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

Zain AliZain Ali
146 views
2/3/2026
AIVision AIWebhookImage AnalysisGoogle Gemini
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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 '] }}

n8n Workflow: Match Medical Symptoms to Products with OpenAI, Qdrant, and Google Sheets RAG

This n8n workflow provides a robust solution for matching user-provided medical symptoms to relevant products by leveraging a RAG (Retrieval Augmented Generation) system. It combines the power of OpenAI for natural language understanding and embeddings, Qdrant for efficient vector storage and retrieval, and Google Sheets for managing product data.

What it does

This workflow automates the following steps:

  1. Triggers Manually: The workflow is initiated manually, allowing for on-demand processing.
  2. Loads Product Data from Google Sheets: It reads a spreadsheet from Google Sheets, which is expected to contain product information.
  3. Splits Data into Batches: The product data is then split into manageable batches for efficient processing.
  4. Loads Documents: Each batch of product data is loaded as documents, preparing them for text processing.
  5. Splits Text: The loaded documents are further processed by a Character Text Splitter to break them into smaller, more manageable chunks.
  6. Generates Embeddings: OpenAI's Embeddings model generates vector embeddings for each text chunk from the product data.
  7. Stores Embeddings in Qdrant: These embeddings are then stored in a Qdrant Vector Store, creating a searchable knowledge base of product information.
  8. Listens for Chat Messages: The workflow is also designed to listen for incoming chat messages, acting as a trigger for symptom matching.
  9. Initializes AI Agent: An AI Agent is initialized with a Simple Memory to maintain conversational context.
  10. Uses OpenAI Chat Model: The AI Agent utilizes an OpenAI Chat Model to understand user queries and generate responses.
  11. Retrieves Relevant Products from Qdrant: When a user provides symptoms via chat, the AI Agent queries the Qdrant Vector Store to retrieve product information most relevant to the symptoms.
  12. Responds to User: The AI Agent then uses the retrieved information to provide a helpful response to the user, suggesting matching products.

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n Instance: A running n8n instance.
  • Google Sheets Account: Access to a Google Sheets spreadsheet containing your product data.
  • OpenAI API Key: An API key for OpenAI to use its Embeddings and Chat models.
  • Qdrant Instance: A running Qdrant instance for vector storage.
  • Credentials: Appropriate credentials configured in n8n for Google Sheets, OpenAI, and Qdrant.

Setup/Usage

  1. Import the Workflow: Download the provided JSON and import it into your n8n instance.
  2. Configure Credentials:
    • Google Sheets: Set up your Google Sheets credential to allow n8n to read your product data.
    • OpenAI: Configure your OpenAI credential with your API key.
    • Qdrant: Set up your Qdrant credential, providing the necessary connection details.
  3. Prepare Google Sheet: Ensure your Google Sheet is structured with product information that can be used for symptom matching. Each row should ideally represent a product with relevant descriptive columns.
  4. First Run (Data Ingestion):
    • Execute the workflow manually by clicking "Execute workflow" on the "When clicking ‘Execute workflow’" node. This will ingest your Google Sheet data into Qdrant.
    • Verify that the data has been successfully embedded and stored in your Qdrant instance.
  5. Second Run (Chat Interaction):
    • Activate the workflow.
    • Send a chat message to the "When chat message received" trigger (e.g., via a connected chat platform, if configured, or by manually simulating a chat message in n8n).
    • The AI Agent will process your symptoms, retrieve matching products from Qdrant, and respond.

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