GitHub 1C Enterprise Daily Trending

MervinPraison/PraisonAI

2024-07-17T01:28:47Z

PraisonAI application combines AutoGen and CrewAI or similar frameworks into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human-agent collaboration.

Praison AI

Praison AI, leveraging both AutoGen and CrewAI or any other agent framework, represents a low-code, centralised framework designed to simplify the creation and orchestration of multi-agent systems for various LLM applications, emphasizing ease of use, customization, and human-agent interaction.

	Cookbook	Open in Colab
Basic	PraisonAI
Include Tools	PraisonAI Tools

TL;DR

pip install praisonai
export OPENAI_API_KEY="Enter your API key"
praisonai --init create a movie script about dog in moon
praisonai

Installation

pip install praisonai

Initialise

export OPENAI_API_KEY="Enter your API key"

Generate your OPENAI API KEY from here: https://platform.openai.com/api-keys

Note: You can use other providers such as Ollama, Mistral ... etc. Details are provided at the bottom.

praisonai --init create a movie script about dog in moon

This will automatically create agents.yaml file in the current directory.

To initialse with a specific agent framework (Optional):

praisonai --framework autogen --init create movie script about cat in mars

Run

praisonai

python -m praisonai

Specify the agent framework (Optional):

praisonai --framework autogen

Full Automatic Mode

praisonai --auto create a movie script about Dog in Moon

User Interface

PraisonAI User Interfaces:

Interface	Description	URL
UI	Multi Agents such as CrewAI or AutoGen	https://docs.praisonai.com/ui/ui
Chat	Chat with 100+ LLMs, single AI Agent	https://docs.praisonai.com/ui/chat
Code	Chat with entire Codebase, single AI Agent	https://docs.praisonai.com/ui/code

pip install -U "praisonai[ui]"
export OPENAI_API_KEY="Enter your API key"
chainlit create-secret
export CHAINLIT_AUTH_SECRET=xxxxxxxx
praisonai ui

python -m praisonai ui

Praison AI Chat

https://docs.praison.ai/chat/

pip install "praisonai[chat]"
export OPENAI_API_KEY="Enter your API key"
praisonai chat

Create Custom Tools

https://docs.praison.ai/tools/custom/

Step 1: Pre-requisite to Create a Custom Tool

agents.yaml file should be present in the current directory.

If it doesn't exist, create it by running the command praisonai --init research about the latest AI News and prepare a detailed report.

Step 2: to Create a Custom Tool

Create a file called tools.py in the same directory as the agents.yaml file.

# example tools.py
from duckduckgo_search import DDGS
from praisonai_tools import BaseTool

class InternetSearchTool(BaseTool):
    name: str = "InternetSearchTool"
    description: str = "Search Internet for relevant information based on a query or latest news"

    def _run(self, query: str):
        ddgs = DDGS()
        results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)
        return results

Step 3: to Create a Custom Tool

Add the tool to the agents.yaml file as show below under the tools section - InternetSearchTool.

framework: crewai
topic: research about the latest AI News and prepare a detailed report
roles:
  research_analyst:
    backstory: Experienced in gathering and analyzing data related to AI news trends.
    goal: Analyze AI News trends
    role: Research Analyst
    tasks:
      gather_data:
        description: Conduct in-depth research on the latest AI News trends from reputable
          sources.
        expected_output: Comprehensive report on current AI News trends.
    tools:
    - InternetSearchTool

Agents Playbook

Simple Playbook Example

framework: crewai
topic: Artificial Intelligence
roles:
  screenwriter:
    backstory: 'Skilled in crafting scripts with engaging dialogue about {topic}.'
    goal: Create scripts from concepts.
    role: Screenwriter
    tasks:
      scriptwriting_task:
        description: 'Develop scripts with compelling characters and dialogue about {topic}.'
        expected_output: 'Complete script ready for production.'

Use 100+ Models

https://docs.praison.ai/models/

Include praisonai package in your project

Option 1: Using RAW YAML

from praisonai import PraisonAI

# Example agent_yaml content
agent_yaml = """
framework: "crewai"
topic: "Space Exploration"

roles:
  astronomer:
    role: "Space Researcher"
    goal: "Discover new insights about {topic}"
    backstory: "You are a curious and dedicated astronomer with a passion for unraveling the mysteries of the cosmos."
    tasks:
      investigate_exoplanets:
        description: "Research and compile information about exoplanets discovered in the last decade."
        expected_output: "A summarized report on exoplanet discoveries, including their size, potential habitability, and distance from Earth."
"""

# Create a PraisonAI instance with the agent_yaml content
praisonai = PraisonAI(agent_yaml=agent_yaml)

# Run PraisonAI
result = praisonai.run()

# Print the result
print(result)

Option 2: Using separate agents.yaml file

Note: Please create agents.yaml file before hand.

from praisonai import PraisonAI

def basic(): # Basic Mode
    praisonai = PraisonAI(agent_file="agents.yaml")
    praisonai.run()

if __name__ == "__main__":
    basic()

Commands to Install Dependencies:

Install all dependencies, including dev dependencies:
```
poetry install
```
Install only documentation dependencies:
```
poetry install --with docs
```
Install only test dependencies:
```
poetry install --with test
```
Install only dev dependencies:
```
poetry install --with dev
```

This configuration ensures that your development dependencies are correctly categorized and installed as needed.

Contributing

Fork on GitHub: Use the "Fork" button on the repository page.
Clone your fork: git clone https://github.com/yourusername/praisonAI.git
Create a branch: git checkout -b new-feature
Make changes and commit: git commit -am "Add some feature"
Push to your fork: git push origin new-feature
Submit a pull request via GitHub's web interface.
Await feedback from project maintainers.

Star History

PaddlePaddle/Paddle

2024-07-17T01:28:47Z

Bringing Characters to Life with Computer Brains in Unity

AI4Animation: Deep Learning for Character Control

This repository explores the opportunities of deep learning for character animation and control. It aims to be a comprehensive framework for data-driven character animation, including data processing, neural network training and runtime control, developed in Unity3D / PyTorch. The various projects below demonstrate such capabilities using neural networks for animating biped locomotion, quadruped locomotion, and character-scene interactions with objects and the environment, plus sports and fighting games, as well as embodied avatar motions in AR/VR. Further advances on this research will continue being added to this project.

SIGGRAPH 2024
Categorical Codebook Matching for Embodied Character Controllers
_{Sebastian Starke, Paul Starke, Nicky He, Taku Komura, Yuting Ye, ACM Trans. Graph. 43, 4, Article 142.}

Translating motions from a real user onto a virtual embodied avatar is a key challenge for character animation in the metaverse. In this work, we present a novel generative framework that enables mapping from a set of sparse sensor signals to a full body avatar motion in real-time while faithfully preserving the motion context of the user. In contrast to existing techniques that require training a motion prior and its mapping from control to motion separately, our framework is able to learn the motion manifold as well as how to sample from it at the same time in an end-to-end manner. To achieve that, we introduce a technique called codebook matching which matches the probability distribution between two categorical codebooks for the inputs and outputs for synthesizing the character motions. We demonstrate this technique can successfully handle ambiguity in motion generation and produce high quality character controllers from unstructured motion capture data. Our method is especially useful for interactive applications like virtual reality or video games where high accuracy and responsiveness are needed.

- Video - Paper - Dataset - Code (+VR Demo) - Windows Demo - Mac Demo - ReadMe -

Unlike existing methods for kinematic character control that learn a direct mapping between inputs and outputs or utilize a motion prior that is trained on the motion data alone, our framework learns from both the inputs and outputs simultaneously to form a motion manifold that is informed about the control signals. To learn such setup in a supervised manner, we propose a technique that we call Codebook Matching which enforces similarity between both latent probability distributions $Z_𝑋$ and $Z_𝑌$. In the context of motion generation, instead of directly predicting the motions outputs from the control inputs, we only predict their probabilities for each of them to appear. By introducing a matching loss between both categorical probability distributions, our codebook matching technique allows to substitute $Z_𝑌$ by $Z_𝑋$ during test time.

Training:
\begin{cases}
    Y \rightarrow Z_Y \rightarrow Y
    \\
    X \rightarrow Z_X
    \\
    Z_X \sim Z_Y
\end{cases}

Inference: 
X \rightarrow Z_X \rightarrow Y

Our method is not limited to three-point inputs but we can also use it to generate embodied character movements with additional joystick or button controls by what we call hybrid control mode. In this setting, the user, engineer or artist can additionally tell the character where to go via a simple goal location while preserving the original context of motion from three-point tracking signals. This changes the scope of applications we can address by walking / running / crouching in the virtual world while standing or even sitting in the real world.

Furthermore, our codebook matching architecture shares many similarities with motion matching and is able to learn a similar structure in an end-to-end manner. While motion matching can bypass ambiguity in the mapping from control to motion by selecting among candidates with similar query distances, our setup selects possible outcomes from predicted probabilities and naturally projects against valid output motions if their probabilities are similar. However, in contrast to database searches, our codebook matching is able to effectively compress the motion data where same motions map to same codes, and can bypass ambiguity issues which existing learning-based methods such as standard feed-forward networks (MLP) or variational models (CVAE) may struggle with. We demonstrate such capabilities by reconstructing the ambiguous toy example functions in the figure below.

SIGGRAPH 2022
DeepPhase: Periodic Autoencoders for Learning Motion Phase Manifolds
_{Sebastian Starke, Ian Mason, Taku Komura, ACM Trans. Graph. 41, 4, Article 136.}

Learning the spatial-temporal structure of body movements is a fundamental problem for character motion synthesis. In this work, we propose a novel neural network architecture called the Periodic Autoencoder that can learn periodic features from large unstructured motion datasets in an unsupervised manner. The character movements are decomposed into multiple latent channels that capture the non-linear periodicity of different body segments while progressing forward in time. Our method extracts a multi-dimensional phase space from full-body motion data, which effectively clusters animations and produces a manifold in which computed feature distances provide a better similarity measure than in the original motion space to achieve better temporal and spatial alignment. We demonstrate that the learned periodic embedding can significantly help to improve neural motion synthesis in a number of tasks, including diverse locomotion skills, style-based movements, dance motion synthesis from music, synthesis of dribbling motions in football, and motion query for matching poses within large animation databases.

- Video - Paper - PAE Code & Demo - Animation Code & Demo - Explanation and Addendum - Tutorial -

- Motion In-Betweening System -

SIGGRAPH 2021
Neural Animation Layering for Synthesizing Martial Arts Movements
_{Sebastian Starke, Yiwei Zhao, Fabio Zinno, Taku Komura, ACM Trans. Graph. 40, 4, Article 92.}

Interactively synthesizing novel combinations and variations of character movements from different motion skills is a key problem in computer animation. In this research, we propose a deep learning framework to produce a large variety of martial arts movements in a controllable manner from raw motion capture data. Our method imitates animation layering using neural networks with the aim to overcome typical challenges when mixing, blending and editing movements from unaligned motion sources. The system can be used for offline and online motion generation alike, provides an intuitive interface to integrate with animator workflows, and is relevant for real-time applications such as computer games.

- Video - Paper -

SIGGRAPH 2020
Local Motion Phases for Learning Multi-Contact Character Movements
_{Sebastian Starke, Yiwei Zhao, Taku Komura, Kazi Zaman. ACM Trans. Graph. 39, 4, Article 54.}

Not sure how to align complex character movements? Tired of phase labeling? Unclear how to squeeze everything into a single phase variable? Don't worry, a solution exists!

Controlling characters to perform a large variety of dynamic, fast-paced and quickly changing movements is a key challenge in character animation. In this research, we present a deep learning framework to interactively synthesize such animations in high quality, both from unstructured motion data and without any manual labeling. We introduce the concept of local motion phases, and show our system being able to produce various motion skills, such as ball dribbling and professional maneuvers in basketball plays, shooting, catching, avoidance, multiple locomotion modes as well as different character and object interactions, all generated under a unified framework.

- Video - Paper - Code - Windows Demo - ReadMe -

SIGGRAPH Asia 2019
Neural State Machine for Character-Scene Interactions
_{Sebastian Starke⁺, He Zhang⁺, Taku Komura, Jun Saito. ACM Trans. Graph. 38, 6, Article 178.}
_{^{(+Joint First Authors)}}

Animating characters can be an easy or difficult task - interacting with objects is one of the latter. In this research, we present the Neural State Machine, a data-driven deep learning framework for character-scene interactions. The difficulty in such animations is that they require complex planning of periodic as well as aperiodic movements to complete a given task. Creating them in a production-ready quality is not straightforward and often very time-consuming. Instead, our system can synthesize different movements and scene interactions from motion capture data, and allows the user to seamlessly control the character in real-time from simple control commands. Since our model directly learns from the geometry, the motions can naturally adapt to variations in the scene. We show that our system can generate a large variety of movements, icluding locomotion, sitting on chairs, carrying boxes, opening doors and avoiding obstacles, all from a single model. The model is responsive, compact and scalable, and is the first of such frameworks to handle scene interaction tasks for data-driven character animation.

- Video - Paper - Code & Demo - Mocap Data - ReadMe -

SIGGRAPH 2018
Mode-Adaptive Neural Networks for Quadruped Motion Control
_{He Zhang⁺, Sebastian Starke⁺, Taku Komura, Jun Saito. ACM Trans. Graph. 37, 4, Article 145.}
_{^{(+Joint First Authors)}}

Animating characters can be a pain, especially those four-legged monsters! This year, we will be presenting our recent research on quadruped animation and character control at the SIGGRAPH 2018 in Vancouver. The system can produce natural animations from real motion data using a novel neural network architecture, called Mode-Adaptive Neural Networks. Instead of optimising a fixed group of weights, the system learns to dynamically blend a group of weights into a further neural network, based on the current state of the character. That said, the system does not require labels for the phase or locomotion gaits, but can learn from unstructured motion capture data in an end-to-end fashion.

- Video - Paper - Code - Mocap Data - Windows Demo - Linux Demo - Mac Demo - ReadMe -

- Animation Authoring Tool -

SIGGRAPH 2017
Phase-Functioned Neural Networks for Character Control
_{Daniel Holden, Taku Komura, Jun Saito. ACM Trans. Graph. 36, 4, Article 42.}

This work continues the recent work on PFNN (Phase-Functioned Neural Networks) for character control. A demo in Unity3D using the original weights for terrain-adaptive locomotion is contained in the Assets/Demo/SIGGRAPH_2017/Original folder. Another demo on flat ground using the Adam character is contained in the Assets/Demo/SIGGRAPH_2017/Adam folder. In order to run them, you need to download the neural network weights from the link provided in the Link.txt file, extract them into the /NN folder, and store the parameters via the custom inspector button.

- Video - Paper - Code (Unity) - Windows Demo - Linux Demo - Mac Demo -

Thesis Fast Forward Presentation from SIGGRAPH 2020

Copyright Information

This project is only for research or education purposes, and not freely available for commercial use or redistribution. The motion capture data is available only under the terms of the Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.

free-educa/books

2024-07-17T01:28:47Z

Secure, cross-platform Git credential storage with authentication to GitHub, Azure Repos, and other popular Git hosting services.

Git Credential Manager

Git Credential Manager (GCM) is a secure Git credential helper built on .NET that runs on Windows, macOS, and Linux. It aims to provide a consistent and secure authentication experience, including multi-factor auth, to every major source control hosting service and platform.

GCM supports (in alphabetical order) Azure DevOps, Azure DevOps Server (formerly Team Foundation Server), Bitbucket, GitHub, and GitLab. Compare to Git's built-in credential helpers (Windows: wincred, macOS: osxkeychain, Linux: gnome-keyring/libsecret), which provide single-factor authentication support for username/password only.

GCM replaces both the .NET Framework-based Git Credential Manager for Windows and the Java-based Git Credential Manager for Mac and Linux.

Install

See the installation instructions for the current version of GCM for install options for your operating system.

