📄 SmartHire CV with RAG | AI-Powered Resume Analysis for Recruiters

📖 Introduction

SmartHire CV with RAG is a next-gen AI tool that simplifies and accelerates resume screening. Upload a PDF CV and instantly extract structured data, generate summaries, and ask custom questions — all powered by a cutting-edge Retrieval-Augmented Generation pipeline with AWS Bedrock embeddings, MongoDB vector search, and GPT-3.5.

Whether you’re a recruiter, HR manager, or talent specialist, SmartHire CV lets you assess candidates in seconds — without losing the context of the original CV. 🤖📄

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🚀 Features

✔️ One-Click Summary Table – Auto-extracts Name, Role, Education, Experience, Skills, Certifications ✔️ RAG-Powered Q\&A – Ask questions like “What tech stacks?” or “Would they fit a Product Owner role?” ✔️ AWS Bedrock Embeddings – Uses Titan-embed-text v2 for accurate semantic search ✔️ MongoDB Atlas $vectorSearch – High-speed vector retrieval at scale ✔️ Concise GPT Responses – Prompts begin with “Please answer concisely…” to ensure brief, focused output ✔️ Multi-CV Management – Upload, index, choose, and delete multiple resumes ✔️ Streamlit Web UI – Clean, no-code interface for non-technical users

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🏗️ Technologies

• 🐍 Python 3.12 – Backend and orchestration
• 🌐 Streamlit – Lightweight frontend
• 🔍 LangChain – RAG pipeline management
• 🧠 OpenAI GPT-3.5 – LLM for Q\&A and summarization
• 🧆 AWS Bedrock – Embedding via Titan model
• 📂 MongoDB Atlas – Vector DB for resume chunks
• 📄 PyMuPDF (fitz) – PDF parsing and text extraction
• 🔐 python-dotenv – Environment variable handling

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📦 Installation

1️⃣ Clone the Repository

git clone https://github.com/Yacine-Mekideche/cv-smart-hire.git
cd cv-smart-hire

2️⃣ Create a .env File

OPENAI_API_KEY=your_openai_api_key
MONGO_URI=your_mongodb_connection_string
AWS_PROFILE=your_aws_profile
AWS_REGION=your_aws_region

3️⃣ Set Up Your Environment

python -m venv venv
# Activate:
venv\Scripts\activate        # Windows
source venv/bin/activate     # macOS/Linux

4️⃣ Install Dependencies

pip install -r requirements.txt

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▶️ Running the App

streamlit run app.py

Once launched in your browser, you can:

• 📄 Upload one or more PDF resumes
• ⚙️ Click “Index CV” to generate embeddings and store in MongoDB
• 📋 Select a CV and click “Generate Full Profile”
• 🗨️ Ask free-form questions in the Chat with CV panel

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🎯 Demo

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🧠 AI Architecture Overview

PDF Resume Upload
       ↓
Parsing & Chunking (PyMuPDF)
       ↓
Embeddings
 • AWS Bedrock (Titan-embed-text v2)
 • OpenAI (fallback)
       ↓
Vector Store (MongoDB Atlas)
       ↓
RAG Pipeline (LangChain)
       ↓
GPT-3.5 Inference
       ↓
Streamlit UI (Summary + Chat)

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📬 Contact Me

💡 Transform your hiring pipeline with AI-powered CV insights.

📩 Business inquiries: contact@iacine.tech

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