1. Project Overview

"Zhīliǎo" is a lightweight knowledge management application developed by the Ziyou Zhiyi team for small and medium-sized teams. The Ziyou Zhiyi team is a research group consisting primarily of faculty members and graduate students from Beijing University of Posts and Telecommunications. "Zhīliǎo" emphasizes "knowledge management" in its functionality and strives for a "lightweight" user experience.

2. Open Source License

Released under the BSD-3-Clause open-source license

3. Screenshots

Product Screenshots

4. Feature Overview

"Zhīliǎo" is built around the entire lifecycle of knowledge management, focusing on knowledge sharing and reuse. It provides knowledge bases and knowledge-sharing communities for small and medium-sized teams while ensuring essential knowledge management capabilities. The product's main features are as follows:

• Knowledge Aggregation: Consolidate knowledge from multiple sources and build knowledge assets;
• Knowledge Discovery: Convenient and accurate searches with hierarchical search capabilities;
• Knowledge Sharing: Convenient sharing within teams and controlled isolation between teams.

"Zhīliǎo" targets small and medium-sized teams. While ensuring essential knowledge management functionality, it adopts a simpler and easier-to-maintain technical architecture, strives for a clean and intuitive UI, and lowers the barrier to entry for users, achieving comprehensive "lightweight" design:

• Focused Features: Retain core functionality while reducing usage costs;
• Easy Deployment: Container-based deployment for quick, one-click setup;
• Simple Maintenance: A streamlined system architecture with built-in system management.

Characteristic Functions

Updated on April 7, 2022

"Zhīliǎo" now officially supports attachment parsing (including text-based formats such as Word, Excel, TXT, and PDF) and providesFull-text search across attachment contents and comments, with logo indicators in search results identifying the source of each resource. The resource details page also displays the attachment parsing status.

Updated on December 26, 2021

This update of "Zhīliǎo" introduces the ability to export resources as PDF files.

Updated on October 15, 2021

The powerful Books feature in "Zhīliǎo" makes it easy to organize large numbers of scattered resources and bring them together for convenient future knowledge browsing, editing, sharing, and other management tasks.

Detailed content, please refer to README.md document.

User Guide for the "Books" Feature

Ziyou Zhiyi has a great tradition: every team member turns their valuable learning achievements in a particular field into tutorials. As a result, the team has accumulated a large number of valuable resources over the years. For example, the team's recent Introduction to Practical Big Data Analytics lecture series was consolidated into a substantial book, providing team members and future newcomers with a rich resource pool ” 。

In future versions of "Zhīliǎo", we will provide an open API, allowing users to conveniently gather the knowledge they need and organize it into customized "Books" , such as datasets, teacher information, work documents, announcements, and notices. At the same time, we aim to technically implement the process of Automation "discovery — parsing — classification — aggregation", enabling every "Book" to be updated efficiently and continuously.

Functional Overview Diagram

5. Tech Stack

Technical Architecture

Directory explanation

├─config: Project configuration files
│
├─kmses: ES module (pending decoupling)
│
├─kmstika: Attachment parsing module
│
├─knowledgems: Web module
│
└─zhi-docker: Docker deployment solution
    ├─docker-compose.yml: Container orchestration configuration
    │
    ├─zhies: ES image configuration
    │
    ├─zhimysql: MySQL image configuration
    │
    └─zhiweb: Web image configuration

Installation and Deployment

The current version provides a Standalone deployment mode based on Docker, enabling one-click deployment of the project and all its dependencies. A Cluster deployment mode is being considered for future versions.

1. Deployment Environment

• A Linux host with 2 CPU cores and 4 GB RAM, using an x86/x64 architecture and running CentOS 7 or Ubuntu 20.04;
• Docker 20.10.0 or later;
• Docker-Compose 1.25.5 or later.

2. Pre-deployment Preparation

Detailed content, please refer to README.md document.

3. Installation and Deployment

Detailed content, please refer to README.md document.