How to choose between RagFlow and Dify

In today's AI application development field, Retrieval-Augmented Generation (RAG) technology has become a key method for building knowledge-intensive applications. RAGFlow and dify are two highly regarded open source frameworks, each with unique advantages and limitations in RAG implementation. This report will deeply analyze the performance of these two tools to help developers and enterprises make wise choices based on specific needs.

The core positioning and design concept of RAGFlow and Dify

There are obvious differences between RAGFlow and Dify in their design concepts, which directly affect their advantages and disadvantages in RAG applications.

RAGFlow focuses on building workflows based on retrieval-enhanced generation, emphasizing modularity and lightweightness, and its core goal is to provide a professional RAG workflow framework. As an engine designed for deep document understanding and retrieval-enhanced generation, RAGFlow combines pre-trained large language models with efficient retrieval technology, providing users with a powerful tool to handle complex problems and scenarios.

In contrast, Dify's core goal is broader, aiming to lower the threshold for LLM application development and quickly build AI applications through simple interfaces and APIs. As an open source LLM application development platform, Dify provides a one-stop solution that enables developers to quickly move from prototyping to product deployment. Dify emphasizes low-code or even no-code development experience, which is suitable for non-technical users to quickly get started, and is more inclined to help developers quickly push LLM applications from the development stage to end users.

This difference in design philosophy makes the two have their own strengths in practical applications: RAGFlow focuses on the quality and depth of RAG, while Dify provides a broader and easier-to-use integration platform.

Analysis of the advantages and disadvantages of RAGFlow

Advantages of RAGFlow

The biggest advantage of RAGFlow lies in its excellent document understanding and processing capabilities. As an open source RAG engine built specifically for deep document understanding, RAGFlow does not use ready-made RAG middleware, but completely redevelops a set of intelligent document understanding systems. This makes RAGFlow perform well in processing complex document formats, especially in parsing complex documents such as PDFs, photocopies, and tables.

In terms of document parsing capabilities, multiple survey results consistently show that RAGFlow has the best document parsing capabilities among the open source RAG projects in the industry. It can automatically identify the layout of documents, including titles, paragraphs, line breaks, and even complex elements such as pictures and tables. This deep document understanding capability is particularly important for professional fields that need to process unstructured data (such as law, medicine, and finance).

Another noteworthy advantage is RAGFlow's retrieval quality optimization. It uses template-based text slicing and visualization adjustment technology, as well as multi-way recall and re-ranking optimization strategies, which can significantly improve the accuracy of retrieval results. This optimization is crucial for scenarios that require high accuracy of answers.

RAGFlow also supports the reverse display and editing of document parsing results, allowing users to more intuitively understand the document processing process and make necessary adjustments to the results, thereby enhancing the controllability of the system.

Disadvantages of RAGFlow

Although RAGFlow performs well in document parsing, it also has some obvious limitations. First, its document parsing speed is relatively slow and prone to failure, which may become an efficiency bottleneck when processing a large number of documents.

In terms of ease of use, RAGFlow does not support low-code development and requires users to manually write workflows, which increases the usage threshold, especially for users with non-technical backgrounds. At the same time, RAGFlow has a high development threshold and requires users to adjust document parsing parameters, which may be a challenge for rapid deployment and iteration.

RAGFlow's integration capabilities are also relatively limited, mainly focusing on RAG applications, and may not be flexible enough for complex application scenarios that require integration with multiple external systems and services. Although the latest version of RAGFlow has been launched and has certain scalability and task flow orchestration capabilities, it still has room for improvement compared to other platforms.

In addition, RAGFlow's ecosystem is relatively small and has limited scalability, which may limit its applicability in a wider range of application scenarios.

Dify 's advantages and disadvantages

Advantages of Dify

The biggest advantage of Dify lies in its user-friendly development experience and comprehensive functional integration. As an open source LLM application development platform, Dify provides a low-code or even no-code development experience, allowing non-technical users to quickly get started. Through an intuitive interface and powerful functions, Dify enables developers to easily build and deploy language model-based applications.

In terms of functional completeness, Dify provides a rich recall mode, supports cross-knowledge base recall, and supports workflow orchestration and QA mode. This comprehensive set of functions makes Dify a versatile AI application development platform suitable for a variety of complex application scenarios.

