RagFlow is an open-source AI Retrieval Augmented Generation (RAG) framework that excels at deep document understanding. It is one of the best RAG frameworks available, offering a range of powerful features and capabilities.

Some of the key updates and features of RagFlow include:

• Support for audio file parsing, integration with various large language models, and the addition of new components like Wikipedia and Baidu to the knowledge graph.
• Workflow support based on graphs, enabling the creation of complex workflows and agents beyond traditional DAGs.
• Compatibility with a wide range of file types, including documents, text, images, and scanned copies.
• Intelligent and explainable template-based chunking, reducing hallucination and providing quick views of key references and traceable citations.
• Emphasis on maintaining data quality and ensuring accurate classification and output, leveraging deep document understanding.
• Open-source and free to use, allowing for further extension and customization.

RagFlow is an ideal choice for building generative AI into your business workflows, unlocking the full potential of RAG technology. Its automated and effortless RAG orchestration, flexible model integration, and intuitive APIs make it a powerful tool for both personal and enterprise use cases.

To get started with RagFlow, you can try the cloud-hosted demo or deploy the framework locally. The local deployment requires a system with at least 4 CPU cores, 16GB of RAM, and 50GB of disk storage, along with Docker installed.

Once set up, you can manage your knowledge base, upload files, and create custom assistants and workflows using the intuitive RagFlow dashboard. The graph-based workflow feature allows you to easily construct personalized RAG-powered agents and applications.

RagFlow is a foundational RAG engine that I highly recommend exploring. Its powerful features, open-source nature, and continuous updates make it a compelling choice for anyone looking to leverage the benefits of retrieval augmented generation in their projects or business operations.

RagFlow, the open-source AI retrieval-augmented generation (RAG) framework, has recently received a series of significant updates that enhance its capabilities. This powerful engine excels at deep document understanding and is considered one of the best RAG frameworks available.

The latest updates include:

1. Expanded Integrations:

   • Support for audio file parsing
   • Integration of new large language models
   • Addition of components like Wikipedia and Baidu to the knowledge graph

2. Workflow-based on Graph:

   • Enables the creation of complex workflows or agents beyond traditional DAG (Directed Acyclic Graph) representations
   • Improves data classification, access control, activity monitoring, and data loss prevention

3. Enhanced File Support:

   • Supports various file types, including documents, texts, images, and scanned copies
   • Intelligent and explainable template-based chunking

4. Improved Quality and Explainability:

   • Reduced hallucination through quick views of key references and traceable citations
   • Focuses on maintaining output quality and data classification accuracy based on deep document understanding

RagFlow is a completely free and open-source framework, allowing users to extend and customize it further. Its foundational RAG engine is a valuable tool for businesses and individuals looking to integrate generative AI into their workflows, ensuring truthful and well-grounded answers.

RagFlow is an open-source AI framework that enables retrieval-augmented generation (RAG) on deep document understanding. It provides a comprehensive solution for building generative AI applications that can process and understand complex data formats.

The key features of RagFlow include:

1. Automated and Effortless RAG Workflows: RagFlow streamlines the orchestration of RAG processes, making it accessible for both personal and enterprise-level use cases.

2. Flexible Model Integration: RagFlow allows you to configure and integrate various large language models and embedding models, providing flexibility in your AI model selection.

3. Multiple Recall, Pair, and Fuse Reranking Abilities: RagFlow offers advanced retrieval and ranking capabilities to ensure the most relevant information is used for generating responses.

4. Intuitive APIs for Seamless Integration: RagFlow provides easy-to-use APIs that enable smooth integration with business applications and workflows.

The RagFlow architecture follows a structured workflow:

1. Question/Query: The process starts with a user's question or query.
2. File Ingestion and Chunking: RagFlow ingests the relevant files and chunks the content into smaller, manageable pieces for efficient processing.
3. Retrieval and Ranking: The framework retrieves the most relevant information from the chunked data and ranks the results using advanced techniques.
4. Generation and Fusion: RagFlow then generates the final response by combining the retrieved information with the capabilities of the large language model.
5. Output: The generated response is provided to the user, with deep document understanding and traceability to the source materials.

RagFlow's graph-based workflow further enhances the flexibility and complexity of the system, allowing users to create custom agents and workflows tailored to their specific needs. This enables better data classification, access control, activity monitoring, and data loss prevention.

By leveraging RagFlow, businesses and individuals can seamlessly integrate generative AI capabilities into their workflows, benefiting from improved response quality, grounded answers, and deep document understanding.

RagFlow is an open-source, retrieval-augmented generation (RAG) framework that enables deep document understanding and streamlined workflows. Here's how you can get started with RagFlow:

Local Deployment

To deploy RagFlow locally, you'll need the following prerequisites:

• CPU with at least 4 cores
• 16GB+ RAM
• 50GB+ disk storage
• Docker installed

1. Clone the RagFlow repository using Git.
2. Navigate to the ragflow folder and run the Docker Compose command to set up the server.
3. Once the server is running, you'll be directed to the RagFlow dashboard where you can manage your knowledge base, chat with your assistant, and explore the graph-based workflow.

