{
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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": [
    {
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      "name": "When chat message received",
      "type": "@n8n/n8n-nodes-langchain.chatTrigger",
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    {
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      "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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      "parameters": {
        "color": 3,
        "width": 800,
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        "content": "## AI Travel Agent Powered by Couchbase.nn### You will need to:n1. Setup your Google API Credentials for the Gemini LLMn2. 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 performn5. 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).nnnOnce all of that is configured you will need to send the loading webhook with some data points (see example).nnThis should create vectorized data in  `points-of-interest` collection.nnOnce 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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      ],
      "parameters": {
        "options": {},
        "jsonData": "={{ $json.body.raw_body.point_of_interest.title }} - {{ $json.body.raw_body.point_of_interest.description }}",
        "jsonMode": "expressionData"
      },
      "typeVersion": 1
    },
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      "type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
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      "type": "n8n-nodes-base.stickyNote",
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      ],
      "parameters": {
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        "height": 460,
        "content": "## CURL Command to Ingest Data.nnHere is an example of how you can load data into your webhook once its active and ready to get requests.nn```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```nn(replace webhook url with the URL listed in the webhook node)nnA 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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      "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
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    {
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      "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": {
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          "mode": "list",
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          "cachedResultUrl": "",
          "cachedResultName": ""
        },
        "couchbaseBucket": {
          "__rl": true,
          "mode": "list",
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        },
        "toolDescription": "The list of Points of Interest from the database.",
        "vectorIndexName": {
          "__rl": true,
          "mode": "list",
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          "cachedResultUrl": "",
          "cachedResultName": ""
        },
        "couchbaseCollection": {
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      "name": "Insert docs with Couchbase Search Vector",
      "type": "n8n-nodes-couchbase.vectorStoreCouchbaseSearch",
      "position": [
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      ],
      "parameters": {
        "mode": "insert",
        "options": {},
        "embedding": "embedding",
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        "couchbaseScope": {
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        "embeddingBatchSize": 1,
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    {
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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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  "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",
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          }
        ]
      ]
    },
    "Simple Memory": {
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