Workflow n8n
Automatisation Search Console avec n8n : chat avec vos données
Ce workflow n8n permet d'automatiser l'interaction avec les données de Google Search Console en utilisant un agent AI. Grâce à cette automatisation n8n, les utilisateurs peuvent poser des questions sur leurs données de recherche et obtenir des réponses instantanées, ce qui facilite la prise de décision basée sur des données précises. Ce type de solution est particulièrement utile pour les équipes marketing et les analystes de données qui cherchent à optimiser leur visibilité en ligne. Le workflow commence par un déclencheur de type Webhook, qui reçoit les entrées de l'utilisateur. Ensuite, les données sont stockées dans une mémoire Postgres via le nœud 'Postgres Chat Memory'. L'agent AI, alimenté par le modèle OpenAI, traite les requêtes et interroge les données de Search Console à l'aide de nœuds HTTP. Les résultats sont ensuite agrégés et renvoyés à l'utilisateur. Ce processus permet non seulement de gagner du temps, mais aussi d'améliorer la précision des analyses. En intégrant ce workflow, les entreprises peuvent réduire les erreurs humaines, accélérer le traitement des données et obtenir des insights précieux en temps réel, augmentant ainsi leur efficacité opérationnelle.
Tags clés :automatisationGoogle Search Consolen8nOpenAIdata analysis
Catégorie: Webhook · Tags: automatisation, Google Search Console, n8n, OpenAI, data analysis❤ 0
Workflow n8n Google Search Console, OpenAI, data analysis : vue d'ensemble
Schéma des nœuds et connexions de ce workflow n8n, généré à partir du JSON n8n.
Workflow n8n Google Search Console, OpenAI, data analysis : détail des nœuds
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"id": "PoiRk5w0xd1ysq4U",
"meta": {
"instanceId": "b9faf72fe0d7c3be94b3ebff0778790b50b135c336412d28fd4fca2cbbf8d1f5",
"templateCredsSetupCompleted": true
},
"name": "AI Agent to chat with you Search Console Data, using OpenAI and Postgres",
"tags": [],
"nodes": [
{
"id": "9ee6710b-19b7-4bfd-ac2d-0fe1e2561f1d",
"name": "Postgres Chat Memory",
"type": "@n8n/n8n-nodes-langchain.memoryPostgresChat",
"position": [
1796,
220
],
"parameters": {
"tableName": "insights_chat_histories"
},
"credentials": {
"postgres": {
"id": "",
"name": "Postgres"
}
},
"typeVersion": 1.1
},
{
"id": "eb9f07e9-ded1-485c-9bf3-cf223458384a",
"name": "OpenAI Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
1356,
240
],
"parameters": {
"model": "gpt-4o",
"options": {
"maxTokens": 16000
}
},
"credentials": {
"openAiApi": {
"id": "",
"name": "OpenAi"
}
},
"typeVersion": 1
},
{
"id": "1d3d6fb7-a171-4590-be42-df7eb0c208ed",
"name": "Set fields",
"type": "n8n-nodes-base.set",
"position": [
940,
-20
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "9f47b322-e42f-42d7-93eb-a57d22adb849",
"name": "chatInput",
"type": "string",
"value": "={{ $json.body?.chatInput || $json.chatInput }}"
},
{
"id": "73ec4dd0-e986-4f60-9dca-6aad2f86bdeb",
"name": "sessionId",
"type": "string",
"value": "={{ $json.body?.sessionId || $json.sessionId }}"
},
{
"id": "4b688c46-b60f-4f0a-83d8-e283f2d7055c",
"name": "date_message",
"type": "string",
"value": "={{ $now.format('yyyy-MM-dd') }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "92dc5e8b-5140-49be-8713-5749b7e2d46b",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
407.32142857142867,
-320
],
"parameters": {
"color": 7,
"width": 347.9910714285712,
"height": 516.8973214285712,
"content": "## Webhook - ChatInput\n\nThis webhook serves as the endpoint for receiving `ChatInput` data. Ensure that you include:\n- `chatInput` – the content you wish to send (😉)\n- `sessionId` – a unique identifier for the session\n\nIf you're using an interface such as **Open WebUI**, the `sessionId` will be generated automatically."
