This is a submission for the Open Source AI Challenge with pgai and Ollama
What I Built
The AI-Powered CV Analysis Platform is a groundbreaking tool that connects job seekers and employers through the power of artificial intelligence. With this platform, users can upload their resumes and job advertisements to receive a detailed analysis. It provides real-time insights, practical recommendations, and personalized guidance to enhance career prospects. Moreover, it helps users discover suitable job opportunities tailored to their skills and experiences, making the job search process smarter and more efficient.
Demo
Home Page Job List: Explore tailored job opportunities directly from the platform's homepage.
Job detail
Resume analysis in progress: Turning your CV into actionable insights for better job opportunities.
AI Suitability Score and Proficiency Levels: Matching your CV to the perfect job with precision and insights.
Discover similar job opportunities tailored to your skills and experience.
Tools Used
TimescaleDB
Used as the primary database for efficient management and retrieval of document and job-related data.
PGAI
Acts as a core database for document storage and processing. It provides the foundation for managing AI-related data integrations.
pgvector
Utilized to store vector embeddings of documents, enabling advanced AI-driven search and similarity matching.
pgvectorscale
Employed to create indexes on the vector embeddings, optimizing performance for vector similarity queries and enhancing the search experience.
OpenAI API
Powers the AI capabilities for document analysis, embedding generation, and providing intelligent insights and recommendations.
Tech Stack
Backend
- Node.js: Server-side runtime environment.
- Express.js: Web framework for building server logic and handling routing.
Frontend
- EJS (Embedded JavaScript): Used to render dynamic HTML pages. Combines server-side data with frontend templates for seamless integration.
Database
- TimescaleDB: Primary database for managing and retrieving document data.
- PostgreSQL: Base relational database supporting structured queries.
- pgvector: For storing document embeddings.
- pgvectorscale: To create and optimize indexes for vector searches.
Final Thoughts
Building the AI-Powered CV Analysis Platform has been an incredible journey, blending traditional database systems with cutting-edge AI capabilities. At first, the idea of integrating advanced AI, like embedding models and similarity searches, directly within the database seemed unconventional. However, as I delved deeper into the project, I realized how transformative this approach could be, especially for simplifying workflows for non-technical users like recruiters and HR professionals.
What excites me most about this project is how it bridges the gap between technical complexity and user-friendly solutions. By utilizing tools like TimescaleDB, pgvector, and OpenAI's API, we've created a system that can streamline CV analysis and job matching without the need for external AI frameworks. This approach not only simplifies integration but also ensures faster, more efficient insights directly from the database.
One of the highlights of this experience was learning to optimize PostgreSQL with extensions like pgvector, and creating a seamless interaction between AI embeddings and SQL queries.
This project has shown me that innovation often lies in reimagining familiar tools in new contexts—and that AI-powered solutions can democratize access to advanced analytics in ways we’re just beginning to explore.
Working with TimescaleDB felt like discovering an exciting new frontier in data management.
TimescaleDB made it feel like the limitations of traditional databases could be overcome without compromising on performance or scalability. Its integration into this project enabled a seamless flow of AI-powered insights, reinforcing the idea that innovation is as much about leveraging the right tools as it is about having the right ideas.
This project has shown me that innovation often lies in reimagining familiar tools in new contexts—and TimescaleDB was instrumental in pushing those boundaries. It turned what seemed like a technical challenge into an exciting opportunity to create something truly transformative.
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