Job-Matchmaking-System

Using Agentic AI and RAG

Job Matchmaking System Using Agentic AI and RAG

Project Overview

The Job Matchmaking System is an intelligent, AI-driven platform designed to help job seekers find the most relevant job opportunities based on their CVs. By leveraging advanced technologies such as Retrieval-Augmented Generation (RAG), Agentic AI, CrewAI, and FAISS, this system offers personalized and highly accurate job recommendations. It extracts critical insights from users' CVs, scrapes job listings from various online portals, and matches them with the most suitable roles, improving the chances of job seekers landing their ideal job.

The project integrates multiple AI components to provide a comprehensive job-seeking solution that not only matches job postings with CV data but also offers personalized career advice and optimization suggestions.

Key Features

• CV Extraction: The system processes and extracts structured data (skills, experience, education, etc.) from the user’s CV in PDF format. This is done by using Natural Language Processing (NLP) models and custom-built parsing techniques.

• Job Scraping: The system scrapes job listings from various online platforms using web scraping techniques. This enables the system to gather up-to-date job data in real time, ensuring that the recommendations are always relevant.

• Job Matching Using RAG: Retrieval-Augmented Generation (RAG) is used to enhance the job matching process. By using embeddings to convert job descriptions and CVs into vector representations, the system matches the most relevant job postings with the user’s CV data, improving both the accuracy and relevance of recommendations.

• FAISS for Efficient Search: FAISS (Facebook AI Similarity Search) is used to perform fast, efficient similarity searches across large job datasets. It helps the system find the most relevant job listings based on user-provided CV data.

• Career Advice: Once job matches are found, the system provides personalized career advice, offering insights on skill gaps, suggestions for CV optimization, and other actionable tips to help users improve their chances of securing job opportunities.

Technologies Used

• Agentic AI: Used for building intelligent, autonomous agents that can make decisions, perform tasks, and interact with other components within the system. This powers the core logic of the matchmaking and recommendation processes.

• CrewAI: Orchestrates the interactions between different AI agents in the system, managing tasks such as CV parsing, job scraping, matching, and advice generation.

• Retrieval-Augmented Generation (RAG): A hybrid AI model that combines information retrieval and generative AI, enabling the system to generate responses based on retrieved data. It is used to match job listings with user CV data and generate meaningful career advice.

• FAISS: A library for efficient similarity search and clustering of high-dimensional vectors. FAISS is used to search for the most relevant job postings based on the embeddings of CV data and job descriptions.

• LangChain: An open-source framework for building applications using language models. LangChain is used to manage the flow of information between language models and agents within the system.

• Embeddings: The transformation of text (CVs and job descriptions) into numerical vector representations, allowing the system to perform similarity matching based on semantic meaning rather than just keyword matching.

• Python: The primary programming language used for building the system, integrating various libraries and frameworks.

How It Works

1. CV Extraction

Users upload their CVs in PDF format to the system. The CV Extraction Agent processes the PDF, extracting key information such as:

• Personal details (e.g., name, contact information)
• Skills (e.g., programming languages, certifications)
• Work experience (e.g., previous roles, achievements)
• Education (e.g., degrees, institutions)
• Other relevant details (e.g., languages spoken, projects)

The extracted data is structured and stored in a machine-readable format, ready for matching with job listings.

2. Job Scraping

The system scrapes job listings from multiple online job portals using web scraping tools. These job postings include details such as:

• Job title
• Company name
• Job description
• Required skills
• Location
• Salary (if available)

The scraping agents are designed to collect job postings in real-time, ensuring the system has access to the most up-to-date job opportunities.

3. Job Matching Using RAG

Once the CV data and job listings are available, the Job Matching Agent uses RAG and FAISS to find the most relevant job postings. Here’s how it works:

• The CV and job descriptions are converted into embeddings (vector representations).
• FAISS is used to perform similarity search, matching the user's CV to job descriptions based on the semantic similarity of their embeddings.
• RAG is used to generate recommendations by retrieving relevant job descriptions and generating an accurate match based on the user's qualifications and the job's requirements.

4. Career Advice Generation

After job matching, the system provides personalized Career Advice. This can include:

• Recommendations for skill development (e.g., learning new technologies, certifications).
• Suggestions for optimizing the CV based on the requirements of the matched job postings.
• Career path suggestions based on the current job market trends and the user’s experience.

5. User Interface

Users interact with the system via a simple interface, where they can:

• Upload their CV.
• View matched job listings.
• Receive personalized career advice and recommendations.

The system is designed to be user-friendly, providing actionable insights and suggestions in a way that’s easy to understand.