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Travel planning agent with Couchbase vector search, Gemini 2.0 Flash and OpenAI

Elliot ScribnerElliot Scribner
1241 views
2/3/2026
DealsHubSpotAutomationRSSCRM
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> Disclaimer: this workflow template uses the n8n-nodes-couchbase community package. Community nodes are unverified and usage of them comes with some risks. See here for instructions on installing n8n community nodes.

This template is intended for use by those interested in learning more about Agentic AI workflow development, as well as those interested in learning how to use the Couchbase Search Vector Store node for practical applications.

This workflow helps users decide on travel destinations based on descriptions of several points of interest loaded into Couchbase and retrieved using Vector Search.

How it Works

This template contains two workflows:

  1. The Data Ingestion workflow uses the following nodes
    1. Webhook node (to listen for HTTP requests)
    2. OpenAI Embeddings node (to generate embeddings on document insertion)
      1. Note: You’ll need to configure OpenAI credentials for this node
    3. Couchbase Vector node (configured for document insertion)
    4. Default Data Loader and Recursive Character Text Splitter
  2. The Chat Application workflow uses the following nodes
    1. Chat Trigger node
    2. AI Tools Agent node connect to:
      • Gemini (as the Chat Model, for generating responses)
      • Simple Memory (as the Memory, to maintain conversation context)
      • Couchbase Search Vector node (as the Tool, for search)
      • OpenAI Embeddings node (as the Embedding model for the Couchbase Search Vector node, to convert queries to vectors)

Set up

Setting up this workflow is easy and only takes around 10 minutes.

Prerequisites

  • A Couchbase Cluster running the Search Service, and corresponding database access credentials
    • Be sure the Couchbase cluster allows the incoming IP address for n8n
    • Create a Vector Search Index using this index definition
    • Create a bucket (called travel-agent), scope (called vectors), and collection (called points-of-interest) in your Cluster
  • OpenAI API Key
  • Gemini API Key

Steps

  1. Configure all necessary credentials (Couchbase, OpenAI, and Gemini)
  2. Select your bucket, scope, and collection for each of the Couchbase vector nodes
  3. Ingest data, either using the cURL statements found on the sticky note within the workflow, or using this shell script to ingest 6 points of interest
  4. Open the chat and test out your travel agent!

Customization and Next Steps

  • This workflow template can be made more robust by enhancing the data model to include more information about each point of interest. For example, the addition of price ranges, ideal seasons to visit, activity types, and accomodation options can help inform the LLM further about each destination, and in turn allow it to provide a more tailored response and be more helpful for travel planning.
  • Alternatively, the data model could be entirely re-configured to suit a wide variety of other use cases. This template can serve as a building block for all sorts of AI Agent applications using RAG and is not limited to only travel recommendations.

n8n Travel Planning Agent with Couchbase Vector Search (Gemini 2.0 Flash and OpenAI)

This n8n workflow demonstrates a sophisticated AI agent designed to assist with travel planning. It leverages a combination of AI models (Google Gemini and OpenAI), vector embeddings, and memory to provide contextual and intelligent responses to user queries.

What it does

This workflow acts as a conversational AI agent that can process user chat messages and provide relevant travel planning assistance. Here's a step-by-step breakdown:

  1. Listens for Chat Messages: The workflow is triggered by an incoming chat message, acting as the user's query for travel planning.
  2. Initializes AI Agent: An AI Agent is set up, configured to use a Google Gemini Chat Model as its primary language model.
  3. Manages Conversation History: A "Simple Memory" buffer is used to maintain the context of the conversation, allowing the AI to remember previous interactions and provide more coherent responses.
  4. Processes Documents for Context (Optional/External): While not directly connected in this specific JSON, the presence of "Default Data Loader," "Recursive Character Text Splitter," and "Embeddings OpenAI" nodes suggests an intended capability to load, chunk, and embed external documents (e.g., travel guides, destination information) into a vector database for retrieval-augmented generation. The AI Agent would then use these embeddings to find relevant information to answer user queries.
  5. Generates AI Response: The AI Agent, using the Google Gemini Chat Model and potentially drawing from memory and external document context (if integrated), generates a comprehensive and helpful response to the user's travel planning query.

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n Instance: A running instance of n8n.
  • Google Gemini API Key: For the "Google Gemini Chat Model" node.
  • OpenAI API Key: For the "Embeddings OpenAI" node (if you intend to use document embeddings).
  • Couchbase Vector Search (External): The directory name suggests integration with Couchbase Vector Search for document retrieval, which would require a Couchbase cluster and relevant configuration (not explicitly shown in this JSON, but implied by the overall solution name).

Setup/Usage

  1. Import the Workflow: Download the provided JSON and import it into your n8n instance.
  2. Configure Credentials:
    • Set up a credential for your Google Gemini API Key in the "Google Gemini Chat Model" node.
    • Set up a credential for your OpenAI API Key in the "Embeddings OpenAI" node if you plan to utilize its functionality.
  3. Activate the Workflow: Once credentials are set, activate the "When chat message received" trigger node to start listening for incoming chat messages.
  4. Integrate Chat Platform: Connect your desired chat platform (e.g., Slack, Telegram, custom frontend) to the "When chat message received" webhook. The specific integration method will depend on your chosen chat platform.
  5. (Optional) Configure Document Loading and Vector Search: If you intend to use the document processing capabilities:
    • Connect the "Default Data Loader" to a source of your travel planning documents.
    • Configure the "Recursive Character Text Splitter" to chunk your documents appropriately.
    • Configure the "Embeddings OpenAI" node to generate embeddings for these chunks.
    • Integrate with Couchbase Vector Search (or another vector database) to store and retrieve these embeddings, allowing the AI Agent to perform RAG (Retrieval Augmented Generation). This part of the integration would likely involve additional n8n nodes or custom code to interact with Couchbase.

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