Current status

Git Credential Manager is currently available for Windows, macOS, and Linux*. GCM only works with HTTP(S) remotes; you can still use Git with SSH:

Feature	Windows	macOS	Linux*
Installer/uninstaller	✓	✓	✓
Secure platform credential storage (see more)	✓	✓	✓
Multi-factor authentication support for Azure DevOps	✓	✓	✓
Two-factor authentication support for GitHub	✓	✓	✓
Two-factor authentication support for Bitbucket	✓	✓	✓
Two-factor authentication support for GitLab	✓	✓	✓
Windows Integrated Authentication (NTLM/Kerberos) support	✓	N/A	N/A
Basic HTTP authentication support	✓	✓	✓
Proxy support	✓	✓	✓
`amd64` support	✓	✓	✓
`x86` support	✓	N/A	✗
`arm64` support	best effort	✓	best effort, no packages
`armhf` support	N/A	N/A	best effort, no packages

(*) GCM guarantees support only for the Linux distributions that are officially supported by dotnet.

Supported Git versions

Git Credential Manager tries to be compatible with the broadest set of Git versions (within reason). However there are some know problematic releases of Git that are not compatible.

Git 1.x

The initial major version of Git is not supported or tested with GCM.
Git 2.26.2

This version of Git introduced a breaking change with parsing credential configuration that GCM relies on. This issue was fixed in commit 12294990 of the Git project, and released in Git 2.27.0.

How to use

Once it's installed and configured, Git Credential Manager is called implicitly by Git. You don't have to do anything special, and GCM isn't intended to be called directly by the user. For example, when pushing (git push) to Azure DevOps, Bitbucket, or GitHub, a window will automatically open and walk you through the sign-in process. (This process will look slightly different for each Git host, and even in some cases, whether you've connected to an on-premises or cloud-hosted Git host.) Later Git commands in the same repository will re-use existing credentials or tokens that GCM has stored for as long as they're valid.

Read full command line usage here.

Configuring a proxy

See detailed information here.

Additional Resources

See the documentation index for links to additional resources.

Experimental Features

Windows broker (experimental)

Future features

Curious about what's coming next in the GCM project? Take a look at the project roadmap! You can find more details about the construction of the roadmap and how to interpret it here.

Contributing

This project welcomes contributions and suggestions. See the contributing guide to get started.

This project follows GitHub's Open Source Code of Conduct.

License

We're MIT licensed. When using GitHub logos, please be sure to follow the GitHub logo guidelines.

thedevdojo/auth

2024-07-18T01:29:19Z

This is the repo for the DevDojo Auth package

ROCm/HIP

2024-07-17T01:29:29Z

HIP: C++ Heterogeneous-Compute Interface for Portability

What is this repository for?

HIP is a C++ Runtime API and Kernel Language that allows developers to create portable applications for AMD and NVIDIA GPUs from single source code.

Key features include:

HIP is very thin and has little or no performance impact over coding directly in CUDA mode.
HIP allows coding in a single-source C++ programming language including features such as templates, C++11 lambdas, classes, namespaces, and more.
HIP allows developers to use the "best" development environment and tools on each target platform.
The HIPIFY tools automatically convert source from CUDA to HIP.
Developers can specialize for the platform (CUDA or AMD) to tune for performance or handle tricky cases.

New projects can be developed directly in the portable HIP C++ language and can run on either NVIDIA or AMD platforms. Additionally, HIP provides porting tools which make it easy to port existing CUDA codes to the HIP layer, with no loss of performance as compared to the original CUDA application. HIP is not intended to be a drop-in replacement for CUDA, and developers should expect to do some manual coding and performance tuning work to complete the port.

DISCLAIMER

The information presented in this document is for informational purposes only and may contain technical inaccuracies, omissions, and typographical errors. The information contained herein is subject to change and may be rendered inaccurate for many reasons, including but not limited to product and roadmap changes, component and motherboard versionchanges, new model and/or product releases, product differences between differing manufacturers, software changes, BIOS flashes, firmware upgrades, or the like. Any computer system has risks of security vulnerabilities that cannot be completely prevented or mitigated.AMD assumes no obligation to update or otherwise correct or revise this information. However, AMD reserves the right to revise this information and to make changes from time to time to the content hereof without obligation of AMD to notify any person of such revisions or changes.THIS INFORMATION IS PROVIDED ‘AS IS.” AMD MAKES NO REPRESENTATIONS OR WARRANTIES WITH RESPECT TO THE CONTENTS HEREOF AND ASSUMES NO RESPONSIBILITY FOR ANY INACCURACIES, ERRORS, OR OMISSIONS THAT MAY APPEAR IN THIS INFORMATION. AMD SPECIFICALLY DISCLAIMS ANY IMPLIED WARRANTIES OF NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR ANY PARTICULAR PURPOSE. IN NO EVENT WILL AMD BE LIABLE TO ANY PERSON FOR ANY RELIANCE, DIRECT, INDIRECT, SPECIAL, OR OTHER CONSEQUENTIAL DAMAGES ARISING FROM THE USE OF ANY INFORMATION CONTAINED HEREIN, EVEN IF AMD IS EXPRESSLY ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. AMD, the AMD Arrow logo, and combinations thereof are trademarks of Advanced Micro Devices, Inc. Other product names used in this publication are for identification purposes only and may be trademarks of their respective companies.

Repository branches

The HIP repository maintains several branches. The branches that are of importance are:

develop branch: This is the default branch, on which the new features are still under development and visible. While this maybe of interest to many, it should be noted that this branch and the features under development might not be stable.
Main branch: This is the stable branch. It is up to date with the latest release branch, for example, if the latest HIP release is rocm-4.3, main branch will be the repository based on this release.
Release branches. These are branches corresponding to each ROCM release, listed with release tags, such as rocm-4.2, rocm-4.3, etc.

Release tagging

HIP releases are typically naming convention for each ROCM release to help differentiate them.

rocm x.yy: These are the stable releases based on the ROCM release. This type of release is typically made once a month.*

More Info

Installation
HIP FAQ
HIP Kernel Language
HIP Porting Guide
HIP Porting Driver Guide
HIP Programming Guide
HIP Logging
Building HIP From Source
HIP Debugging
HIP RTC
HIP Terminology (including Rosetta Stone of GPU computing terms across CUDA/HIP/OpenCL)
HIPIFY
Supported CUDA APIs:
- Runtime API
- Driver API
- cuComplex API
- Device API
- cuBLAS
- cuRAND
- cuDNN
- cuFFT
- cuSPARSE
Developer/CONTRIBUTING Info
Release Notes

How do I get set up?

See the Installation notes.

Simple Example

The HIP API includes functions such as hipMalloc, hipMemcpy, and hipFree. Programmers familiar with CUDA will also be able to quickly learn and start coding with the HIP API. Compute kernels are launched with the "hipLaunchKernelGGL" macro call. Here is simple example showing a snippet of HIP API code:

hipMalloc(&A_d, Nbytes);
hipMalloc(&C_d, Nbytes);

hipMemcpy(A_d, A_h, Nbytes, hipMemcpyHostToDevice);

const unsigned blocks = 512;
const unsigned threadsPerBlock = 256;
hipLaunchKernelGGL(vector_square,   /* compute kernel*/
                dim3(blocks), dim3(threadsPerBlock), 0/*dynamic shared*/, 0/*stream*/,     /* launch config*/
                C_d, A_d, N);  /* arguments to the compute kernel */

hipMemcpy(C_h, C_d, Nbytes, hipMemcpyDeviceToHost);

The HIP kernel language defines builtins for determining grid and block coordinates, math functions, short vectors, atomics, and timer functions. It also specifies additional defines and keywords for function types, address spaces, and optimization controls (See the HIP Kernel Language for a full description). Here's an example of defining a simple 'vector_square' kernel.

template <typename T>
__global__ void
vector_square(T *C_d, const T *A_d, size_t N)
{
    size_t offset = (blockIdx.x * blockDim.x + threadIdx.x);
    size_t stride = blockDim.x * gridDim.x;

    for (size_t i=offset; i<N; i+=stride) {
        C_d[i] = A_d[i] * A_d[i];
    }
}

The HIP Runtime API code and compute kernel definition can exist in the same source file - HIP takes care of generating host and device code appropriately.

HIP Portability and Compiler Technology

HIP C++ code can be compiled with either,

On the NVIDIA CUDA platform, HIP provides header file in the repository hipother which translate from the HIP runtime APIs to CUDA runtime APIs. The header file contains mostly inlined functions and thus has very low overhead - developers coding in HIP should expect the same performance as coding in native CUDA. The code is then compiled with nvcc, the standard C++ compiler provided with the CUDA SDK. Developers can use any tools supported by the CUDA SDK including the CUDA profiler and debugger.
On the AMD ROCm platform, HIP provides a header and runtime library built on top of HIP-Clang compiler in the repository Common Language Runtime (CLR). The HIP runtime implements HIP streams, events, and memory APIs, and is a object library that is linked with the application. The source code for all headers and the library implementation is available on GitHub. HIP developers on ROCm can use AMD's ROCgdb for debugging and profiling.

Thus HIP source code can be compiled to run on either platform. Platform-specific features can be isolated to a specific platform using conditional compilation. Thus HIP provides source portability to either platform. HIP provides the hipcc compiler driver which will call the appropriate toolchain depending on the desired platform.

Examples and Getting Started

A sample and blog that uses any of HIPIFY tools to convert a simple app from CUDA to HIP:
```
cd samples/01_Intro/square
# follow README / blog steps to hipify the application.
```
Guide to Porting a New Cuda Project

More Examples

The GitHub repository HIP-Examples contains a hipified version of benchmark suite. Besides, there are more samples in Github HIP samples, showing how to program with different features, build and run.

Tour of the HIP Directories

include:
- hip_runtime_api.h : Defines HIP runtime APIs and can be compiled with many standard Linux compilers (GCC, ICC, CLANG, etc), in either C or C++ mode.
- hip_runtime.h : Includes everything in hip_runtime_api.h PLUS hipLaunchKernelGGL and syntax for writing device kernels and device functions. hip_runtime.h can be compiled using a standard C++ compiler but will expose a subset of the available functions.
- amd_detail/** , nvidia_detail/** : Implementation details for specific platforms. HIP applications should not include these files directly.
bin: Tools and scripts to help with hip porting
- hipcc : Compiler driver that can be used to replace nvcc in existing CUDA code. hipcc will call nvcc or HIP-Clang depending on platform and include appropriate platform-specific headers and libraries.
- hipconfig : Print HIP configuration (HIP_PATH, HIP_PLATFORM, HIP_COMPILER, HIP_RUNTIME, CXX config flags, etc.)
docs: Documentation - markdown and doxygen info.

Reporting an issue

Use the GitHub issue tracker. If reporting a bug, include the output of "hipconfig --full" and samples/1_hipInfo/hipInfo (if possible).

strawberrymusicplayer/strawberry

2024-07-17T01:29:29Z

🍓 Strawberry Music Player

Strawberry is a music player and music collection organizer. It is a fork of Clementine released in 2018 aimed at music collectors and audiophiles. It's written in C++ using the Qt toolkit.

Resources:

Website: https://www.strawberrymusicplayer.org/
Wiki: https://wiki.strawberrymusicplayer.org/
Forum: https://forum.strawberrymusicplayer.org/
Github: https://github.com/strawberrymusicplayer/strawberry
Buildbot: https://buildbot.strawberrymusicplayer.org/
Latest builds: https://builds.strawberrymusicplayer.org/
openSUSE buildservice: https://build.opensuse.org/package/show/home:jonaski:audio/strawberry
Ubuntu PPA: https://launchpad.net/~jonaski/+archive/ubuntu/strawberry
Ubuntu Unstable PPA: https://launchpad.net/~jonaski/+archive/ubuntu/strawberry-unstable

‼ Opening an issue

Read the FAQ: https://wiki.strawberrymusicplayer.org/wiki/FAQ
Search for the issue to see if it is already solved, or if there is an open issue for it already. If there is an open issue already, you can comment on it if you have additional information that could be useful to us.
For technical problems, discussion, questions and feature suggestions use the forum (https://forum.strawberrymusicplayer.org/) instead. The forum is better suited for discussion.
We do not take feature requests from users on GitHub. Any issues related to feature requests will be closed. This does not necessarily mean that we won't add new features, but we don't have time to take feature requests or answer questions about new features from users. It is still possible to suggest or discuss new features on the forum (https://forum.strawberrymusicplayer.org/).
We do not maintain the Flatpak package. Do not report issues related to Flatpak unless the issue can be reproduced with a native package, use Flatpak support instead https://flatpak.org/about/

💰 Sponsoring

The program is free software, released under GPL. If you like this program and can make use of it, consider sponsoring or donating to help fund the project. There are currently 4 options for sponsoring:

Funding developers is a way to contribute to open source projects you appreciate, it helps developers get the resources they need, and recognize contributors working behind the scenes to make open source better for everyone.

✔ Features

Play and organize music
Supports WAV, FLAC, WavPack, Ogg FLAC, Ogg Vorbis, Ogg Opus, Ogg Speex, MPC, TrueAudio, AIFF, MP4, MP3, ASF and Monkey's Audio.
Audio CD playback
Native desktop notifications
Playlist management
Smart and dynamic playlists
Advanced audio output and device configuration for bit-perfect playback on Linux
In-player song loudness analysis and song playback loudness normalization, as per EBU R 128
Edit tags on audio files
Fetch tags from MusicBrainz
Album cover art from Last.fm, Musicbrainz, Discogs, Musixmatch, Deezer, Tidal, Qobuz and Spotify
Song lyrics from Genius, Musixmatch, ChartLyrics, lyrics.ovh, lololyrics.com, songlyrics.com, azlyrics.com and elyrics.net
Support for multiple backends
Audio analyzer
Audio equalizer
Transfer music to mass-storage USB players, MTP compatible devices and iPod Nano/Classic
Scrobbler with support for Last.fm, Libre.fm and ListenBrainz
Subsonic, Tidal, Spotify and Qobuz streaming support

It has so far been tested to work on Linux, OpenBSD, FreeBSD, macOS and Windows.

Access to macOS and Windows releases are currently restricted to sponsors, a 5 USD monthly sponsorship is required. You can sponsor strawberry through Patreon for direct access to new releases. If you are sponsoring through GitHub, Ko-fi or PayPal, please e-mail support AT strawberrymusicplayer.org for access to downloads.

❗ Requirements

To build Strawberry from source you need the following installed on your system with the additional development packages/headers:

CMake
C/C++ compiler (GCC, Clang or MSVC)
pkg-config or pkgconf
Boost
GLib
Qt 6 or Qt 5.12 or higher with components Core, Gui, Widgets, Concurrent, Network and Sql
SQLite 3.9 or newer
Protobuf
ALSA (Required on Linux)
D-Bus (Required on Linux)
GStreamer or VLC
TagLib 1.11.1 or higher or TagParser
ICU

Optional dependencies:

Song fingerprinting and MusicBrainz tagging: Chromaprint
Moodbar: fftw3
PulseAudio integration: PulseAudio
Audio CD: libcdio
MTP devices: libmtp
iPod Classic devices: libgpod
EBU R 128 loudness normalization libebur128

You should also install the gstreamer plugins base and good, and optionally bad, ugly and libav to support all audio formats.

🔧 Compiling from source

Get the code:

git clone --recursive https://github.com/strawberrymusicplayer/strawberry

Compile and install:

cd strawberry
mkdir build
cd build
cmake .. -DBUILD_WITH_QT6=ON
make -j $(nproc)
sudo make install

Strawberry is backwards compatible with Qt 5, to compile with Qt 5 use:

cmake .. -DBUILD_WITH_QT5=ON

To compile on Windows with Visual Studio 2019 or 2022, see https://github.com/strawberrymusicplayer/strawberry-msvc

🐧 Packaging status

IvorySQL/IvorySQL

2024-07-18T01:29:26Z

Open Source Oracle Compatible PostgreSQL.

IvorySQL is advanced, fully featured, open source Oracle compatible PostgreSQL with a firm commitment to always remain 100% compatible and a Drop-in replacement of the latest PostgreSQL. IvorySQL adds a “compatible_db” toggle switch to switch between Oracle and PostgreSQL compatibility modes. One of the highlights of IvorySQL is PL/iSQL procedural language that supports oracle’s PL/SQL syntax and Oracle style Packages.

