n8n-workflows/workflows/1703_Stickynote_Webhook_Automation_Webhook.json

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  "meta": {
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    "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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      "typeVersion": 1.1
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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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      ],
      "parameters": {
        "options": {},
        "modelName": "models/gemini-2.0-flash"
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    {
      "id": "a4b15997-de4d-4c78-b623-e936442134af",
      "name": "Sticky Note",
      "type": "n8n-nodes-base.stickyNote",
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      "parameters": {
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        "width": 800,
        "height": 500,
        "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",
      "type": "n8n-nodes-base.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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        "httpMethod": "POST"
      },
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    },
    {
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      "name": "Default Data Loader",
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      "position": [
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      ],
      "parameters": {
        "options": {},
        "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",
      "position": [
        1280,
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      ],
      "parameters": {
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      "name": "Sticky Note1",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
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      "parameters": {
        "width": 720,
        "height": 460,
        "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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      "id": "b2cf8788-849c-4420-b448-bd49caa4941e",
      "name": "Simple Memory",
      "type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
      "position": [
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      ],
      "parameters": {},
      "typeVersion": 1.3
    },
    {
      "id": "0bf7fef9-f999-42a8-a6a8-ab111fe9a084",
      "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."
        }
      },
      "typeVersion": 1.8
    },
    {
      "id": "3af3c8ce-582b-407c-847a-8063f9ad2e1a",
      "name": "Retrieve docs with Couchbase Search Vector",
      "type": "n8n-nodes-couchbase.vectorStoreCouchbaseSearch",
      "position": [
        860,
        500
      ],
      "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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      },
      "typeVersion": 1.1
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    {
      "id": "77a4e857-607a-4bbc-a28d-8a715f9415d5",
      "name": "Insert docs with Couchbase Search Vector",
      "type": "n8n-nodes-couchbase.vectorStoreCouchbaseSearch",
      "position": [
        1100,
        1120
      ],
      "parameters": {
        "mode": "insert",
        "options": {},
        "embedding": "embedding",
        "textFieldKey": "description",
        "couchbaseScope": {
          "__rl": true,
          "mode": "list",
          "value": "",
          "cachedResultUrl": "",
          "cachedResultName": ""
        },
        "couchbaseBucket": {
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          "mode": "list",
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        "embeddingBatchSize": 1,
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      },
      "typeVersion": 1.1
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    {
      "id": "4c0274c3-6647-4f45-b7d4-d63cfe2102ea",
      "name": "Generate OpenAI Embeddings using text-embedding-3-small",
      "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
      "position": [
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      ],
      "parameters": {
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    },
    {
      "id": "83f864fa-a298-4738-a102-ca2d283377de",
      "name": "Generate OpenAI Embeddings using text-embedding-3-small1",
      "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
      "position": [
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      ],
      "parameters": {
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  ],
  "active": true,
  "pinData": {},
  "settings": {
    "callerPolicy": "workflowsFromSameOwner",
    "executionOrder": "v1"
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  "versionId": "80e40e5a-35a3-4fa4-b90e-ac9d76897bbd",
  "connections": {
    "Webhook": {
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          {
            "node": "Insert docs with Couchbase Search Vector",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "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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    },
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            "type": "ai_textSplitter",
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          }
        ]
      ]
    },
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            "type": "ai_embedding",
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            "type": "ai_embedding",
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}