},
"typeVersion": 1
},
{
"id": "ca9f3732-9b62-4f44-b970-77d5d470ec76",
"name": "Webhook - ChatInput",
"type": "n8n-nodes-base.webhook",
"position": [
500,
-20
],
"webhookId": "a6820b65-76cf-402b-a934-0f836dee6ba0",
"parameters": {
"path": "a6820b65-76cf-402b-a934-0f836dee6ba0/chat",
"options": {},
"httpMethod": "POST",
"responseMode": "responseNode",
"authentication": "basicAuth"
},
"credentials": {
"httpBasicAuth": {
"id": "",
"name": "basic-auth"
}
},
"typeVersion": 2
},
{
"id": "9d684873-6dfe-4709-928d-293b187dfb30",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
820,
-320
],
"parameters": {
"color": 7,
"width": 347.9910714285712,
"height": 516.8973214285712,
"content": "## Set fields\n\nThis node sets three fields:\n- `chatInput`: retrieved from the previous webhook node\n- `sessionId`: retrieved from the previous webhook node\n- `date_message`: formatted within this node. This will be used later to help the AI agent determine the date range for retrieving Search Console data."
},
"typeVersion": 1
},
{
"id": "8750215a-1e33-4ac8-a6da-95efa8ffed65",
"name": "Respond to Webhook",
"type": "n8n-nodes-base.respondToWebhook",
"position": [
2600,
-20
],
"parameters": {
"options": {}
},
"typeVersion": 1.1
},
{
"id": "1b879496-5c0f-4bd5-b4cb-18df2662aef2",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
1240,
-320
],
"parameters": {
"color": 7,
"width": 1154.2857142857138,
"height": 516.8973214285712,
"content": "## AI Agent - Tools Agent\n\nThis AI Agent is configured with a system prompt that instructs it to:\n- On the first user message, **retrieve available Search Console properties** and offer the user the option to **fetch data from these properties**\n- Based on the user’s natural language input, **construct an API call** to the selected Search Console property and retrieve the requested data\n- Present the data in a **markdown-formatted table**\n\nThe AI Agent has a friendly tone and is designed to **confirm the user’s data requirements accurately** before executing any API requests.\n"
},
"typeVersion": 1
},
{
"id": "c44c6402-9ddd-4a7b-bc5a-b6c3679a3f68",
"name": "Call Search Console Tool",
"type": "@n8n/n8n-nodes-langchain.toolWorkflow",
"position": [
2196,
220
],
"parameters": {
"name": "SearchConsoleRequestTool",
"workflowId": {
"__rl": true,
"mode": "list",
"value": "PoiRk5w0xd1ysq4U",
"cachedResultName": "My workflow 10"
},
"description": "Call this tool when you need to get the website_list or custom_insights",
"jsonSchemaExample": ""
},
"typeVersion": 1.2
},
{
"id": "b1701a89-c5b3-47fb-99d5-4896a6d5c7a2",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
1234,
220
],
"parameters": {
"color": 6,
"width": 328.9664285714292,
"height": 468.13107142857154,
"content": "\n\n\n\n\n\n\n\n\n\n\n### AI Agent Sub-node - OpenAI Chat Model\n\nThis sub-node utilizes the selected **OpenAI Chat Model**. You can replace it with any LLM that **supports tool calling**.\n\n### ⚠️ Choose Your Model\nIn this template, the **default model is `gpt-4o`**, a **costly option**. If you'd like a more **affordable alternative**, select `gpt4-o-mini`, though note that responses may occasionally be of slightly lower quality compared to `gpt-4o`."