Exploring the RagFlow Demo

1. You can try out the RagFlow demo, which is fully hosted in the cloud, to get a general idea of its capabilities.
2. In the dashboard, you can create a new knowledge base, upload files, and configure the chunking, retrieval, and model settings.
3. Once your files are processed, you can start chatting with your assistant and see how RagFlow provides accurate and well-cited answers by leveraging the retrieval-augmented generation approach.
4. The graph-based workflow feature allows you to create custom agents and orchestrate complex AI-powered tasks, further enhancing the flexibility and power of RagFlow.

RagFlow's key features include:

• Automated and effortless RAG workflows
• Support for various file types (documents, images, tables, etc.)
• Intelligent and explainable template-based chunking
• Reduced hallucination and grounded answers
• Flexible integration with different language models
• Intuitive APIs for seamless business integration

By exploring the local deployment and the RagFlow demo, you can experience the power of this open-source RAG framework and consider how it can be integrated into your own workflows or business applications.

RagFlow, the open-source AI retrieval-augmented generation (RAG) framework, offers robust knowledge base management and customization capabilities that empower users to tailor the system to their specific needs. Here's a closer look at these features:

1. Knowledge Base Management: RagFlow allows you to create and manage your own knowledge bases. You can upload various file types, including documents, images, and even URLs, and the system will automatically parse and chunk the content for efficient retrieval and generation. This enables you to build a comprehensive knowledge repository tailored to your use case.

2. Model Integration: RagFlow supports the integration of a wide range of large language models, from OpenAI to NVIDIA and open-source alternatives. This flexibility allows you to choose the model that best fits your requirements, whether it's for improved accuracy, specialized domain knowledge, or specific capabilities.

3. Chunking and Retrieval Optimization: RagFlow offers intelligent and explainable template-based chunking, which helps reduce hallucination and ensures the output is grounded in the source material. Additionally, you can leverage the Raptor retrieval enhancement feature to further improve the relevance and accuracy of the answers generated.

4. Workflow Automation: The graph-based workflow feature in RagFlow enables you to create complex, automated workflows and agents that go beyond traditional DAG (Directed Acyclic Graph) representations. This allows for better data classification, access control, activity monitoring, and data loss prevention, streamlining your AI-powered processes.

5. Intuitive User Interface: RagFlow's web-based dashboard provides an intuitive and user-friendly interface for managing your knowledge bases, configuring models, and interacting with the generated responses. The dashboard makes it easy to onboard and collaborate with team members, ensuring seamless integration into your workflows.

By leveraging these powerful features, you can unlock the full potential of retrieval-augmented generation within your business or personal projects. RagFlow's open-source nature and extensive customization options make it a versatile and compelling choice for those seeking to build advanced AI-powered solutions.

The graph workflow in RAGflow is a powerful feature that enables you to create complex, personalized RAG-based assistants. This workflow allows you to drag-and-drop various components to construct your desired agent or chatbot, streamlining the process of building retrieval-augmented generation (RAG) applications.

The graph workflow provides a range of components, including retrieval, generation, answer categorization, message relevance rewriting, and keyword extraction. These components can be seamlessly integrated to create a tailored workflow that suits your specific needs, whether it's a customer service chatbot, an HR assistant, or a multilingual interpreter.

One of the key advantages of the graph workflow is its flexibility. You can start from scratch and build your agent from the ground up, or you can leverage the pre-built assistants provided by RAGflow, such as the web search assistant, to jumpstart your development. This allows you to quickly prototype and iterate on your ideas, saving valuable time and resources.

Moreover, the graph workflow supports a variety of integrations, including popular knowledge sources like Wikipedia, PubMed, and Archive.org. This enables you to seamlessly incorporate external data sources into your RAG-powered applications, ensuring that your agents have access to a rich and diverse knowledge base.

Overall, the graph workflow in RAGflow is a game-changer for anyone looking to build personalized, retrieval-augmented AI assistants. Its intuitive drag-and-drop interface, pre-built components, and integration capabilities make it a powerful tool for both individual developers and enterprise-level teams.

RagFlow is an open-source AI framework engine that excels at retrieval-augmented generation, deep document understanding, and streamlining workflows for businesses and personal use cases. The recent updates to RagFlow have significantly enhanced its capabilities, including support for audio file parsing, integration of new large language models, the ability to support workflows based on graphs, and improved support for various file formats.

One of the standout features of RagFlow is its workflow based on graphs, which enables users to create complex workflows or agents beyond traditional DAG (Directed Acyclic Graph) representations. This feature allows for better data classification, access control, activity monitoring, and data loss prevention.

RagFlow is an ideal choice for building generative AI into your business, as it unleashes the full potential of your workflows and ensures truthful question answering from complex data formats. The framework's automated and effortless rag workflows, flexible configuration of language models and embedding models, and intuitive APIs make it a powerful tool for seamless integration with businesses.

Overall, RagFlow is a foundational RAG engine that is highly recommended for anyone looking to leverage the power of retrieval-augmented generation and deep document understanding in their applications or workflows. Its open-source nature and continuous updates make it a valuable asset in the AI ecosystem.