The IvorySQL project is released under the Apache 2 license and encourages all types of contributions. For IvorySQL community no contribution is too small, and we want to thank all our community contributors.

We are committed to following the principles of the open source way

We are committed to abiding by the principles of open-source ways and we strongly believe in building a healthy and inclusive community. We maintain that good ideas can come from anywhere, and the best ideas should win. Only by including diverse perspectives, we can reach the best decision. While the first version of IvorySQL is mainly focused on Oracle Compatibility features, going forward the future road map and feature set will be determined by the community in an open-source way.

Installation

We recommend following our Quick Start for how to install and running IvorySQL.

Furthermore, for more detailed installation instructions, please refer to the Installation Docs. We provide four installation methods for IvorySQL, as outlined below:

Contributing to the IvorySQL

There are plenty of ways to contribute to IvorySQL. You can contribute by providing the documentation updates, by providing the translations for the documentation. If you have design skills you can contribute to the IvorySQL website project. Testing the IvorySQL and reporting issues or by issuing pull requests for bug fixes or new features or answering the questions on mailing lists are some ways to contribute to the IvorySQL project and all types of contributions are welcomed and appreciated by the IvorySQL community.

Join IvorySQL mailing lists to get started

Documentation

Please check the online documentation.

Omkar-Sonawane-23/Maa-Vimala

2024-07-17T01:29:53Z

Maa-Vimala

Calling all keyboard enthusiasts! 🚀 Ever wish you could conquer the digital realm without lifting a finger off your beloved keys? Well, say hello to our latest creation—a game-changing tool for developers who live for the thrill of a keyboard shortcut.

Features

Perform Search using Keyword like
- 📺 Youtube
- 🔍 Google
- 📚 Wikipedia
Use only one word to perform Word Search (Without Space)
Perform Calculations 🧮
Weather Information ☁️
Set ToDo's (Coming Soon ❗)

How To Add This on a New Tab

git clone https://github.com/Web403/Maa-Vimala

path/to/Maa-Vimala/index.html Go To Browser -> Setting search for Home page set to Custom Url Paste your Path To Index File.

Contributing

Pull requests are welcome. Read Contribute.md File First For major changes, please open an issue first to discuss what you would like to change.

How To Use It?

Just Start Typing
Press Enter After Complete.
Watch the video for more 📽️.

![Watch the video]

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

Join the Discord

https://discord.gg/uD7x8Mah

twmb/franz-go

2024-07-17T01:31:00Z

franz-go contains a feature complete, pure Go library for interacting with Kafka from 0.8.0 through 3.6+. Producing, consuming, transacting, administrating, etc.

franz-go - A complete Apache Kafka client written in Go

Franz-go is an all-encompassing Apache Kafka client fully written Go. This library aims to provide every Kafka feature from Apache Kafka v0.8.0 onward. It has support for transactions, regex topic consuming, the latest partitioning strategies, data loss detection, closest replica fetching, and more. If a client KIP exists, this library aims to support it.

This library attempts to provide an intuitive API while interacting with Kafka the way Kafka expects (timeouts, etc.).

Features

Feature complete client (Kafka >= 0.8.0 through v3.4+)
Full Exactly-Once-Semantics (EOS)
Idempotent & transactional producers
Simple (legacy) consumer
Group consumers with eager (roundrobin, range, sticky) and cooperative (cooperative-sticky) balancers
All compression types supported: gzip, snappy, lz4, zstd
SSL/TLS provided through custom dialer options
All SASL mechanisms supported (GSSAPI/Kerberos, PLAIN, SCRAM, and OAUTHBEARER)
Low-level admin functionality supported through a simple Request function
High-level admin package with many helper types to make cluster administration easy.
Utilizes modern & idiomatic Go (support for contexts, variadic configuration options, ...)
Highly performant by avoiding channels and goroutines where not necessary
Written in pure Go (no wrapper lib for a C library or other bindings)
Ability to add detailed log messages or metrics using hooks
Plug-in metrics support for prometheus, zap, etc.
An admin client with many helper functions for easy admin tasks
A schema registry client and convenience Serde type for encoding and decoding

Works with any Kafka compatible brokers:

Redpanda: the fastest and most efficient Kafka compatible event streaming platform
Kafka: the original Java project
Microsoft Event Hubs
- Event Hubs does not support producing with compression; be sure to use kgo.ProducerBatchCompression(kgo.NoCompression).
Amazon MSK

Install

This repo contains multiple tags to allow separate features to be developed and released independently. The main client is in franz-go. Plugins are released from plugin/{plugin}. The raw-protocol package is released from pkg/kmsg, and the admin package is released from pkg/kadm.

The main client is located in the package github.com/twmb/franz-go/pkg/kgo, while the root of the project is at github.com/twmb/franz-go. There are a few extra packages within the project, as well as a few sub-modules. To use the main kgo package,

go get github.com/twmb/franz-go

To use a plugin,

go get github.com/twmb/franz-go/plugin/kzap

To use kadm,

go get github.com/twmb/franz-go/pkg/kadm

As an example, your require section in go.mod may look like this:

require (
	github.com/twmb/franz-go v1.12.0
	github.com/twmb/franz-go/pkg/kmsg v1.4.0
)

Getting started

Here's a basic overview of producing and consuming:

seeds := []string{"localhost:9092"}
// One client can both produce and consume!
// Consuming can either be direct (no consumer group), or through a group. Below, we use a group.
cl, err := kgo.NewClient(
	kgo.SeedBrokers(seeds...),
	kgo.ConsumerGroup("my-group-identifier"),
	kgo.ConsumeTopics("foo"),
)
if err != nil {
	panic(err)
}
defer cl.Close()

ctx := context.Background()

// 1.) Producing a message
// All record production goes through Produce, and the callback can be used
// to allow for synchronous or asynchronous production.
var wg sync.WaitGroup
wg.Add(1)
record := &kgo.Record{Topic: "foo", Value: []byte("bar")}
cl.Produce(ctx, record, func(_ *kgo.Record, err error) {
	defer wg.Done()
	if err != nil {
		fmt.Printf("record had a produce error: %v\n", err)
	}

})
wg.Wait()

// Alternatively, ProduceSync exists to synchronously produce a batch of records.
if err := cl.ProduceSync(ctx, record).FirstErr(); err != nil {
	fmt.Printf("record had a produce error while synchronously producing: %v\n", err)
}

// 2.) Consuming messages from a topic
for {
	fetches := cl.PollFetches(ctx)
	if errs := fetches.Errors(); len(errs) > 0 {
		// All errors are retried internally when fetching, but non-retriable errors are
		// returned from polls so that users can notice and take action.
		panic(fmt.Sprint(errs))
	}

	// We can iterate through a record iterator...
	iter := fetches.RecordIter()
	for !iter.Done() {
		record := iter.Next()
		fmt.Println(string(record.Value), "from an iterator!")
	}

	// or a callback function.
	fetches.EachPartition(func(p kgo.FetchTopicPartition) {
		for _, record := range p.Records {
			fmt.Println(string(record.Value), "from range inside a callback!")
		}

		// We can even use a second callback!
		p.EachRecord(func(record *kgo.Record) {
			fmt.Println(string(record.Value), "from a second callback!")
		})
	})
}

This only shows producing and consuming in the most basic sense, and does not show the full list of options to customize how the client runs, nor does it show transactional producing / consuming. Check out the examples directory for more!

API reference documentation can be found on . Supplementary information can be found in the docs directory:

docs
├── admin requests — an overview of how to issue admin requests
├── metrics and logging — a small writeup on how to enable metrics & logging in franz-go, as well as a few thoughts on latency tracking
├── package layout — describes the packages in franz-go
├── producing and consuming — descriptions of producing & consuming & the guarantees
└── transactions — a description of transactions and the safety even in a pre-KIP-447 world

Who uses this?

In alphabetical order,

If you use this library and want on the list above, please either open a PR or comment on #142!

Version Pinning

By default, the client issues an ApiVersions request on connect to brokers and defaults to using the maximum supported version for requests that each broker supports. If you want to pin to an exact version, you can use the MaxVersions option.

Kafka 0.10.0 introduced the ApiVersions request; if you are working with brokers older than that, you must use the kversions package. Use the MaxVersions option for the client if you do so.

Metrics & logging

Note there exists plug-in packages that allow you to easily add prometheus metrics, go-metrics, zap logging, etc. to your client! See the plugin directory for more information! These plugins are provided under dedicated modules, e.g. github.com/twmb/franz-go/plugin/kprom@v1.0.0.

The franz-go client takes a neutral approach to metrics by providing hooks that you can use to plug in your own metrics.

All connections, disconnections, reads, writes, and throttles can be hooked into, as well as per-batch produce & consume metrics. If there is an aspect of the library that you wish you could have insight into, please open an issue and we can discuss adding another hook.

Hooks allow you to log in the event of specific errors, or to trace latencies, count bytes, etc., all with your favorite monitoring systems.

In addition to hooks, logging can be plugged in with a general Logger interface. A basic logger is provided if you just want to write to a given file in a simple format. All logs have a message and then key/value pairs of supplementary information. It is recommended to always use a logger and to use LogLevelInfo.

See this example for an expansive example of integrating with prometheus! Alternatively, see this example for how to use the plug-in prometheus package!

Benchmarks

This client is quite fast; it is the fastest and most cpu and memory efficient client in Go.

For 100 byte messages,

This client is 4x faster at producing than confluent-kafka-go, and up to 10x-20x faster (at the expense of more memory usage) at consuming.
This client is 2.5x faster at producing than sarama, and 1.5x faster at consuming.
This client is 2.4x faster at producing than segment's kafka-go, and anywhere from 2x to 6x faster at consuming.

To check benchmarks yourself, see the bench example. This example lets you produce or consume to a cluster and see the byte / record rate. The compare subdirectory shows comparison code.

Supported KIPs

Theoretically, this library supports every (non-Java-specific) client facing KIP. Any KIP that simply adds or modifies a protocol is supported by code generation.

KIP	Kafka release	Status
KIP-1 — Disallow acks > 1	0.8.3	Supported & Enforced
KIP-4 — Request protocol changes	0.9.0 through 0.10.1	Supported
KIP-8 — Flush method on Producer	0.8.3	Supported
KIP-12 — SASL & SSL	0.9.0	Supported
KIP-13 — Throttling (on broker)	0.9.0	Supported
KIP-15 — Close with a timeout	0.9.0	Supported (via context)
KIP-19 — Request timeouts	0.9.0	Supported
KIP-22 — Custom partitioners	0.9.0	Supported
KIP-31 — Relative offsets in message sets	0.10.0	Supported
KIP-32 — Timestamps in message set v1	0.10.0	Supported
KIP-35 — ApiVersion	0.10.0	Supported
KIP-40 — ListGroups and DescribeGroups	0.9.0	Supported
KIP-41 — max.poll.records	0.10.0	Supported (via PollRecords)
KIP-42 — Producer & consumer interceptors	0.10.0	Partial support (hooks)
KIP-43 — SASL PLAIN & handshake	0.10.0	Supported
KIP-48 — Delegation tokens	1.1	Supported
KIP-54 — Sticky partitioning	0.11.0	Supported
KIP-57 — Fix lz4	0.10.0	Supported
KIP-62 — background heartbeats & improvements	0.10.1	Supported
KIP-70 — On{Assigned,Revoked}	0.10.1	Supported
KIP-74 — Fetch response size limits	0.10.1	Supported
KIP-78 — ClusterID in Metadata	0.10.1	Supported
KIP-79 — List offsets for times	0.10.1	Supported
KIP-81 — Bound fetch memory usage	WIP	Supported (through a combo of options)
KIP-82 — Record headers	0.11.0	Supported
KIP-84 — SASL SCRAM	0.10.2	Supported
KIP-86 — SASL Callbacks	0.10.2	Supported (through callback fns)
KIP-88 — OffsetFetch for admins	0.10.2	Supported
KIP-91 — Intuitive producer timeouts	2.1	Supported (as a matter of opinion)
KIP-97 — Backwards compat for old brokers	0.10.2	Supported
KIP-98 — EOS	0.11.0	Supported
KIP-101 — OffsetForLeaderEpoch v0	0.11.0	Supported
KIP-102 — Consumer close timeouts	0.10.2	Supported (via context)
KIP-107 — DeleteRecords	0.11.0	Supported
KIP-108 — CreateTopic validate only field	0.10.2	Supported
KIP-110 — zstd	2.1	Supported
KIP-112 — Broker request protocol changes	1.0	Supported
KIP-113 — LogDir requests	1.0	Supported
KIP-117 — Admin client	0.11.0	Supported (via kmsg)
KIP-124 — Request rate quotas	0.11.0	Supported
KIP-126 — Ensure proper batch size after compression	0.11.0	Supported (avoided entirely)
KIP-133 — Describe & Alter configs	0.11.0	Supported
KIP-140 — ACLs	0.11.0	Supported
KIP-144 — Broker reconnect backoff	0.11.0	Supported
KIP-152 — More SASL; SASLAuthenticate	1.0	Supported
KIP-183 — Elect preferred leaders	2.2	Supported
KIP-185 — Idempotency is default	1.0	Supported
KIP-192 — Cleaner idempotence semantics	1.0	Supported
KIP-195 — CreatePartitions	1.0	Supported
KIP-204 — DeleteRecords via admin API	1.1	Supported
KIP-207 — New error in ListOffsets	2.2	Supported
KIP-219 — Client-side throttling	2.0	Supported
KIP-222 — Group operations via admin API	2.0	Supported
KIP-226 — Describe configs v1	1.1	Supported
KIP-227 — Incremental fetch	1.1	Supported
KIP-229 — DeleteGroups	1.1	Supported
KIP-249 — Delegation tokens in admin API	2.0	Supported
KIP-255 — SASL OAUTHBEARER	2.0	Supported
KIP-266 — Fix indefinite consumer timeouts	2.0	Supported (via context)
KIP-279 — OffsetForLeaderEpoch bump	2.0	Supported
KIP-289 — Default group.id to null	2.2	Supported
KIP-294 — TLS verification	2.0	Supported (via dialer)
KIP-302 — Use multiple addrs for resolved hostnames	2.1	Supported (via dialer)
KIP-320 — Fetcher: detect log truncation	2.1	Supported
KIP-322 — DeleteTopics disabled error code	2.1	Supported
KIP-339 — IncrementalAlterConfigs	2.3	Supported
KIP-341 — Sticky group bugfix	?	Supported
KIP-342 — OAUTHBEARER extensions	2.1	Supported
KIP-345 — Static group membership	2.4	Supported
KIP-357 — List ACLs per principal via admin API	2.1	Supported
KIP-360 — Safe epoch bumping for `UNKNOWN_PRODUCER_ID`	2.5	Supported
KIP-361 — Allow disable auto topic creation	2.3	Supported
KIP-368 — Periodically reauthenticate SASL	2.2	Supported
KIP-369 — An always round robin produce partitioner	2.4	Supported
KIP-373 — Users can create delegation tokens for others	3.3	Supported
KIP-380 — Inter-broker protocol changes	2.2	Supported
KIP-389 — Group max size error	2.2	Supported
KIP-392 — Closest replica fetching w/ rack	2.2	Supported
KIP-394 — Require member.id for initial join request	2.2	Supported
KIP-396 — Commit offsets manually	2.4	Supported
KIP-405 — Kafka Tiered Storage	3.5	Supported (protos)
KIP-412 — Dynamic log levels w/ IncrementalAlterConfigs	2.4	Supported
KIP-429 — Incremental rebalance (see KAFKA-8179)	2.4	Supported
KIP-430 — Authorized ops in DescribeGroups	2.3	Supported
KIP-447 — Producer scalability for EOS	2.5	Supported
KIP-455 — Replica reassignment API	2.4	Supported
KIP-460 — Leader election API	2.4	Supported
KIP-464 — CreateTopic defaults	2.4	Supported
KIP-467 — Per-record error codes when producing	2.4	Supported (and ignored)
KIP-480 — Sticky partition producing	2.4	Supported
KIP-482 — Tagged fields (KAFKA-8885)	2.4	Supported
KIP-496 — OffsetDelete admin command	2.4	Supported
KIP-497 — New AlterISR API	2.7	Supported
KIP-498 — Max bound on reads	?	Supported
KIP-511 — Client name/version in ApiVersions request	2.4	Supported
KIP-514 — Bounded Flush	2.4	Supported (via context)
KIP-516 — Topic IDs	???	Supported as it is implemented
KIP-518 — List groups by state	2.6	Supported
KIP-519 — Configurable SSL "engine"	2.6	Supported (via dialer)
KIP-525 — CreateTopics v5 returns configs	2.4	Supported
KIP-526 — Reduce metadata lookups	2.5	Supported
KIP-533 — Default API timeout (total time, not per request)	2.5	Supported (via RetryTimeout)
KIP-546 — Client Quota APIs	2.5	Supported
KIP-554 — Broker side SCRAM APIs	2.7	Supported
KIP-559 — Protocol info in sync/join	2.5	Supported
KIP-568 — Explicit rebalance triggering on the consumer	2.6	Supported
KIP-569 — Docs & type in DescribeConfigs	2.6	Supported
KIP-570 — Leader epoch in StopReplica	2.6	Supported
KIP-580 — Exponential backoff	2.6	Supported
KIP-584 — Versioning scheme for features	?	Supported (nothing to do yet)
KIP-588 — Producer recovery from txn timeout	2.7	Supported
KIP-590 — Envelope (broker only)	2.7	Supported
KIP-595 — New APIs for raft protocol	2.7	Supported
KIP-599 — Throttling on create/delete topic/partition	2.7	Supported
KIP-602 — Use all resolved addrs by default	2.6	Supported (via dialer)
KIP-651 — Support PEM	2.7	Supported (via dialer)
KIP-654 — Aborted txns with unflushed data is not fatal	2.7	Supported (default behavior)
KIP-664 — Describe producers / etc.	2.8 (mostly)	Supported
KIP-679 — Strongest producer guarantee by default	3.0	Supported (by default always)
KIP-699 — Batch FindCoordinators	3.0	Supported
KIP-700 — DescribeCluster	2.8	Supported
KIP-704 — AlterISR => AlterPartition	3.2	Supported
KIP-709 — Batch OffsetFetch	3.0	Supported
KIP-730 - AllocateProducerIDs	3.0	Supported
KIP-734 — Support MaxTimestamp in ListOffsets	3.0	Supported (simple version bump)
KIP-735 — Bump default session timeout	?	Supported
KIP-778 — KRaft Upgrades (protocol changes only)	3.2	Supported
KIP-784 — Add ErrorCode to DescribeLogDirs response	3.1	Supported
KIP-792 — Generation field in consumer group protocol	3.4	Supported
KIP-794 — Better sticky partitioning	3.3	Supported (`UniformBytesPartitioner`)
KIP-800 — Reason in Join/Leave group	3.1	Supported
KIP-814 — SkipAssignment for static group leaders	3.1	Supported
KIP-827 — `DescribeLogDirs.{Total,Usable}Bytes`	3.3	Supported
KIP-836 — `DescribeQuorum` voter lag info	3.3	Supported
KIP-841 — `AlterPartition.TopicID`	3.3	Supported
KIP-848 — Next gen consumer rebalance protocol	3.7	Unsupported (proto supported)
KIP-866 — ZK to Raft RPC changes	3.4	Supported
KIP-893 — Nullable structs in the protocol	3.5	Supported
KIP-899 — Allow clients to rebootstrap	?	Supported (`UpdateSeedBrokers`)
KIP-903 — Stale broker epoch fencing	3.5	Supported (proto)
KIP-919 — Admin client talk to KRaft , Controller registration	3.7	Supported (proto)
KIP-951 — Leader discovery optimizations	3.7	Supported

