Here’s how it works:
1. HTML Fetching with ScrapingBee: We use ScrapingBee to fetch the HTML content from the target webpage. The tool handles dynamic content by rendering JavaScript, which ensures we get a fully loaded page.
2. Custom HTML Cleaning for Efficiency: After retrieving the HTML, InstantAPI.ai cleans the content internally to remove non-essential elements like scripts, styles, and metadata. This cleaning process not only helps to focus on relevant data but also increases both speed and cost/token efficiency when processing the content further.
3. OpenAI API for Structuring Data: The tool leverages OpenAI to interpret the cleaned HTML content and format it into a structured API response. It identifies the implied API method, processes any relevant parameters, and ensures the output aligns with the user's requirements.
4. Customizable Parameters: Users can specify details like API method names, response structure, and even the country code for web requests, offering a high degree of flexibility.
5. Built-in Error Handling: We've included error handling mechanisms to ensure reliable operation, making it robust for various use cases.
6. I also built a no-code solution which integrates the API through Google Sheets. This will be released over the next week.
The tool is built using a combination of custom functions and libraries to handle everything from sanitizing inputs to managing complex API interactions. One of the challenges we focused on was optimizing the processing speed and reducing costs, which we managed by refining our internal HTML cleaning process.
For those interested in seeing it in action, I've created a demo video: https://www.loom.com/share/9691221bb05347a298cc36b1bd8e0c8b?sid=2e5d70e6-ddbb-4f5f-9b84-3fab6963e885
I’d love to get feedback from the HN community, especially on how we can improve the tool’s accuracy and functionality.
