AI & researchAgentChat TriggerDocument Default Data LoaderEmbeddings Open AiLM Chat Google Gemini

Travel Planning Agent with Couchbase Vector Search, Gemini 2.0 Flash and OpenAI

Configured with Agent, Chat Trigger, Document Default Data Loader, Embeddings Open Ai, and additional nodes. Review the connections and setup before adapting this template.

Download workflow JSON View files on GitHub ↗11 nodes · MIT

Workflow preview

Node categories in this workflow

Trigger
AI & models
Files / media
Transform
Data store

Icons describe general node roles, inferred from node types. Inspect the parameters for the configured action.

Drag to pan; use + and − to zoom. Reconstructed from the exported node positions; nothing runs here. Dashed lines represent non-main connections. Full node names and notes appear below.

How it works

The exported workflow contains the following configured nodes. Their connections are shown above; inspect the JSON for the complete logic, expressions, and external actions.

Workflow node names, types, and operations
Node	Category	Type	Operation
When chat message received	Trigger	Chat Trigger	See node parameters
Google Gemini Chat Model	AI & models	LM Chat Google Gemini	See node parameters
Webhook	Trigger	Webhook	See node parameters
Default Data Loader	Files / media	Document Default Data Loader	See node parameters
Recursive Character Text Splitter	Transform	Text Splitter Recursive Character Text Splitter	See node parameters
Simple Memory	Data store	Memory Buffer Window	See node parameters
AI Travel Agent	AI & models	Agent	See node parameters
Retrieve docs with Couchbase Search Vector	Data store	Vector Store Couchbase Search	See node parameters
Insert docs with Couchbase Search Vector	Data store	Vector Store Couchbase Search	See node parameters
Generate OpenAI Embeddings using text-embedding-3-small	AI & models	Embeddings Open Ai	See node parameters
Generate OpenAI Embeddings using text-embedding-3-small1	AI & models	Embeddings Open Ai	See node parameters

Setup

Download the JSON and import it into an isolated n8n instance. Leave the workflow inactive.
Connect your own credentials through n8n’s credential store. Replace REPLACE_WITH_* values with your resource identifiers.
Inspect API destinations, Code nodes, database queries, file paths, and outbound actions.
Run with synthetic data and harmless destinations. Enable triggers only after checking the actual outputs.

Requirements

An n8n instance with the listed node types and compatible versions. Service-connected workflows may require accounts, credentials, or paid APIs.

Credential types declared in the source

None declared. HTTP requests and Code nodes may still need configuration.

Workflow notes

AI Travel Agent Powered by Couchbase.

You will need to:

Setup your Google API Credentials for the Gemini LLM
Setup your OpenAI Credentials for the OpenAI embedding nodes.
Create a Couchbase cluster (using Couchbase Capella in the cloud, or Couchbase Server)
Add Database credentials with appropriate permissions for the operations you want to perform
Configure Allowed IP addresses for your n8n instance. Use 0.0.0.0/0 for easier testing.
Create a bucket, scope, and collection. We recommend the following:
- Bucket: travel-agent
- Scope: vectors
- Collection: points-of-interest
Navigate to the Data Tools, click the Search tab, and click Import Search Index. Upload the following JSON file found here.

Once all of that is configured you will need to send the loading webhook with some data points (see example).

This should create vectorized data in points-of-interest collection.

Once you have data points there try to ask the Agent questions about the data points and test the response. Eg. "Where should I go for a romantic getaway?"

CURL Command to Ingest Data.

Here is an example of how you can load data into your webhook once its active and ready to get requests.

curl -X POST "webhook url" \
  -H "Content-Type: application/json" \
  -d '{
    "raw_body": {
      "point_of_interest": {
        "title": "Eiffel Tower",
        "description": "Iconic iron lattice tower located on the Champ de Mars in Paris, France."
      }
    }
  }'

(replace webhook url with the URL listed in the webhook node)

A shell script to bulk insert six data points can be found here. Be sure to activate the workflow and use the production Webhook URL when running the script.

Travel Planning Agent with Couchbase Vector Search, Gemini 2.0 Flash and OpenAI

Purpose and structure

This folder contains the Travel Planning Agent with Couchbase Vector Search, Gemini 2.0 Flash and OpenAI workflow. Its executable nodes use agent, chatTrigger, documentDefaultDataLoader, embeddingsOpenAi, lmChatGoogleGemini, memoryBufferWindow, textSplitterRecursiveCharacterTextSplitter, vectorStoreCouchbaseSearch, webhook. The table and connection map below are derived from the JSON, not an inferred guarantee of behavior.

Setup

Import inactive. Configure your own credentials and replace REPLACE_WITH_* values. Review external destinations and Code nodes before running.

Entry points

When chat message received, Webhook

Verification

Sanitized source candidate; not runtime-tested. This overview is generated from node metadata. Read the folder README for the connection map, credential types, and license.

Attribution

https://github.com/Zie619/n8n-workflows/blob/94007c1445d9258a7da116646b79473e7c7c3282/workflows/Stickynote/1703_Stickynote_Webhook_Automation_Webhook.json MIT; preserve source notices.

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