Missing from above but included in librdkafka is:

KIP-85, which does not seem relevant for franz-go
KIP-92 for consumer lag metrics, which is better suited for an external system via the admin api
KIP-223 for more metrics
KIP-235, which is confusing but may be implement via a custom dialer and custom kerberos?
KIP-359 to verify leader epoch when producing; this is easy to support but actually is not implemented in Kafka yet
KIP-421 for dynamic values in configs; librdkafka mentions it does not support it, and neither does franz-go for the same reason (we do not use a config file)
KIP-436 is about yet another metric
KIP-517, more metrics

trigaten/The_Prompt_Report

2024-07-18T01:31:23Z

The Prompt Report Code Repository

Generative Artificial Intelligence (GenAI) systems are being increasingly deployed across all parts of industry and research settings. Developers and end users interact with these systems through the use of prompting or prompt engineering. While prompting is a widespread and highly researched concept, there exists conflicting terminology and a poor ontological understanding of what constitutes a prompt due to the area’s nascency. This repository is the code for The Prompt Report, our research that establishes a structured understanding of prompts, by assembling a taxonomy of prompting techniques and analyzing their use. This code allows for the automated review of papers, the collection of data, and the running of experiments. Our dataset is available on Hugging Face and our paper is available on ArXiv.org. Information is also available on our website.

Prompt Engineering Survey

Install requirements

after cloning, run pip install -r requirements.txt from root

Setting up API keys

Make a file at root called .env.

For OpenAI: https://platform.openai.com/docs/quickstart
For Hugging Face: https://huggingface.co/docs/hub/security-tokens, also run huggingface-cli login
For Sematic Scholar: https://www.semanticscholar.org/product/api#api-key

Use the reference example.env file to fill in your API keys/tokens.

OPENAI_API_KEY=sk.-...
SEMANTIC_SCHOLAR_API_KEY=...
HF_TOKEN=...

Setting up keys for running tests

Then to load the .env file, type:
pip install pytest-dotenv

You can also choose to update the env file by doing:
py.test --envfile path/to/.env

In the case that you have several .env files, create a new env_files in the pytest config folder and type:

env_files =
.env
.test.env
.deploy.env

Structure of the Repository

The script main.py calls the necessary functions to download all the papers, deduplicate and filter them, and then run all the experiments.

The core of the repository is in src/prompt_systematic_review. The config_data.py script contains configurations that are important for running experiments and saving time. You can see in main.py how some of these options are used.

The source folder is divided into 4 main sections: 3 scripts (automated_review.py, collect_papers.py,config_data.py) that deal with collecting the data and running the automated review, the utils folder that contains utility functions that are used throughout the repository, the get_papers folder that contains the scripts to download the papers, and the experiments folder that contains the scripts to run the experiments.

At the root, there is a data folder. It comes pre-loaded with some data that is used for the experiments, however the bulk of the dataset can either be generated by running main.py or by downloading the data from Hugging Face. It is in data/experiments_output that the results of the experiments are saved.

Notably, the keywords used in the automated review/scraping process are in src/prompt_systematic_review/utils/keywords.py. Anyone who wishes to run the automated review can adjust these keywords to their liking in that file.

Running the code

TLDR;

git clone https://github.com/trigaten/Prompt_Systematic_Review.git && cd Prompt_Systematic_Review
pip install -r requirements.txt
# create a .env file with your API keys
nano .env
git lfs install
git clone https://huggingface.co/datasets/PromptSystematicReview/ThePromptReport
mv ThePromptReport/* data/
python main.py

Running main.py will download the papers, run the automated review, and run the experiments. However, if you wish to save time and only run the experiments, you can download the data from Hugging Face and move the papers folder and all the csv files in the dataset into the data folder (should look like data/papers/*.pdf and data/master_papers.csv etc). Adjust main.py accordingly.

Every experiment script has a run_experiment function that is called in main.py. The run_experiment function is responsible for running the experiment and saving the results. However each script can be run individually by just running python src/prompt_systematic_review/experiments/<experiment_name>.py from root.

There is one experiment, graph_internal_references that, because of weird issues with parallelism, is better run from root as an individual script. To avoid it causing issues with other experiments, it is run last as it is ordered at the bottom of the list in experiments/__init__.py.

Notes

Sometimes a paper title may appear differently on the arXiv API. For example, "Visual Attention-Prompted Prediction and Learning" (arXiv:2310.08420), according to arXiv API is titled "A visual encoding model based on deep neural networks and transfer learning"

wordshub/free-font

2024-07-18T01:31:48Z

大概是2020年最全的免费可商用字体，这里收录的商免字体都能找到明确的授权出处，可以放心使用，持续更新中...

前言

汉字字体制作是一个庞大的工程，不同于西文字库，汉字常用字库表就有 6763 个汉字，GBK标准中共有20902 个汉字，而新出版的 GB_18064，共有六万多个字符。而且汉字的字形相对较为复杂，一套中文字体的完成需要耗费大量专业人士的精力和时间，我们倡导大家使用正版字体，为中文字体的制作创造一个良性的环境。

然而目前国内字体的授权体系还不是那么完善，不同的厂商对不同的使用场景都不同的授权，而授权协议里不那么好理解的专业术语往往也使用户望而却步。对于哪些刚起步的创业公司或者个人来讲动辄几千的授权费用也是一笔不小的开支，这里收录了一些在网上收集整理的可以免费商用的中文字体供大家使用。

这里收录的字体都能找到其开源或者开放免费商用授权的出处，有些在网络上声称免费商用但找不到授权出处的字体不在收录之列。

传统	艺术	手写
方正黑体	站酷庆科黄油体	一点钢笔鹤体
方正楷体	站酷文艺体	清松手写体
方正书宋	站酷酷黑	庞门正道粗书体
方正仿宋	站酷小微LOGO体	沐瑶随心手写体
全字库说文解字	站酷高端黑	沐瑶软笔手写体
思源黑体	庞门正道标题体	演示悠然小楷
思源宋体	庞门正道轻松体	演示春风楷
文泉驿正黑	胡晓波骚包体	演示夏行楷
文泉驿微米黑	胡晓波真帅体	演示秋鸿楷
源云明体	胡晓波男神体	演示佛系体
源流明体	装甲明朝	智勇手书体
源样明体	包图小白体	手书体
一点明体	优设标题黑	杨任东竹石体
OPPO Sans	锐字真言体	新叶念体
一点颜体	联盟起艺卢帅正锐黑体	贤二体
王汉宗系列	中文像素体	手写杂字体
cwTeX字体	摄图摩登小方体	麦克笔手绘体
花园明朝	卓健橄榄简体	钟齐志莽行书
源界明朝体	字体视界法棍体	江西拙楷
全字库正楷体	濑户体	问藏书房
全字库正宋体	和田研字体
阿里巴巴普惠体	超级细
思源真黑	千图小兔体
	黄令东齐伋体
	Smart Font UI
	Kanjyuku Gothic

中文

方正系列

「方正黑体」的设计源于铅字时代的黑体字稿，是一款专门为正文排版设计的黑体。方正黑体的笔画两端留有喇叭口，撇、捺、钩笔画的粗细变化较大，中宫比较紧凑。其字重适中、字形风格中性朴实，不但适用于印刷媒介，还可用于屏幕阅读，是一款非常经典的正文黑体。

「方正书宋」源自上海印刷技术研究所的宋二，原字形距今已有近60年。最初的设计就是专门针对最常用的小字号正文排版。方正书宋的字形端正清秀，中宫适度。凭着清晰爽目、久读不易疲劳、印刷适性好的优点，长期被作为杂志和书籍的正文首选字体，所以也是一款非常经典的正文宋体。

「仿宋体」产生于20世纪初，由当时的铅字制作者仿宋版书中的瘦细字体而制成。方正仿宋源于铅字时代的字稿，其字身略窄，笔画瘦硬，横竖笔画等粗，起笔处有斜势棱角，折笔处棱角分明，整体字形挺拔俊秀，给人以悦目之感。因清晰美观，容易辨认，长期被用于工程图纸文字和书刊中的引文。

「方正楷体」源于上海印刷技术研究所“华文楷体”字稿。原“华文楷体”诞生于20世纪40年代，新中国成立后一直是主要使用的铅字楷体。在此期间，上海字模一厂、上海印刷技术研究所等单位进行了数次改良，并添加了简体字。方正楷体的结构秀丽匀称，笔画圆润柔和，横竖笔画的粗细变化不大。这款字体被大量应用于报纸、杂志和书籍中的正文，在教科书中最为常见。

庞门正道系列

「庞门正道标题体」由庞门正道联合了13位设计师于2016年推出的第一款免费商用字体。

「庞门正道粗书体」由庞门正道公众号2018年发布的第二款免费商用字体，由车港敏设计完成

「庞门正道轻松体」由庞门正道在2019年发布的第三款免费字体。

站酷系列

「站酷高端黑」由字体设计师胡晓波、刘兵克发起，参与汉字百人舞100位字体设计师共同完成。是2014年圣诞发布的第一款由站酷冠名的公益字体，字体设计师胡晓波、刘兵克在站酷网发起“汉字百人舞”的征集设计师集体造字，前后参与设计师近百人。2014年12月发布第一版，2015年11月发布最新修订版。包含6763个汉字、数字和英文字母。

「站酷文艺体」由字体设计师刘兵克确定字形和规范，由刘兵克工作室以及刘兵克字体设计直播课学员共同创作完成，设计师郑庆科完成了最终的字库生成工作。字形新颖独特，简洁有力，清新淡雅，文艺范十足。包含7155个常用字，52个英文字母，10 个阿拉伯数字，67个常用标点及符号。

「站酷小微LOGO体」由上海佐兹品牌设计工作室设计总监李大卫 (David 小马哥）带领其工作室团队共成完成。是李大卫送给自己女儿出生三周年的一个礼物。其目的是为了填补免费字体中的LOGO体空缺。字体笔画浑厚，识别性高,放大放小空间间隙都能如意灵通。2018年1月最新修订版。采用GB2312汉字编码，共收录6763个汉字。

「站酷庆科黄油体」，由字体设计师郑庆科开发，线条圆润。并且字体数量多，达到了9千个多个汉字。站酷庆科黄油体是一款字型创新、线条圆润的字体。笔划的每一个直角，都被处理成了半径为4pt的圆角。在设计中需遵循笔划简化但不省略，笔划角度尽量为垂直角度。字体部首的右下角均为45°缺角，提高了字体视觉中心，并有效的解决了一些字体因笔划交叉，字体阅读困难的毛病。

「站酷酷黑」由胡晓波发起，字游工作室成员进行基础字形设计。胡晓波设计班十名学员共同设计完成。[站酷酷黑体验版］在站酷十周年之际横空出世。字形笔画粗犷有力，用宽扁型的字面构建出厚重的字体形态，笔画细节上的修饰既增强了字体的设计感，又让字体多了一些小小的精致感，中宫的饱满使得文字之间的排版组合非常醒目好看。2016年7月发布体验版。包含3500个常用字，52个英文字母，10个阿拉伯数字。

「站酷快乐体」由刘兵克学员和工作室团队设计。是在“站酷快乐体”基础上耗时7个月进行的大幅修改与提升，经过多轮校审之后已正式对外公布并提供下载。2015年9月发布第一版，2016年6月发布最新修订版。采用GB2312汉字编码，共收录6763个汉字。

胡晓波系列

「胡晓波男神体」由设计师胡晓波设计的一款免费字体

「胡晓波骚包体」由设计师胡晓波设计的一款免费字体

「胡晓波真帅体」是由设计师胡晓波设计的一款免费字体

沐瑶系列

「沐瑶软笔手写体」（Muyao-Softbrush ）是一款手写体，自来站酷设计师@春颜秋色送给女儿的礼物。也是以女儿名字命名的一款可免费商用的手写字体。遵循GB2312标准，共包含6763个汉字加数字英文和常用标点。

「沐瑶随心手写体」站酷设计师@春颜秋色免费开发的第二款公益字体。沐瑶是以其女儿的名字命名的。字体本身是基于GB2312-80标准，共包含6763个汉字和数百个符号。

思源系列

「思源黑体」是一套 OpenType/CFF 泛中日韩字体。这个开源项目不仅提供了可用的 OpenType 字体，还提供了利用 AFDKO makeotf 和 otf2otc 工具创建这些 OpenType 字体时的所有源文件。

