Kinyoo
dd254fab68
Brings the main line's 14 commits onto the design-system branch. Where the two disagreed, the rule was: take the incoming behaviour, keep this branch's semantic tokens. - The knowledge space header and its file list: incoming wins outright, since the whole point is to land the list-view rework here. FileTable is deleted, FileListView/FileListRow/FileListToolbar arrive, and the hex those ported files carried is converted to the semantic tokens this branch runs on (#1d2129/#4e5969/#86909c/#e5e6eb/#f7f8fa → text-1/text-2/text-3/border-base/fill-1). #8a94a6, #ebebeb and #ECECEC stay: no semantic token maps to them. - DropdownMenuCheckboxItem keeps this branch's tokens while taking the incoming indicatorSide behaviour. - ChannelPreviewDrawer keeps its tokens and takes the incoming removal of the `rejected` branch (a rejected application must be resubmittable). - The two square cards take incoming whole — the missing-icon and broken-favicon fixes are behaviour, and their new #A9AEB8 has no token. - useFileUpload and useWebsocket take incoming: those are the main line's lint cleanup, already the newer form. Suppressions pruned after the merge; client and @bisheng/ui typecheck clean, client lint reports nothing. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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 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
- 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.
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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).
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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.
- 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.
- 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.
- 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
