MaxKB

MaxKB (Max Knowledge Base) is a ready-to-use Retrieval-Augmented Generation (RAG) chatbot framework designed to deliver robust knowledge-based Q&A across diverse domains. It features a flexible workflow engine, a comprehensive function library, and MCP tool-use capabilities to orchestrate AI processes for complex business scenarios. MaxKB supports document ingestion, automatic crawling, text splitting, vectorization, and seamless integration into existing systems with zero coding required. It is model-agnostic, supporting both private and public LLMs, and is widely applicable to intelligent customer service, internal knowledge bases, academic research, and education.

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Key Capabilities

• Ready-to-use RAG chatbot with strong workflow orchestration and MCP tool-use
• Direct document uploads and automatic crawling of online documents
• Automatic text splitting and vectorization for efficient retrieval
• Retrieval-Augmented Generation to reduce hallucinations and improve answer quality
• Flexible orchestration to meet complex business process needs
• Zero-coding, rapid integration into third-party systems
• Model-agnostic: supports private models (e.g., DeepSeek, Llama, Qwen) and public models (e.g., OpenAI, Claude, Gemini)
• Strong applicability to customer service, knowledge bases, academia, and education

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How to Get Started

1. Install/Access MaxKB through the appropriate deployment channel (GitHub project or official distribution).
2. Upload documents or configure automatic crawling to populate the knowledge base.
3. Configure the RAG workflow using the built-in workflow engine and function library.
4. Integrate with your systems using zero-coding connectors to enable AI-powered Q&A within your apps or services.
5. Test and iterate to tune retrieval settings, model selection, and MCP tool usage for optimal results.

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Use Cases

• Intelligent customer service Q&A powered by your corporate knowledge base
• Internal knowledge management and employee support
• Academic research assistance and study aids
• Education-focused tutoring and information retrieval

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Safety and Considerations

• Model choices and data handling should comply with your organization’s privacy and security policies.
• Regularly review outputs for accuracy and update knowledge sources to minimize outdated information.

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Core Features

• Ready-to-use RAG chatbot framework with robust workflow engine
• Direct document uploads and automatic web crawling for knowledge ingestion
• Automatic text splitting and vectorization for efficient retrieval
• Retrieval-Augmented Generation to improve answer quality and reduce hallucinations
• Flexible orchestration for complex business scenarios via MCP tool-use
• Zero-coding rapid integration into third-party systems
• Model-agnostic support for private and public LLMs
• Suitable for customer service, internal knowledge bases, academia, and education

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Technical Highlights

• Input: Documents (upload) and dynamic online sources
• Processing: Text splitting, embedding/vectorization, and indexing
• Retrieval: Efficient querying against your knowledge base
• Generation: LLM-based responses enhanced by retrieved context
• Orchestration: Custom workflows combining tools, APIs, and modules
• Integration: Plug-and-play with existing systems without coding