📖 Find the most relevant workflow templates using RAG, Qdrant and Gemini
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Description
🤖 AI Workflow Recommender (RAG + Qdrant + Gemini)
This workflow helps users find the most relevant n8n templates using AI.
It combines Retrieval-Augmented Generation (RAG), vector search (Qdrant), and Gemini to understand user intent and recommend workflows based on meaning, not just keywords.
⚙️ How it works
- Collect workflow templates from the n8n API using multiple search queries
- Process and clean the data (split, format, deduplicate)
- Convert workflows into embeddings using Gemini
- Store embeddings in a vector database (Qdrant)
- Accept user queries via chat interface
- Convert queries into embeddings
- Retrieve relevant workflows using semantic search
- Generate AI-powered recommendations with explanations and template links
🚀 What this workflow does
- Understands user intent (not just keywords)
- Finds relevant workflows using semantic similarity
- Recommends the best workflows with explanations
- Provides ready-to-use template links
🧩 Setup steps
- Set up Qdrant (Cloud or self-hosted)
- Add Google Gemini API credentials
- Run the Data Ingestion workflow to populate the database
- Activate the RAG chatbot workflow
⚠️ Important
- Make sure the vector database is populated before using the chatbot
- Ensure embedding model and vector dimension match
- Free-tier APIs may have rate limits
🎥 Tutorial
🔗 Nodes Used
HTTP Request, AI Agent, Simple Memory, Default Data Loader, Chat Trigger, Qdrant Vector Store
📥 Import
Download workflow.json and import into n8n:
Workflow menu → Import from File