ScoutifyAI - AI-Powered Personalized Job Search Agent

ScoutifyAI is an advanced AI-driven job search agent designed to revolutionize how you find career opportunities. By leveraging Large Language Models (LLMs) and vector search, ScoutifyAI provides hyper-personalized job recommendations, deep skill analysis, and actionable career insights tailored to your unique profile.

Key Features

• Intelligent Resume Analysis: ScoutifyAI extracts key technical skills and identifies the most suitable job titles from your uploaded resumes.
• Hyper-Personalized Search: Fetches job listings from global APIs and filters them using sophisticated AI logic based on your specific roles, skills, and preferences.
• Semantic Matching: Uses LLM-generated embeddings and Pinecone vector search to find jobs that truly match your profile beyond simple keywords.
• Deep Fit Analysis: Provides comprehensive job-fit scores and identifies specific skill gaps between your profile and job requirements.
• Actionable Career Insights: Aggregates skill gaps across searches to highlight high-impact areas for professional development.
• Interactive Dashboard: A Streamlit-based UI for user interaction, managing jobs profiles, and viewing results.

Tech Stack

• Backend: Python, FastAPI (Asynchronous)
• Frontend: Streamlit
• AI/ML:
  • OpenAI API (for LLM tasks like query generation, analysis, embeddings)
  • Pinecone (Vector Database for semantic search)
• Database: Supabase (PostgreSQL for user data, job details, search history)
• External Data: RapidAPI (LinkedIn Job Search API)
• Containerization: Docker

Getting Started

Follow these steps to set up and run the application locally:

1. Prerequisites

• Python 3.8+
• Pinecone Account
• Supabase Account
• OpenAI API Key
• RapidAPI Key (Subscription to "LinkedIn Job Search API" required)

2. Configuration

Copy the .env.example file to create your own .env file and fill in your API keys:

cp .env.example .env

Important

Pinecone Setup: Ensure you create a Pinecone index named job-search-tool with a namespace named job-list.

3. Database Setup (Supabase)

Run the setup_supabase.sql script in your Supabase SQL Editor to create the necessary tables and seed the default user.

4. Running with Docker Compose (Recommended)

You can run the entire stack with a single command:

docker compose up --build

This will start both services:

• Backend: http://localhost:8000
• Frontend: http://localhost:8501

Usage (Overview)

1. Open the Streamlit interface at http://localhost:8501.
2. Upload Resume: Go to the Upload page to process your PDF resume.
3. Search Jobs: Define job preferences (roles, skills, location). Support for voice input is available.
4. Analyze Results: Review matched jobs, AI-generated match percentages, and skill gap insights.
5. Career Insights: View aggregated trends from your searches to see where to focus your learning.