Recipe Recommendations with Qdrant and Mistral — n8n ワークフロー

高 複雑度⚡ トリガー24個のノード🏷️ Miscellaneous👁 6,207回閲覧作成者：Jimleuk

概要

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

使用ノード

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

ワークフロープレビュー

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

仕組み

  1. 1

    トリガー

    このワークフローは トリガー トリガーで開始します。

  2. 2

    処理

    データは 24 個のノードを流れます, connecting agent, chattrigger, code。

  3. 3

    出力

    ワークフローは自動化を完了し、設定された宛先に結果を配信します。

ノード詳細 (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

このワークフローのインポート方法

  1. 1右側の JSONをダウンロード ボタンをクリックしてワークフローファイルを保存します。
  2. 2n8nインスタンスを開き、ワークフロー → 新規 → ファイルからインポート に進みます。
  3. 3ダウンロードした recipe-recommendations-with-qdrant-and-mistral ファイルを選択し、インポートをクリックします。
  4. 4各サービスノードの 認証情報（APIキー、OAuthなど）を設定します。
  5. 5ワークフローをテスト をクリックして動作確認し、有効化します。

またはn8nの JSONからインポート に直接貼り付け：

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

インテグレーション

agentchattriggercodedocumentdefaultdataloaderembeddingsmistralcloudexecuteworkflowtriggerhtmlhttprequestlmchatmistralcloudmanualtriggermergesettextsplitterrecursivecharactertextsplittertoolworkflowvectorstoreqdrantwait

このワークフローを取得

ワンクリックでダウンロード＆インポート

JSONをダウンロードn8n.ioで見る
ノード24
複雑度high
トリガーtrigger
閲覧数6,207
カテゴリMiscellaneous

作成者

Jimleuk

Jimleuk

@jimleuk

タグ

agentchattriggercodedocumentdefaultdataloaderembeddingsmistralcloudexecuteworkflowtriggerhtmlhttprequestlmchatmistralcloudmanualtrigger
⚡

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