「思源宋体」是 Adobe Type 发布的最新泛 CJK 字体，它是对应于思源黑体的宋体字体；我们发布这两种字体是为了响应统一设计的需求，以服务于东亚 15 亿人口。

思源宋体支持四种不同的东亚语言（简体中文、繁体中文、日语和朝鲜语），7 种粗细类型中的每一种都有 65,535 个字形，可共同呈现一致的视觉美感。该字体还包含多个西方字形，支持拉丁语、希腊语和西里尔文脚本，这些字形均来源于 Source Serif。

「思源柔黑体」是基于思源黑体修改而成的圆体版思源字体，作者是日本的自家製フォント工房。思源柔黑体提供了三种不同圆角程度的版本，以及七种不同粗细的版本。思源柔黑体保留思源黑体原有的优雅及可读性，在文字边角以圆角进行处理，整体看起来更显得活泼、可爱，也较有活力。思源柔黑体一共释出三种版本，差异在于圆角的圆滑程度不同，L 圆角较轻、X 圆角最重，介于中间的是标准版。

「思源真黑体」也是同样出自日本自家製字型工房，堪称是思源柔黑体的姊妹作，从思源黑体修改而来，在细部上做了些许的调整和变化，使它看起来更为锐利。思源真黑体一共有七种字重，以及等宽字体、比例字体选择。

文泉驿系列

「文泉驿正黑体」是一个"自由字体"。该字体包含了所有常用简体中文、繁体中文所需要的汉字(最新版本包含超过27842个汉字，完整覆盖GB2312/Big5/GBK以及GB18030标准字符集)。该字体同时还包含了日文、韩文和其他几十种语言符号。除此以外，该字体还嵌入了最新版本的文泉驿点阵宋体的中英文点阵，使得屏幕汉字显示清晰锐利，易于阅读。

作为黑体中文字体，文泉驿正黑为非衬线字体，笔画对比度明显，特别适合屏幕汉字显示以及文档标题字体。

「文泉驿微米黑」是一个"自由字体"。该字体包含了所有常用简体中文、繁体中文所需要的汉字(最新版本包含超过20932个汉字，完整覆盖GB2312/Big5以及GBK标准字符集)。该字体同时还包含了日文、韩文和其他几十种语言符号。以外，该字体还包含了高质量的Droid Sans拉丁符号和Droid Sans Mono等宽字体，并内置Hinting和Kerning信息。微米黑字体文件极小，特别使用于便携式电脑设备。该字体版权受到法律保护，字体版权人为Google和文泉驿信任委员会。请遵循官方字体授权使用并衍生本字体

明体系列

台湾最活跃的字型社团「字嗨」管理员 But Ko ，释出三款改造自思源宋体的繁体中文字体，开放给需要的使用者免费下载！三款字体分别为：源样明体、源流明体及源云明体。

这三款字体的共同特色是修改自「思源宋体」，源样明体采用思源宋体韩文版本的字符，配合繁体中文习惯的置中标点符号，可说是兼具排版及传统印刷体风格的繁体中文字体；源流明体在笔画上有所调整，使字型看起来更有力道；至于源云明体在横笔的起笔及竖笔的起笔、收笔处加入粗细变化，笔画交叉处有朦胧处理。

和思源宋体一样，源样明体、源流明体都有七种字重，源云明体则是 Light、Regular、Medium 和 SemiBold 四种，能广泛使用于各种环境。

「源云明体」是基于思源宋体的而改造的开放原始码中文字型。

「源流明体」是基于思源宋体的而改造的开放原始码中文字型。

「源样明体」是基于思源宋体的开放原始码中文字型。採用思源宋体韩文（KR）版本的字符，配合繁体中文惯用的置中标点，可排版传统印刷体风格的文件。

刻石录系列

「一点明体」是一套依照传承字形标准化文件《传承字形部件检校表》的推荐字形标准，并以 TrueType格式封装、依照 Unicode 编码的 OpenType字型。I.明体 名称里的 I 是罗马数字 一，I. 念作 一点，象徵笔画的基本：点与线。目前I.明体已由开源字型组织一点字坊全力维护。

「一点钢笔鹤体」由王汉宗粗钢体修改而来，刻石錄重新制作和修整原字型中缺少的或有欠美观的部份，例如半形英数、圈圈字、常用符号等。

「一点颜体」是由王汉宗颜体修改而来，重新制作和修整原字型中缺少的或有欠美观的部份，例如全半形英数、平假名、片假名、拉丁字母、俄文字母、阿拉伯数字、圈圈字、常用符号等。

王汉宗系列

简介

王汉宗自由字型由研发天蚕字库的台湾中原大学数学系王汉宗教授先分别在2000年和2004年先后捐出十套WCL系列字型和32套新字型，全部以GNU GPL许可分发。

争议

2005年，台湾著名字型厂商文鼎科技认为王汉宗字型与文鼎的字体极度相似，一些自由软件网站决定在问题明了前先移除字体下载以避免法律问题。

然而云端字体服务公司 justfont 则不认为存在授权问题，在提供字体列表中可见到 17 个王汉宗自由字型。文鼎公司觉得王汉宗有许多套字体是 copy 自文鼎字体。但是如果说是完全抄袭，也会有争议，因为虽然骨架相同，但描绘点并不一样，法律上顶多只能算是「疑似」抄袭。

另外就「隶书」、「仿宋」、等字体来说，文鼎公司也并非独创，有许多字体公司的同名字型几乎长得其实一模一样。这么一来其实很难说谁抄谁。


字体文件	字体中文名	字体英文名	名称缩写	文鼎科技主张侵权
WCL-01.ttf	波浪体	HanWangWCL01	wcl01
WCL-02.ttf	特明体	HanWangWCL02	wcl02	是
WCL-03.ttf	波卡体空阴	HanWangWCL03	wcl03
WCL-04.ttf	综艺体双空阴	HanWangWCL04	wcl04	是
WCL-05.ttf	标楷体空心	HanWangWCL05	wcl05
WCL-06.ttf	仿宋体	HanWangWCL06	wcl06
WCL-07.ttf	粗钢体	HanWangWCL07	wcl07
WCL-08.ttf	粗黑体实阴	HanWangWCL08	wcl08
WCL-09.ttf	粗圆体双空	HanWangWCL09	wcl09
WCL-10.ttf	海报体半天水	HanWangWCL10	wcl10
wt001.ttf	细明	HanWangMingLight	hwml
wt002.ttf	中明	HanWangMingMedium	hwmm	是
wt003.ttf	粗明	HanWangMingBold	hwmb	是
wt004.ttf	特明	HanWangMingHeavy	hwmh	是
wt005.ttf	超明	HanWangMingBlack	hwmbk
wt006.ttf	细圆	HanWangYenLight	hwyl	是
wt009.ttf	特圆	HanWangYenHeavy	hwyh	是
wt011.ttf	细黑	HanWangHeiLight	hwhl
wt014.ttf	特黑	HanWangHeiHeavy	hwhh
wt021.ttf	中隶书	HanWangLiSuMedium	hwlsm	是
wt024.ttf	中仿宋	HanWangFangSongMedium	hwfsm	是
wt028.ttf	叠圆	HanWangKanDaYan	hwkdy	是
wt034.ttf	勘亭流	HanWangKanTan	hwkt	是
wt040.ttf	综艺体	HanWangZonYi	hwzy	是
wt064.ttf	颜体	HanWangYanKai	hwyk
wt071.ttf	中行书	HanWangShinSuMedium	hwssm	是
wtcc02.ttf	酷俪海报	HanWangCC02	hwcc02
wtcc15.ttf	正海报	HanWangCC15	hwcc15
wthc06.ttf	钢笔行楷	HanWangGB06	hwhc
wtg-06cut1.ttf	超黑俏皮动物	HanWangGSolid06cut1	hwgsc1
wp010-05.ttf	中明注音体	HanWangMingMediumChuIn	hwmmci
wp110-05.ttf	中明破音字1	HanWangMingMediumChuIn1	hwmmci1
wp210-05.ttf	中明破音字2	HanWangMingMediumChuIn2	hwmmci2
wp310-05.ttf	中明破音字3	HanWangMingMediumChuIn3	hwmmci3
wp010-08.ttf	中楷注音体	HanWangKaiMediumChuIn	hwkmci
wp110-08.ttf	中楷破音字1	HanWangKaiMediumChuIn1	hwkmci1
wp210-08.ttf	中楷破音字2	HanWangKaiMediumChuIn2	hwkmci2
wp310-08.ttf	中楷破音字3	HanWangKaiMediumChuIn3	hwkmci3
wts11.ttf	粗楷体简体	KaiBold-Gb5	wts11	是
wts43.ttf	中魏碑简体	WeiBeiMedium-Gb5	wts43
wts47.ttf	中仿宋简体	FangSongMedium-Gb5	wts47	是
wts55.ttf	细新宋简体	SinSongThin-Gb5	wts55

其他

「千图小兔体」是千图网联合国内专业字体设计机构 iFonts 共同发布的一款萌系创意字体，历时半年多悉心打磨完成。向所有千图网用户开放免费下载。将可爱的千小兔融入字体设计中，尽显年轻、活力与俏皮，适用于可爱、少女风的海报、店招等场景。

「演示佛系体」是由一生酷爱书法的纪国才老先生，在80岁这年亲手写了一套独具风格的字体，并授权 Keynote研究所 和 秋叶PPT 面向全社会联合发布，全渠道免费商用！

「演示秋鸿楷」是Keynote研究所的人生哥制作发布的一款毛笔字体。字稿的作者是叶运鹏：高中教师丨辽宁人丨98年加入农民书画家协会。该字体简体中文楷书，包含标准的6763个字符。

「演示夏行楷」是Keynote研究所的人生哥制作发布的一款毛笔字体。字稿作者是刘锡栋：中国漓江书画艺术家协会理事丨北京中国书画研究院研究员丨中国民族硬笔书法家协会会员丨中国楹联学会会员丨黑龙江楹联家协会会员丨高级政工师，已退休该字体简体中文行楷，包含标准的6763个字符。

「演示春风楷」是Keynote研究所的人生哥制作发布的一款毛笔字体。字稿的作者是徐占海，军人出身，1983年入伍。每日笔耕不辍练习写字。该字体简体中文楷书，包含标准的6763个字符。

「演示悠然小楷」是秋叶PPT联合keynote研究所推出的一款书法字体，字稿的作者是孟祥媛，一位还在大学读书的书法专业学生。

初看「演示悠然小楷」，可能并不觉得惊艳，再品，却会发现透着质朴、率真的气息。正如尚在象牙塔中的作者本人，那是未经雕琢的拙朴书生气。接下来，一起体会悠然小楷的这股「拙气」。悠然小楷笔画分明，结构清晰，没有过度的连笔和飞白，对阅读识别更为友好；小楷字形较小，天然拥有更大的字间距，即便作为长段文字使用，阅读上也不会有太大视觉压力；悠然小楷的笔画力度分布均衡，起收有序，没有太过强烈的对比，自然沉劲而不漂浮；在小楷的基础上有些行楷的影子，为字体增添了灵动，但又有克制，形成停而不断、笔断意连之态；部分笔画细节可能并不完美，不够顺滑，但这份未经雕琢，又何尝不是一种难得的拙朴；所以见过很多大开大合的书法字体后，再见悠然小楷，可能没有很惊艳之感。但艳丽与喧嚣并不一定代表力量，沉劲入骨更难能可贵。

「和田研字体」（和田研細丸ゴシック / 和田研中丸ゴシック；WadaLabChuMaruGo2004Emoji）是WadaLab,RareEarth(日本)发布于2004年的一款字体，年代相对久远，但是迄今为止依然在持续维护更新中，最新的版本发布于2020年02月24日。以简单的元素来表达字体的美感且每一个字体的直角转折处都以椭圆角来做替代，整个字体的设计都充满了浓浓的古典风格。在字体包里面包含了两款字体，分别是“和田研细丸”和“和田研中丸”，细丸笔画较细，而中丸笔画较粗。而字体文件上带有P的，则是代表等宽字体。这款字体虽然是日本的，但是包含的繁体字符还是比较全的，常用字基本都覆盖了。和田研字体日本一个开源的字体项目，无论是商用还是非商用都可以免费使用。您可以对此字体进行重新修改发布，也可以以嵌入在其他软件上的形式进行分发。大家可以请放心下载使用

「摄图摩登小方体」是摄图网联合iFonts共同推出的一款公益创意黑体字，由摄图网委托iFonts制作，版权归摄图网所有，本字库供社会各界全渠道商用。「摄图摩登小方体」采用GB2312编码，共包含6995个字符，其中6763个简体汉字，52个大小写字母，10个数字，170个标点符号。

字体的设计灵感来自对未来空间的想象，传达给人一种未来与现实的交际感。笔画方正质朴而又不失灵气活力，适中的字体笔画粗度和几何感的细节设计，让字体有良好的视觉应用表现。通过对字体负形空间的处理，使字体整体感觉稳重端正而又不呆板，更加具有趣味性。适合电商、广告、美食等商业宣传和包装设计，以及社交网络的个性化用字。

作为公益开源字体，摄图摩登小方体已经向全社会开放下载并可免费商用（包括个人及企业商业用途）。

「中文像素体」是一个像素中文字体，里面具有6000多个常用字，其是使用HZK16字模制作。可以在在商业和免费游戏项目中使用这个字体。

「黄令东齐伋体」，一款可以媲美康熙字典体的免费商用字体！可以免费商用！黄令东齐伋体摘自明代木版印刷书籍《凌閔刻本》，采用半自动计算机视觉和OCR制作而成的一款字体。其笔画风格书法特征浓郁，比常规的明朝体更具韵律感，富有人文气息。黄令东齐伋体非常适宜用于需要展现人文感、手作感、古拙感的场景。黄令东齐伋体（QIJIFALLBACK Regular）是基于思源宋体改造而来，所以对中文汉字很全，无论是简体还是繁体都基本上不缺字! 依据SIL Open Font License 1.1授权协议，可以用于任何用途，包括免费商用。

「钟齐志莽行书」（Zhi Mang Xing Regular）是谷歌开源字体项目里面其中一款字体，遵循SIL Open Font License 1.1授权协议，可以用于任何用途，包括免费商用。如有疑虑，可点击下方官网链接，查看开源字体授权OFL协议

「包图小白体」定义为一款简单可爱的创意字体。在字形笔画上去除了折笔的弧形，换之以平直的笔画，竖弯钩转为竖折，同时弱化了钩笔画，省去了许多笔画末尾的小尾巴，显得更加直白可爱。粗短的笔画，像柯基的小短腿，相比细长的字体能给人更多的轻松感。整体形态采用了镂空的制作技巧，增强了字体的立体感，适合用于品牌标志、海报、包装、影视综艺、游戏、漫画等场景。包图小白体采用GB2312编码，共收容7707个字符，包含6763个简体汉字、110个大写字母、139个小写字母、102个数字、95个标点符号、其他特殊类符号288个、其他字母210个（包含日文平假名片假名）。

「江西拙楷」由站酷设计师@Fontree （黄煜臣）个人开发并发布，这是一套手写楷体，相比电脑中标准化制作的楷体，这套字体的笔画会带有一些书写的痕迹，每个字的笔画是没有统一标准的，所以看上去显得不够规范，但是会有一种手写的自然之感。

「优设标题黑」由优设与字由合作完成，是一款适用性广，速度感、力量感极强的专业美术标题字体。它以黑体字型为基础，整体字形沉稳，同时采用较大字面和粗壮的笔画来强化力量感。每个字体水平倾斜 8° 的设计，赋予了字体极强的速度感，为了让字体倾斜后也能保持稳固，设计师将整体字身设定宽扁。而起笔和弯钩上独具匠心的尖角设计，不仅突显了设计的几何感，而且方便后期修改。

