An intelligent question-answering and knowledge management platform that leverages LLMs, semantic search, and vector databases to extract, understand, and interact with business data from multiple sources.
Research & Development Notice
This project was developed as a Research & Development (R&D) initiative focused on exploring intelligent knowledge systems and LLM-powered applications. It is not publicly deployed. This page presents a detailed overview of its architecture, features, and capabilities.
MEF QnA is a full-stack AI-powered application designed for intelligent question answering, document understanding, and business knowledge management. It combines a modern frontend with a powerful backend that utilizes Large Language Models (LLMs) and vector databases to deliver context-aware and semantically rich responses.
The platform enables users to extract data from websites, upload documents, and interact with an AI assistant capable of understanding and retrieving relevant information across multiple data sources.
Frontend (Client Layer):
Next.js-based modern UI with state management via Zustand, styled with Tailwind CSS, and integrated with Supabase for authentication and real-time data.
Backend (Server Layer):
FastAPI with modular routers handling scraping, semantic querying, feedback collection, and integrations. Provides RESTful APIs with proper validation and error handling.
Data Layer:
Supabase (PostgreSQL) for structured data storage and ChromaDB for vector storage, enabling semantic search capabilities powered by embeddings and LLMs.
Secure authentication and user management system
Interactive conversational interface with history
Website content extraction via URL input
Support for PDF, DOCX, and other formats
Configure AI alignment and business objectives
Create, edit, and manage FAQ systems
Evaluate and improve AI responses
Slack and chat widget integrations
Advanced scraping with support for structured data
ChromaDB integration with chunking and metadata
Powered by embeddings and Large Language Models
Ingestion and processing for multiple formats
Business goals and trigger-based responses
Collect and analyze user feedback
Slack and chat widget integration endpoints
Settings, API keys, and system configuration
AI-powered semantic search across multiple data sources
Unified knowledge base from web and document ingestion
Conversational AI for real-time user interaction
Feedback-driven continuous improvement of AI responses
Business-aligned AI behavior using goals and triggers
End-to-end AI knowledge management system with scalability
Integration of LLMs with real-world business workflows
Advanced vector search architecture for semantic matching
Modern, intuitive frontend experience with responsive design
Extensible integration ecosystem for third-party services
I'm always open to discussing new projects, innovative ideas, and opportunities to build scalable solutions.