Uriel' Next BlogAI Semantic Search Chatbot
This project is a hands-on AI Semantic Search Chatbot that I built from scratch. It allows users to upload their own documents and instantly search them semantically through a conversational chatbot interface.
The system demonstrates how Artificial Intelligence and Natural Language Processing (NLP) can be integrated with Python and modern web technologies to deliver a practical and interactive user experience.
Why AI & Semantic Search?
AI and NLP are at the core of modern intelligent systems. They enable computers to understand and process human language in meaningful ways.
I chose to focus on Semantic Search because it represents a real-world problem that requires combining multiple domains — language understanding, information retrieval, and user interaction.
Building this project gave me a strong understanding of how embeddings, vector databases, and conversational logic work together to create intelligent search experiences.
Key Features
- Semantic search across uploaded user documents
- AI-powered chatbot interface for intuitive interaction
- Natural Language Processing for understanding user intent
- Integration of embeddings and vector similarity search
- Document upload and preprocessing pipeline
- Backend built with Python and Flask
- Efficient data handling and request routing
- Modular architecture with clear separation between logic layers
- Frontend with modern and minimal UI for smooth interaction
- Error handling, logging, and scalability considerations
Project Highlights
The project demonstrates how to apply AI and Python concepts to build a complete, production-like system.
All components — from file upload and document parsing to embedding generation and semantic querying — were built and structured by me to ensure full understanding of the entire pipeline.
This project showcases my ability to combine AI, backend logic, and frontend integration into a cohesive system. It highlights practical knowledge of NLP, embeddings, and semantic search while maintaining clean and scalable code architecture.
Feel free to explore the live project here and see how all parts come together.
For more details, contact us.