Retrieval augmentation Generation (RAG) has become a powerful technique for augmenting large language models.

The RAG framework combines the benefits of a search-based system and a generative model for more accurate, context-aware and timely responses. With the growing demand for complex AI solutions, a number of open source RAG frameworks have popped up on GitHub, each with unique features and capabilities. What are the functions of the RAG framework?

Oversimplified RAG workflow

Retrieval augmentation Generation (RAG) is an artificial intelligence framework that enhances the power of large language models (LLMS) by integrating external sources of knowledge.

RAG works by retrieving relevant information from the knowledge base and using it to enhance the LLM's input, allowing the model to generate more accurate, up-to-date, and context-relevant responses.

This approach helps overcome limitations such as knowledge deadlines and reduces the risk of hallucinations in the LLM output.

Why can't I just use LangChain?

While LangChain is a powerful tool for building LLM applications, it is not a direct replacement for RAG. Instead, LangChain can be used to implement RAG systems. Here's why you need RAG in addition to using LangChain:

• External knowledge: RAG allows you to incorporate domain-specific or up-to-date information into the LLM's training data that may not be there.
• Improved accuracy: By reacting to retrieved information, RAG can greatly reduce errors and hallucinations.
• Customization: RAG enables you to customize responses to specific data sets or knowledge bases, which is critical for many business applications.
• Transparency: RAG makes it easier to trace the source of the information used to generate the response, improving auditability.

Essentially, LangChain provides the tools and abstractions to build LLM applications, while RAG is a specific technique that can be implemented using LangChain to improve the quality and reliability of LLM output.

GitHub's 10 best RAG frameworks

In this article, we will explore the top 10 RAG frameworks currently available on GitHub. These frameworks represent the cutting edge of RAG technology and are worth investigating by developers, researchers, and organizations looking to implement or improve their AI-powered applications.

1. Haystack

GitHub star rating: 14.6k stars

picture

Haystack is a powerful and flexible framework for building end-to-end question answering and search systems. It uses a modular architecture that allows developers to easily create pipelines for a variety of NLP tasks, including document retrieval, question answering, and summarization:

• Supports multiple document storage (Elasticsearch, FAISS, SQL, etc.)
• Integration with popular language models (BERT, RoBERTa, DPR, etc.
• Scalable architecture for handling large numbers of files
• Easy to use API for building custom NLP pipelines

Haystack's versatility and extensive documentation make it an excellent choice for both beginners and experienced developers implementing RAG systems.

https://github.com/deepset-ai/haystack

2. RAGFlow

GitHub star rating: 11.6k

picture

RAGFlow is a relatively new entrant to the RAG framework space, but has quickly gained traction due to its focus on simplicity and efficiency. The framework aims to simplify the process of building RAG based applications by providing a set of pre-built components and workflows:

• Intuitive workflow design interface
• Pre-configured RAG pipes for common use cases
• Integration with popular vector databases
• Support for custom embedding models

RAGFlow's user-friendly approach makes it an attractive option for developers who want to quickly create and deploy RAG application prototypes without having to delve into the underlying complexities.

https://github.com/infiniflow/ragflow

3. Txtai

GitHub Stars: 7.5k

picture

txtai is a versatile AI data platform that goes beyond the traditional RAG framework. It provides a comprehensive set of tools for building semantic search, language model workflows, and document processing pipelines:

• Embedded database for efficient similarity search
• Apis for integrating language models and other AI services
• An extensible architecture for custom workflows
• Multiple languages and data types are supported

txtai's all-in-one approach makes it an excellent choice for businesses looking to implement a variety of AI capabilities within a single framework.

https://github.com/neuml/txtai

4. STORM

GitHub star rating: 5,000 stars

Stanford open source RAG model

STORM (Stanford Open Source RAG Model) is a research-oriented RAG framework developed at Stanford University. STORM may have fewer stars compared to some other frameworks, but its academic background and focus on cutting-edge technologies make it a valuable resource for researchers and developers interested in the latest advancements in RAG technology:

• Implement novel RAG algorithms and techniques
• Focus on improving the accuracy and efficiency of the retrieval mechanism
• Integration with the most advanced language models
• Lots of documents and research papers

