Advanced RAG Implementation Guide

This repository contains a structured guide to implementing Retrieval-Augmented Generation (RAG). Each notebook in this repository is designed to teach a specific aspect of RAG, starting from the fundamentals to building an end-to-end pipeline.

Acknowledgment

This guide is inspired by and based on the work of ThatAIGuy. Full credit goes to the original author for their invaluable resources and insights.

Topics

• 1_fundamentals_of_rag.ipynb
  Introduces the basics of Retrieval-Augmented Generation.

• 2_langchain_retrieval_pipeline.ipynb
  Covers how to set up a retrieval pipeline using LangChain for streamlined workflows.

• 3_overview_of_document_loaders.ipynb
  Provides an overview of document loaders and their role in processing data for retrieval tasks.

• 4_document_loaders.ipynb
  A deeper dive into using various document loaders with practical examples.

• 5_text_splitter_transformation.ipynb
  Explains text splitting and transformations to optimize data for embedding and retrieval.

• 6_text_embedding_models.ipynb
  Focuses on text embedding models and their configurations for generating meaningful vector representations.

• 7_vector_stores_and_retrievers.ipynb
  Discusses vector stores and retrievers, showcasing how to store and retrieve information efficiently.

• 8_retrievers.ipynb
  Detailed exploration of retriever types and their integration with vector stores.

• 9_End_to_End_RAG_Chain.ipynb
  Combines all concepts into an end-to-end Retrieval-Augmented Generation pipeline.

How to Use

1. Clone this repository.
2. Install the required dependencies:

   pip install -r requirements.txt

3. Navigate through the notebooks in order, starting with 1_fundamentals_of_rag.ipynb.

Prerequisites

• Python 3.10 or higher
• Google API Key
• Jupyter Notebook or Jupyter Lab
• All dependencies listed in requirements.txt

Contributing

Contributions are welcome! Feel free to fork this repository and submit a pull request.