter

#5
CA

Call n8n Workflow Tool

n8n-nodes-langchain.toolWorkflow

#6
DE

Default Data Loader

n8n-nodes-langchain.documentDefaultDataLoader

#7
EM

Embeddings Mistral Cloud

n8n-nodes-langchain.embeddingsMistralCloud

#8
MI

Mistral Cloud Chat Model

n8n-nodes-langchain.lmChatMistralCloud

#9
QD

Qdrant Vector Store

n8n-nodes-langchain.vectorStoreQdrant

#10

이 워크플로 가져오는 방법

  1. 1오른쪽의 JSON 다운로드 버튼을 클릭하여 워크플로 파일을 저장합니다.
  2. 2n8n 인스턴스를 열고 워크플로 → 새로 만들기 → 파일에서 가져오기로 이동합니다.
  3. 3다운로드된 recipe-recommendations-with-qdrant-and-mistral 파일을 선택하고 가져오기를 클릭합니다.
  4. 4각 서비스 노드에 대한 자격 증명（API 키, OAuth 등）을 설정합니다.
  5. 5워크플로 테스트를 클릭하여 모든 것이 작동하는지 확인한 후 활성화합니다.

또는 n8n → JSON에서 가져오기에 직접 붙여넣기:

{ "name": "Recipe Recommendations with Qdrant and Mistral", "nodes": [...], ...}

통합

agentchattriggercodedocumentdefaultdataloaderembeddingsmistralcloudexecuteworkflowtriggerhtmlhttprequestlmchatmistralcloudmanualtriggermergesettextsplitterrecursivecharactertextsplittertoolworkflowvectorstoreqdrantwait

이 워크플로 가져오기

한 번의 클릭으로 다운로드 및 가져오기

JSON 다운로드n8n.io에서 보기
노드24
복잡도high
트리거trigger
조회수6,207
카테고리Miscellaneous

제작자

Jimleuk

Jimleuk

@jimleuk

태그

agentchattriggercodedocumentdefaultdataloaderembeddingsmistralcloudexecuteworkflowtriggerhtmlhttprequestlmchatmistralcloudmanualtrigger
⚡

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