「锐字真言体」是锐字家族字体当中第一款免费可商用的字体。真言体笔触浑厚有力，笔画曲折有度，字形个性鲜明，刚柔并济，落笔简洁有序，给人以遒劲有力、端正凝练的感受。直角与圆角的错落搭配使得字体婉转有度，落落大方，具有自己独到的风格！这款字体特别适用于文字标题、竞技视觉、广告设计、个性品牌设计推广、企业宣传及时尚品牌的设计应用。

「联盟起艺卢帅正锐黑体」由站酷设计师@设计师卢帅在站酷发布，共计8000余字

「问藏书房」字体是由问藏书房联合造字工房一起的设计的一款免费字体，字体整体现代简约，中宫紧凑，视觉阅读极具层次感，字字俊秀且呈现古雅之风，设计团队以精心的设计赋予了汉字全新美感！字符编码采用中华人民共和国GB2312-80字符集标准，按需添加101个汉字。共收容字数6864CJK汉字，ASCII常用字符100个。

清松手写体

「清松手写体」是一款来自台湾的字体，它由游清松建立，为了创建这个字体，他还成立了一个小组：「顺其字然」，首先先在稿纸上用原子笔手写，然后，透过「守写字」网站产生出初稿字体，再透过字体软件后制加工的手写字体。「顺其字然」手写字体，没有一般计算机字型的生硬感，多了几分手写般的自然感。不论是中文、英文、符号数字，适合在计算机屏幕上阅读、排版，打印出来效果也很棒的！

目前青松手写体已经发布了三个版本，另有两个版本正在准备中，清松字体的另一大特色是可以在小组里提出增字的要求，更新的频率也很快。

字体	状态	风格	更新记录
清松手写体1	2018/10/17 发布	圆润	1.35（2019/10/11）
清松手写体2	2019/3/27 发布	清秀	1.01（2019/07/21）
清松手写体3	2019/8/4 发布	萌	1.20（2020/01/05）
清松手写体4	准备中	POP
清松手写体5	风格已确定	行楷

「新叶念体」采用GB2312编码，约6900字，王亚设计在站酷发布。

「贤二体」是龙泉专寺动漫中心与汉仪字库联合推出的一款免费商用字体。贤二体的字形偏长，起笔收笔锋利且粗细对比明显，笔画笨拙微带曲线感。字体组合结构松散，重心不稳，达到诙谐幽默，惹人喜爱的效果。

「手书体」由站酷设计师@Joker9 设计，收录基本汉字6763个，字体风格为手写风格，可以用于slogan、banner、海报、商标等设计里面。

「杨任东竹石体」于2017年4月26日世界知识产权日正式发布，本字体版本为1.23字体按照GB2312-80书写，并增补了少量常见汉字；整套字体有7个字重，共计50000余个字符

cwTeX系列

「cwTeX 字体」最初是由 Tsong-Min Wu 及 Tsong-Huey Wu 于 1999 至 2004 年间制作作并以 GNU GENERAL PUBLIC LICENSE Version 2 or latter 发布。于 2014 年，Tsong-Min Wu、Tsong-Huey Wu、Edward G.J. Lee 及 Chen-Pan Liao 四人同意将汉字及中文常用标点符号之字符另外独立并以 SIL Open Font License (Version 1.1) 授出，以利与其他 OFL 授权之字体整合。

目前 cwtex-q-fonts 共有五种字体，两种授权方式，0.3版本之前以 GPL 2.0 及以上授权，0.3版本之后以及 OFL 1.1 授权，这里收录的是0.4版本。

「字体视界法棍体」由原字体（《义启嘟嘟体》登记号：沪作登字-2016-F-00712739）更名而来。由上海义启信息科技有限公司字库部员工通过字体软件进行设计与制作，为了让更多的人可以免费使用商用字体，公司决定改名为《字体视界法棍体》，并于2019年7月17日字体节这天在字体视界官网发布许可广大用户免费商用。

字体视界法棍体笔画中间呈弧形外拓，充满张力，加粗字形笔画显得更可爱个性；结构空间变化不像正体那样小，以拙为巧，憨态可掬。这款字体笔画纤细充满张力，规律加粗，曲线折角充分展现它可爱不粗笨的气质笔画，结构外圆内疏，左右呼应如手写，各个字生机勃勃又融为一体，整体一气呵成。

采用 GB2312 编码，共收容 7698 个字符，包含 6771 个汉字，110 个大写字母，139 个小写字母，102 个数字（包含阿拉伯数字，罗马数字等），98 个标点符号，其他特殊符号 164 个，其他字母 224 个。

「卓健橄榄简体」由卓米品牌设计发布，并于于2019年底授权限时免费商用。收容7050个常用字，52 个英文字母，139个阿拉伯数字及常用标点符号，卓健橄榄简体是一款相对较时尚的字体，部分笔画斜切处理，让字体更具时尚感和时尚属性，整体中心偏上，更符合现代审美。

官方给出的说法是限时免费下载，免费商用。具体是指可以免费商用，限时下载，还是限时免费商用，官方没有给出明确解释，这点存有疑问。如果有商用需求，请与官方联系求证。

「OPPO Sans」是 OPPO 全新品牌字体，首发于 ColorOS 6 操作系统，由 OPPO 与国内知名字体厂商汉仪合作开发出的全新中文字库，总共 226003 个字节。OPPO Sans 紧随品牌升级的步调，用文字全方位探索科技美感，字体整体简洁优雅，字型设计富有现代感，极具科技美感。

字体采用全新骨架设计，中宫自然舒适，更加挺拔优雅；同时简化笔画，把握更多细节处理，去除出脚，使字体显得更加干净。简洁富有变化的字体线条还融入 OPPO 产品曲线美学，极具现代感和未来感，字体更加现代，笔画更加均匀，视觉表现更加整洁，非常适合用来作为设计素材使用，实用性相当广泛。

全字库系列

「全字库字体」是台湾地区为了解决电脑中文字数不足而做的一款开源字体，供全社会免费商用。

「阿里巴巴普惠体」由阿里巴巴在举办的「2019 阿里巴巴UCAN 设计大会」发布了，该字体是永久免费商用。

日文

「花园明朝体」（HanaMin 花园フォント）是一款基于日语的自由开源汉字字体。这款字体的特点是涵盖了目前 CJK (中日韩统一表意文字)字库中几乎所有的汉字与字符，而且作为开源项目，任何人可自由下载使用。如果在软件中找不到此字体，请查找名称：HanaMin。此字体对简体中文支持比较好。

「源界明朝体」这款字体是以「思源宋体」（日文为「源ノ明朝」）为基础，加入破坏效果使其最接近可读状态，可以简单理解成思源宋体的破坏版，源界明朝在视觉上具有相当大的张力，可作为图片内的标题和大字使用，整体来说相当吸睛且颇具效果。源界明朝体为日文字体，对部分简体中文支持不太好。

「手写杂字体」这是一个日本人制作的一款有点可爱风格的字体，所以会是日本字形（然而作者表示其实日本标准很多地方他都没遵循，所以叫杂字体）。作者的描述是自由使用与重新配布，可商用，但是保留著作权，字体仍在不断更新加字中。此款手写杂字体，虽为日文字体，但对中文繁体以及中文简体都支持得灰常不错。

「装甲明朝」是日本网友以思源宋体为基础，修改后开放免费下载的新字型。字体风格硬朗有气派，若是拿来做越野车、大型装甲机车等玩具淘宝海报设计就非常适用了。Soukou Mincho 装甲明朝体支持繁体（分部）、简体（部分）、英文、日文以及数字符号。

「濑户字体」是一款偏可爱风的字体，支持简体中文、繁体中文、日文，包含CJK常用汉字、平片假名、JIS第一至四级，共30000余字。这是由日本的一位业余字体设计师濑户のぞみ小姐所制作的。

「麦克笔手绘体」一款日本网站提供的免费商用字体，たぬき油性マジック(TanukiMagic) 字型风格和马克笔手绘的 POP 效果如出一辙。

「超极细」日文称为【超極細ゴシック体】，据说是目前字体线条最细的一款字体。超极细本身是日文字体，但同时支持繁体，所以也可作繁体字体使用。

「Kanjiku Gothic」是一种免费字体，是免费下载和商业字体。该免费字体包含第二级汉字，可以由文字处理器和Adobe图形软件等应用程序自由处理和使用。平假名，片假名和字母是原始制作的，并且汉字辅以IPAex Gothic改进的字体。

「Smart Font UI」是一种时尚而酷炫的日语字体，可以免费下载并在市场上出售。此免费字体还包括第二级汉字，并且是一种哥特式字体，可以自由处理并与Excel，Word和Adobe Illustrator等应用程序一起使用。创建用于平假名，片假名，字母和数字的原始字体，并使用IPAex Gothic改进字体补充汉字字符。

英文

数字

关于授权

这里的授权形式分为两类：免费商用和开源。

可能在不少人看来，「开源」就是没有限制。但实际情况恰恰相反。开源字体和开源软件一样，不但受制于各种类型的许可证，而且由于开源许可证的社区属性，关于其用法的争议甚至比权利义务一目了然的商业许可更大，在使用前必须认真检索和理解。值得注意的是常见的开源字体许可证对字体的使用限制不大，主要限制在于对字体的编辑和再分发。

免费商用字体

商用免费不是一种开源软件的授权协议，主要指个人或者字体开发厂商没有开源但是免费提供给个人和商业机构使用的字体。目前中文字体的版权没有一个统一的标准，不同的字体厂商，对免费商用的字体有不同的限制，有些字体免费商用之前还需要获得厂商的授权，使用这类字体时需要仔细阅读他们的条款，避免不必要的麻烦。个人设计师发布并承诺开放免费商用的字体这没有这些限制。

厂商对于免费商用的字体有收回的权利，之前有过这样的案例，某些厂商对一些曾经免费商用的字体收回了授权。使用字体厂商发布的免费商用字体前需要确认这个字体是否还面对公众免费商用，这里也会尽量及时更新这类字体的授权信息。

开源字体

GNU通用公共许可证 (GNU General Public License)是GNU运动为保证其软件在后续的发展中仍保持开源开放而为其软件设立的“使用条款”。

GPL 最大的特点是：如果你在自己的作品中使用了受制于 GPL 的软件，那么该作品也必须以 GPL 许可分发。 这显然会产生一个大问题：如果我在文档中用了 GPL 字体，这份文件是否也就因此「感染」了 GPL、变成了开源的东西？ 这是一个没有定论的问题，但其潜在争议很大，以至于 GPL 的推广者自由软件基金会额外推出了一条名为「GPL 字型例外」（GPL font exception）的条款，可以附在 GPL 字体许可的后面，阻断其向使用该字体的文件的传播。

由于其争议性这里收录的文泉系列字体需慎用。

SIL开放字体许可证 (SIL Open Font License) 由SIL国际制定，初版于2005年发布，如今已是诸多开源字体的首选授权条款，这种许可证比 GPL 要开明，不会扩散到使用 SIL 字体的作品上，而只要求对字体作出修改后的作品本身要以相同许可分发。

IPA开放字型授权条款是日本「IPA」以符合「开放原始码促进会（Open Source Initiative）」于2009年开源定义的使用条款。

免责声明：本文涉及对字体/字库/字库软件等的分发、使用等著作权相关法律问题的讨论。其中很多目前在法律上并无明确规定，学理上存在争议，不同国家、甚至相同法域内也存在相互冲突的判例。因此，文章内容仅代表个人观点，仅供参考，不应被作为正式的法律意见，作者不对采纳文中建议或观点所可能产生的任何责任或后果承担责任。

参考链接

免费字体获取和使用指南 - 少数派

免费商用字体 - Free字体

设计师福利，这是一份最最全面的免费可商用字体清单 - 摹客

免费商用中文字体分享 - 站酷

JuliaWeb/RemoteREPL.jl

2024-07-18T01:32:00Z

Connect a REPL to a remote Julia process

leanprover/leansat

2024-07-18T01:32:13Z

This package provides an interface and foundation for verified SAT reasoning

netdata/helmchart

2024-07-17T01:32:54Z

Helm chart for kubernetes deployments

mem0ai/mem0

2024-07-17T01:33:56Z

Unified GUI Censorship Resistant Solution Powered by Xray

Marzban

Unified GUI Censorship Resistant Solution Powered by Xray

English / فارسی / 简体中文 / Русский

Overview

Marzban (the Persian word for "border guard" - pronounced /mærz'ban/) is a proxy management tool that provides a simple and easy-to-use user interface for managing hundreds of proxy accounts powered by Xray-core and built using Python and Reactjs.

Why using Marzban?

Marzban is user-friendly, feature-rich and reliable. It lets you to create different proxies for your users without any complicated configuration. Using its built-in web UI, you are able to monitor, modify and limit users.

Features

Built-in Web UI
Fully REST API backend
Multiple Nodes support (for infrastructure distribution & scalability)
Supports protocols Vmess, VLESS, Trojan and Shadowsocks
Multi-protocol for a single user
Multi-user on a single inbound
Multi-inbound on a single port (fallbacks support)
Traffic and expiry date limitations
Periodic traffic limit (e.g. daily, weekly, etc.)
Subscription link compatible with V2ray (such as V2RayNG, OneClick, Nekoray, etc.), Clash and ClashMeta
Automated Share link and QRcode generator
System monitoring and traffic statistics
Customizable xray configuration
TLS and REALITY support
Integrated Telegram Bot
Integrated Command Line Interface (CLI)
Multi-language
Multi-admin support (WIP)

Installation guide

Run the following command

sudo bash -c "$(curl -sL https://github.com/Gozargah/Marzban-scripts/raw/master/marzban.sh)" @ install

Once the installation is complete:

You will see the logs that you can stop watching them by closing the terminal or pressing Ctrl+C
The Marzban files will be located at /opt/marzban
The configuration file can be found at /opt/marzban/.env (refer to configurations section to see variables)
The data files will be placed at /var/lib/marzban
You can access the Marzban dashboard by opening a web browser and navigating to http://YOUR_SERVER_IP:8000/dashboard/ (replace YOUR_SERVER_IP with the actual IP address of your server)

Next, you need to create a sudo admin for logging into the Marzban dashboard by the following command

marzban cli admin create --sudo

That's it! You can login to your dashboard using these credentials

To see the help message of the Marzban script, run the following command

marzban --help

If you are eager to run the project using the source code, check the section below

Manual install (advanced)

Install xray on your machine

You can install it using Xray-install

bash -c "$(curl -L https://github.com/XTLS/Xray-install/raw/main/install-release.sh)" @ install

Clone this project and install the dependencies (you need Python >= 3.8)

git clone https://github.com/Gozargah/Marzban.git
cd Marzban
wget -qO- https://bootstrap.pypa.io/get-pip.py | python3 -
python3 -m pip install -r requirements.txt

Alternatively, to have an isolated environment you can use Python Virtualenv

Then run the following command to run the database migration scripts

alembic upgrade head

If you want to use marzban-cli, you should link it to a file in your $PATH, make it executable, and install the auto-completion:

sudo ln -s $(pwd)/marzban-cli.py /usr/bin/marzban-cli
sudo chmod +x /usr/bin/marzban-cli
marzban-cli completion install

Now it's time to configuration

Make a copy of .env.example file, take a look and edit it using a text editor like nano.

