Project Specifications
- Category Web Application
- Client Personal Project
- Live Demo Launch App
- Source Code
Key Features
RAG-Based AI Architecture
Integrates Large Language Models into a retrieval-augmented system to deliver context-aware, highly accurate responses grounded in specific document data.
Vector DB & Semantic Search
Leverages Supabase (PostgreSQL with pgvector) to store high-dimensional embeddings and execute similarity searches for fast context retrieval.
LLM Integration & Orchestration
Utilizes the Vercel AI SDK to manage OpenAI model interactions, handle prompt engineering workflows, and stream real-time responses to the user UI.
Interactive Conversational Interface
Provides a responsive UI where users can perform natural language queries and receive dynamic, context-backed answers.
End-to-End Ingestion Pipeline
Implements document parsing, chunk embedding creation, vector storage, context injection, and dynamic LLM inference.