Introduction
qKnow is an open-source, enterprise-grade intelligent knowledge platform centered on knowledge graphs and integrated with vector knowledge bases. It aims to build a unified knowledge hub that combines "structured + unstructured" data. By deeply integrating knowledge engineering with large language model (LLM) technologies, qKnow incorporates core capabilities such as knowledge extraction, knowledge fusion, and knowledge reasoning. It efficiently extracts knowledge from both structured databases and unstructured documents, helping enterprises build intelligent knowledge systems that are semantically clear, dynamically evolving, traceable, and controllable.
The Commercial Edition of the qKnow platform has now been comprehensively upgraded. Building upon its robust knowledge graph capabilities, it innovatively introduces parallel management of multiple knowledge bases and a hybrid retrieval mechanism, enabling a dual-engine approach driven by "graph + vector" to significantly expand the breadth and depth of knowledge storage, organization, and application. Furthermore, the platform deeply integrates LLM capabilities to construct a native-AI application matrix covering knowledge-based Q&A, intelligent writing, compliance review, and more—advancing enterprise knowledge from being merely "searchable" to being "usable, writable, and reviewable," ushering in a new era of intelligent knowledge utilization.
qKnow offers both an Open-Source Edition and a Commercial Edition to meet diverse knowledge management needs across different stages of development and business complexity.
✨✨✨ Open-Source Demo ✨✨✨ https://qknow-demo.qiantong.tech, Username: qKnow, Password: qKnow123
✨✨✨ Commercial Edition Demo ✨✨✨ https://qknow-pro.qiantong.tech, Request a demo account Contact Customer Service
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Open-Source Edition vs. Commercial Edition
Open-Source Edition: Focuses on core knowledge management capabilities, including Knowledge Center, Concept & Relationship Configuration, Structured/Unstructured Knowledge Extraction, Graph Exploration, Data Source Management (supporting MySQL, Oracle), and basic system management. Lightweight, easy to use, and flexible to deploy, it's ideal for SMEs, research teams, or developers to quickly get started and build an initial enterprise knowledge graph at low cost.
Commercial Edition: Offers comprehensive features and an advanced architecture, fully supporting advanced knowledge governance capabilities such as parallel management of multiple knowledge graphs, federated retrieval across multiple knowledge bases, knowledge fusion, and knowledge reasoning. In terms of knowledge application, it deeply integrates LLM technology to provide:
- Knowledge-Based Q&A: Supports joint retrieval from multiple knowledge sources, citation tracking, and related resource recommendations for precise, traceable intelligent answers.
- Knowledge Recommendation: Proactively recommends trending, relevant, and latest knowledge based on user intent and profile, enabling knowledge to "find people."
- Intelligent Writing Assistant: Generates outlines, expands content, refines text, and exports with one click by leveraging enterprise templates and business data.
- Intelligent Document Review: Automatically identifies grammar, terminology, sensitive words, and other issues using rule engines and LLM semantic understanding, improving text quality.
- Smart Chain Workshop: Supports integration with external agents like Dify, enabling flexible orchestration and collaborative invocation of AI workflows.
The Commercial Edition also provides enterprise-grade permission control, customizable Embedding models, recall testing, and SLA-backed technical support, making it suitable for large organizations in finance, manufacturing, energy, and government sectors with high demands for knowledge accuracy, security, and intelligence.
Open-Source and Commercial Editions Complement Each Other:
- The Open-Source Edition serves as a lightweight entry point, helping users quickly validate the value of knowledge management.
- The Commercial Edition acts as an enterprise solution, offering end-to-end knowledge governance and deep AI application capabilities for scalable, intelligent knowledge asset operations.
Regardless of the edition chosen, qKnow is committed to becoming a trusted intelligent knowledge hub for enterprises, accelerating knowledge accumulation, unlocking data potential, and empowering digital transformation and intelligent decision-making.
Use Cases
Ideal for enterprises and institutions aiming to build intelligent Q&A, semantic search, and agent-based applications using knowledge graphs and vectorized knowledge bases, qKnow serves as a critical platform for advancing intelligent knowledge management and AI-integrated applications.
