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# Mastering LangChain RAG

Welcome to the **Mastering LangChain RAG** series repository. This repository contains all the code examples and resources for the six-part series on LangChain's Retrieval-Augmented Generation (RAG) technology.

## Overview

This series aims to equip developers, data scientists, and AI enthusiasts with the knowledge to implement and optimize RAG in their projects. Each part of the series builds on the previous one, gradually introducing more advanced concepts and techniques.

## Series Outline

1. **[Quick Start Guide to LangChain RAG](https://medium.com/@eric_vaillancourt/mastering-langchain-rag-a-comprehensive-tutorial-series-part-1-28faf6257fea)**: Basics of setting up LangChain RAG.
2. **[Integrating Chat History](https://medium.com/@eric_vaillancourt/mastering-langchain-rag-integrating-chat-history-part-2-4c80eae11b43)**: Incorporate chat history into your RAG model.
3. **[Implementing Streaming Capabilities](https://medium.com/@eric_vaillancourt/mastering-langchain-rag-implementing-streaming-capabilities-part-3-e3f4885ea66a)**: Handle real-time data processing with RAG.
4. **Returning Sources with Results**: Configure RAG to provide sources along with responses.
5. **Adding Citations to Your Results**: Include citations in your results for verifiability.
6. **Putting It All Together**: Build a comprehensive RAG application integrating all components.

## Folder Structure

Each folder corresponds to a part of the series and contains relevant code examples and resources:

- `01 - Quickstart`: Code for the quick start guide to LangChain RAG.
- `02 - Integrating Chat History`: Code for integrating chat history into your RAG model.
- `03 - Implementing Streaming Capabilities`: Code for implementing streaming capabilities with RAG.
- `04 - Returning Sources with Results`: Code for configuring RAG to provide sources along with responses.
- `05 - Adding Citations to Your Results`: Code for adding citations to your results.
- `06 - Putting It All Together`: Comprehensive project integrating all components learned in the series.

## Getting Started

To get started with any part of the series, navigate to the corresponding folder and follow the instructions in the `README.md` file within that folder. Each `README.md` provides detailed steps to set up and run the code examples for that specific part.

## Prerequisites

Ensure you have the following installed:

- Python 3.11 or higher
- pip (Python package installer)
- Git

## Installation

Clone this repository to your local machine:

```bash
git clone https://github.com/ericvaillancourt/RAG-tutorial.git
cd RAG-tutorial
```

Navigate to the folder of the part you are interested in:

```bash
cd "01 - Quickstart"
```

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.

## Contributing

We welcome contributions to this project. If you have suggestions for improvements or new features, please create an issue or submit a pull request.

## Support

If you find this series helpful, consider supporting my work by [buying me a coffee](https://www.buymeacoffee.com/evaillancourt).

## Contact

For any questions or suggestions, feel free to contact me at [eric@ericvaillancourt.ca](mailto:eric@ericvaillancourt.ca).

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