For those looking to explore the cutting edge of RAG technology, STORM provides a solid foundation backed by rigorous scholarship.

https://github.com/stanford-oval/storm

5. LLM-App

GitHub star rating: 3.4K

picture

LLM-App is a collection of templates and tools for building dynamic RAG applications. Key features of LLM-App include:

• Ready-to-use Docker containers for rapid deployment
• Supports dynamic data sources and real-time updates
• Integration with popular LLM and vector databases
• Customizable templates for a variety of RAG use cases

LLM-App's emphasis on operational aspects and real-time capabilities makes it an attractive option for businesses looking to deploy production-ready RAG systems.

https://github.com/pathwaycom/llm-app

6. Cognita

GitHub star: 3K star

picture

Cognita is a new entrant in the RAG framework space, focused on providing a unified platform for building and deploying AI applications. While it has a low star rating compared to some other frameworks, its comprehensive approach and emphasis on MLOps principles make it worth considering:

• An end-to-end platform for RAG application development
• Integration with popular ML frameworks and tools
• Built-in monitoring and observability
• Support model version and experiment tracking

For businesses looking to streamline the entire ML lifecycle, Cognita's holistic approach to AI application development makes ita compelling choice.

https://github.com/truefoundry/cognita

7. R2R

GitHub stars: 2.5K stars

picture

R2R (Retrieval to Retrieval) is a specialized RAG framework that focuses on improving the retrieval process through iterative improvements. While it may have fewer stars, its innovative retrieval methods make it a framework worth watching:

• Implement novel search algorithms
• Supports multi-step retrieval process
• Integration with various embedded models and vector storage
• Tools for analyzing and visualizing retrieval performance

For developers and researchers interested in advancing retrieval technology, R2R offers a unique and powerful set of tools.

8.Neurite

GitHub star: 909 stars

Neurite

Neurite is an emerging RAG framework that aims to simplify the process of building AI-powered applications. While it has a smaller user base compared to some other frameworks, its focus on developer experience and rapid prototyping makes it worth exploring:

• Intuitive API for building RAG pipes
• Support for multiple data sources and embedded models
• Built-in caching and optimization mechanisms
• Extensible architecture for custom components

Neurite emphasizes simplicity and flexibility, which makes it an attractive option for developers looking to quickly implement RAG functionality in their applications.

https://github.com/satellitecomponent/Neurite

9. FlashRAG

GitHub star: 905 stars

FlashRAG from the Laboratory of Natural Language Processing and Information Retrieval, Renmin University of China

FlashRAG is a lightweight and efficient RAG framework developed by the Natural Language Processing and Information Retrieval Laboratory of Renmin University of China. The main features of FlashRAG include

• Optimize the search algorithm and improve the search speed
• Supports distributed processing and scaling
• Integration with popular language models and vector storage
• Benchmarking and performance analysis tools

For applications where speed and efficiency are critical, FlashRAG offers a dedicated set of tools and optimizations.

https://github.com/RUC-NLPIR/FlashRAG

10. Canopy

GitHub star: 923 stars

Canopy is a RAG framework developed by Pinecone, a company known for its vector database technology. It leverage Pinecone's expertise in efficient vector search to provide a powerful, scalable RAG solution that:

• Tight integration with Pinecone's vector database
• Streaming and real-time updates are supported
• Advanced query processing and reordering capabilities
• Tools for managing and versioning knowledge bases

With its focus on scalability and integration with the Pinecone ecosystem, Canopy is an excellent choice for businesses that have already used or are considering using Pinecone for their vector search needs.

https://github.com/pinecone-io/canopy

Write at the end

The world of RAG frameworks is diverse and evolving rapidly, and the ten frameworks we explore all have unique advantages and features. From comprehensive, proven Haystack to emerging professional frameworks such as FlashRAG and R2R, there is always a solution for every need and use case:

• Specific requirements of the project
• The degree of customization and flexibility you need
• Scalability and performance characteristics of the framework
• Community scale and activities around the framework
• Quality of available documentation and support

By carefully evaluating these factors and experimenting with different frameworks, you can find the RAG solution that best suits your needs and helps you build smarter, more context-aware AI applications. It is critical for developers and organizations looking to leverage the power of AI in their applications and services to stay informed of the latest developments in RAG technology.