},
"typeVersion": 1
},
{
"id": "cd1a7cec-5845-47b1-a2c8-d3b458a02eb0",
"name": "Sticky Note4",
"type": "n8n-nodes-base.stickyNote",
"position": [
1656,
220
],
"parameters": {
"color": 6,
"width": 328.9664285714292,
"height": 468.13107142857154,
"content": "\n\n\n\n\n\n\n\n\n\n\n### AI Agent Sub-node - Postgres Chat Memory\n\nConnect your **Postgres credentials** and specify a **table name** to store the chat history. In this template, the default table name is `insights_chat_histories`, and the **context window length is set to 5**.\n\n**👋 Tip:** If you don’t have a Postgres database, you can quickly **set one up with [Supabase](https://supabase.com/)**.\n"
},
"typeVersion": 1
},
{
"id": "290a07d1-c7ed-434d-9851-2a2dcdd35bdf",
"name": "Sticky Note6",
"type": "n8n-nodes-base.stickyNote",
"position": [
2076,
220
],
"parameters": {
"color": 6,
"width": 328.9664285714292,
"height": 468.13107142857154,
"content": "\n\n\n\n\n\n\n\n\n\n\n### AI Agent Sub-node - Call Search Console Tool\n\nThis **tool is used by the AI Agent** to:\n- Retrieve the **list of accessible properties in Search Console**\n- **Fetch Search Console data** based on the user’s natural language request\n\n"
},
"typeVersion": 1
},
{
"id": "07805c90-7ba5-44d0-b6eb-5a65efb0f8be",
"name": "Sticky Note5",
"type": "n8n-nodes-base.stickyNote",
"position": [
2480,
-320
],
"parameters": {
"color": 7,
"width": 347.9910714285712,
"height": 516.8973214285712,
"content": "## Respond to Webhook\n\nThis node is used to send a response back to the user.\n\n**👋 Tip:** `intermediateSteps` are configured, allowing you to use raw data fetched from Search Console to **create charts or other visualizations** if desired.\n"
},
"typeVersion": 1
},
{
"id": "9a927a40-45e4-4fd5-ab3e-b77578469f82",
"name": "Sticky Note7",
"type": "n8n-nodes-base.stickyNote",
"position": [
400,
800
],
"parameters": {
"color": 7,
"width": 370.3910714285712,
"height": 492.3973214285712,
"content": "## Tool Call Trigger\n\nThis **node is triggered when the AI Agent needs to retrieve the `website_list`** (accessible Search Console properties) or **`custom_insights`** based on user data.\n"
},
"typeVersion": 1
},
{
"id": "c54a4653-0f09-46b0-bd20-68919b96e154",
"name": "Tool calling",
"type": "n8n-nodes-base.executeWorkflowTrigger",
"position": [
500,
1080
],
"parameters": {},
"typeVersion": 1
},
{
"id": "cc7303ee-1afa-4859-83e7-3af0e963a0f1",
"name": "Switch",
"type": "n8n-nodes-base.switch",
"position": [
1300,
1080
],
"parameters": {
"rules": {
"values": [
{
"outputKey": "custom_insights",
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "a30fe6a6-7d0a-4f14-8492-ae021ddc8ec6",
"operator": {
"type": "string",
"operation": "contains"
},
"leftValue": "={{ $json.request_type }}",
"rightValue": "custom_insights"
}
]
},
"renameOutput": true
},
{
"outputKey": "website_list",
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "1b7d6039-6474-4a73-b157-584743a9d7f0",
"operator": {
"type": "string",
"operation": "contains"
},
"leftValue": "={{$json.request_type}}",
"rightValue": "website_list"
}
]
},
"renameOutput": true
}
]
},
"options": {}
},
"typeVersion": 3.2
},
{
"id": "6860ff98-4050-4f64-b8c1-a153e3388df0",
"name": "Set fields - Consruct API CALL",
"type": "n8n-nodes-base.set",
"position": [
920,
1080
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "06373437-8288-4171-9f98-e8a417220dd4",
"name": "request_type",
"type": "string",
"value": "={{ $json.query.parseJson().request_type }}"
},
{
"id": "da45c0c5-05f6-4107-81aa-8c08c972d9bf",
"name": "start_date",
"type": "string",
"value": "={{ $json.query.parseJson().startDate }}"
},
{
"id": "59d55034-c612-43d7-9700-4cacdb630ec2",
"name": "end_date",
"type": "string",
"value": "={{ $json.query.parseJson().endDate }}"
},
{
"id": "4c2478c0-7f96-4d3d-a632-089307dc989e",
"name": "dimensions",
"type": "string",
"value": "={{ $json.query.parseJson().dimensions }}"
},
{
"id": "eceefbf9-44e5-4617-96ea-58aca2a29618",
