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This project demonstrates a reverse matching approach to recruitment — prioritizing technical potential and academic projects over years of experience.

What is RAG Recruitment Assistant?

The RAG Recruitment Assistant is an intelligent system that uses Retrieval-Augmented Generation (RAG) architecture to match candidates with opportunities based on semantic understanding rather than keyword filtering. Built with LangChain, FAISS, and Gemini 1.5 Flash, it analyzes student profiles to identify talent potential.

Quick Start

Get started in 5 minutes with Google Colab

Installation

Set up the environment locally

RAG Architecture

Understand the core architecture

Examples

See real-world usage examples

Key Features

Semantic Search

FAISS vector store enables similarity-based candidate discovery beyond keyword matching

LLM Reasoning

Gemini 1.5 Flash generates intelligent justifications for candidate recommendations

Structured Extraction

Pydantic models extract profile data with validated schemas

Reverse Matching

Prioritize projects and potential over years of experience

Batch Processing

Analyze multiple CVs simultaneously with parallel processing

Visualization

Interactive dashboards with Plotly for talent insights

Technology Stack

This project leverages cutting-edge AI and ML technologies:
  • LangChain — Framework for LLM application development
  • FAISS — Facebook’s vector similarity search library
  • Gemini 1.5 Flash — Google’s fast, efficient LLM
  • HuggingFace Embeddings — Sentence transformers for semantic encoding
  • Pydantic — Data validation and structured extraction
  • Plotly — Interactive visualizations

Use Cases

Identify promising student candidates based on academic projects, hackathon wins, and technical skills rather than work history.
Build searchable talent databases that surface candidates based on semantic similarity to job requirements.
Automate the first-pass screening of resumes with LLM-powered analysis and structured data extraction.
Understand the skills distribution across candidate pools to inform training and hiring strategies.

Getting Started

1

Install Dependencies

Set up Python 3.10+ and install required packages
pip install -r requirements.txt
2

Configure API Key

Set your Google API key for Gemini access
export GOOGLE_API_KEY="your_api_key"
3

Run the Notebook

Open Talent_Scout_3000x.ipynb in Google Colab or Jupyter and execute the cells

Need Help?

Check out our configuration guide for detailed setup instructions

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