I am currently working on entrepreneurship with RhinoLink-Tech / 连犀智语, focusing on AI-native data semantic governance and intelligent execution platforms.
My work centers on transforming fragmented enterprise data, business semantics, rules, knowledge assets, and workflows into a connected, explainable, governable, and executable intelligent system.
AI Agent + Data Semantics + Knowledge Network + Governance + Intelligent Execution
I have 12 years of engineering experience across AI Agent systems, data infrastructure, data virtualization, big data engines, cloud-native systems, and distributed storage.
| Direction | Description |
|---|---|
| 🧠 AI-native Data Semantic Governance | Build semantic layers for enterprise data intelligence |
| 🤖 Enterprise AI Agent Infrastructure | Connect data, tools, knowledge, and execution |
| 🔎 RAG-based Knowledge Retrieval | Retrieve metadata, metrics, documents, and knowledge assets |
| 🕸️ Knowledge Network & Semantic Modeling | Organize business semantics into reusable knowledge networks |
| 🧩 Task Engine & Intelligent Execution | Transform governance problems into executable tasks |
| ⚙️ Data Virtualization & Federated Query | Provide unified access to heterogeneous data sources |
| ☁️ Cloud-native Data Infrastructure | Build scalable and reliable data systems |
I am serving RhinoLink-Tech / 连犀智语 in building an AI-native platform that connects data, semantics, knowledge, governance tasks, and execution workflows.
The goal is not to build just another chatbot, but to build an intelligent data workbench that can:
- understand enterprise business semantics
- organize knowledge into reusable networks
- turn governance problems into executable tasks
- connect data discovery, data Q&A, data analysis, governance, and action
- make AI outputs traceable, explainable, and auditable
Semovix is positioned as a data semantic governance and intelligent workbench platform.
It focuses on:
- semantic understanding
- metadata governance
- knowledge network construction
- task-driven governance workflows
- intelligent execution
- enterprise AI workbench
From Semantics to Action
让语义走向行动
Xino is the intelligent assistant and official IP of the platform.
It represents an enterprise AI work partner that understands business semantics, coordinates tasks, and helps users move from understanding to execution.
| Domain | Keywords |
|---|---|
| AI Agent | LangChain, ReAct, Plan-and-Execute, Tool Calling, Memory |
| RAG | Chroma, FAISS, HyDE, Query Rewriting, Embeddings |
| Knowledge Graph | Neo4j, SPARQL, Entity Modeling, Graph Reasoning |
| Data Semantics | Metadata, Business Terms, Metrics, Ontology, Semantic Mapping |
| Data Governance | Data Assets, Data Lineage, Data Quality, Data Security |
| Data Infrastructure | Trino, Presto, OpenLookeng, Hive, Spark |
| Cloud Native | Docker, Kubernetes, Helm |
| Distributed Systems | Ceph, MinIO, HDFS, S3, Raft, CRUSH |
| Programming | C/C++, Go, Java, Python |
An intelligent data semantic platform focused on data discovery, semantic understanding, governance, and AI-powered interaction.
Focus Areas:
Data Semantics · Metadata · Data Discovery · RAG · Knowledge Graph · Data Governance · AI Workbench
An enterprise-oriented Agent foundation project focused on decision-agent infrastructure, context engineering, knowledge networks, and governed execution.
Focus Areas:
AI Agent · Decision Agent · Context Engineering · Knowledge Network · Tool Orchestration · Governed Execution
A natural-language-driven data interaction direction that helps users find, understand, and query enterprise data.
Keywords:
AI Agent · RAG · Knowledge Graph · Metadata · Data Governance · Federated Query
A unified data access layer for heterogeneous data sources, supporting cross-source query, metadata management, query optimization, permission control, and data exploration.
Keywords:
Trino · Presto · Spark · Hive · OpenLookeng · Federated Query · Data Governance
A distributed object storage and cloud-native infrastructure direction focused on high-performance backend architecture and large-scale data systems.
Keywords:
C/C++ · S3 · Ceph · MinIO · HDFS · Raft · CRUSH · Kubernetes
I maintain and contribute to projects around:
- AI-native data semantic governance
- enterprise AI Agent infrastructure
- intelligent data discovery and data Q&A
- semantic data platforms and knowledge networks
- task-driven governance and intelligent execution
- data virtualization and federated query
- cloud-native data infrastructure
My GitHub activity reflects continuous exploration in AI Agent, data semantics, knowledge networks, and intelligent data infrastructure.
- AI Agent for enterprise data intelligence
- data semantic governance and semantic modeling
- RAG + Knowledge Graph for trusted data interaction
- task engine and intelligent execution workflows
- AI-native development and product engineering
- open-source intelligent data infrastructure
AI should not only answer questions.
It should understand business, organize knowledge, and drive action.