"name": "rowLimit",
"type": "number",
"value": "={{ $json.query.parseJson().rowLimit }}"
},
{
"id": "4e18386e-8548-4385-b620-43efbb11cd63",
"name": "startRow",
"type": "number",
"value": "={{ $json.query.parseJson().startRow}}"
},
{
"id": "a9323a7b-08b4-4015-b3d7-632bcdf56f4e",
"name": "property",
"type": "string",
"value": "={{ encodeURIComponent($json.query.parseJson().property) }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "0a2dfb28-17ee-477f-b9ea-f1d8e05e3745",
"name": "Sticky Note8",
"type": "n8n-nodes-base.stickyNote",
"position": [
820,
800
],
"parameters": {
"color": 7,
"width": 370.3910714285712,
"height": 492.3973214285712,
"content": "## Set Fields - Construct API Call\n\nThis node configures fields based on the JSON sent by the AI agent:\n- The `request_type` field determines the route: `website_list` (to retrieve the list of websites) or `custom_insights` (to get insights from Search Console)\n- Additional fields are set to construct the API call, following the **[Search Console API Documentation](https://developers.google.com/webmaster-tools/v1/searchanalytics/query?hl=en)**\n"
},
"typeVersion": 1
},
{
"id": "e6ef5c28-01e4-4a0b-9081-b62ec28be635",
"name": "Set fields - Create searchConsoleDataArray",
"type": "n8n-nodes-base.set",
"position": [
2180,
980
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "2cffd36f-72bd-4535-8427-a88028ea0c4c",
"name": "searchConsoleData",
"type": "array",
"value": "={{ $json.rows }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "abc80061-a794-4e1d-a055-bd88ea5c93eb",
"name": "Set fields - Create searchConsoleDataArray 2",
"type": "n8n-nodes-base.set",
"position": [
2180,
1340
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "2cffd36f-72bd-4535-8427-a88028ea0c4c",
"name": "searchConsoleData",
"type": "array",
"value": "={{ $json.siteEntry }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "24981eea-980e-4e07-9036-d0042c5b2fbe",
"name": "Search Console - Get Custom Insights",
"type": "n8n-nodes-base.httpRequest",
"position": [
1620,
980
],
"parameters": {
"url": "=https://www.googleapis.com/webmasters/v3/sites/{{ $json.property }}/searchAnalytics/query",
"method": "POST",
"options": {},
"jsonBody": "={\n \"startDate\": \"{{ $json.start_date }}\",\n \"endDate\": \"{{ $json.end_date }}\",\n \"dimensions\": {{ $json.dimensions }},\n \"rowLimit\": {{ $json.rowLimit }},\n \"startRow\": 0,\n \"dataState\":\"all\"\n}",
"sendBody": true,
"sendQuery": true,
"sendHeaders": true,
"specifyBody": "json",
"authentication": "genericCredentialType",
"genericAuthType": "oAuth2Api",
"queryParameters": {
"parameters": [
{}
]
},
"headerParameters": {
"parameters": [
{
"name": "Content-Type",
"value": "application/json"
}
]
}
},
"credentials": {
"oAuth2Api": {
"id": "",
"name": "search-console"
}
},
"typeVersion": 4.2
},
{
"id": "645ff407-857d-4629-926b-5cfc52cfa8ba",
"name": "Sticky Note9",
"type": "n8n-nodes-base.stickyNote",
"position": [
1520,
800
],
"parameters": {
"color": 7,
"width": 370.3910714285712,
"height": 364.3185243941325,
"content": "## Search Console - Get Custom Insights\n\nThis node **performs the API call to retrieve data from Search Console**.\n"
},
"typeVersion": 1
},
{
"id": "15aa66e2-f288-4c86-8dad-47e22aa9104f",
"name": "Sticky Note10",
"type": "n8n-nodes-base.stickyNote",
"position": [
1520,
1180
],
"parameters": {
"color": 7,
"width": 370.3910714285712,
"height": 334.24982142857124,
"content": "## Search Console - Get List of Properties\n\nThis node **performs the API call to retrieve the list of accessible properties from Search Console**.\n"
},
"typeVersion": 1
},
{
"id": "cd804a52-833a-451a-8e0c-f640210ee2c4",
"name": "## Search Console - Get List of Properties",
"type": "n8n-nodes-base.httpRequest",
"position": [
1620,
1340
],
"parameters": {
"url": "=https://www.googleapis.com/webmasters/v3/sites",
"options": {},
"sendHeaders": true,
"authentication": "genericCredentialType",