You probably like to modify the admin credentials.

cp .env.example .env
nano .env

Check configurations section for more information

Eventually, launch the application using command below

python3 main.py

To launch with linux systemctl (copy marzban.service file to /var/lib/marzban/marzban.service)

systemctl enable /var/lib/marzban/marzban.service
systemctl start marzban

To use with nginx

server {
    listen 443 ssl http2;
    listen [::]:443 ssl http2;
    server_name  example.com;

    ssl_certificate      /etc/letsencrypt/live/example.com/fullchain.pem;
    ssl_certificate_key  /etc/letsencrypt/live/example.com/privkey.pem;

    location ~* /(dashboard|api|docs|redoc|openapi.json) {
        proxy_pass http://0.0.0.0:8000;
        proxy_set_header Host $host;
        proxy_set_header X-Real-IP $remote_addr;
        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
    }

    # xray-core ws-path: /
    # client ws-path: /marzban/me/2087
    #
    # All traffic is proxed through port 443, and send to the xray port(2087, 2088 etc.).
    # The '/marzban' in location regex path can changed any characters by yourself.
    #
    # /${path}/${username}/${xray-port}
    location ~* /marzban/.+/(.+)$ {
        proxy_redirect off;
        proxy_pass http://127.0.0.1:$1/;
        proxy_http_version 1.1;
        proxy_set_header Upgrade $http_upgrade;
        proxy_set_header Connection "upgrade";
        proxy_set_header Host $http_host;
        proxy_set_header X-Real-IP $remote_addr;
        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
    }
}

server {
    listen 443 ssl http2;
    listen [::]:443 ssl http2;
    server_name  marzban.example.com;

    ssl_certificate      /etc/letsencrypt/live/example.com/fullchain.pem;
    ssl_certificate_key  /etc/letsencrypt/live/example.com/privkey.pem;

    location / {
        proxy_pass http://0.0.0.0:8000;
        proxy_set_header Host $host;
        proxy_set_header X-Real-IP $remote_addr;
        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
    }
}

By default the app will be run on http://localhost:8000/dashboard. You can configure it using changing the UVICORN_HOST and UVICORN_PORT environment variables.

Configuration

You can set settings below using environment variables or placing them in .env file.

Variable	Description
SUDO_USERNAME	Superuser's username
SUDO_PASSWORD	Superuser's password
SQLALCHEMY_DATABASE_URL	Database URL (SQLAlchemy's docs)
UVICORN_HOST	Bind application to this host (default: `0.0.0.0`)
UVICORN_PORT	Bind application to this port (default: `8000`)
UVICORN_UDS	Bind application to a UNIX domain socket
UVICORN_SSL_CERTFILE	SSL certificate file to have application on https
UVICORN_SSL_KEYFILE	SSL key file to have application on https
XRAY_JSON	Path of Xray's json config file (default: `xray_config.json`)
XRAY_EXECUTABLE_PATH	Path of Xray binary (default: `/usr/local/bin/xray`)
XRAY_ASSETS_PATH	Path of Xray assets (default: `/usr/local/share/xray`)
XRAY_SUBSCRIPTION_URL_PREFIX	Prefix of subscription URLs
XRAY_FALLBACKS_INBOUND_TAG	Tag of the inbound that includes fallbacks, needed in the case you're using fallbacks
XRAY_EXCLUDE_INBOUND_TAGS	Tags of the inbounds that shouldn't be managed and included in links by application
CUSTOM_TEMPLATES_DIRECTORY	Customized templates directory (default: `app/templates`)
CLASH_SUBSCRIPTION_TEMPLATE	The template that will be used for generating clash configs (default: `clash/default.yml`)
SUBSCRIPTION_PAGE_TEMPLATE	The template used for generating subscription info page (default: `subscription/index.html`)
HOME_PAGE_TEMPLATE	Decoy page template (default: `home/index.html`)
TELEGRAM_API_TOKEN	Telegram bot API token (get token from @botfather)
TELEGRAM_ADMIN_ID	Numeric Telegram ID of admin (use @userinfobot to found your ID)
TELEGRAM_PROXY_URL	Run Telegram Bot over proxy
JWT_ACCESS_TOKEN_EXPIRE_MINUTES	Expire time for the Access Tokens in minutes, `0` considered as infinite (default: `1440`)
DOCS	Whether API documents should be available on `/docs` and `/redoc` or not (default: `False`)
DEBUG	Debug mode for development (default: `False`)
WEBHOOK_ADDRESS	Webhook address to send notifications to. Webhook notifications will be sent if this value was set.
WEBHOOK_SECRET	Webhook secret will be sent with each request as `x-webhook-secret` in the header (default: `None`)
NUMBER_OF_RECURRENT_NOTIFICATIONS	How many times to retry if an error detected in sending a notification (default: `3`)
RECURRENT_NOTIFICATIONS_TIMEOUT	Timeout between each retry if an error detected in sending a notification in seconds (default: `180`)
NOTIFY_REACHED_USAGE_PERCENT	At which percentage of usage to send the warning notification (default: `80`)
NOTIFY_DAYS_LEFT	When to send warning notifaction about expiration (default: `3`)
USERS_AUTODELETE_DAYS	Delete expired (and optionally limited users) after this many days (Negative values disable this feature, default: `-1`)
USER_AUTODELETE_INCLUDE_LIMITED_ACCOUNTS	Weather to include limited accounts in the auto-delete feature (default: `False`)
USE_CUSTOM_JSON_DEFAULT	Enable custom JSON config for ALL supported clients (default: `False`)
USE_CUSTOM_JSON_FOR_V2RAYNG	Enable custom JSON config only for V2rayNG (default: `False`)
USE_CUSTOM_JSON_FOR_STREISAND	Enable custom JSON config only for Streisand (default: `False`)
USE_CUSTOM_JSON_FOR_V2RAYN	Enable custom JSON config only for V2rayN (default: `False`)

API

Marzban provides a REST API that enables developers to interact with Marzban services programmatically. To view the API documentation in Swagger UI or ReDoc, set the configuration variable DOCS=True and navigate to the /docs and /redoc.

Backup

It's always a good idea to backup your Marzban files regularly to prevent data loss in case of system failures or accidental deletion. Here are the steps to backup Marzban:

By default, all Marzban important files are saved in /var/lib/marzban (Docker versions). Copy the entire /var/lib/marzban directory to a backup location of your choice, such as an external hard drive or cloud storage.
Additionally, make sure to backup your env file, which contains your configuration variables, and also, your Xray config file. If you installed Marzban using marzban-scripts (recommended installation approach), the env and other configurations should be inside /opt/marzban/ directory.

By following these steps, you can ensure that you have a backup of all your Marzban files and data, as well as your configuration variables and Xray configuration, in case you need to restore them in the future. Remember to update your backups regularly to keep them up-to-date.

Telegram Bot

Marzban comes with an integrated Telegram bot that can handle server management, user creation and removal, and send notifications. This bot can be easily enabled by following a few simple steps, and it provides a convenient way to interact with Marzban without having to log in to the server every time.

To enable Telegram Bot:

set TELEGRAM_API_TOKEN to your bot's API Token
set TELEGRAM_ADMIN_ID to your Telegram account's numeric ID, you can get your ID from @userinfobot

Marzban CLI

Marzban comes with an integrated CLI named marzban-cli which allows administrators to have direct interaction with it.

If you've installed Marzban using easy install script, you can access the cli commands by running

marzban cli [OPTIONS] COMMAND [ARGS]...

For more information, You can read Marzban CLI's documentation.

Marzban Node

The Marzban project introduces the Marzban-node, which revolutionizes infrastructure distribution. With Marzban-node, you can distribute your infrastructure across multiple locations, unlocking benefits such as redundancy, high availability, scalability, flexibility. Marzban-node empowers users to connect to different servers, offering them the flexibility to choose and connect to multiple servers instead of being limited to only one server. For more detailed information and installation instructions, please refer to the Marzban-node official documentation

Webhook notifications

You can set a webhook address and Marzban will send the notifications to that address.

the requests will be sent as a post request to the adress provided by WEBHOOK_ADDRESS with WEBHOOK_SECRET as x-webhook-secret in the headers.

Example request sent from Marzban:

Headers:
Host: 0.0.0.0:9000
User-Agent: python-requests/2.28.1
Accept-Encoding: gzip, deflate
Accept: */*
Connection: keep-alive
x-webhook-secret: something-very-very-secret
Content-Length: 107
Content-Type: application/json



Body:
{"username": "marzban_test_user", "action": "user_updated", "enqueued_at": 1680506457.636369, "tries": 0}

Different action typs are: user_created, user_updated, user_deleted, user_limited, user_expired, user_disabled, user_enabled

Donation

If you found Marzban useful and would like to support its development, you can make a donation in one of the following crypto networks:

TRON network (TRC20): TX8kJoDcowQPBFTYHAJR36GyoUKP1Xwzkb
ETH, BNB, MATIC network (ERC20, BEP20): 0xFdc9ad32454FA4fc4733270FCc12ddBFb68b83F7
Bitcoin network: bc1qpys2nefgsjjgae3g3gqy9crsv3h3rm96tlkz0v
Dogecoin network: DJAocBAu8y6LwhDKUktLAyzV8xyoFeHH6R
TON network: EQAVf-7hAXHlF-jmrKE44oBwN7HGQFVBLAtrOsev5K4qR4P8

Thank you for your support!

License

Made in [Unknown!] and Published under AGPL-3.0.

Contributors

We ❤️‍🔥 contributors! If you'd like to contribute, please check out our Contributing Guidelines and feel free to submit a pull request or open an issue. We also welcome you to join our Telegram group for either support or contributing guidance.

Check open issues to help the progress of this project.

Thanks to the all contributors who have helped improve Marzban:

Made with contrib.rocks

lostb1t/replex

2024-07-17T01:34:30Z

Remix your Plex hubs

Replex

Remix your plex hubs

Features

Merge hubs (recommended rows) from different libraries into a single hub (interleave). Aka have movies and shows in a single row.
Choose between styles, shelf (default) or hero.
Remove watched items from hubs.
Auto load artwork for hero styles.
Filter hubs by its restrictions (per user hub)
Disable user state: remove watched badges from hub items.
Disable leaf count: remove episode count from artwork.
Force maximum quality.
Auto select version based on resolution of the client.
Fallback to different version if selected version is video transcoding.
Works on every client not only plex web!
Plays nice with PMM (and without).

How does it work

Replex is an proxy that transforms the communication between the plex media server and plex clients. This allows replex to change some dials that otherwise wouldnt be possible.

Installation

Docker compose example including plex:

version: "3"
services:
  plex:
    image: lscr.io/linuxserver/plex:latest
    container_name: plex
    environment:
      - PUID=1000
      - PGID=1000
      - TZ=Etc/UTC
      - VERSION=docker
      # claim from https://plex.tv/claim 
      - PLEX_CLAIM=
    ports:
      - 32400:32400
     volumes:
       - /path/to/library:/config
       - /path/to/tvseries:/tv
       - /path/to/movies:/movies
    restart: unless-stopped
  replex:
    image: ghcr.io/lostb1t/replex:latest
    container_name: replex
    environment:
      REPLEX_HOST: http://plex:32400
      REPLEX_TOKEN: ***** # server admin plex token: https://support.plex.tv/articles/204059436-finding-an-authentication-token-x-plex-token/
    ports:
      - 3001:80
    restart: unless-stopped
    depends_on:
      - plex

Add your proxy url to plex "Custom server access URLs" (ex http://0.0.0.0:3001) Then access your proxy url http://0.0.0.0:3001

Note: DO NOT run the plex container in host mode. It will cause plex to connect to the local ip instead of the custom url for local clients and bypass replex.

Note: Plex clients are a bit broken with custom urls and unsecured connections. Most wont work if the custom server url is not secure. So for testing the direct web client is fine but after that you want to setup SSL. See the "Remote access" section for more info.

Settings

Settings are set via environment variables

Setting	Default	Description
REPLEX_HOST		Url of your plex instance. ex: http://0.0.0.0:32400
REPLEX_TOKEN		server admin plex token, needed for hero images. To find your token see: https://support.plex.tv/articles/204059436-finding-an-authentication
REPLEX_INTERLEAVE	true	Interleave home hubs. Collection hubs with the same name from different libraries are interleaved (combined) into one.
REPLEX_EXCLUDE_WATCHED	true	If set to true, hide watched items for hubs.
REPLEX_HUB_RESTRICTIONS	true	Apply collections restrictions to their hub's. Plex does not apply restrictions to hubs, so you cannot have different collection hubs for users. this fixes that.
REPLEX_DISABLE_CONTINUE_WATCHING	false	Disable/remove the continue watching row
REPLEX_DISABLE_USER_STATE	true	Remove watched badges from hub items. * does not work on all clients
REPLEX_DISABLE_LEAF_COUNT	false	Remove episode count label from show artwork.
REPLEX_HERO_ROWS		Comma seperated list of hubidentifiers to make builtin hubs hero style. For custom collections see Hhb style. Options are: home.movies.recent movies.recent movie.recentlyadded movie.topunwatched movie.recentlyviewed hub.movie.recentlyreleased movie.recentlyreleased home.television.recent tv.recentlyadded tv.toprated tv.inprogress tv.recentlyaired
REPLEX_FORCE_MAXIMUM_QUALITY	false	This will force clients to use the maximum quality. Meaning that if a client requests anything other then the maximum quality this will be ignored and the maximum quality (direct play/stream when server allows for original) is used instead. This doesn't prevent transcoding. It only sets the bitrate to original quality. So if a client needs a different codec, container or audio it should still transcode.
REPLEX_FORCE_DIRECT_PLAY_FOR	false	Force direct play for the given resolutions. Options are "4k", "1080" and "720". This wil result in an error message if the client does not support directplay. Not recommended
REPLEX_VIDEO_TRANSCODE_FALLBACK_FOR		If the selected media triggers a video transcode. Fallback to another version of the media. Only triggers on video transcoding. Remuxing is still allowed. Options are "4k" and "1080". Example if REPLEX_VIDEO_TRANSCODE_FALLBACK_FOR is set to "4k" then 4k transcodes will fallback to another version if avaiable
REPLEX_AUTO_SELECT_VERSION	false	If you have multiple versions of a media item then this setting will choose the one thats closest to the client resolution. So a 1080p TV will get the 1080P version while 4k gets the 4k version. A user can still override this by selecting a different version from the client.
REPLEX_DISABLE_RELATED	false	See: https://github.com/lostb1t/replex/issues/26.
REPLEX_REDIRECT_STREAMS	false	Redirect streams to another endpoint.
REPLEX_REDIRECT_STREAMS_HOST	REPLEX_HOST	Alternative streams endpoint
REPLEX_CACHE_TTL	1800	Time to live for general caches in seconds. Set to 0 to disable (higly recommended to keep enabled besides testing purposes).

Interleaved rows

Collections hubs with the same name from different libraries will be merged into one on the home screen. So an collection hub named "Trending" in the Movie library will be merged with an collection named "Trending" from a shows library on home.

Note, this does not work on builtin hubs. As i personally dont see then need of mixing those. You can recreate the builtin rows with smart collections if you wish to have that functionality, or with PMM ofcourse.

Hub style

For custom collections you can change the hub style to hero by setting the label "REPLEXHERO" on an collection.

For built in rows you can use the hubidentifier in the REPLEX_HERO_ROWS. See the setting for available know options.

Note: hero style elements uses coverart from plex. Banner or background is not used. Note: Hero elements are not supported for continue watching by plex. You can replicate this functionality by creating a smart collection which filters on in progress and settinf REPLEX_DISABLE_CONTINUE_WATCHING

Exclude watched items

If you want to hide watched items from your hubs, you can set REPLEX_EXCLUDE_WATCHED to true. Alternatively, you can add the label "REPLEX_EXCLUDE_WATCHED" to a collection to exclude watched items from that collection only.

Remote access (force clients to use the proxy)

Because this app sits before Plex the builtin remote access (and auto SSL) will not work and needs to be disabled.

For testing purposes you can access through the browser at http://[replexip]:[replexport] (ex: http://localhost:3001) But if you want other clients to connect to replex you need to setup a reverse proxy with a domain and preferable ssl.

A few easy to setup reverse proxys are: https://caddyserver.com or https://nginxproxymanager.com

Once you have your domain hooked up to replex add your replex url to 'Custom server access URLs' field under network. and lastly disable remote access under remote access.

Clear you clients caches to force plex reloading the custom server url

Note: SSL is highly suggested, some clients default to not allowing insecure connections. And some clients dont even support insecure connections (app.plex.tv)

Reverse proxy

There should be no need for this but if you have a reverse proxy running and dont want to proxy streaming through plex then you can route the following paths and it subpaths directly to plex.

