https://github.com/codewithcharan/advanced-rag

Advanced RAG Implementation Guide
https://github.com/codewithcharan/advanced-rag

embeddings generative-ai langchain retrieval-augmented-generation vector-database

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Advanced RAG Implementation Guide

• Host: GitHub
• URL: https://github.com/codewithcharan/advanced-rag
• Owner: CodeWithCharan
• License: mit
• Created: 2024-12-01T18:40:00.000Z (about 2 months ago)
• Default Branch: main
• Last Pushed: 2024-12-06T13:40:06.000Z (about 2 months ago)
• Last Synced: 2024-12-22T15:15:00.961Z (about 1 month ago)
• Topics: embeddings, generative-ai, langchain, retrieval-augmented-generation, vector-database
• Language: Jupyter Notebook
• Homepage:
• Size: 7.76 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# 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](https://github.com/bansalkanav/Generative-AI-Scratch-2-Advance-By-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:  

   ```bash

   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.