Creating Advanced Data-Driven GPTs Without APIs: Using Decomposed URLs & Algorithmic Analysis

I'm excited to share my latest GPT discovery: crafting advanced, data-driven GPTs without relying on traditional APIs. This leap forward is achieved through the creative use of decomposed URLs and a code interpreter for intricate algorithmic analysis. As a practical showcase, I've created the Algorithmic Hotel Assistant.

Try it: https://chat.openai.com/g/g-utlPOlEHu-algorithmic-hotel-assistant

This tool is an ingenious amalgamation of Booking.com 's complex URL structures with the robust data processing power of ChatGPT Code Interpreter and Python's Pandas library, further enhanced by Bing search integration for comprehensive data acquisition.

What's truly exciting is that this approach isn't limited to hotel searches. Imagine applying this technology to job searches, creating a system that's finely tuned to match specific experiences and skills with relevant job opportunities.

Understanding and Utilizing Decomposed URL Structures

Websites like Booking.com employ complex URL structures filled with various parameters to display search results. By dissecting these URLs, it becomes possible to directly tweak parameters like dates, number of guests, and hotel features, thereby bypassing the need for an official API.

Dynamic URL Generation in Action

The Algorithmic Hotel Assistant dynamically crafts URLs based on user input. For instance, modifying the checkin and checkout parameters in the URL fetches results for specific dates. This process efficiently streamlines and refines the search experience.

Decomposing a Booking.com URL: An Example

https://www.booking.com/searchresults.html?label=gen173nr-1FCAQoggJCD3NlYXJjaF9uZXcgeW9ya0gzWARoJ4gBAZgBMbgBF8gBDNgBAegBAfgBA4gCAagCA7gChPHdqgbAAgHSAiQ5YWU3MDM3Zi1kMmQ3LTRlODAtOWM3ZS01MzZlMTQ0NWI3OWHYAgXgAgE&aid=304142&ss=New+York&ssne=New+York&ssne_untouched=New+York&lang=en-us&src=searchresults&dest_id=20088325&dest_type=city&checkin=2023-12-13&checkout=2023-12-16&group_adults=2&no_rooms=1&group_children=0&nflt=mealplan%3D1%3Bmealplan%3D999%3Bdistance%3D3000%3Bht_id%3D201%3Bfc%3D2%3Bclass%3D4%3Breview_score%3D80%3Bht_id%3D204%3Bdi%3D970%3BSustainablePropertyFilter%3D1%3Bclass%3D5%3Bpopular_activities%3D253%3Bpopular_activities%3D447%3Bprivacy_type%3D3%3Bmin_bathrooms%3D1%3Bpopular_nearby_landmarks%3D11045%3Btdb%3D3%3Bhotelfacility%3D3%3Bhotelfacility%3D16%3Bpopular_activities%3D11        

Breaking it down:

• Base URL: https://www.booking.com/searchresults.html
• Tracking and Identification Parameters:label, aid: Used for tracking and affiliate identification.
• Search Query: ss: 'New York', the search destination.ssne: 'New York', a variant of the search destination.lang: 'en-us', setting the language preference.
• Booking Details: checkin: '2023-12-13', and checkout: '2023-12-16', specifying stay dates.group_adults: '2', number of adults.no_rooms: '1', number of rooms.group_children: '0', number of children.
• Filters and Preferences: nflt: Contains multiple filters like meal plans, distance from a point, hotel type, facilities, class, review score, sustainability filter, activities, privacy type, minimum bathrooms, nearby landmarks, etc.

Dynamic URL Generation and Data Analysis

Using such a decomposed URL, the Algorithmic Hotel Assistant dynamically generates custom URLs based on user preferences. It then utilizes the bing search capability to extract hotel data, which is processed using Pandas in Python via the code interpreter using a data algorithm for scoring and ranking.

