RAG Chatbot
Full-Stack Development, AI, NLP
Role
Full-Stack Developer, AI Integrator
Responsibilities
- Frontend development with React
- Backend development with FastAPI
- Implementation of RAG logic with LLMs
- Database integration with PostgreSQL and pgvector
Technologies
- React & FastAPI
- PostgreSQL & pgvector
- LLM & RAG (Retrieval Augmented Generation)
Project Overview
This project was created to demonstrate an understanding of full-stack development and the practical application of advanced AI concepts. The RAG chatbot is an interactive prototype that provides context-aware answers to user queries by retrieving information from a document database. It's a testament to the idea that a blend of logical backend systems and intuitive frontend design can create powerful and intelligent tools.
Development & Challenges
One of the biggest challenges was accurately implementing the Retrieval Augmented Generation (RAG) logic. This required a deep understanding of embeddings, semantic chunking, and how to effectively query a vector database like pgvector to retrieve the most relevant document snippets. Overcoming this involved careful data preprocessing and fine-tuning the query process to ensure accurate and precise answers from the chatbot.
Impact & Conclusion
The RAG Chatbot serves as a powerful example of how I can integrate complex technologies like LLMs and vector databases into a seamless full-stack application. The skills learned from this project, from managing backend APIs to designing a responsive user interface, are directly applicable to building sophisticated digital products. It showcases a passion for continuous learning and problem-solving in the AI and development space.
Click here to view the project on GitHub.