You basically need to tell Gemini to perform pagination with some notion of "max entries per json string"and specify some delimiter token you can use to find json string boundaries, split the response on your specified delimiter, and attempt to decode each section and merge them until you find something invalid within the Gemini context window.

Gemini can't fully count as it generates but it will be approximately close and allow the API to at least return a valid and useable response even if it's not literally every product on the page

Hello Brother again,

Just Reviewed your project codebase, you have just given the html content to gemini pro model, this ain't going to work in large scale web scraping, AI(No matters which LLM you use paid or free, None of them is capable of performing complex web scraping of its own, scraping is not a straight forward Software Engineering Task, its quite complicated) is not that much capable as of now that it can parse complicated information out of HTML, it can definitely work for such websites that have clear DOM structure, to be exact, those website which have clear names in div classes such as product_description/description, product_title/title, product_price/price, but for large scale web scraping and sophisticated kind of scraping you have to write a generic web scraping code written in core python.

You have to understand that any complex web scraping system have these components :

1.Loading the Web Page ::
For this, one can use Dynamic Browser such as Selenium, Bright Data Browser and other private Browsers, Playwright or directly using raw http libraries "requests", "urllib3", "httpx" and the holy "curl" cli.

1. Captcha Bypass (If strict rate limiting in place) :

2. Possible Workarounds :
   2.1. Use High Availability Proxies such as Bright Data Proxies, I have used them in one of my large scale web scraping projects and they are really good.
   2.2. Bypass captcha's using Specifically Crafted Scripts for common captcha's such as cloud flare, Imperva, Google ReCaptcha(v1, v2, v3, v4), GeeTest Slide Captcha(v1 to v4) and recent Puzzle Pieces Based Captcha's and last those move objects according to static image direction.
   2.3. Solve using Captcha's Solving Paid APIs.

3. Parse the HTML Source and get the required Data:
   One can parse the html source using standing bs4 library and as well as lxml library for parsing the lxml based web pages, for example parsing robots.txt

This parsing can be done using two methods :

1. Static Parsing
2. Dynamic Parsing

For Specific Websites Scrapers, One can use static Parsing Techniques where sections are pre defined which one have to extract with defined classes names, html tag names that may or may not change over time.

For Generic Use Cases Websites,One must use dynamic parsing techniques which implements the "parents-childs-sibbling" relationship based scraping approach.

So, in this scenario, One must have to write Different Sections Specific Generic Code using the dynamic parsing approach, it won't be done using few lines of python code this type of generic API requires several months of dedicated coding over different market Domains..

then only one can develop truly Generic Crawl API.

1. Scraping Logic for Handling Different Kind of Paginations Such as simple Num Clicks Based Pagination(for e.g., 1-> 2->3->4...Max Page), Infinite Scrolling Pagination Scraping Logic, Load More Button Click Kind Pagination Logic.

***Note : Large Scale Web Scraping i.e., Real World Web Scraping Scenario's often requires Speedy Execution, if one always going to use the dynamic Browser for every page loading, its going to take weeks to scrape even Millions Pages, so one must know when to use the Dynamic Web browsers & when to use raw HTTP based Browsing Capabilities.

I hope it will help you to see the big picture behind large scale web scraping systems and anybody who will be reading this huge comment will learn real world scraping key aspects.

Any more clarification, if you need, you can let me know.

Here is the snapshot of your code method:

Thanks & Regards
Tanuj Sharma