Some Important Details

🚀 RAG-Based AI Chatbot Platform (LLM Integration)
This project represents a core milestone in my journey toward AI Engineering, focusing on building production-ready systems that integrate Large Language Models into real-world applications.

🗄️ Vector Database & Embeddings Utilized Supabase (PostgreSQL + pgvector) to store and query high-dimensional vector embeddings, enabling efficient semantic search and similarity matching for relevant context retrieval.

⚙️ LLM Integration Layer Integrated OpenAI models through the Vercel AI SDK, using it as an abstraction layer to handle prompt orchestration, streaming responses, and model interaction.

💬 Conversational Interface Developed a responsive, user-friendly chat interface where users can query the system and receive context-aware, dynamically generated responses grounded in retrieved data.

🔄 End-to-End AI Pipeline Built a complete flow including: Document ingestion & embedding generation Vector storage & retrieval Context injection into prompts Response generation with LLMs