Match medical symptoms to products with OpenAI, Qdrant & Google Sheets RAG

🧠 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:

  --- 
  Product: {{ $json['Product Name '] }}
  Use-case: {{ $json['Symptoms Involved'] }}
  Link: {{ $json['ForeverBetty Product Page Link '] }}