Arabic AI Assistant with Retrieval-Augmented Generation (RAG)

Developed an Arabic Retrieval-Augmented Generation (RAG) chatbot that enables users to ask natural language questions and receive accurate, context-aware responses based on a domain-specific knowledge base.

Key Features:

• Retrieves relevant documents using semantic search and vector embeddings.

• Generates grounded responses with Large Language Models (LLMs) to minimize hallucinations.

• Supports Arabic conversational interactions through an intuitive chat interface.

• Utilizes a vector database for efficient document retrieval and scalable knowledge management.

• Designed with a modular architecture for integration into enterprise knowledge management workflows.

Technologies: Python, LlamaIndex, Qdrant, Gemini Embeddings, Groq, Chainlit