Data Semantics + Knowledge Network + AI Agent + Governance + Execution
- GitHub: https://github.com/LuckyCaptain-go
- Company: https://github.com/RhinoLink-Tech
- Email: 332901848@qq.com
正在建设面向企业智能的数据语义治理与智能执行平台
AI Agent · 数据语义 · RAG · 知识图谱 · 数据治理 · 智能执行
我目前主要在创业，服务于 RhinoLink-Tech / 连犀智语，聚焦于 AI 原生数据语义治理平台 与 智能执行系统 的建设。
我的工作重点是将企业中分散的数据、业务语义、规则、知识资产和流程任务连接起来，构建一个可理解、可解释、可治理、可执行的智能系统。
AI Agent + 数据语义 + 知识网络 + 数据治理 + 智能执行
我拥有 12 年工程经验，长期积累于 AI Agent、数据基础设施、数据虚拟化、大数据引擎、云原生系统和分布式存储等方向。
- AI 原生数据语义治理
- 企业级 AI Agent 基础设施
- 基于 RAG 的元数据与知识检索
- 知识网络与语义建模
- 任务引擎与智能执行
- 数据虚拟化与联邦查询
- 云原生数据基础设施
我正在服务 RhinoLink-Tech / 连犀智语，建设面向企业的数据语义治理与智能执行平台。
我们的目标不是做一个简单的聊天机器人，而是构建一个能够连接数据、语义、知识、治理任务和执行流程的智能数据工作台。
它应该能够：
- 理解企业业务语义
- 将知识组织成可复用的网络
- 将治理问题转化为可执行任务
- 连接数据发现、数据问答、数据分析、数据治理与行动
- 让 AI 输出可追溯、可解释、可审计
Semovix 定位为数据语义治理与智能工作台平台。
核心关注：
- 语义理解
- 元数据治理
- 知识网络构建
- 任务驱动的数据治理流程
- 智能执行
- 企业 AI 工作台
From Semantics to Action
让语义走向行动
Xino 是平台的智能助手与官方 IP。
它代表一种企业 AI 工作伙伴：能够理解业务语义、协同任务，并帮助用户从理解走向执行。
| 领域 | 关键词 |
|---|---|
| AI Agent | LangChain, ReAct, Plan-and-Execute, Tool Calling, Memory |
| RAG | Chroma, FAISS, HyDE, Query Rewriting, Embeddings |
| 知识图谱 | Neo4j, SPARQL, 实体建模, 图推理 |
| 数据语义 | 元数据, 业务术语, 指标, 本体, 语义映射 |
| 数据治理 | 数据资产, 数据血缘, 数据质量, 数据安全 |
| 数据基础设施 | Trino, Presto, OpenLookeng, Hive, Spark |
| 云原生 | Docker, Kubernetes, Helm |
| 分布式系统 | Ceph, MinIO, HDFS, S3, Raft, CRUSH |
| 编程语言 | C/C++, Go, Java, Python |
一个面向数据发现、语义理解、数据治理与 AI 交互的智能数据语义平台。
方向：
数据语义 · 元数据 · 数据发现 · RAG · 知识图谱 · 数据治理 · AI 工作台
一个面向企业级 Agent 的基础框架，关注决策 Agent、上下文工程、知识网络与受控执行。
方向：
AI Agent · 决策 Agent · 上下文工程 · 知识网络 · 工具编排 · 受控执行
面向企业数据场景的自然语言交互方向，帮助用户完成数据发现、数据理解和数据查询。
关键词：
AI Agent · RAG · 知识图谱 · 元数据 · 数据治理 · 联邦查询
面向异构数据源的统一数据访问层，支持跨源查询、元数据管理、查询优化、权限控制与数据探查。
关键词：
Trino · Presto · Spark · Hive · OpenLookeng · 联邦查询 · 数据治理
面向大规模数据系统的分布式对象存储与云原生基础设施方向，关注高性能后端架构与系统可靠性。
关键词：
C/C++ · S3 · Ceph · MinIO · HDFS · Raft · CRUSH · Kubernetes
我维护和参与的项目主要围绕：
- AI 原生数据语义治理
- 企业级 AI Agent 基础设施
- 智能数据发现与数据问答
- 语义数据平台与知识网络
- 任务驱动的数据治理与智能执行
- 数据虚拟化与联邦查询
- 云原生数据基础设施
我的 GitHub 主要体现了我在 AI Agent、数据语义、知识网络和智能数据基础设施 方向的持续探索。
- 企业数据智能中的 AI Agent
- 数据语义治理与语义建模
- RAG + 知识图谱驱动的可信数据交互
- 任务引擎与智能执行流程
- AI 原生开发与产品工程
- 开源智能数据基础设施
AI 不应该只回答问题。
它应该理解业务、组织知识，并驱动行动。
数据语义 + 知识网络 + AI Agent + 数据治理 + 智能执行
- GitHub: https://github.com/LuckyCaptain-go
- Company: https://github.com/RhinoLink-Tech
- Email: 332901848@qq.com