Top 10 RAG Frameworks Github Repos 2024

Retrieval-Augmented Generation (RAG) has emerged as a powerful technique for enhancing the capabilities of large language models.

RAG frameworks combine the strengths of retrieval-based systems with generative models, allowing for more accurate, context-aware, and up-to-date responses. As the demand for sophisticated AI solutions grows, numerous open-source RAG frameworks have surfaced on GitHub, each offering unique features and capabilities.

What Does RAG Framework Do?

RAG Workflow Oversimplifed

Retrieval-Augmented Generation (RAG) is an AI framework that enhances the capabilities of large language models (LLMs) by incorporating external knowledge sources.

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

This approach helps overcome limitations such as knowledge cutoff dates and reduces the risk of hallucinations in LLM outputs.

Why Can’t I Just Use LangChain?

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

1. External knowledge: RAG allows you to incorporate domain-specific or up-to-date information that may not be present in the LLM’s training data.
2. Improved accuracy: By grounding responses in retrieved information, RAG can significantly reduce errors and hallucinations.
3. Customization: RAG enables you to tailor responses to specific datasets or knowledge bases, which is crucial for many business applications.
4. Transparency: RAG makes it easier to trace the sources of information used in generating responses, improving auditability.

In essence, while LangChain provides the tools and abstractions to build LLM applications, RAG is a specific technique that can be implemented using LangChain to enhance the quality and reliability of LLM outputs.

Top 10 Best RAG Frameworks on GitHub that You Can Use Now

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

1. Haystack by deepset-ai

GitHub Stars: 14.6k stars

RAG Framework: Haystack by deepset-ai

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

• Support for multiple document stores (Elasticsearch, FAISS, SQL, etc.)
• Integration with popular language models (BERT, RoBERTa, DPR, etc.)
• Scalable architecture for processing large volumes of documents
• 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 looking to implement RAG systems.

GitHub — deepset-ai/haystack: :mag: LLM orchestration framework to build customizable…

2. RAGFlow by infiniflow

GitHub Stars: 11.6k

RAG Framework: RAGFlow by infiniflow

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

• Intuitive workflow design interface
• Pre-configured RAG pipelines 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 prototype and deploy RAG applications without diving deep into the underlying complexities.

GitHub — infiniflow/ragflow: RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine…

3. txtai by neuml

GitHub Stars: 7.5k

RAG Framework: txtai by neuml

txtai is a versatile AI-powered data platform that goes beyond traditional RAG frameworks. It offers a comprehensive suite of tools for building semantic search, language model workflows, and document processing pipelines.Key capabilities of txtai include:

• Embeddings database for efficient similarity search
• API for integrating language models and other AI services
• Extensible architecture for custom workflows
• Support for multiple languages and data types

txtai’s all-in-one approach makes it an excellent choice for organizations looking to implement a wide range of AI-powered features within a single framework.

GitHub — neuml/txtai: 💡 All-in-one open-source embeddings database for semantic search, LLM…

4. STORM by stanford-oval

GitHub Stars: 5k Stars

STORM by stanford-oval

STORM (Stanford Open-source RAG Model) is a research-oriented RAG framework developed by Stanford University. While it may have fewer stars compared to some other frameworks, its academic pedigree and focus on cutting-edge techniques make it a valuable resource for researchers and developers interested in the latest advancements in RAG technology.Notable aspects of STORM include:

• Implementation of novel RAG algorithms and techniques
• Focus on improving the accuracy and efficiency of retrieval mechanisms
• Integration with state-of-the-art language models
• Extensive documentation and research papers

For those looking to explore the frontiers of RAG technology, STORM offers a solid foundation backed by academic rigor.

