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Match Medical Symptoms to Products with OpenAI, Qdrant & Google Sheets RAG

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Created by: Zain Ali || zain104

Zain Ali

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Last update 4 days ago

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🧠 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 '] }}