Retrieval-Augmented Generation (RAG) System

I developed an end-to-end Retrieval-Augmented Generation (RAG) system that enables Large Language Model (LLMs) to generate accurate, context-aware answers based on custom data instead of relying solely on pretrained knowledge.

? What I Built

Designed a complete RAG pipeline:

Document ingestion (PDF, DOCX, TXT, CSV)

Text chunking & preprocessing

Embedding generation

Vector storage

Similarity-based retrieval

Context-aware response generation using an LLM

Implemented embeddings to convert text into vector representations.

Integrated a Vector Database (FAISS / ChromaDB) for efficient semantic search.

Connected the retrieval system with an LLM (OpenAI API / open-source model like LLaMA or Mistral).

Engineered prompts to ensure accurate and grounded answers.

Enabled source attribution so responses reference the original documents.

Built a simple user interface (e.g., Streamlit) for easy interaction.

? Key Features

Supports Arabic and English documents.

Dynamic document upload capability.

Context-aware question answering.

Clean, modular, and scalable code structure.

? Outcome

The system successfully retrieves the most relevant document chunks and generates precise, explainable answers grounded in the provided knowledge base, making it suitable for knowledge assistants, internal documentation systems, and AI-powered chatbots.