CareerGPT AI-Powered Career Platform Development by Mohid NaghmanCareerGPT AI-Powered Career Platform Development by Mohid Naghman

CareerGPT AI-Powered Career Platform Development

Mohid Naghman

Completed work

AI Agent Developer

AI Engineer

Streamlit

Human Resources

🚀 CareerGPT - Your AI-Powered Career Intelligence Platform

CareerGPT is a sophisticated, multi-agent AI assistant designed to provide comprehensive career guidance. Built with a stateful LangGraph backend and a modern Streamlit frontend, this platform helps users explore careers, create personalized learning paths, receive in-depth resume analysis, and search for real-time job openings.

Demo: (#1 (comment))

✨ Core Features

🧠 Intelligent Agentic Workflow: A robust "hub and spoke" design using LangGraph. A central supervisor agent intelligently routes user requests to specialized agents for different tasks.

🗣️ Stateful & Context-Aware Conversations: The application remembers context across turns, enabling advanced features like follow-up questions about an analyzed resume.

📚 RAG-Powered Knowledge Base: The CareerAdvisor agent uses a local FAISS vector store to provide accurate, context-aware answers to general career questions.

🔎 Real-Time Job Search: The JobSearch agent integrates with the Tavily Search API to find, analyze, and summarize live job postings from the internet.

📄 Interactive Resume Coaching: Upload a resume for a detailed analysis, then ask specific questions and get contextual answers from the ResumeQAAgent.

✨ Polished, Modern UI: A responsive and intuitive user interface built with Streamlit and a custom, professional theme.

🛠️ Tech Stack

Category Technology / Service Frontend Streamlit Backend Python, LangChain, LangGraph LLMs Groq (Llama 3), Google AI (Gemini for Embeddings) Vector DB FAISS (Facebook AI Similarity Search) Tools Tavily Search API

📂 Project Structure

/CareerGPT/
├── agents/
│   ├── __init__.py
│   └── chains.py             # Logic for each agent's "brain"
├── data/
│   └── career_docs/          # Source PDFs for the RAG knowledge base
├── static/
│   └── style.css             # External CSS for the UI
├── utils/
│   ├── __init__.py
│   └── file_parser.py        # PDF and DOCX parsing utility
├── .env                      # Your secret API keys (Ignored by Git)
├── .gitignore                # Specifies files/folders for Git to ignore
├── Graph_backend.py          # The core LangGraph agentic workflow
├── requirements.txt          # Project dependencies
├── setup.py                  # One-time script to build the vector store
├── streamlit_ui.py           # The main Streamlit application file
└── README.md                 # You are here!

⚙️ Setup and Installation

Follow these steps to get CareerGPT running on your local machine.

1. Clone the Repository

git clone https://github.com/your-username/CareerGPT.git
cd CareerGPT

2. Create and Activate a Virtual Environment

# Create the environment
python -m venv venv

# Activate it
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate

3. Install Dependencies

Install all required packages using the requirements.txt file.

pip install -r requirements.txt

4. Configure Environment Variables

You will need API keys from the following services:

Groq (for fast LLM inference)

Google AI Studio (for text embeddings)

Tavily AI (for the job search tool)

Create a file named .env in the project root. This file is included in .gitignore to keep your keys secure.

GROQ_API_KEY="your_groq_api_key_here"
GOOGLE_API_KEY="your_google_api_key_here"
TAVILY_API_KEY="your_tavily_api_key_here"

5. Build the Vector Store

Before running the app, you need to build the local FAISS index from the documents in /data/career_docs/.

python setup.py

This will create a faiss_index folder in your project root.

6. Run the Application

You're all set! Launch the Streamlit app:

streamlit run streamlit_ui.py

Navigate to http://localhost:8501 in your browser.

🚀 Future Enhancements

This project has a strong foundation with many possibilities for future development:

Interview Coach Agent: A new agent to conduct mock interviews and provide feedback.

Persistent User Sessions: Integrate a database (like SQLite) to save and load conversations.

Advanced RAG: Implement more sophisticated retrieval strategies like re-ranking.

Long-Term Memory: Use a vector store to give the chatbot long-term memory about the user's career goals and progress.

Like this project

Completed work

Posted Aug 2, 2025

Developed CareerGPT, an AI platform for career guidance using LangGraph and Streamlit.

Likes

Views