Webhook - ChatInput

This webhook serves as the endpoint for receiving ChatInput data. Ensure that you include:

• chatInput – the content you wish to send (😉)
• sessionId – a unique identifier for the session

If you're using an interface such as Open WebUI, the sessionId will be generated automatically.

Set fields

This node sets three fields:

• chatInput: retrieved from the previous webhook node
• sessionId: retrieved from the previous webhook node
• 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.

AI Agent - Tools Agent

This AI Agent is configured with a system prompt that instructs it to:

• On the first user message, retrieve available Search Console properties and offer the user the option to fetch data from these properties
• Based on the user’s natural language input, construct an API call to the selected Search Console property and retrieve the requested data
• Present the data in a markdown-formatted table

The AI Agent has a friendly tone and is designed to confirm the user’s data requirements accurately before executing any API requests.

AI Agent Sub-node - OpenAI Chat Model

This sub-node utilizes the selected OpenAI Chat Model. You can replace it with any LLM that supports tool calling.

⚠️ Choose Your Model

In 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.

AI Agent Sub-node - Postgres Chat Memory

Connect 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.

👋 Tip: If you don’t have a Postgres database, you can quickly set one up with Supabase.

AI Agent Sub-node - Call Search Console Tool

This tool is used by the AI Agent to:

• Retrieve the list of accessible properties in Search Console
• Fetch Search Console data based on the user’s natural language request

Respond to Webhook

This node is used to send a response back to the user.

👋 Tip: intermediateSteps are configured, allowing you to use raw data fetched from Search Console to create charts or other visualizations if desired.

Tool Call Trigger

This node is triggered when the AI Agent needs to retrieve the website_list (accessible Search Console properties) or custom_insights based on user data.

Set Fields - Construct API Call

This node configures fields based on the JSON sent by the AI agent:

• The request_type field determines the route: website_list (to retrieve the list of websites) or custom_insights (to get insights from Search Console)
• Additional fields are set to construct the API call, following the Search Console API Documentation

Search Console - Get Custom Insights

This node performs the API call to retrieve data from Search Console.

Search Console - Get List of Properties

This node performs the API call to retrieve the list of accessible properties from Search Console.

Set Fields - Create searchConsoleDataArray

These nodes create an array based on the response from the Search Console API.

Array Aggregation - Response to AI Agent

These nodes aggregate the array from the previous step and send it back to the AI Agent through the field named output as response.

AI Agent to Chat with Your Search Console Data

This 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.

Follow this n8n documentation to set up the OAuth credentials.

Important Notes

Correctly Configure Scopes for Search Console API Calls

• 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:

Of 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.

Configure Authentication for the Webhook

Since 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.

🤩💖 Example of awesome things you can do with this AI Agent

AI Agent to chat with you Search Console Data, using OpenAI and Postgres

Purpose and structure

This folder contains the AI Agent to chat with you Search Console Data, using OpenAI and Postgres workflow. Its executable nodes use agent, aggregate, executeWorkflowTrigger, httpRequest, lmChatOpenAi, memoryPostgresChat, respondToWebhook, set, switch, toolWorkflow, webhook. The table and connection map below are derived from the JSON, not an inferred guarantee of behavior.

Setup

Import inactive. Configure your own credentials and replace REPLACE_WITH_* values. Review external destinations and Code nodes before running.

Entry points

Webhook - ChatInput, Tool calling

Verification

Sanitized source candidate; not runtime-tested. This overview is generated from node metadata. Read the folder README for the connection map, credential types, and license.

Attribution

https://github.com/Zie619/n8n-workflows/blob/94007c1445d9258a7da116646b79473e7c7c3282/workflows/Webhook/1252_Webhook_Respondtowebhook_Automation_Webhook.json MIT; preserve source notices.