This project implements a Retrieval-Augmented Generation (RAG) system for AWS, combining document retrieval with language generation to produce more accurate and contextually relevant responses.

This is the main notebook that contains all the core components of the RAG system:

• Data Cleaning
• Indexing
• Vector Database Integration
• Retrieval Mechanism
• Embedding Model Implementation
• Text Generation

This notebook focuses specifically on the embedding model used in the RAG system. It likely contains:

• Embedding model architecture
• Finetuning process (if applicable)

1. Ensure you have all the necessary dependencies installed.

2. Start with embedding_model.ipynb to understand and set up the embedding model.

3. Move on to main_rag.ipynb to see how all components work together in the RAG system.

4. dataset: https://www.kaggle.com/datasets/harshsinghal/aws-case-studies-and-blogs/code