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Recipe recommendations with Qdrant and Mistral

JimleukJimleuk
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2/3/2026
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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 later retrieval.
  • Our AI Agent is setup to recommend recipes from our Qdrant vector store. However, instead of the default similarity search, we'll use the Recommendation API instead.
  • Qdrant's Recommendation API allows you to provide a negative prompt; in our case, the user can specify recipes or ingredients to avoid.
  • The AI Agent is now able to suggest a recipe recommendation better suited for the user and increase customer satisfaction.

Requirements

  • Qdrant vector store instance to save the recipes
  • Mistral.ai account for embeddings and LLM agent

Customising the workflow

This workflow can work for a variety of different audiences. Try different sets of data such as clothes, sports shoes, vehicles or even holidays.

n8n Workflow: Recipe Recommendations with Qdrant and Mistral

This n8n workflow provides a robust system for generating recipe recommendations using a combination of Qdrant for vector search and Mistral AI for natural language processing. It allows users to query for recipes based on their preferences and retrieves relevant suggestions from a vector database.

What it does

This workflow automates the following steps:

  1. Triggers on Chat Message or Manual Execution: The workflow can be initiated either by a chat message (e.g., from a chatbot integration) or manually.
  2. Prepares Input Data: It takes the incoming chat message or manual input and prepares it for processing.
  3. Embeds User Query: The user's recipe query is converted into a numerical vector embedding using the Mistral Cloud Embeddings model. This allows for semantic search.
  4. Searches Qdrant Vector Store: The generated embedding is used to perform a similarity search in the Qdrant Vector Store to find the most relevant recipe documents.
  5. Loads Relevant Documents: The documents retrieved from Qdrant are loaded for further processing.
  6. Splits Text for LLM: The content of the retrieved documents is split into manageable chunks using a Recursive Character Text Splitter to optimize for the Language Model's context window.
  7. Generates Recommendations with AI Agent: An AI Agent powered by the Mistral Cloud Chat Model uses the split text and the original user query to generate coherent and helpful recipe recommendations.
  8. Calls Sub-Workflows (Optional): The workflow includes a "Call n8n Workflow Tool" which suggests the capability to integrate and execute other n8n workflows as part of the AI agent's decision-making process.
  9. Processes and Merges Results: The output from the AI agent is processed, and potentially merged with other data, before being presented as the final recommendation.
  10. Delays Execution (Optional): A "Wait" node is included, allowing for a configurable delay in the workflow's execution, which can be useful for rate limiting or asynchronous operations.
  11. Performs HTTP Requests: The workflow can make HTTP requests, likely for interacting with external APIs or services, possibly to fetch additional data or send out the final recommendations.
  12. Transforms Data (HTML, Set, Code): Various nodes (HTML, Edit Fields (Set), Code) are used throughout the workflow to extract, transform, and manipulate data as needed.

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n Instance: A running n8n instance.
  • Mistral AI Account: API access to Mistral Cloud for embeddings and chat models. You will need to configure credentials for the "Embeddings Mistral Cloud" and "Mistral Cloud Chat Model" nodes.
  • Qdrant Instance: A running Qdrant vector database instance. You will need to configure credentials for the "Qdrant Vector Store" node.
  • Workflow for "Call n8n Workflow Tool": If you intend to use the "Call n8n Workflow Tool," you will need to have a separate n8n workflow accessible for it to call.

Setup/Usage

  1. Import the workflow: Download the JSON provided and import it into your n8n instance.
  2. Configure Credentials:
    • For the Embeddings Mistral Cloud node, provide your Mistral AI API key.
    • For the Mistral Cloud Chat Model node, provide your Mistral AI API key.
    • For the Qdrant Vector Store node, configure the connection details (e.g., host, port, API key) to your Qdrant instance.
  3. Populate Qdrant: Ensure your Qdrant instance is populated with recipe data, vectorized using a compatible embedding model (preferably Mistral's embedding model to match the workflow). The data should be structured in a way that the workflow can retrieve and process it effectively.
  4. Activate Trigger:
    • If using the Chat Trigger, configure it to listen for messages from your desired platform (e.g., Slack, Telegram, Discord).
    • If using the Manual Trigger, you can execute the workflow directly from the n8n UI for testing.
    • If using the Execute Workflow Trigger, ensure another workflow is configured to call this one.
  5. Customize Nodes (Optional):
    • Adjust the "Edit Fields (Set)" node to modify or add data as required.
    • Modify the "Code" node for any custom JavaScript logic.
    • Fine-tune the "Recursive Character Text Splitter" parameters for optimal text chunking.
    • Review and adjust the HTTP Request nodes if they are meant to interact with specific external services.
  6. Activate the workflow: Once configured, activate the workflow to start receiving recipe recommendation requests.

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