Google Autocomplete Keyword Scraper

This n8n workflow demonstrates a basic structure for interacting with external APIs, processing data in batches, and responding to a chat-based trigger. While the provided JSON defines a foundational framework, this specific configuration is ready to be extended for tasks like scraping Google Autocomplete suggestions.

What it does

This workflow outlines the following steps:

1. Listens for Chat Messages: The workflow is initiated by a Chat Trigger node, indicating it's designed to respond to incoming chat messages or commands.
2. Processes Items in Batches: It includes a Loop Over Items (Split in Batches) node, suggesting that subsequent operations will handle data in manageable chunks, which is useful for API rate limits or large datasets.
3. Introduces a Delay: A Wait node is present, allowing for a configurable pause between operations. This is crucial when dealing with APIs that have rate limits or when you need to simulate human interaction.
4. Performs an HTTP Request: An HTTP Request node is included, which is the core component for making calls to external web services or APIs. This is where the logic for fetching Google Autocomplete suggestions would be configured.
5. Executes Custom Code: A Code node is available for running custom JavaScript. This can be used for data transformation, advanced logic, or preparing data before or after an API call.
6. Responds to the Webhook: A Respond to Webhook node is configured, indicating that the workflow is designed to send a response back to the system that triggered it (likely the chat platform).

Prerequisites/Requirements

• n8n Instance: A running instance of n8n.
• Chat Platform Integration: Depending on the specific chat platform you intend to use (e.g., Slack, Telegram, Discord), you will need to configure the Chat Trigger node with the appropriate credentials and webhook URLs.
• API Endpoint: If you intend to use this for Google Autocomplete, you will need to identify and configure the correct Google Autocomplete API endpoint within the HTTP Request node. No specific API key is explicitly required by the node definition itself, but the target API might require one.

Setup/Usage

1. Import the Workflow:
   • Download the provided JSON.
   • In your n8n instance, go to "Workflows" and click "New".
   • Click the three dots menu (...) in the top right and select "Import from JSON".
   • Paste the JSON content or upload the file.
2. Configure the Chat Trigger:
   • Open the "When chat message received" (Chat Trigger) node.
   • Configure it to listen to your desired chat platform and channel. This typically involves setting up a webhook URL in your chat platform and pasting it into the n8n node, or configuring API credentials.
3. Configure the HTTP Request:
   • Open the "HTTP Request" node.
   • Set the URL to the Google Autocomplete API endpoint (e.g., https://suggestqueries.google.com/complete/search?client=chrome&q={{ $json.query }}).
   • Configure the Method (likely GET).
   • Adjust any Headers or Query Parameters as needed, using expressions to dynamically insert the search query from the incoming chat message (e.g., q: {{ $json.query }}).
4. Adjust Loop and Wait (Optional):
   • Modify the "Loop Over Items" node's batch size if you expect to process many items.
   • Adjust the "Wait" node's duration to control the delay between API calls, especially to respect rate limits.
5. Implement Custom Logic (Optional):
   • Use the "Code" node to transform the API response, extract specific suggestions, or filter results before responding.
6. Configure Respond to Webhook:
   • Open the "Respond to Webhook" node.
   • Set the Response Mode and Body to format the output you want to send back to the chat platform (e.g., a list of autocomplete suggestions).
7. Activate the Workflow:
   • Once configured, activate the workflow by toggling the "Active" switch in the top right corner of the workflow editor.

Now, when a message is received by the configured chat trigger, the workflow will execute, make an HTTP request, and respond accordingly.