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Personalized tour package recommendations with GPT-4o, Pinecone & Lovable UI

Mohan GopalMohan Gopal
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2/3/2026
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Personalized Tour Package Recommendations via n8n + Pinecone + Lovable UI

I've created an intelligent Travel Itinerary Planner that connects a Lovable front-end UI with a smart backend powered by n8n, Pinecone, and OpenAI to deliver personalized tour packages based on natural language queries.

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What It Does

Users type in their travel destination and duration (e.g., "Paris 5 days trip" or "Bali Trip for 7 Days, would love water sports, adventures and trekking included, also some historical monuments") through a Lovable UI.

This triggers a webhook in n8n, which processes the request, searches vectorized tour data in Pinecone, and generates a personalized itinerary using OpenAI’s GPT.

The results are then structured and sent back to the frontend UI for display in an interactive, reorderable format.

Workflow Architecture

Lovable UI ➝ Webhook ➝ Tour Recommendation Agent ➝ Vector Search ➝ OpenAI Response ➝ Structured Output ➝ Response to Lovable

Tools & Components Used

Webhook Acts as the entry point between the Lovable frontend and n8n.

Captures the user query (destination, duration) and forwards it into the workflow.

OpenAI Chat Model To interpret the user query.

To generate a user-friendly, structured tour package from the matched results.

Simple Memory Keeps chat state and context for follow-up queries (extendable for future features like multi-step planning or saved itineraries).

Question Answering with Vector Store Searches vector embeddings of pre-loaded tour data.

Finds the most relevant tour packages by comparing query embeddings.

Pinecone Vector Store Stores tour packages and activity data in vectorized format.

Enables fast and scalable semantic search across destinations, themes (e.g., "adventure", "cultural"), and duration.

OpenAI Embeddings Embeds all tour and activity documents stored in Pinecone.

Converts input user queries into embedding vectors for semantic search.

Structured Output Parser Parses the final OpenAI-generated response into a consistent, frontend-consumable JSON format.

Frontend (Lovable UI) User types in destination or their travel package needs in the Tour Search.

Lovable queries the n8n workflow.

Displays beautifully structured, editable itineraries.

How to Set It Up

  1. Webhook Setup in n8n Create a POST webhook node.

Set Webhook URL and connect it with Lovable frontend.

  1. Pinecone & Embeddings Convert your static tour package documents (PDFs, JSON, CSV, etc.) into embeddings using OpenAI.

Store the embeddings in a Pinecone namespace (e.g., kuala-lumpur-3-days).

  1. Configure “Answer with Vector Store” Tool Connect the tool to your Pinecone instance and pass query embedding for matching.

  2. Connect to OpenAI Chat Use the GPT model to process query + context from Pinecone to generate an engaging itinerary description.

Optionally chain a second model to format it into UI-consumable output.

  1. Output Parser & Return Use Structured Output Parser to parse the response and pass it to Respond to Webhook node for UI display.

Ideal Use Cases

Smart itinerary planning for OTAs or DMCs

Personalized travel recommendations in chatbots or apps

Travel advisors and agents automating package generation

Benefits

Highly relevant, contextual travel suggestions

Natural query understanding via OpenAI

Seamless frontend-backend integration via Webhook

If you’re building personalized experiences for travelers using AI, give this approach a try!

Let me know if you’d like the JSON for this workflow or help setting up the Pinecone data pipeline.

Personalized Tour Package Recommendations with GPT-4o, Pinecone & Lovable UI

This n8n workflow provides personalized tour package recommendations by leveraging the power of GPT-4o for natural language understanding and Pinecone for efficient vector-based search. It acts as a backend for a "Lovable UI" (as hinted by the directory name), taking user queries and returning tailored tour suggestions.

What it does

This workflow automates the following steps:

  1. Receives User Query: Listens for incoming HTTP requests (likely from a UI) containing a user's tour preferences or questions.
  2. Initializes AI Agent: Sets up an AI Agent (likely GPT-4o via LangChain) to process the user's request.
  3. Embeds User Query: Converts the user's natural language query into a vector embedding using OpenAI's embedding model.
  4. Searches Pinecone Vector Store: Uses the generated embedding to search a Pinecone vector store, retrieving relevant tour package information. This acts as a tool for the AI agent to get factual data.
  5. Generates Personalized Recommendations: The AI Agent, equipped with the retrieved tour information and its language model capabilities, generates personalized tour package recommendations.
  6. Parses Output: Structures the AI's response into a usable format (e.g., JSON).
  7. Responds to Webhook: Sends the personalized recommendations back to the calling UI or application.

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n Instance: A running n8n instance.
  • OpenAI API Key: For the OpenAI Chat Model (GPT-4o) and Embeddings.
  • Pinecone API Key and Environment: For accessing your Pinecone vector store, which should contain your tour package data as embeddings.
  • Lovable UI (External): An external application or UI that makes HTTP requests to this workflow's webhook and consumes its responses.

Setup/Usage

  1. Import the Workflow: Import the provided JSON into your n8n instance.
  2. Configure Credentials:
    • Set up your OpenAI API Key credentials for the "OpenAI Chat Model" and "Embeddings OpenAI" nodes.
    • Set up your Pinecone API Key and Environment credentials for the "Pinecone Vector Store" node.
  3. Activate the Webhook: The "Webhook" node will provide a unique URL once the workflow is activated. This URL is where your external UI should send user queries.
  4. Populate Pinecone: Ensure your Pinecone vector store is populated with embeddings of your tour package data. The "Pinecone Vector Store" node will query this data.
  5. Test the Workflow: Send a test request to the webhook URL with a sample user query (e.g., {"query": "I want a relaxing beach vacation for a family of four in Europe."}) and observe the generated recommendations.

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