Welcome to langgraph-AI-interview-agent Discussions!

[zzzlip]

📢 Multimodal Interview Assessment Agent: An AI-Powered Interview System Based on Large Models and Agents
I'm excited to introduce my open-source project: the Multimodal Interview Assessment Agent. This is a comprehensive recruitment and interview assistance system built on the powerful langgraph and llamaindex frameworks.

My goal with this project is to leverage the capabilities of Large Models and Agents to automate and intelligentize the often-tedious job interview process. By doing so, I hope to genuinely help students and job seekers improve their interview performance and increase their success rate. This project is also a question for the "China Software Cup" iFLYTEK A3 competition.

🔗 GitHub Repository:

https://github.com/zzzlip/langgraph-AI-interview-agent

✨ What Problem Does This Project Solve?
The job search journey is filled with challenges, from struggling with resume writing to lacking quantitative feedback on interview performance and targeted practice. This project aims to provide a one-stop solution with the following core features:
Intelligent Resume Assessment & Optimization: Evaluates your resume across five key dimensions: Future Potential, Educational Background, Tech Stack Match, Experience Match, and Resume Structure. It generates an intuitive radar chart and a detailed report with actionable optimization suggestions.
Simulated Interviews & In-depth Training: Utilizes RAG to build a knowledge base of interview questions from major tech companies. Based on your resume and target job, it intelligently generates questions covering Technical Fundamentals, Project Deep Dives, Business Acumen, and Soft Skills.
Algorithmic Skills Testing: Integrates with the Codeforces platform to select relevant coding challenges based on job requirements and automatically verifies your submission status.
Full-Cycle Mock Interviews: Simulates the entire interview pipeline, from resume screening and coding tests to technical interviews (supporting multiple rounds). It provides a comprehensive analysis of your answer quality, body language, and vocal tone, complete with a detailed feedback report.
🚀 Technical Highlights
Advanced Agent Framework: Built with langgraph, utilizing its unique features like concurrent state graphs, human-in-the-loop interaction, and cycles to create a complex and efficient agent workflow.
Multi-Model Integration: Seamlessly integrates several powerful LLMs, including Google Gemini, DeepSeek, and Qwen.
RAG Strategy: Implements a Retrieval-Augmented Generation strategy with a custom knowledge base, using techniques like query reformulation, metadata filtering, and re-ranking to enhance the relevance and quality of generated questions.
Multimodal Analysis: Capable of processing audio and video data to analyze body language, vocal tone, and emotion during mock interviews, offering more holistic feedback.
💬 What We'd Love to Discuss With You
I'm open-sourcing this project not just to share a finished product, but to spark conversation and learn from the community. I would love to hear your thoughts on:
Features & Ideas: Do you find the current features helpful for job seekers? What new features or improvements would you suggest?
Technical Implementation: Are you interested in the project's use of langgraph, its RAG architecture, or the multimodal analysis approach? Let's dive into the technical details!
Future Development: The project currently lacks a front-end UI, and the RAG knowledge base could be expanded. We warmly welcome anyone interested in contributing, whether it's through front-end development, data collection, or back-end optimization. Your help would be invaluable.
User Feedback: If you try running the project, please share your experience! We're eager to hear about any issues you encounter, your overall impressions, or any ideas you might have.
This project has been a deep dive into the world of Agents and RAG for me. I know there's still plenty of room for improvement, and I look forward to hearing your feedback.
Let's work together to make it even better and help more people succeed in their job search