Features
- AI-powered chat with project-based context awareness
- Upload and query documents using vector embeddings
- Multi-project workspace with isolated knowledge bases
- Persistent chat sessions with memory
- Fast semantic search using ChromaDB
- Clean and intuitive UI for learning workflows
Key Achievements
- Built a context-aware AI chat system using RAG architecture.
- Enabled multi-project and multi-chat support with dynamic routing.
- Improved response accuracy by integrating vector search (ChromaDB).
- Designed scalable backend APIs for AI-driven workflows.
Problems Tackled
- Handling context separation between multiple projects and chats.
- Managing efficient document retrieval for accurate AI responses.
- Designing a scalable architecture for AI + frontend integration.
- Ensuring low-latency responses with growing data size.
Learning Outcomes
- Hands-on implementation of Retrieval-Augmented Generation (RAG).
- Working with vector databases like ChromaDB for semantic search.
- Designing context-aware AI systems with chat memory.
- Deep understanding of Next.js App Router and dynamic routing.
- Structuring scalable full-stack applications using TypeScript.
Future Growth
- Implementing persistent chat storage with database integration.
- Adding streaming responses for real-time AI interaction.
- Optimizing retrieval pipeline for faster performance.
- Enhancing UI/UX for better learning experience.