{
"id": "iGAzT789R7Q1fOOE",
"meta": {
"instanceId": "7a1e9dd164c758cbdeb7cf88274e567a937a36ed99d4d22ff24b645841097c48",
"templateId": "3577",
"templateCredsSetupCompleted": true
},
"name": "Travel Planning Agent with Couchbase Vector Search, Gemini 2.0 Flash and OpenAI",
"tags": [],
"nodes": [
{
"id": "0f361616-a552-43ed-9754-794780113955",
"name": "When chat message received",
"type": "@n8n\/n8n-nodes-langchain.chatTrigger",
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],
"webhookId": "c22b2240-ff07-44e5-a1aa-63584150a1cb",
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{
"id": "e8b9815d-0fe5-4e7c-a20b-1602384580cd",
"name": "Google Gemini Chat Model",
"type": "@n8n\/n8n-nodes-langchain.lmChatGoogleGemini",
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"modelName": "models\/gemini-2.0-flash"
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"name": "Sticky Note",
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"parameters": {
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"content": "## AI Travel Agent Powered by Couchbase.\n\n### You will need to:\n1. Setup your Google API Credentials for the Gemini LLM\n2. Setup your OpenAI Credentials for the OpenAI embedding nodes.\n3. Create a Couchbase cluster (using [Couchbase Capella](https:\/\/cloud.couchbase.com\/) in the cloud, or Couchbase Server)\n4. Add [Database credentials](https:\/\/docs.couchbase.com\/cloud\/clusters\/manage-database-users.html#create-database-credentials) with appropriate permissions for the operations you want to perform\n5. Configure [Allowed IP addresses](https:\/\/docs.couchbase.com\/cloud\/clusters\/allow-ip-address.html) for your n8n instance. Use `0.0.0.0\/0` for easier testing.\n6. Create a bucket, scope, and collection. We recommend the following:\n - Bucket: `travel-agent`\n - Scope: `vectors`\n - Collection: `points-of-interest`\n7. Navigate to the Data Tools, click the Search tab, and click Import Search Index. Upload the following JSON file found [here](https:\/\/gist.github.com\/ejscribner\/6f16343d4b44b1af31e8f344557814b0).\n\n\nOnce all of that is configured you will need to send the loading webhook with some data points (see example).\n\nThis should create vectorized data in `points-of-interest` collection.\n\nOnce 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?\""
},
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"name": "Webhook",
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"webhookId": "3ca6fbdd-a157-4e9d-9042-237048da85b6",
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"path": "3ca6fbdd-a157-4e9d-9042-237048da85b6",
"options": {
"rawBody": true
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"parameters": {
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"jsonData": "={{ $json.body.raw_body.point_of_interest.title }} - {{ $json.body.raw_body.point_of_interest.description }}",
"jsonMode": "expressionData"
},
"typeVersion": 1
},
{
"id": "63fc308f-4d1c-4d24-9b20-68d7e6c2dbba",
"name": "Recursive Character Text Splitter",
"type": "@n8n\/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
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"content": "## CURL Command to Ingest Data.\n\nHere is an example of how you can load data into your webhook once its active and ready to get requests.\n\n```\ncurl -X POST \"webhook url\" \\\n -H \"Content-Type: application\/json\" \\\n -d '{\n \"raw_body\": {\n \"point_of_interest\": {\n \"title\": \"Eiffel Tower\",\n \"description\": \"Iconic iron lattice tower located on the Champ de Mars in Paris, France.\"\n }\n }\n }'\n```\n\n(replace webhook url with the URL listed in the webhook node)\n\nA shell script to bulk insert six data points can be found [here](https:\/\/gist.github.com\/ejscribner\/355a46a0a383a4878e65e2230b92c6b5). Be sure to activate the workflow and use the production Webhook URL when running the script."
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"name": "Simple Memory",
"type": "@n8n\/n8n-nodes-langchain.memoryBufferWindow",
"position": [
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],
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{
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"name": "AI Travel Agent",
"type": "@n8n\/n8n-nodes-langchain.agent",
"position": [
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],
"parameters": {
"options": {
"maxIterations": 10,
"systemMessage": "You are a helpful assistant for a trip planner. You have a vector search capability to locate points of interest, Use it and don't invent much."
}
},
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{
"id": "3af3c8ce-582b-407c-847a-8063f9ad2e1a",
"name": "Retrieve docs with Couchbase Search Vector",
"type": "n8n-nodes-couchbase.vectorStoreCouchbaseSearch",
"position": [
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],
"parameters": {
"mode": "retrieve-as-tool",
"topK": 10,
"options": [],
"toolName": "PointofinterestKB",
"embedding": "embedding",
"textFieldKey": "description",
"couchbaseScope": {
"__rl": true,
"mode": "list",
"value": "",
"cachedResultUrl": "",
"cachedResultName": ""
},
"couchbaseBucket": {
"__rl": true,
"mode": "list",
"value": ""
},
"toolDescription": "The list of Points of Interest from the database.",
"vectorIndexName": {
"__rl": true,
"mode": "list",
"value": "",
"cachedResultUrl": "",
"cachedResultName": ""
},
"couchbaseCollection": {
"__rl": true,
"mode": "list",
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{
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"name": "Insert docs with Couchbase Search Vector",
"type": "n8n-nodes-couchbase.vectorStoreCouchbaseSearch",
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"parameters": {
"mode": "insert",
"options": [],
"embedding": "embedding",
"textFieldKey": "description",
"couchbaseScope": {
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"embeddingBatchSize": 1,
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"name": "Generate OpenAI Embeddings using text-embedding-3-small",
"type": "@n8n\/n8n-nodes-langchain.embeddingsOpenAi",
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"name": "Generate OpenAI Embeddings using text-embedding-3-small1",
"type": "@n8n\/n8n-nodes-langchain.embeddingsOpenAi",
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"active": true,
"pinData": [],
"settings": {
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"executionOrder": "v1"
},
"versionId": "80e40e5a-35a3-4fa4-b90e-ac9d76897bbd",
"connections": {
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"main": [
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"type": "main",
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}
]
]
},
"Simple Memory": {
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"type": "ai_memory",
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]
]
},
"Default Data Loader": {
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"type": "ai_document",
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}
]
]
},
"Google Gemini Chat Model": {
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"type": "ai_languageModel",
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]
]
},
"When chat message received": {
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"type": "main",
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}
]
]
},
"Recursive Character Text Splitter": {
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"type": "ai_textSplitter",
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]
},
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},
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