NeuralQuery

Intelligent Search & Filtering Engine built with Next.js 16 and Supabase.

View Live Demo

Features

• Intelligent Search: Keyword search across title, description, and company using PostgreSQL pg_trgm for fuzzy matching.
• Advanced Filtering: Combine Location, Experience, and Salary range filters seamlessly.
• Dynamic Sorting: Sort by Salary or Date (Newest first).
• Pagination: Efficient server-side pagination.
• Deep Linking: All search states (query, filters, sort, page) are synced to the URL, enabling easy sharing and navigation history.
• Optimization: Debounced search inputs to reduce database load.
• Rate Limiting: IP-based rate limiting to prevent abuse.

Loading Skeleton to make it UX friendly

Tech Stack

• Frontend: Next.js 16 (App Router), React 19, Tailwind CSS.
• Backend: Next.js Server Actions / API Routes.
• Database: Supabase (PostgreSQL).
• Styling: Tailwind CSS + Lucide Icons.

Setup Instructions

1. Clone and Install

   git clone <repo-url>
   cd neuralquery
   npm install

2. Supabase Setup

   • Create a new project on Supabase.
   • Go to the SQL Editor in your Supabase dashboard.
   • Copy the contents of supabase/schema.sql and run it. This will:
     • Enable pg_trgm extension.
     • Create the jobs table.
     • Enable Row Level Security (RLS).
     • Create optimized indexes.
     • Seed the database with sample data.

3. Environment Variables

   • Copy .env.example to .env.local
   • Fill in your Supabase URL and Anon Key.

   cp .env.example .env.local

4. Run Development Server

   npm run dev

   Open http://localhost:3000.

API Endpoints

Create Record

POST /api/records

Request Body:

{
  "title": "Software Engineer",
  "description": "Develop web applications",
  "company": "Tech Corp",
  "location": "Remote",
  "experience": 3,
  "salary": 120000
}

List & Search Records

GET /api/records

Query Parameters:

• q: Search query
• location: Filter by location
• minExperience, maxExperience: Filter by years of experience
• minSalary, maxSalary: Filter by salary range
• sortBy: created_at (default) or salary
• order: asc or desc (default)
• page: Page number (default 1)
• limit: Items per page (default 10)

Architecture & Logic

Search Logic

The core search logic resides in lib/services/jobs.ts. It constructs a dynamic Supabase query based on provided parameters:

• Keyword Search: Uses OR logic combined with ilike operators on title, description, and company columns. (Note: For large scale, tsvector Full Text Search would be the next upgrade).
• Filtering: Applies AND filters for accurate drill-down.
• Performance:
  • GIN Indexes (gin_trgm_ops) are used on text columns for fast pattern matching.
  • B-Tree Indexes are used on numeric/date columns for range queries and sorting.

Project Structure

• app/: Next.js App Router pages and API routes.
• components/: Reusable UI components (Client components for interactivity).
• lib/: Shared types and business logic (Service layer).
• utils/: Database client utilities.

🚧 Future Improvements & Limitations

• Authentication: Usage of Supabase Auth is potential next step. Currently, the application is open for demonstration purposes (anyone can post jobs). In a production environment, Row Level Security (RLS) policies would be restricted to authenticated users.
• API Security: The API endpoints (POST /api/records) currently have no authentication or rate limiting beyond basic IP-based throttle. production use would require API keys or OAuth.
• Full Text Search: Migrate to tsvector for scalable full-text search.
• Analytics: Dashboard for job view counts and application tracking.