Another significant advantage of Dify is its powerful tool extension capability. It provides a wealth of tool extensions, which makes it very convenient for developers to integrate various functions. Dify also emphasizes model neutrality, allowing users to use various models without restrictions. This approach enables developers to explore different paths in AI application development without being bound by specific models or frameworks.

In terms of the ecosystem, Dify has comprehensive documentation and supports the development of source code contributors, which provides a foundation for broader community participation and ecosystem expansion. Dify also includes security content review capabilities, which is particularly important for enterprise-level applications.

Disadvantages of Dify

Although Dify is feature-rich, it has some limitations in the core capabilities of RAG. Compared with RAGFlow, Dify's knowledge base retrieval effect is not good, which may affect the core quality of RAG applications. Although Dify emphasizes that the integration of RAGFlow's retrieval enhancement generation technology and Dify can provide deep document understanding and question answering capabilities, this also indirectly illustrates Dify's own shortcomings in this regard.

In terms of performance, Dify is not suitable for complex task chains, which may limit its performance in advanced RAG applications that require complex reasoning and multi-step processing. At the same time, Dify is also relatively inflexible and has limited functionality, especially compared to specialized RAG frameworks.

It is worth noting that Dify's copyright information is subject to a conditional Apache-2.0 license that prohibits its use in building multi-tenant SaaS services and does not allow the removal of copyright information. This may pose a legal obstacle for companies that want to build commercial SaaS services based on Dify.

Selection Advice: When to Use RAGFlow or Dify

Based on the above analysis, we can provide selection suggestions for different scenarios:

Scenarios suitable for choosing RAGFlow

If your needs are mainly focused on the following aspects, RAGFlow may be a better choice:

1. Professional fields that need to process complex format documents (such as scanned PDFs, forms) and have high requirements for answer accuracy, such as knowledge base question answering systems in the legal, medical and financial industries.

2. Scenarios that have extremely high requirements for document parsing quality and require accurate extraction of document structure and content.

3. Projects that pursue retrieval quality rather than development speed, especially applications with strict requirements on retrieval accuracy.

4. Existing technical teams are able to handle more complex development and adjustment work, and have special needs for customized document processing.

Suitable scenarios for choosing Dify

If your needs are more in line with the following scenarios, Dify may be more suitable:

1. Teams or individuals who need to quickly build and launch LLM applications, especially projects that pursue development efficiency.

2. In scenarios where non-technical users want to complete application development through simple configuration, there is an obvious demand for low-code/no-code.

3. Product applications that require user interface, data management, and model management, such as customer service, FAQ systems, etc.

4. Projects that require flexible construction of complex AI applications (such as multi-agent collaboration) or rapid integration with business systems.

5. Enterprises that need to integrate multiple models and tools to build comprehensive AI solutions.

Conclusion and Future Outlook

As two excellent open source frameworks, RAGFlow and Dify have their own characteristics in RAG application development. RAGFlow focuses on deep document understanding and high-quality retrieval, and is suitable for professional applications that require high document processing and answer accuracy; while Dify provides a more comprehensive and easier-to-use AI application development platform, suitable for rapid construction and deployment of a variety of AI applications.

It is worth noting that the combination of the two may bring greater value. As some studies have shown, the combination of Dify and RAGFlow can provide users with unprecedented document understanding and question answering capabilities, making the development of intelligent applications more efficient and intelligent. This combination fully utilizes RAGFlow's deep document understanding and retrieval enhancement generation capabilities and seamlessly integrates it into Dify's application development process.

As AI technology continues to advance, we can expect these two frameworks to continue to develop and improve. RAGFlow may further improve its speed and stability in document processing while enhancing its ease of use; while Dify may strengthen its knowledge base retrieval capabilities and provide more customization options. For developers and enterprises, understanding the advantages and disadvantages of these two frameworks and choosing or combining them according to specific needs will be the key to building efficient RAG applications.

References and Industry Insights

In terms of comprehensive evaluation, Dify has more complete functions and better scalability, but it needs to pay attention to copyright issues, and the poor RAG search effect also needs some optimization. RAGFlow performs well in document parsing and understanding, but it needs to be improved in terms of ease of use and development efficiency. No matter which tool you choose, you should consider the complexity of the data, development resources and business goals to ensure that you choose the solution that best suits your needs.