Our previous blog tackled the definition of RAG or Retrieval-Augmented Generation and how it works. We explored how RAG combines information retrieval and natural language generation to improve the quality of text generation systems.

In this article, we’ll explore some of the top open-source GitHub repositories that can be used as a foundation for creating your own RAG. Whether you’re a seasoned programmer or just getting started, a wealth of resources is available to help you build your very own RAG.

To stay up-to-date with the latest trends and developments in the tech industry we will learn what a GitHub repository is, its advantages of open source for RAG, and the criteria for selecting the right open-source GitHub repositories for creating your own RAG.

Let’s Talk About Github

What is GitHub? GitHub is a web-based platform that allows developers to collaborate and share their code. It provides issue-tracking and project management features, making it an essential tool for software development. Millions of developers from all over the globe use it.

What is An Open-Source Github Repository?

Open-source software licenses allow users to freely use, modify, and distribute the code. Repositories on GitHub not only contain the actual code but also include version control, which helps manage changes to the source code over time.

Here are some key features of an open-source GitHub repository:

• Accessibility: The code is publicly available for anyone to view, download, and use.
• Collaboration: Multiple contributors can work on the same project from different locations, contributing to the codebase, documentation, and issue tracking.
• Community Engagement: A repository’s community can report bugs, request features, or propose changes through pull requests.
• Transparency: Changes and contributions are tracked, providing transparency and a history of the development process.
• License: Open source projects typically have a license, such as MIT, GPL, Apache, etc., that dictates how the software can be used and shared.

GitHub itself encourages open-source development by offering free plans for hosting open-source projects, as well as providing tools for collaboration and discussion amongst developers.

Criteria for Selection

Several criteria must be considered when selecting an open-source GitHub repository for your own RAG. It’s like choosing the right computer based on your needs and workload.

Keep in mind that you must consider the criteria carefully. Not everything is applicable to your model, so select the most appropriate ones for your RAG system.

Below are some key criteria you may want to consider:

• Popularity: Look at the number of stars, forks, and contributors to determine the repository’s popularity within the community. Assess the frequency of commits, open issues, and pull requests to ensure the repository is actively maintained. Consider the level of community support available, which includes active discussions, quick responses to issues, and the presence of active users willing to help.
• Feature Set: Evaluate whether the repository provides all the essential features and capabilities to support your RAG system’s specific requirements.
• License & Documentation: Check for clear and comprehensive documentation that can help users understand and use the repository effectively. Ensure the repository has an open-source license that suits your needs and complies with your project’s requirements.
• Scalability & Dependencies: Evaluate how well the repository’s framework or codebase can handle growth and increased demand as your RAG system scales. Consider the number and maintenance of the repository’s external libraries or dependencies, as this can affect the ease of integration and future maintenance.
• Quality of Code: Evaluate the code quality, including readability, structure, and adherence to coding standards. Consider how well the repository’s code integrates with other tools and systems you may be using. Be on the look out for repositories with thorough tests to ensure the stability and reliability of the code. Look for case studies or examples of successful projects using the repository.

Now that you have an idea about what to look out for when searching for the ideal repos for your RAG, we’ve taken the liberty of gathering the top 5 Open source RAG Repositories to date.

Cognita by Truefoundry:

• Cognita is an open-source framework designed to organize your RAG codebase and customize RAG configurations easily. It simplifies the process of testing locally and deploying in a production-ready environment. Cognita addresses key issues like chunking and embedding jobs, query service deployment, LLM/embedding model deployment, and vector DB deployment.

Retrieval-augmented-generation Topic on GitHub:

• This GitHub topic provides a comprehensive infrastructure for developing search, recommendation, and RAG applications. Think of it as the knowledge base for most developers. It effectively integrates search language models with advanced tools for fine-tuning ranking and relevance, offering a robust framework for building AI-native applications.

Verba by Weaviate:

• Verba is an open-source modular RAG application that simplifies the creation of personalized answers using state-of-the-art techniques. It offers a user-friendly interface and a customizable architecture, making it easy for users to jump into RAG without extensive technical expertise.

• Verba supports HuggingFace models and enables importing documents through various sources, such as Unstructured data and Github repositories.

System Design Primer

System Design Primer contains a comprehensive collection of resources focused on end-to-end system design. The content within this publication is tailored to address the challenges associated with large-scale systems such as TensorFlow

Real-world

The Real-world repo is often considered the “mother of all demo apps” due to its ability to facilitate the development of intricate, real-world Full-Stack applications. It harnesses the power of cutting-edge technologies such as React, Angular, Node, and Django, among others.

LlamaIndex

LlamaIndex can be utilized to create your own retrieval-augmented generation (RAG) system. With LlamaIndex, you can build applications that integrate private or domain-specific data with large language models (LLMs) like GPT-4 to enhance the accuracy and relevance of responses. The process involves ingesting, structuring, and accessing custom data sources, enabling the LLM to dynamically generate responses based on up-to-date and relevant information.

RAG is a powerful technique that enhances the capabilities of LLMs by incorporating external data, enabling them to generate more accurate and informed responses. Choosing an open-source GitHub repository for your RAG system is a critical decision, as your system’s integrity is at stake. It is safe to consider criteria such as popularity, activity, maintenance, documentation, license, community support, code quality, test coverage, and interoperability to align with your specific requirements. There is no harm in testing and researching as many repositories as possible.

Although not all RAGs are the same, the top 6 RAG repositories covered offer diverse options for developers. Evaluate them based on your project’s needs and long-term goals to make an informed decision. The right open-source GitHub repository can be foundational in implementing and evolving your RAG system while contributing to the broader ecosystem of RAG applications.