"genericAuthType": "oAuth2Api",
"headerParameters": {
"parameters": [
{
"name": "Content-Type",
"value": "application/json"
}
]
}
},
"credentials": {
"oAuth2Api": {
"id": "",
"name": "search-console"
}
},
"typeVersion": 4.2
},
{
"id": "3eac4df1-00ac-4262-b520-3a7e218c7e57",
"name": "Sticky Note11",
"type": "n8n-nodes-base.stickyNote",
"position": [
2040,
800
],
"parameters": {
"color": 7,
"width": 370.3910714285712,
"height": 725.1298214285712,
"content": "## Set Fields - Create `searchConsoleDataArray`\n\nThese nodes **create an array based on the response from the Search Console API**.\n"
},
"typeVersion": 1
},
{
"id": "86db5800-a735-4749-a800-63d78908610b",
"name": "Sticky Note12",
"type": "n8n-nodes-base.stickyNote",
"position": [
2520,
800
],
"parameters": {
"color": 7,
"width": 370.3910714285712,
"height": 722.6464176100125,
"content": "## Array Aggregation - Response to AI Agent\n\nThese nodes **aggregate the array from the previous** step and send it back to the AI Agent through the field named output as `response`.\n"
},
"typeVersion": 1
},
{
"id": "aefbacc7-8dfc-4655-bc4d-f0498c823711",
"name": "Array aggregation - response to AI Agent",
"type": "n8n-nodes-base.aggregate",
"position": [
2640,
980
],
"parameters": {
"options": {},
"aggregate": "aggregateAllItemData",
"destinationFieldName": "response"
},
"typeVersion": 1
},
{
"id": "e5334c72-981c-4375-ae8e-9a3a0457880b",
"name": "Array aggregation - response to AI Agent1",
"type": "n8n-nodes-base.aggregate",
"position": [
2660,
1340
],
"parameters": {
"options": {},
"aggregate": "aggregateAllItemData",
"destinationFieldName": "response"
},
"typeVersion": 1
},
{
"id": "2e93a798-6c26-4d34-a553-ba01b64ca3fe",
"name": "Sticky Note13",
"type": "n8n-nodes-base.stickyNote",
"position": [
-398.45627799387194,
-320
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"parameters": {
"width": 735.5589746610085,
"height": 1615.4504601771982,
"content": "# AI Agent to Chat with Your Search Console Data\n\nThis **AI Agent enables you to interact with your Search Console data** through a **chat interface**. Each node is **documented within the template**, providing sufficient information for setup and usage. You will also need to **configure Search Console OAuth credentials**.\n\nFollow this **[n8n documentation](https://docs.n8n.io/integrations/builtin/credentials/google/oauth-generic/#configure-your-oauth-consent-screen)** to set up the OAuth credentials.\n\n## Important Notes\n\n### Correctly Configure Scopes for Search Console API Calls\n- It’s essential to **configure the scopes correctly** in your Google Search Console API OAuth2 credentials. Incorrect **configuration can cause issues with the refresh token**, requiring frequent reconnections. Below is the configuration I use to **avoid constant re-authentication**:\n\n\n\nOf course, you'll need to add your **client_id** and **client_secret** from the **Google Cloud Platform app** you created to access your Search Console data.\n\n### Configure Authentication for the Webhook\n\nSince the **webhook will be publicly accessible**, don’t forget to **set up authentication**. I’ve used **Basic Auth**, but feel free to **choose the method that best meets your security requirements**.\n\n## 🤩💖 Example of awesome things you can do with this AI Agent\n\n\n\n"
},
"typeVersion": 1
},
{
"id": "fa630aa9-3c60-4b27-9477-aaeb79c7f37d",
"name": "AI Agent",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
1676,
-20
],
"parameters": {
"text": "=user_message : {{ $json.chatInput }}\ndate_message : {{ $json.date_message }}",
"options": {
