Grocer

This project is a conceptual design for a Next.js application that automatically aggregates nearby grocery store ads, extracts sales information with a language model, stores the data in Firebase, and highlights deals that are cheaper than typical online prices.

Architecture Overview

1. Next.js Web Application

   • Provides the user-facing interface with React components and server-side rendering.
   • Utilizes Next.js API routes or serverless functions to handle backend logic.

2. Ad Retrieval Service

   • Scheduled process (e.g., cron job or serverless function) that fetches digital or PDF ads from local grocery stores.
   • Location is determined from user preferences or IP geolocation to pull nearby stores only.

3. LLM Processing

   • Ads are fed into a large language model (via an external API such as OpenAI) to parse product names, quantities, and sale prices.
   • Extracted data is normalized into a structured format.

4. Firebase Firestore Database

   • Stores parsed sale information including store, product, price, and sale dates.
   • Facilitates real-time updates and synchronization for the Next.js client.

5. Price Comparison Service

   • Compares extracted sale prices to known online prices using third-party APIs or web scraping.
   • Determines which items are significantly cheaper locally and labels them as "worth buying."

6. Deal Recommendation Engine

   • Uses business logic (e.g., historical prices or price drop percentage) to highlight top deals.
   • Results are surfaced in the user interface with filtering and sorting options.

Key Features

• Automatic Ad Aggregation: Detects and downloads weekly ads from nearby grocery stores without user intervention.
• LLM-Powered Extraction: Leverages a language model to parse unstructured ad content into structured product entries.
• Firebase Integration: Stores and syncs deals across user sessions with secure authentication.
• Deal Finder: Compares local sale prices with online retailers to recommend cost-effective purchases.
• Responsive Web UI: Built with Next.js and React for a modern, mobile-friendly experience.

Future Enhancements

• Push notifications for newly detected deals.
• Budget tracking and shopping list integration.
• User feedback loop for improving extraction accuracy.

Development Plan

The following high-level roadmap outlines the steps required to implement the project. Each stage should include code reviews, automated tests, and clear documentation to meet professional engineering standards.

1. Project Setup

   • Initialize a Next.js application using TypeScript (create-next-app with the --typescript flag).
   • Configure ESLint and Prettier for consistent code style. Enforce formatting with a pre-commit hook via Husky.
   • Establish a monorepo or organized folder structure for "web" and serverless functions if needed.
   • Set up a GitHub Actions workflow (or similar CI) to run linting and tests on each pull request.

2. Core Services

   • Ad Retrieval Service: Build a module that periodically downloads ads from nearby stores. Start with open APIs or screen-scraping using libraries like Cheerio. Abstract network calls so they can be mocked in tests.
   • LLM Processing: Integrate with a language-model API. Create a thin wrapper that handles retries, logging, and cost monitoring. Unit-test the parsing logic using sample ad content.
   • Database Layer: Set up Firebase Firestore with strict security rules. Define TypeScript interfaces for stored objects and centralize all database interactions in a dedicated module.

3. Backend Logic

   • Implement Next.js API routes (or serverless functions) that coordinate ad retrieval, invoke the LLM, and write parsed data to Firestore.
   • Add a price comparison utility that looks up online prices from third-party APIs. Plan for rate limiting and potential API failures.
   • Develop a recommendation engine that scores each item based on price savings and user preferences.

4. Frontend Features

   • Build authentication flows using Firebase Auth. Protect sensitive routes and ensure users can manage their profile data.
   • Create responsive pages to display deals, using React components with TypeScript props and state. Provide filters for store, category, and price.
   • Surface "worth buying" recommendations with clear explanations of the savings.

5. Testing and Quality Assurance

   • Write unit tests with Jest and integration tests with React Testing Library or Cypress for critical user flows.
   • Use mock services for the LLM and external price APIs to keep tests fast and deterministic.
   • Maintain high test coverage and enforce it through the CI pipeline.

6. Deployment and Monitoring

   • Deploy the Next.js application to Vercel or a similar platform, storing API keys and Firebase credentials in environment variables.
   • Set up logging and error reporting (e.g., using Sentry) for serverless functions and client-side code.
   • Monitor Firestore usage and LLM costs, adding alerts for unusual spikes.

7. Iteration and Future Work

   • After the MVP is stable, implement the features listed in the "Future Enhancements" section.
   • Continuously refine extraction accuracy and recommendation rules based on real user feedback and metrics.