Match medical symptoms to products with OpenAI, Qdrant & Google Sheets RAG
Needs changesNeeds changes. This template uses 1 thing(s) that n8n 3.0 removes: AI Agent v1 (old agent modes). 1 breaking item(s) found. Static check, last run 2026-10-19.
What to replace before upgrading
| Removed in 3.0 | Replace with |
|---|---|
| AI Agent v1 (old agent modes) RAG Agent (v1.7) | Latest AI Agent node (Tools Agent). For SQL Agent, use a Postgres or MySQL tool with a recent AI Agent. |
Nodes used (11)
- AI Agent
@n8n/n8n-nodes-langchain.agent - Manual Trigger
n8n-nodes-base.manualTrigger - Qdrant Vector Store
@n8n/n8n-nodes-langchain.vectorStoreQdrant - Embeddings OpenAI
@n8n/n8n-nodes-langchain.embeddingsOpenAi - Default Data Loader
@n8n/n8n-nodes-langchain.documentDefaultDataLoader - Character Text Splitter
@n8n/n8n-nodes-langchain.textSplitterCharacterTextSplitter - Loop Over Items (Split in Batches)
n8n-nodes-base.splitInBatches - Chat Trigger
@n8n/n8n-nodes-langchain.chatTrigger - OpenAI Chat Model
@n8n/n8n-nodes-langchain.lmChatOpenAi - Simple Memory
@n8n/n8n-nodes-langchain.memoryBufferWindow - Google Sheets
n8n-nodes-base.googleSheets
What it does
🧠 RAG AI Medical Agent – n8n Workflow 👥 Who’s it for This workflow is perfect for: - Healthcare ecommerce businesses that want to automate product recommendations. - Founders or developers building an AI assistant using retrieval-augmented generation (RAG) with product data. - Anyone wanting to combine OpenAI, Qdrant vector search, and Google Sheets to power intelligent medical queries. --- ⚙️ How it works / What it does This RAG-based workflow allows users to ask medical questions related to hair or scalp issues (e.g., hair loss, thinning). It: 1. Retrieves product info from a Google Sheet. 2. Converts product data into text embeddings using OpenAI. 3. Stores those embeddings in a Qdrant vector database. 4. On chat message trigger, performs a vector similarity search to match user symptoms with relevant products. 5. Uses an AI agent to respond with top 3 matching products from your catalog. --- 🛠️ How to set up Step 1: 🗂 Get your data - Make sure your Google Sheet contains the following columns: - Product Name - Symptoms Involved - Product Description - ForeverBetty Product Page Link - Category (optional but recommended) Step 2: 🔐 Connect your accounts - Add your Google Sheets OAuth2 credentials in the "Get all products" node. - Add your OpenAI API key in the embedding nodes. - Add your Qdrant credentials in the vector store nodes. Step 3: 🧠 Populate the Vector DB 1. Click “Execute workflow” manually. 2. This pulls data from the Google Sheet. 3. Each row is: - Formatted properly into a vector-friendly string. - Converted into an embedding using OpenAI. - Stored into Qdrant. Step 4: 💬 Enable Chat Interface - Use the ChatTrigger to receive user queries. - The agent searches Qdrant for relevant vectors. - Replies with product suggestions via LangChain's LLM agent. --- 📋 Requirements - 🧠 n8n - 📄 A Google Sheet with product data. - 🔐 Google Sheets OAuth2 credentials. - 🧠 OpenAI API key (for embeddings + chat LLM). - 🗃️ Qdrant Vector DB instance (Cloud or self-hosted). --- 🧩 How to customize it 🔄 Change the data structure - Update the "Set Data Properly in vector database" node to modify what fields are embedded. - Example: handlebars --- Product: {{ $json'Product Name ' }} Use-case: {{ $json'Symptoms Involved' }} Link: {{ $json'ForeverBetty Product Page Link ' }}