Planificateur de Voyage Intelligent avec Recherche Vectorielle

{
    "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",
            "position": [
                380,
                240
            ],
            "webhookId": "c22b2240-ff07-44e5-a1aa-63584150a1cb",
            "parameters": {
                "options": []
            },
            "typeVersion": 1.1
        },
        {
            "id": "e8b9815d-0fe5-4e7c-a20b-1602384580cd",
            "name": "Google Gemini Chat Model",
            "type": "@n8n\/n8n-nodes-langchain.lmChatGoogleGemini",
            "position": [
                560,
                480
            ],
            "parameters": {
                "options": [],
                "modelName": "models\/gemini-2.0-flash"
            },
            "typeVersion": 1
        },
        {
            "id": "a4b15997-de4d-4c78-b623-e936442134af",
            "name": "Sticky Note",
            "type": "n8n-nodes-base.stickyNote",
            "position": [
                1260,
                280
            ],
            "parameters": {
                "color": 3,
                "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?\""
            },
            "typeVersion": 1
        },
        {
            "id": "34866f8e-00b0-4706-82d7-491b9531a8b6",
            "name": "Webhook",
            "type": "n8n-nodes-base.webhook",
            "position": [
                800,
                1000
            ],
            "webhookId": "3ca6fbdd-a157-4e9d-9042-237048da85b6",
            "parameters": {
                "path": "3ca6fbdd-a157-4e9d-9042-237048da85b6",
                "options": {
                    "rawBody": true
                },
                "httpMethod": "POST"
            },
            "typeVersion": 2
        },
        {
            "id": "26d4e62a-42b0-4e09-8585-827e5bcc9fff",
            "name": "Default Data Loader",
            "type": "@n8n\/n8n-nodes-langchain.documentDefaultDataLoader",
            "position": [
                1180,
                1360
            ],
            "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,
                1540
            ],
            "parameters": {
                "options": []
            },
            "typeVersion": 1
        },
        {
            "id": "84f8c32b-8e0c-457c-aaec-17827042674d",
            "name": "Sticky Note1",
            "type": "n8n-nodes-base.stickyNote",
            "position": [
                -60,
                1060
            ],
            "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."
            },
            "typeVersion": 1
        },
        {
            "id": "b2cf8788-849c-4420-b448-bd49caa4941e",
            "name": "Simple Memory",
            "type": "@n8n\/n8n-nodes-langchain.memoryBufferWindow",
            "position": [
                720,
                480
            ],
            "parameters": [],
            "typeVersion": 1.3
        },
        {
            "id": "0bf7fef9-f999-42a8-a6a8-ab111fe9a084",
            "name": "AI Travel Agent",
            "type": "@n8n\/n8n-nodes-langchain.agent",
            "position": [
                600,
                240
            ],
            "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",
                    "value": "",
                    "cachedResultUrl": "",
                    "cachedResultName": ""
                }
            },
            "typeVersion": 1.1
        },
        {
            "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": {
                    "__rl": true,
                    "mode": "list",
                    "value": ""
                },
                "vectorIndexName": {
                    "__rl": true,
                    "mode": "list",
                    "value": "",
                    "cachedResultUrl": "",
                    "cachedResultName": ""
                },
                "embeddingBatchSize": 1,
                "couchbaseCollection": {
                    "__rl": true,
                    "mode": "list",
                    "value": "",
                    "cachedResultUrl": "",
                    "cachedResultName": ""
                }
            },
            "typeVersion": 1.1
        },
        {
            "id": "4c0274c3-6647-4f45-b7d4-d63cfe2102ea",
            "name": "Generate OpenAI Embeddings using text-embedding-3-small",
            "type": "@n8n\/n8n-nodes-langchain.embeddingsOpenAi",
            "position": [
                960,
                740
            ],
            "parameters": {
                "options": []
            },
            "typeVersion": 1.2
        },
        {
            "id": "83f864fa-a298-4738-a102-ca2d283377de",
            "name": "Generate OpenAI Embeddings using text-embedding-3-small1",
            "type": "@n8n\/n8n-nodes-langchain.embeddingsOpenAi",
            "position": [
                1000,
                1340
            ],
            "parameters": {
                "options": []
            },
            "typeVersion": 1.2
        }
    ],
    "active": true,
    "pinData": [],
    "settings": {
        "callerPolicy": "workflowsFromSameOwner",
        "executionOrder": "v1"
    },
    "versionId": "80e40e5a-35a3-4fa4-b90e-ac9d76897bbd",
    "connections": {
        "Webhook": {
            "main": [
                [
                    {
                        "node": "Insert docs with Couchbase Search Vector",
                        "type": "main",
                        "index": 0
                    }
                ]
            ]
        },
        "Simple Memory": {
            "ai_memory": [
                [
                    {
                        "node": "AI Travel Agent",
                        "type": "ai_memory",
                        "index": 0
                    }
                ]
            ]
        },
        "Default Data Loader": {
            "ai_document": [
                [
                    {
                        "node": "Insert docs with Couchbase Search Vector",
                        "type": "ai_document",
                        "index": 0
                    }
                ]
            ]
        },
        "Google Gemini Chat Model": {
            "ai_languageModel": [
                [
                    {
                        "node": "AI Travel Agent",
                        "type": "ai_languageModel",
                        "index": 0
                    }
                ]
            ]
        },
        "When chat message received": {
            "main": [
                [
                    {
                        "node": "AI Travel Agent",
                        "type": "main",
                        "index": 0
                    }
                ]
            ]
        },
        "Recursive Character Text Splitter": {
            "ai_textSplitter": [
                [
                    {
                        "node": "Default Data Loader",
                        "type": "ai_textSplitter",
                        "index": 0
                    }
                ]
            ]
        },
        "Retrieve docs with Couchbase Search Vector": {
            "ai_tool": [
                [
                    {
                        "node": "AI Travel Agent",
                        "type": "ai_tool",
                        "index": 0
                    }
                ]
            ]
        },
        "Generate OpenAI Embeddings using text-embedding-3-small": {
            "ai_embedding": [
                [
                    {
                        "node": "Retrieve docs with Couchbase Search Vector",
                        "type": "ai_embedding",
                        "index": 0
                    }
                ]
            ]
        },
        "Generate OpenAI Embeddings using text-embedding-3-small1": {
            "ai_embedding": [
                [
                    {
                        "node": "Insert docs with Couchbase Search Vector",
                        "type": "ai_embedding",
                        "index": 0
                    }
                ]
            ]
        }
    }
}