Description

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.

Key Features

1) Scrapes HelloFresh's weekly menu to fetch and store recipe data
2) Utilizes Qdrant vector store for efficient recipe recommendation
3) Implements Mistral embeddings for enhanced vectorization of recipes
4) Features an AI agent that converses with users for personalized recipe suggestions
5) Leverages Qdrant's Recommend API for fine-tuned recommendations by incorporating user preferences
6) Saves original recipe data in a SQLite database for comprehensive access and retrieval.

Required Tools

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