{
  "name": "Travel planning assistant with MongoDB Atlas, Gemini LLM and vector search",
  "nodes": [
    {
      "id": "3742b914-9f9d-4c6e-bfdf-f494295182a3",
      "name": "When chat message received",
      "type": "@n8n/n8n-nodes-langchain.chatTrigger",
      "position": [
        0,
        0
      ]
    },
    {
      "id": "5b7fcae2-78ab-45f7-933b-3acf993832e6",
      "name": "MongoDB Chat Memory",
      "type": "@n8n/n8n-nodes-langchain.memoryMongoDbChat",
      "position": [
        320,
        220
      ]
    },
    {
      "id": "eaba53fd-fc1c-404f-8720-eeea6cde088e",
      "name": "Google Gemini Chat Model",
      "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
      "position": [
        180,
        240
      ]
    },
    {
      "id": "af440c3f-e81f-4e40-a349-6272c3b23517",
      "name": "MongoDB Atlas Vector Store",
      "type": "@n8n/n8n-nodes-langchain.vectorStoreMongoDBAtlas",
      "position": [
        480,
        280
      ]
    },
    {
      "id": "17f2e6f3-d79c-4588-b4ee-bbfff61bc38d",
      "name": "Embeddings OpenAI",
      "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
      "position": [
        580,
        500
      ]
    },
    {
      "id": "fc7ab263-9b1c-4e98-ae51-74248b91fe82",
      "name": "Sticky Note",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        780,
        -420
      ],
      "parameters": {
        "width": 900,
        "height": 960,
        "content": "## AI Traveling Agent Powered by MongoDB Atlas for Memory and vector search.\n\n**Atlas MongoDB Memory Node**\n\n- The memory node allows the agent to persist and retrieve conversation based on threads in"
      }
    },
    {
      "id": "5a0353d2-410a-4059-8dc1-56a438e22cea",
      "name": "AI Traveling Planner Agent",
      "type": "@n8n/n8n-nodes-langchain.agent",
      "position": [
        220,
        0
      ]
    },
    {
      "id": "e4c2c92d-6291-42c8-9d03-5abfe1a85a83",
      "name": "Webhook",
      "type": "n8n-nodes-base.webhook",
      "position": [
        420,
        760
      ]
    },
    {
      "id": "8ec1fa93-3eea-44e2-a66d-7f1e961cfa94",
      "name": "Default Data Loader",
      "type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
      "position": [
        520,
        1200
      ]
    },
    {
      "id": "f723cca8-7bf4-4c93-932f-b558d21e8a4d",
      "name": "Recursive Character Text Splitter",
      "type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
      "position": [
        1060,
        1400
      ]
    },
    {
      "id": "c4a5f12e-de9b-44d0-93b2-a06cb56a1a91",
      "name": "MongoDB Atlas Vector Store1",
      "type": "@n8n/n8n-nodes-langchain.vectorStoreMongoDBAtlas",
      "position": [
        740,
        880
      ]
    },
    {
      "id": "cf3b0e71-73d5-4a54-bb64-a2d951cd7726",
      "name": "Embeddings OpenAI1",
      "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
      "position": [
        800,
        1100
      ]
    },
    {
      "id": "386538c3-81e7-4797-a4b6-81dea83fa778",
      "name": "Sticky Note1",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -440,
        940
      ],
      "parameters": {
        "width": 720,
        "height": 360,
        "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 \"https://<account>.app.n8n.cloud/webhook-tes"
      }
    },
    {
      "id": "0aa2676e-9f93-4b71-bd69-a4a8b2069496",
      "name": "Sticky Note2",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        1040,
        620
      ],
      "parameters": {
        "width": 720,
        "height": 360,
        "content": "## Vector Search data ingestion\n\nUsing webhook to ingest data to the MongoDB `points_of_interest` \ncollection. \n\nThis can be done in other ways like loading from wbesites/git/files or other supported "
      }
    }
  ],
  "connections": {
    "Webhook": {
      "main": [
        [
          {
            "node": "MongoDB Atlas Vector Store1",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Embeddings OpenAI": {
      "ai_embedding": [
        [
          {
            "node": "MongoDB Atlas Vector Store",
            "type": "ai_embedding",
            "index": 0
          }
        ]
      ]
    },
    "Embeddings OpenAI1": {
      "ai_embedding": [
        [
          {
            "node": "MongoDB Atlas Vector Store1",
            "type": "ai_embedding",
            "index": 0
          }
        ]
      ]
    },
    "Default Data Loader": {
      "ai_document": [
        [
          {
            "node": "MongoDB Atlas Vector Store1",
            "type": "ai_document",
            "index": 0
          }
        ]
      ]
    },
    "MongoDB Chat Memory": {
      "ai_memory": [
        [
          {
            "node": "AI Traveling Planner Agent",
            "type": "ai_memory",
            "index": 0
          }
        ]
      ]
    },
    "Google Gemini Chat Model": {
      "ai_languageModel": [
        [
          {
            "node": "AI Traveling Planner Agent",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "MongoDB Atlas Vector Store": {
      "ai_tool": [
        [
          {
            "node": "AI Traveling Planner Agent",
            "type": "ai_tool",
            "index": 0
          }
        ]
      ]
    },
    "When chat message received": {
      "main": [
        [
          {
            "node": "AI Traveling Planner Agent",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Recursive Character Text Splitter": {
      "ai_textSplitter": [
        [
          {
            "node": "Default Data Loader",
            "type": "ai_textSplitter",
            "index": 0
          }
        ]
      ]
    }
  }
}