Recipe Recommendations with Qdrant and Mistral — n8n 工作流

高 复杂度⚡ 触发器24 个节点🏷️ Miscellaneous👁 6,207 次查看作者：Jimleuk

概览

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

使用的节点

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

工作流预览

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

工作原理

  1. 1

    触发器

    工作流由 触发器 触发器启动。

  2. 2

    处理

    数据流经 24 个节点, connecting agent, chattrigger, code。

  3. 3

    输出

    工作流完成自动化并将结果发送到配置的目标。

节点详情 (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

如何导入此工作流

  1. 1点击右侧 下载 JSON 按钮保存工作流文件。
  2. 2打开你的 n8n 实例，依次点击 工作流 → 新建 → 从文件导入。
  3. 3选择下载的 recipe-recommendations-with-qdrant-and-mistral 文件并点击导入。
  4. 4为每个服务节点配置 凭证（API 密钥、OAuth 等）。
  5. 5点击 测试工作流 验证一切正常，然后激活它。

或直接在 n8n → 从 JSON 导入 中粘贴：

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

集成

agentchattriggercodedocumentdefaultdataloaderembeddingsmistralcloudexecuteworkflowtriggerhtmlhttprequestlmchatmistralcloudmanualtriggermergesettextsplitterrecursivecharactertextsplittertoolworkflowvectorstoreqdrantwait

获取此工作流

一键下载并导入

下载 JSON在 n8n.io 上查看
节点24
复杂度high
触发器trigger
查看次数6,207
分类Miscellaneous

创建者

Jimleuk

Jimleuk

@jimleuk

标签

agentchattriggercodedocumentdefaultdataloaderembeddingsmistralcloudexecuteworkflowtriggerhtmlhttprequestlmchatmistralcloudmanualtrigger
⚡

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