"systemMessage": "=Assist users by asking natural, conversational questions to understand their data needs and building a custom JSON API request to retrieve Search Console data. Handle assumptions internally, confirming them with the user in a friendly way. Avoid technical jargon and never imply that the user is directly building an API request.\n\nPre-Step: Retrieve the Website List\nImportant: Initial Action: Before sending your first message to the user, retrieve the list of connected Search Console properties.\n\nTool Call for Website List:\n\nTool name: SearchConsoleRequestTool\nRequest:\n{\n \"request_type\": \"website_list\" // Always include `request_type` in the API call.\n}\nUsage: Use this list to personalize your response in the initial interaction.\nStep-by-Step Guide\nStep 1: Initial Interaction and Introduction\nGreeting:\n\n\"Hi there! I’m here to help you gain valuable insights from your Search Console data. Whether you're interested in a specific time frame, performance breakdown by pages, queries, or other dimensions, I've got you covered.\n\nI can help you retrieve data for these websites:\n\nhttps://example1.com\nhttps://example2.com\nhttps://example3.com\nWhich of these properties would you like to analyze?\"\nStep 2: Handling User Response for Property Selection\nAction: When the user selects a property, use the property URL exactly as listed (e.g., \"https://example.com\") when constructing the API call.\n\nStep 3: Understanding the User's Needs\nAcknowledgment and Setting Defaults:\n\nIf the user expresses a general need (e.g., \"I want the last 3 months of page performance\"), acknowledge their request and set reasonable defaults.\n\nExample Response:\n\n\"Great! I'll gather the top 300 queries from the last 3 months for https://example.com. If you'd like more details or adjustments, just let me know.\"\n\nFollow-up Questions:\n\nConfirming Dimensions: If the user doesn’t specify dimensions, ask:\n\n\"For this analysis, I’ll look at page performance. Does that sound good, or would you like to include other details like queries, devices, or other dimensions?\"\n\nNumber of Results: If the user hasn’t specified the number of results, confirm:\n\n\"I can show you the top 100 results. Let me know if you'd like more or fewer!\"\n\nStep 4: Gathering Specific Inputs (If Necessary)\nAction: If the user provides specific needs, capture and confirm them naturally.\n\nExample Response:\n\n\"Perfect, I’ll pull the data for [specified date range], focusing on [specified dimensions]. Anything else you’d like me to include?\"\n\nImplicit Defaults:\n\nDate Range: Assume \"last 3 months\" if not specified.\nRow Limit: Default to 100, adjustable based on user input.\nStep 5: Confirming Input with the User\nAction: Summarize the request to ensure accuracy.\n\nExample Response:\n\n\"Here’s what I’m preparing: data for https://example.com, covering the last 3 months, focusing on the top 100 queries. Let me know if you’d like to adjust anything!\"\n\nStep 6: Constructing the JSON for Custom Insights\nAction: Build the API call based on the conversation.\n\n{\n \"property\": \"<USER_PROVIDED_PROPERTY_URL>\", // Use the exact property URL.\n \"request_type\": \"custom_insights\",\n \"startDate\": \"<ASSUMED_OR_USER_SPECIFIED_START_DATE>\",\n \"endDate\": \"<ASSUMED_OR_USER_SPECIFIED_END_DATE>\",\n \"dimensions\": [\"<IMPLIED_OR_USER_SPECIFIED_DIMENSIONS>\"], // Array of one or more: \"page\", \"query\", \"searchAppearance\", \"device\", \"country\"\n \"rowLimit\": 300 // Default or user-specified limit.\n}\nStep 7: Presenting the Data\nWhen Retrieving Custom Insights:\n\nImportant: Display all retrieved data in an easy-to-read markdown table format.\nStep 8: Error Handling\nAction: Provide clear, user-friendly error messages when necessary.\n\nExample Response:\n\n\"Hmm, there seems to be an issue retrieving the data. Let’s review what we have or try a different approach.\"\n\nAdditional Notes\nProactive Assistance: Offer suggestions based on user interactions, such as adding dimensions or refining details.\nTone: Maintain a friendly and helpful demeanor throughout the conversation.",
"returnIntermediateSteps": true
},
"promptType": "define"
},
"typeVersion": 1.6
}
],