The Enhanced Scoring Algorithm

The assistant employs a scoring algorithm that normalizes price and reviews, calculates location and amenity scores, and combines these with user-defined weights:

# Normalization and Scoring
max_price = hotels["Price per Night ($)"].max()
hotels["Normalized Price"] = 1 - (hotels["Price per Night ($)"] / max_price)

max_reviews = hotels["Number of Reviews"].max()
hotels["Normalized Reviews"] = hotels["Number of Reviews"] / max_reviews

# Location and Amenity Scores
hotels["Location Score"] = calculate_location_score(hotels["Location"])
hotels["Amenity Score"] = calculate_amenity_score(hotels["Amenities"], user_preferences)

# Weighted Scoring and Sorting
hotels["Score"] = (rating_weight * hotels["Rating"]) + ...
hotels.sort_values("Score", ascending=False, inplace=True)        

Enhanced Scoring Algorithm: A Deeper Dive

Here's a closer look at how the scoring algorithm within the Algorithmic Hotel Assistant works:

1. Normalization of Price:max_price = hotels["Price per Night ($)"].max(): This line finds the highest price per night among all hotels in the dataset, setting a benchmark for comparison.hotels["Normalized Price"] = 1 - (hotels["Price per Night ($)"] / max_price): Each hotel's price per night is then normalized on a scale from 0 to 1. By subtracting this value from 1, we ensure that a lower price contributes to a higher normalized score, making it more desirable.
2. Normalization of Number of Reviews:max_reviews = hotels["Number of Reviews"].max(): Identifies the hotel with the maximum number of reviews, which is used for scaling the reviews of all hotels.hotels["Normalized Reviews"] = hotels["Number of Reviews"] / max_reviews: The number of reviews for each hotel is normalized against this maximum value. A higher number of reviews thus results in a higher normalized score, indicating popularity or reliability.
3. Calculating Location and Amenity Scores:hotels["Location Score"] = calculate_location_score(hotels["Location"]): The location score is computed based on the hotel's proximity to key points of interest or central areas. This function can consider factors like distance to city center, tourist attractions, or business districts.hotels["Amenity Score"] = calculate_amenity_score(hotels["Amenities"], user_preferences): This line evaluates how well the amenities offered by a hotel align with the user's preferences. It can consider factors like free WiFi, gym facilities, breakfast options, etc.
4. Weighted Scoring and Sorting:hotels["Score"] = (rating_weight * hotels["Rating"]) + ...: The final score for each hotel is a weighted sum of its rating, normalized price, normalized reviews, location score, and amenity score. The weights (like rating_weight) are adjustable, allowing for personalization based on user priorities.hotels.sort_values("Score", ascending=False, inplace=True): The hotels are then sorted based on their total scores, with higher scores indicating a better match to the user's preferences.

The development of the Algorithmic Hotel Assistant is a prime example of the transformative potential of algorithmic searches using ChatGPT across various areas. This tool, which skillfully merges the nuances of decomposed URLs with the power of data analytics, showcases a new era of personalized, efficient solutions for everyday applications, ranging from travel to job searching.

The integration of Bing search and the code interpreter further enhances this tool, creating a potent combination of algorithmic search and in-depth analysis. This synergy not only streamlines the search process but also brings a level of precision and personalization to the results that was previously unattainable. It's a glimpse into a future where advanced data-driven technologies simplify and enrich our daily decision-making processes.

?Update:

Here are the assistant instructions. https://gist.github.com/ruvnet/8bf97b88224d7005089884b96f5fd92f

??Bonus: Decomposing a Indeed Job Search

Each part of this URL contributes to fine-tuning the job search results on Indeed, allowing for a customized and targeted job hunting experience based on specific titles, salary expectations, locations, job types, and recency of postings.

Understanding and manipulating these parameters could enable a more personalized and efficient job search process, similar to how the Algorithmic Hotel Assistant operates for hotel bookings.

https://indeed.com/jobs?q=prompt+engineer+%24100%2C000&l=Oakville%2C+ON&sc=0kf%3Ajt%28fulltime%29%3B&fromage=3&lang=en&vjk=af62b4eb1c392f71        

• Base URL: https://ca.indeed.com/jobsThis is the primary entry point for job search queries on Indeed's Canadian site.

Parameters:

• q: 'prompt engineer $100,000'This is the main search query, indicating the job title or keywords ('prompt engineer') and a specific salary ('$100,000').
• l: 'Oakville, ON'Specifies the location of the job search, in this case, Oakville, Ontario.
• sc: '0kf:jt(fulltime);'This parameter seems to encode specific search configurations, possibly relating to job type, here indicating a full-time position.
• fromage: '3'Represents the age of the job listing in days. '3' means it's filtering for jobs posted within the last three days.
• lang: 'en'Sets the language for the search results, which is English in this case.
• vjk: 'af62b4eb1c392f71'This appears to be a unique identifier for a specific job listing or search session.