🧠 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:
- Retrieves product info from a Google Sheet.
- Converts product data into text embeddings using OpenAI.
- Stores those embeddings in a Qdrant vector database.
- On chat message trigger, performs a vector similarity search to match user symptoms with relevant products.
- 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
- Click “Execute workflow” manually.
- This pulls data from the Google Sheet.
- 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