"active": true,
"pinData": {},
"settings": {
"executionOrder": "v1"
},
"versionId": "abda3766-7d18-46fb-83e7-c2343ff26385",
"connections": {
"Switch": {
"main": [
[
{
"node": "Search Console - Get Custom Insights",
"type": "main",
"index": 0
}
],
[
{
"node": "## Search Console - Get List of Properties",
"type": "main",
"index": 0
}
]
]
},
"AI Agent": {
"main": [
[
{
"node": "Respond to Webhook",
"type": "main",
"index": 0
}
]
]
},
"Set fields": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
]
]
},
"Tool calling": {
"main": [
[
{
"node": "Set fields - Consruct API CALL",
"type": "main",
"index": 0
}
]
]
},
"OpenAI Chat Model": {
"ai_languageModel": [
[
{
"node": "AI Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Webhook - ChatInput": {
"main": [
[
{
"node": "Set fields",
"type": "main",
"index": 0
}
]
]
},
"Postgres Chat Memory": {
"ai_memory": [
[
{
"node": "AI Agent",
"type": "ai_memory",
"index": 0
}
]
]
},
"Call Search Console Tool": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"Set fields - Consruct API CALL": {
"main": [
[
{
"node": "Switch",
"type": "main",
"index": 0
}
]
]
},
"Search Console - Get Custom Insights": {
"main": [
[
{
"node": "Set fields - Create searchConsoleDataArray",
"type": "main",
"index": 0
}
]
]
},
"## Search Console - Get List of Properties": {
"main": [
[
{
"node": "Set fields - Create searchConsoleDataArray 2",
"type": "main",
"index": 0
}
]
]
},
"Set fields - Create searchConsoleDataArray": {
"main": [
[
{
"node": "Array aggregation - response to AI Agent",
"type": "main",
"index": 0
}
]
]
},
"Set fields - Create searchConsoleDataArray 2": {
"main": [
[
{
"node": "Array aggregation - response to AI Agent1",
"type": "main",
"index": 0
}
]
]
}
}
}Workflow n8n Google Search Console, OpenAI, data analysis : pour qui est ce workflow ?
Ce workflow s'adresse aux équipes marketing, aux analystes de données et aux responsables SEO des entreprises de taille moyenne à grande. Il est conçu pour ceux qui souhaitent automatiser l'analyse de leurs données de recherche et améliorer leur stratégie digitale. Un niveau technique intermédiaire est recommandé pour la mise en place et la personnalisation.
Workflow n8n Google Search Console, OpenAI, data analysis : problème résolu
Ce workflow résout le problème de l'accès rapide et efficace aux données de Google Search Console. Il élimine les frustrations liées à la recherche manuelle d'informations et réduit le temps consacré à l'analyse des performances. Grâce à l'automatisation, les utilisateurs peuvent obtenir des réponses instantanées à leurs questions, ce qui leur permet de prendre des décisions éclairées et de réagir rapidement aux changements dans leurs données de recherche.
Workflow n8n Google Search Console, OpenAI, data analysis : étapes du workflow
Étape 1 : Le workflow est déclenché par un Webhook qui reçoit les entrées de l'utilisateur.
- Étape 1 : Les données sont stockées dans la mémoire Postgres à l'aide du nœud 'Postgres Chat Memory'.
- Étape 2 : L'agent AI traite la requête en utilisant le modèle OpenAI.
- Étape 3 : Les données de Search Console sont récupérées via des appels HTTP, notamment pour obtenir des insights personnalisés.
- Étape 4 : Les résultats sont agrégés et renvoyés à l'utilisateur, offrant une réponse rapide et précise.
Workflow n8n Google Search Console, OpenAI, data analysis : guide de personnalisation
Pour personnaliser ce workflow, vous pouvez modifier l'URL du Webhook pour l'adapter à votre application. Assurez-vous que la table de mémoire Postgres est correctement configurée pour stocker vos données. Vous pouvez également ajuster les paramètres du modèle OpenAI pour affiner les réponses de l'agent AI. Pour intégrer d'autres outils, examinez les nœuds HTTP et ajustez les appels API en fonction de vos besoins spécifiques. Enfin, surveillez le flux en utilisant les outils de logging disponibles dans n8n pour assurer un fonctionnement optimal.