| Use Case | Enhanced Description |
|---|---|
| Knowledge Integration & Unified Governance | For enterprises with vast amounts of scattered documents, databases, or unstructured data, qKnow supports automatic knowledge extraction from heterogeneous sources to build a unified knowledge system, enabling centralized management, classification, and standardized governance of knowledge assets. |
| Knowledge Quality Enhancement & Intelligent Evolution | To address issues like knowledge duplication, conflicts, or information gaps, the platform leverages LLM-driven knowledge fusion and reasoning to intelligently identify redundant entities, merge synonymous terms, and predict potential relationships, continuously improving the accuracy and completeness of the knowledge graph. |
| Breaking Down Information Silos | Supports integration with multiple independent systems (e.g., document repositories, project management systems, databases). Through multi-graph management and federated retrieval across multiple knowledge bases, it breaks down data barriers, enabling unified search and cross-system, cross-format knowledge analysis. |
| Native AI Applications & Decision Support | Deeply integrates LLM capabilities to support enterprise-specific intelligent Q&A, conversational recommendations, AI-assisted writing, and compliance review—ensuring AI outputs are traceable and evidence-based, aiding efficient decision-making and content generation. |
| Accelerating Digital Transformation | Builds an operational intelligent knowledge foundation for governments and enterprises, transforming tacit experience into explicit assets. It promotes deep reuse of knowledge across business processes, customer service, and internal collaboration, enabling knowledge-driven intelligent upgrades. |
Key Advantages
| Advantage | Description |
|---|---|
| Knowledge Graphs as the Skeleton, LLMs as the Intelligence | Structured knowledge forms the cognitive framework, while LLMs drive semantic understanding and content generation, creating an intelligent hub that "thinks and converses." |
| Enterprise-Grade Capabilities, Lightweight Onboarding | The Commercial Edition supports high availability and fine-grained permissions; the Open-Source Edition is ready-to-use and low-cost, suitable for teams at any development stage. |
| Modular Design, Expandable Like LEGO | Functional components can be flexibly combined—enable knowledge extraction, graph management, intelligent Q&A, etc., as needed—supporting smooth evolution. |
| Open-Source Collaboration, Community-Driven Growth | Open and transparent code encourages community contributions, fostering a sustainable and trustworthy knowledge intelligence ecosystem. |
| Technology with Empathy, Knowledge with Context | Emphasizes knowledge provenance and context, enabling machine understanding that aligns more closely with human thinking—serving people, not replacing them. |
Core Features Overview
qKnow adopts a modular design with a clear and extensible feature architecture, encompassing eight core modules: Knowledge Graph, Knowledge Base, Knowledge Q&A, Knowledge Recommendation, Intelligent Writing Assistant, Document Intelligence Review, Smart Chain Workshop, and System Management.
Covering the entire chain of knowledge construction, governance, application, and AI integration, qKnow is ready-to-use and highly flexible, fully meeting diverse enterprise needs—from basic management to intelligent applications.
For a detailed feature list, see: Full Feature List
Tech Stack
qKnow uses a front-end/back-end separation architecture. The backend is built on Spring Boot, and the frontend on Vue 3, integrated with mainstream middleware and data tools.
| Tech Stack | Framework | Description |
|---|---|---|
| Backend | Spring Boot | Main framework, simplifies configuration and development |
| MyBatis-Plus | ORM framework, simplifies database operations | |
| Spring Framework | Core support for dependency injection, AOP, etc. | |
| Quartz | Job scheduling | |
| Spring Security | Security framework for authentication and authorization | |
| Alibaba Druid | Database connection pool for optimized performance | |
| Frontend | Vue 3 | Progressive frontend framework |
| Vite | Fast build tool, replacing Vue CLI | |
| Element Plus | UI component library | |
| Axios | HTTP client | |
| Pinia | State management (replaces Vuex) | |
| Vue Router | Frontend routing | |
| Vis | Knowledge graph visualization for dynamic, interactive charts | |
| Echarts | Data visualization library supporting multiple chart types | |
| Third-party Dependencies | DeepKE | Knowledge extraction tool using deep learning for entity and relation extraction |
| MySQL | Core relational database | |
| Neo4j | Graph database | |
| Redis | Data caching and high-performance reads | |
| Swagger | API documentation generator | |
| Docker (Optional) | Containerized deployment support |
Deployment Requirements
Before deploying qKnow, ensure the following environments and tools are properly installed:
| Environment | Item | Recommended Version | Description |
|---|---|---|---|
| Backend | JDK | 1.8 or higher | OpenJDK 8 or 11 recommended |
| Maven | 3.6+ | Project build and dependency management | |
| MySQL | 5.7 or 8.0 | Relational database | |
| Neo4j | 4.4.40 | Graph database | |
| Redis | 5.0+ | Supports caching and messaging | |
| OS | Windows / Linux / Mac | Runs on common platforms | |
| Frontend | Node.js | 16+ | Required for build tools |
| npm / pnpm / yarn | Any one | Package managers | |
| Vue CLI / Vite | Latest | Scaffolding tools |
Community
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💡 If you have suggestions or feature requests, please submit an Issue to help us improve the platform.