GitHub — stanford-oval/storm: An LLM-powered knowledge curation system that researches a topic and…

5. LLM-App by pathwaycom

GitHub Stars: 3.4K

LLM-App by pathwaycom

LLM-App is a collection of templates and tools for building dynamic RAG applications. It stands out for its focus on real-time data synchronization and containerized deployment.Key features of LLM-App include:

• Ready-to-run Docker containers for quick deployment
• Support for dynamic data sources and real-time updates
• Integration with popular LLMs and vector databases
• Customizable templates for various RAG use cases

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

GitHub — pathwaycom/llm-app: Dynamic RAG for enterprise. Ready to run with Docker,⚡in sync with…

6. Cognita by truefoundry

GitHub Stars: 3k stars

Cognita by truefoundry

Cognita is a newer entrant in the RAG framework space, focusing on providing a unified platform for building and deploying AI applications. While it has fewer stars compared to some other frameworks, its comprehensive approach and emphasis on MLOps principles make it worth considering.Notable features of Cognita include:

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

Cognita’s holistic approach to AI application development makes it a compelling choice for organizations looking to streamline their entire ML lifecycle.

GitHub — truefoundry/cognita: RAG (Retrieval Augmented Generation) Framework for building modular…

7. R2R by SciPhi-AI

GitHub Stars: 2.5k stars

R2R by SciPhi-AI

R2R (Retrieval-to-Retrieval) is a specialized RAG framework that focuses on improving the retrieval process through iterative refinement. While it may have fewer stars, its innovative approach to retrieval makes it a framework to watch.Key aspects of R2R include:

• Implementation of novel retrieval algorithms
• Support for multi-step retrieval processes
• Integration with various embedding models and vector stores
• Tools for analyzing and visualizing retrieval performance

For developers and researchers interested in pushing the boundaries of retrieval techniques, R2R offers a unique and powerful set of tools.

8. Neurite by satellitecomponent

GitHub Stars: 909 stars

Neurite by satellitecomponent

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.Notable features of Neurite include:

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

Neurite’s emphasis on simplicity and flexibility makes it an attractive option for developers looking to quickly implement RAG functionality in their applications.

GitHub — satellitecomponent/Neurite: Fractal Graph-of-Thought. Experimental Mind-Mapping for…

9. FlashRAG by RUC-NLPIR

GitHub Stars: 905 Stars

FlashRAG by RUC-NLPIR

FlashRAG is a lightweight and efficient RAG framework developed by the Natural Language Processing & Information Retrieval Lab at Renmin University of China. While it may have fewer stars, its focus on performance and efficiency makes it a noteworthy contender.Key aspects of FlashRAG include:

• Optimized retrieval algorithms for improved speed
• Support for distributed processing and scaling
• Integration with popular language models and vector stores
• Tools for benchmarking and performance analysis

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

GitHub — RUC-NLPIR/FlashRAG: ⚡FlashRAG: A Python Toolkit for Efficient RAG Research

10. Canopy by pinecone-io

GitHub Stars: 923

Canopy is a RAG framework developed by Pinecone, a company known for its vector database technology. It leverages Pinecone’s expertise in efficient vector search to provide a powerful and scalable RAG solution.Notable features of Canopy include:

• Tight integration with Pinecone’s vector database
• Support for streaming and real-time updates
• Advanced query processing and reranking capabilities
• Tools for managing and versioning knowledge bases

Canopy’s focus on scalability and integration with Pinecone’s ecosystem makes it an excellent choice for organizations already using or considering Pinecone for their vector search needs.

GitHub — pinecone-io/canopy: Retrieval Augmented Generation (RAG) framework and context engine…

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Conclusion

The world of RAG frameworks is diverse and rapidly evolving, with each of the ten frameworks we’ve explored offering unique strengths and capabilities. From the comprehensive and well-established Haystack to emerging specialized frameworks like FlashRAG and R2R, there’s a solution to fit a wide range of needs and use cases.When choosing a RAG framework, consider factors such as:

• The specific requirements of your project
• The level of customization and flexibility you need
• The scalability and performance characteristics of the framework
• The size and activity of the community around the framework
• The quality of documentation and support available

By carefully evaluating these factors and experimenting with different frameworks, you can find the RAG solution that best fits your needs and helps you build more intelligent, context-aware AI applications.As the field of AI continues to advance, we can expect these frameworks to evolve and new ones to emerge. Staying informed about the latest developments in RAG technology will be crucial for developers and organizations looking to leverage the power of AI in their applications and services.