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dataelement/bisheng

BISHENG is an open LLM devops platform for next generation Enterprise AI applications. Powerful and comprehensive features include: GenAI workflow, RAG, Agent, Unified model management, Evaluation, SFT, Dataset Management, Enterprise-level System Management, Observability and more.

本周新增
7 星
近 3 周倍数
—
距上次推送
28 天
距上次发布
—
许可证
Apache-2.0

星标增长

每根柱子是这个周期记录下来的 star 增长:优先取逐个 stargazer 到达时间分桶的到达数,还没被 stargazer 扫描覆盖的仓库改用该周期的 star 净增;两个写者都没记录的周期画成虚线空档,而不是当成 0。柱高按各自窗口的峰值归一,两张图不共用坐标轴。指向任意一根柱子, tooltip 里是该周期的九项指标:当前值与周期内的净增。

指向或点击一根柱子,看当天的九项指标。

9/209/239/269/2910/110/410/710/10

2026 年 9 月 21 日 新增 7 个星标; 2026 年 9 月 22 日 新增 5 个星标; 2026 年 9 月 23 日 新增 4 个星标; 2026 年 9 月 24 日 新增 4 个星标; 2026 年 9 月 25 日 新增 1 个星标; 2026 年 9 月 26 日 新增 1 个星标; 2026 年 9 月 27 日 新增 2 个星标; 2026 年 9 月 28 日 新增 4 个星标; 2026 年 9 月 29 日 新增 1 个星标; 2026 年 9 月 30 日 新增 2 个星标; 2026 年 10 月 1 日 新增 2 个星标; 2026 年 10 月 2 日 新增 1 个星标; 2026 年 10 月 3 日 新增 2 个星标; 2026 年 10 月 4 日 新增 2 个星标; 2026 年 10 月 5 日 新增 0 个星标; 2026 年 10 月 6 日 新增 3 个星标; 2026 年 10 月 7 日 新增 1 个星标; 2026 年 10 月 8 日 新增 -1 个星标; 2026 年 10 月 9 日 新增 1 个星标; 2026 年 10 月 10 日 新增 3 个星标

窗口内最高的一天是 2026 年 9 月 21 日,新增 7 个星标。

证据时间轴

这个项目还没有记录到异动、发布或许可证变化。

README
The document stored for this repository, with its Chinese translation when one has been produced

Proudly made by Chinese,May we, like the creators of Deepseek and Black Myth: Wukong, bring more wonder and greatness to the world.

源自中国匠心,希望我们能像 [Deepseek]、[黑神话:悟空] 团队一样,给世界带来更多美好。

Bisheng banner

license

简体中文 | English | 日本語

dataelement%2Fbisheng | Trendshift

BISHENG is an open LLM application devops platform, focusing on enterprise scenarios. It has been used by a large number of industry leading organizations and Fortune 500 companies.

"Bi Sheng" was the inventor of movable type printing, which played a vital role in promoting the transmission of human knowledge. We hope that BISHENG can also provide strong support for the widespread implementation of intelligent applications. Everyone is welcome to participate.

Features

  1. Lingsight, a general-purpose agent with expert-level taste: Through the AGL(Agent Guidance Language) framework, we embed domain experts’ preferences, experience, and business logic into the AI, enabling the agent to exhibit “expert-level understanding” when handling tasks.

sence1

  1. Unique BISHENG Workflow

    • 🧩 Independent and comprehensive application orchestration framework: Enables the execution of various tasks within a single framework (while similar products rely on bot invocation or separate chatflow and workflow modules for different tasks).
    • 🔄 Human in the loop: Allows users to intervene and provide feedback during the execution of workflows (including multi-turn conversations), whereas similar products can only execute workflows from start to finish without intervention.
    • 💥 Powerful: Supports loops, parallelism, batch processing, conditional logic, and free combination of all logic components. It also handles complex scenarios such as multi-type input/output, report generation, content review, and more.
    • 🖐️ User-friendly and intuitive: Operations like loops, parallelism, and batch processing, which require specialized components in similar products, can be easily visualized in BISHENG as a "flowchart" (drawing a loop forms a loop, aligning elements creates parallelism, and selecting multiple items enables batch processing).

    sence0

  2. Designed for Enterprise Applications: Document review, fixed-layout report generation, multi-agent collaboration, policy update comparison, support ticket assistance, customer service assistance, meeting minutes generation, resume screening, call record analysis, unstructured data governance, knowledge mining, data analysis, and more.
    The platform supports the construction of highly complex enterprise application scenarios and offers deep optimization with hundreds of components and thousands of parameters.

sence1

  1. Enterprise-grade features are the fundamental guarantee for application implementation: security review, RBAC, user group management, traffic control by group, SSO/LDAP, vulnerability scanning and patching, high availability deployment solutions, monitoring, statistics, and more.

sence2

  1. High-Precision Document Parsing: Our high-precision document parsing model is trained on a vast amount of high-quality data accumulated over past 5 years. It includes high-precision printed text, handwritten text, and rare character recognition models, table recognition models, layout analysis models, and seal models., table recognition models, layout analysis models, and seal models. You can deploy it privately for free.

sence3

  1. A community for sharing best practices across various enterprise scenarios: An open repository of application cases and best practices.

Quick start

Please ensure the following conditions are met before installing BISHENG:

  • CPU >= 4 Virtual Cores
  • RAM >= 16 GB
  • Docker 19.03.9+
  • Docker Compose 1.25.1+

Recommended hardware condition: 18 virtual cores, 48G. In addition to installing BISHENG, we will also install the following third-party components by default: ES, Milvus, and Onlyoffice.

Download BISHENG

git clone https://github.com/dataelement/bisheng.git
# Enter the installation directory
cd bisheng/docker

# If the system does not have the git command, you can download the BISHENG code as a zip file.
wget https://github.com/dataelement/bisheng/archive/refs/heads/main.zip
# Unzip and enter the installation directory
unzip main.zip && cd bisheng-main/docker

Start BISHENG

docker compose -f docker-compose.yml -p bisheng up -d

After the startup is complete, access http://IP:3001 in the browser. The login page will appear, proceed with user registration.

By default, the first registered user will become the system admin.

For more installation and deployment issues, refer to::Self-hosting

Acknowledgement

This repo benefits from langchain langflow unstructured and LLaMA-Factory . Thanks for their wonderful works.

Thank you to our contributors:

Community & contact

Welcome to join our discussion group

Wechat QR Code

页面上的每个数字都来自记录下来的 stargazer 到达时间,没有评分公式,也没有 AI 判定。 任何结论都可以用 /api/v1/repos/{id}/stats 里的原始增量自己重算。