Building a Web Scraper with NCP Protocol for N8N Workflows

Web scraping automation has become an essential tool for data collection and analysis. This article explores how to create a powerful web scraper using the NCP protocol and integrate it seamlessly into N8N workflows.

The potential applications for this web scraping solution are vast, from extracting real estate listings to comparing product prices across e-commerce platforms. Let’s dive into how to build this powerful tool step by step.

Setting Up the Project Environment

To begin, you’ll need to set up your project environment using UB, a tool that helps manage dependencies and project structures. If you don’t already have it installed, you can download it via your terminal.

Start by creating a new project folder and initializing your virtual environment with the following steps:

1. Create your project folder
2. Open your terminal within the project directory
3. Create a virtual environment
4. Install the necessary dependencies, including the LCP protocol

Your project structure should include a source folder containing the majority of your code, and a ChioMain file that serves as the entry point for your server.

Setting Up API Authentication

The next step involves configuring your OpenAI API key in the ChioMain file. This is critical for the LLM functionality that will process and structure your scraped data. For security reasons, add your ChioMain file to .gitignore to prevent exposing your API key in public repositories.

Building the Data Structure

Before writing the scraper itself, define the output format for your scraped data. This example uses a Pydantic model to validate and structure the data, ensuring proper organization and type checking:

The Pydantic model specifies the columns or relevant data points you want to extract from your target web page, providing a structured framework for your output.

Creating the Scraper Tool

The core of the project is the Scraper Tool class, which initializes a browser instance with specific configurations to avoid detection as a bot. The class includes several key functions:

1. A function to retrieve HTML data from the target website
2. A filtering function that extracts only the relevant items from the page
3. An LLM function that structures the extracted HTML into your desired format
4. A cleanup function that ensures resources are properly released

The scraper uses a targeted approach to minimize token usage by filtering the HTML before sending it to the language model. This improves both performance and cost-efficiency.

Implementing the Main Server

The main server implements the NCP protocol to expose your scraper as a tool that can be called from other applications (like N8N). The server defines functions that accept parameters like:

• The query (e.g., location for real estate searches)
• Instructions for the AI model on how to process the data

These parameters are then passed to the web scraper function, which processes the request and returns structured data.

Exposing Your Server to the Internet

To make your scraper accessible to N8N, you’ll need to expose your local server to the internet. This can be done using ngrok, which creates a secure tunnel to your local port (8000 in this example).

Once exposed, you’ll receive a URL that can be used to configure your N8N workflow.

Configuring the N8N Workflow

In N8N, set up a workflow that utilizes your scraper as follows:

1. Add the NCP Client node
2. Configure it with your ngrok URL, adding the ‘/sse’ endpoint
3. Set up the message port as ‘/message’

Your tool should be automatically detected by N8N. The workflow can then be triggered by a prompt that specifies what data to scrape (such as apartments in a specific city).

Processing and Displaying Results

The final step involves processing the scraped data and displaying it in a useful format. In this example, the workflow includes a code block that parses the formatted data and loads it into Google Sheets for easy viewing and analysis.

When executed, the workflow sends a request to the scraper, which extracts the data, processes it through the LLM, and returns structured information that can be easily visualized and analyzed.

Practical Applications

This web scraping solution has numerous practical applications:

• Real estate market research
• Price comparison across e-commerce platforms
• Job listing aggregation
• News and content monitoring
• Travel deal tracking

By combining the power of web scraping with the flexibility of N8N workflows and the intelligence of LLMs, you can create sophisticated data collection systems that automate research and analysis tasks.

Conclusion

Building a web scraper with the NCP protocol and integrating it with N8N provides a powerful tool for automated data collection and processing. The combination of web scraping, workflow automation, and AI processing creates a versatile solution that can be adapted to various business needs and use cases.

By following the steps outlined above, you can create a robust web scraping system that extracts, processes, and presents data in a structured and useful format, enabling more informed decision-making and analysis.