/video/:/transcode/universal/session
/library/parts

Redirect streams

If you have for example an appbox it might not be ideal to stream media through replex. As that will take a lot of network resources. You can redirect streams by enabling REPLEX_REDIRECT_STREAMS and optionally set REPLEX_REDIRECT_STREAMS_HOST if it needs to be different from REPLEX_HOST

Note: Plex doesnt handle redirects wel, and will not remeber it. So every chuck of a stream will first hit replex and then gets redirected to actuall download that chuck from the redirect url. So a bit wastefull

Known limitations

hero hubs on Android devices dont load more content. so hero hubs have a maximum of 100 items on Android.
On android mobile hero elements in libraries are slightly cutoff. This is plex limitation.
when exclude_watched is true a maximum item limit per library is opposed of 250 items. So if you have a mixed row of 2 libraries the max results of that row will be 500 items.
disable_user_state: For movies this works in the webapp. Shows work accross clients

Help it doesnt work!

Replex works on on app.plex.tv but not on my clients

disable GDM in plex and make sure plex is not directly acccesible. you can use this url to check what servers plex communicates to your clients: https://clients.plex.tv/api/v2/resources?includeIPv6=1&includeRelay=1&X-Plex-Language=en-NL&X-Plex-Token=YOURTOKEN&X-Plex-Client-Identifier=1234
Try to clear the cache on the client. Old plex domains might linger.

teloxide/teloxide

2024-07-17T01:34:30Z

🤖 An elegant Telegram bots framework for Rust

v0.11 -> v0.12 migration guide >>

`teloxide`

A full-featured framework that empowers you to easily build Telegram bots using Rust. It handles all the difficult stuff so you can focus only on your business logic.

Highlights

Declarative design. teloxide is based upon dptree, a functional chain of responsibility pattern that allows you to express pipelines of message processing in a highly declarative and extensible style.

Feature-rich. You can use both long polling and webhooks, configure an underlying HTTPS client, set a custom URL of a Telegram API server, do graceful shutdown, and much more.
Simple dialogues. Our dialogues subsystem is simple and easy-to-use, and, furthermore, is agnostic of how/where dialogues are stored. For example, you can just replace a one line to achieve persistence. Out-of-the-box storages include Redis and Sqlite.

Strongly typed commands. Define bot commands as an enum and teloxide will parse them automatically — just like JSON structures in serde-json and command-line arguments in structopt.

Setting up your environment

Download Rust.
Create a new bot using @Botfather to get a token in the format 123456789:blablabla.
Initialise the TELOXIDE_TOKEN environmental variable to your token:

# Unix-like
$ export TELOXIDE_TOKEN=<Your token here>

# Windows command line
$ set TELOXIDE_TOKEN=<Your token here>

# Windows PowerShell
$ $env:TELOXIDE_TOKEN=<Your token here>

Make sure that your Rust compiler is up to date (teloxide currently requires rustc at least version 1.70):

# If you're using stable
$ rustup update stable
$ rustup override set stable

# If you're using nightly
$ rustup update nightly
$ rustup override set nightly

Run cargo new my_bot, enter the directory and put these lines into your Cargo.toml:

[dependencies]
teloxide = { version = "0.12", features = ["macros"] }
log = "0.4"
pretty_env_logger = "0.4"
tokio = { version =  "1.8", features = ["rt-multi-thread", "macros"] }

API overview

The dices bot

This bot replies with a dice to each received message:

[examples/throw_dice.rs]

use teloxide::prelude::*;

#[tokio::main]
async fn main() {
    pretty_env_logger::init();
    log::info!("Starting throw dice bot...");

    let bot = Bot::from_env();

    teloxide::repl(bot, |bot: Bot, msg: Message| async move {
        bot.send_dice(msg.chat.id).await?;
        Ok(())
    })
    .await;
}

Commands

Commands are strongly typed and defined declaratively, similar to how we define CLI using structopt and JSON structures in serde-json. The following bot accepts these commands:

/username <your username>
/usernameandage <your username> <your age>
/help

[examples/command.rs]

use teloxide::{prelude::*, utils::command::BotCommands};

#[tokio::main]
async fn main() {
    pretty_env_logger::init();
    log::info!("Starting command bot...");

    let bot = Bot::from_env();

    Command::repl(bot, answer).await;
}

#[derive(BotCommands, Clone)]
#[command(rename_rule = "lowercase", description = "These commands are supported:")]
enum Command {
    #[command(description = "display this text.")]
    Help,
    #[command(description = "handle a username.")]
    Username(String),
    #[command(description = "handle a username and an age.", parse_with = "split")]
    UsernameAndAge { username: String, age: u8 },
}

async fn answer(bot: Bot, msg: Message, cmd: Command) -> ResponseResult<()> {
    match cmd {
        Command::Help => bot.send_message(msg.chat.id, Command::descriptions().to_string()).await?,
        Command::Username(username) => {
            bot.send_message(msg.chat.id, format!("Your username is @{username}.")).await?
        }
        Command::UsernameAndAge { username, age } => {
            bot.send_message(msg.chat.id, format!("Your username is @{username} and age is {age}."))
                .await?
        }
    };

    Ok(())
}

Dialogues management

A dialogue is typically described by an enumeration where each variant is one possible state of the dialogue. There are also state handler functions, which may turn a dialogue from one state to another, thereby forming an FSM.

Below is a bot that asks you three questions and then sends the answers back to you:

[examples/dialogue.rs]

use teloxide::{dispatching::dialogue::InMemStorage, prelude::*};

type MyDialogue = Dialogue<State, InMemStorage<State>>;
type HandlerResult = Result<(), Box<dyn std::error::Error + Send + Sync>>;

#[derive(Clone, Default)]
pub enum State {
    #[default]
    Start,
    ReceiveFullName,
    ReceiveAge {
        full_name: String,
    },
    ReceiveLocation {
        full_name: String,
        age: u8,
    },
}

#[tokio::main]
async fn main() {
    pretty_env_logger::init();
    log::info!("Starting dialogue bot...");

    let bot = Bot::from_env();

    Dispatcher::builder(
        bot,
        Update::filter_message()
            .enter_dialogue::<Message, InMemStorage<State>, State>()
            .branch(dptree::case![State::Start].endpoint(start))
            .branch(dptree::case![State::ReceiveFullName].endpoint(receive_full_name))
            .branch(dptree::case![State::ReceiveAge { full_name }].endpoint(receive_age))
            .branch(
                dptree::case![State::ReceiveLocation { full_name, age }].endpoint(receive_location),
            ),
    )
    .dependencies(dptree::deps![InMemStorage::<State>::new()])
    .enable_ctrlc_handler()
    .build()
    .dispatch()
    .await;
}

async fn start(bot: Bot, dialogue: MyDialogue, msg: Message) -> HandlerResult {
    bot.send_message(msg.chat.id, "Let's start! What's your full name?").await?;
    dialogue.update(State::ReceiveFullName).await?;
    Ok(())
}

async fn receive_full_name(bot: Bot, dialogue: MyDialogue, msg: Message) -> HandlerResult {
    match msg.text() {
        Some(text) => {
            bot.send_message(msg.chat.id, "How old are you?").await?;
            dialogue.update(State::ReceiveAge { full_name: text.into() }).await?;
        }
        None => {
            bot.send_message(msg.chat.id, "Send me plain text.").await?;
        }
    }

    Ok(())
}

async fn receive_age(
    bot: Bot,
    dialogue: MyDialogue,
    full_name: String, // Available from `State::ReceiveAge`.
    msg: Message,
) -> HandlerResult {
    match msg.text().map(|text| text.parse::<u8>()) {
        Some(Ok(age)) => {
            bot.send_message(msg.chat.id, "What's your location?").await?;
            dialogue.update(State::ReceiveLocation { full_name, age }).await?;
        }
        _ => {
            bot.send_message(msg.chat.id, "Send me a number.").await?;
        }
    }

    Ok(())
}

async fn receive_location(
    bot: Bot,
    dialogue: MyDialogue,
    (full_name, age): (String, u8), // Available from `State::ReceiveLocation`.
    msg: Message,
) -> HandlerResult {
    match msg.text() {
        Some(location) => {
            let report = format!("Full name: {full_name}\nAge: {age}\nLocation: {location}");
            bot.send_message(msg.chat.id, report).await?;
            dialogue.exit().await?;
        }
        None => {
            bot.send_message(msg.chat.id, "Send me plain text.").await?;
        }
    }

    Ok(())
}

More examples >>

Tutorials

"Migrating my family finance bot from Python to Rust (teloxide) because I am tired of exceptions (part 1)" by Troy Köhler.
"Migrating my family finance bot from Python to Rust (teloxide) [part 2]" by Troy Köhler.

FAQ

Q: Where I can ask questions?

Issues is a good place for well-formed questions about the library design, enhancements, and bug reports.
GitHub Discussions is a place where you can ask us for help in a less formal manner.
If you need quick help in real-time, you should ask a question in our official Telegram group.

Q: Do you support the Telegram API for clients?

A: No, only the bots API.

Q: Can I use webhooks?

A: You can! teloxide has a built-in support for webhooks in dispatching::update_listeners::webhooks module. See how it's used in examples/ngrok_ping_pong_bot and examples/heroku_ping_pong_bot.

Q: Can I handle both callback queries and messages within a single dialogue?

A: Yes, see examples/purchase.rs.

Community bots

Feel free to propose your own bot to our collection!

raine/tgreddit — A bot that sends the top posts of your favorite subreddits to Telegram.
magnickolas/remindee-bot — Telegram bot for managing reminders.
WaffleLapkin/crate_upd_bot — A bot that notifies about crate updates.
mattrighetti/GroupActivityBot — Telegram bot that keeps track of user activity in groups.
alenpaul2001/AurSearchBot — Telegram bot for searching in Arch User Repository (AUR).
ArtHome12/vzmuinebot — Telegram bot for food menu navigate.
studiedlist/EddieBot — Chatting bot with several entertainment features.
modos189/tg_blackbox_bot — Anonymous feedback for your Telegram project.
0xNima/spacecraft — Yet another telegram bot to downloading Twitter spaces.
0xNima/Twideo — Simple Telegram Bot for downloading videos from Twitter via their links.
mattrighetti/libgen-bot-rs — Telegram bot to interface with libgen.
zamazan4ik/npaperbot-telegram — Telegram bot for searching via C++ proposals.
studentenherz/dlebot — A bot to query definitions of words from the Spanish Language Dictionary.
fr0staman/fr0staman_bot — Feature rich Telegram game-like bot with pigs 🐽.
franciscofigueira/transferBot — Telegram bot that notifies of crypto token transfers.

Show bots using `teloxide` older than v0.6.0

mxseev/logram — Utility that takes logs from anywhere and sends them to Telegram.
alexkonovalov/PedigreeBot — A Telegram bot for building family trees.
Hermitter/tepe — A CLI to command a bot to send messages and files over Telegram.
myblackbeard/basketball-betting-bot — The bot lets you bet on NBA games against your buddies.
dracarys18/grpmr-rs — Modular Telegram Group Manager Bot written in Rust.
ArtHome12/cognito_bot — The bot is designed to anonymize messages to a group.
crapstone/hsctt — A bot that converts HTTP status codes into text.

See 1600+ other public repositories using teloxide >>

Contributing

See CONRIBUTING.md.

qgis/QGIS-Hugo

2024-07-17T01:34:42Z

Hugo static website for the new QGIS.org

codercup/unibest

2024-07-17T01:35:36Z

unibest - 最好用的 uniapp 开发框架。unibest 是由 uniapp + Vue3 + Ts + Vite4 + UnoCss + UniUI 驱动的跨端快速启动模板，使用 VS Code 开发，具有代码提示、自动格式化、统一配置、代码片段等功能，同时内置了大量平时开发常用的基本组件，开箱即用，让你编写 uniapp 拥有 best 体验。

unibest - 最好的 uniapp 开发框架

unibest —— 最好的 uniapp 开发模板，由 uniapp + Vue3 + Ts + Vite5 + UnoCss + wot-ui + z-paging 构成，使用了最新的前端技术栈，无需依靠 HBuilderX，通过命令行方式运行 web、小程序 和 App（编辑器推荐 VSCode，可选 webstorm）。

unibest 内置了 约定式路由、layout布局、请求封装、请求拦截、登录拦截、UnoCSS、i18n多语言 等基础功能，提供了 代码提示、自动格式化、统一配置、代码片段 等辅助功能，让你编写 uniapp 拥有 best 体验（ unibest 的由来）。

📖 文档地址 | 📱 DEMO 地址

⚙️ 环境

node>=18
pnpm>=7.30

📂 快速开始

执行 pnpm create unibest 创建项目

执行 pnpm i 安装依赖

执行 pnpm dev 运行 H5

📦 运行（支持热更新）

web平台： pnpm dev:h5, 然后打开 http://localhost:9000/。
weixin平台：pnpm dev:mp-weixin 然后打开微信开发者工具，导入本地文件夹，选择本项目的dist/dev/mp-weixin 文件。
APP平台：pnpm dev:app, 然后打开 HBuilderX，导入刚刚生成的dist/dev/app 文件夹，选择运行到模拟器(开发时优先使用)，或者运行的安卓/ios基座。

🔗 发布

web平台： pnpm build:h5，打包后的文件在 dist/build/h5，可以放到web服务器，如nginx运行。如果最终不是放在根目录，可以在 manifest.config.ts 文件的 h5.router.base 属性进行修改。
weixin平台：pnpm build:mp-weixin, 打包后的文件在 dist/build/mp-weixin，然后通过微信开发者工具导入，并点击右上角的“上传”按钮进行上传。
APP平台：pnpm build:app, 然后打开 HBuilderX，导入刚刚生成的dist/build/app 文件夹，选择发行 - APP云打包。

📄 License

MIT

捐赠

chatchat-space/Langchain-Chatchat

2024-07-17T01:35:36Z

Langchain-Chatchat（原Langchain-ChatGLM, Qwen 与 Llama 等）基于 Langchain 与 ChatGLM 等语言模型的 RAG 与 Agent 应用 | Langchain-Chatchat (formerly langchain-ChatGLM), local knowledge based LLM (like ChatGLM, Qwen and Llama) RAG and Agent app with langchain

🌍 READ THIS IN ENGLISH

📃 LangChain-Chatchat (原 Langchain-ChatGLM)

基于 ChatGLM 等大语言模型与 Langchain 等应用框架实现，开源、可离线部署的 RAG 与 Agent 应用项目。

概述

🤖️ 一种利用 langchain 思想实现的基于本地知识库的问答应用，目标期望建立一套对中文场景与开源模型支持友好、可离线运行的知识库问答解决方案。

💡 受 GanymedeNil 的项目 document.ai 和 AlexZhangji 创建的 ChatGLM-6B Pull Request 启发，建立了全流程可使用开源模型实现的本地知识库问答应用。本项目的最新版本中可使用 Xinference、Ollama 等框架接入 GLM-4-Chat、 Qwen2-Instruct、 Llama3 等模型，依托于 langchain 框架支持通过基于 FastAPI 提供的 API 调用服务，或使用基于 Streamlit 的 WebUI 进行操作。

✅ 本项目支持市面上主流的开源 LLM、 Embedding 模型与向量数据库，可实现全部使用开源模型离线私有部署。与此同时，本项目也支持 OpenAI GPT API 的调用，并将在后续持续扩充对各类模型及模型 API 的接入。

⛓️ 本项目实现原理如下图所示，过程包括加载文件 -> 读取文本 -> 文本分割 -> 文本向量化 -> 问句向量化 -> 在文本向量中匹配出与问句向量最相似的 top k个 -> 匹配出的文本作为上下文和问题一起添加到 prompt中 -> 提交给 LLM生成回答。

📺 原理介绍视频

从文档处理角度来看，实现流程如下：

🚩 本项目未涉及微调、训练过程，但可利用微调或训练对本项目效果进行优化。

🌐 AutoDL 镜像中 0.3.0 版本所使用代码已更新至本项目 v0.3.0 版本。

🐳 Docker 镜像将会在近期更新。

🧑‍💻 如果你想对本项目做出贡献，欢迎移步开发指南获取更多开发部署相关信息。

功能介绍

0.3.x 版本功能一览

功能	0.2.x	0.3.x
模型接入	本地：fastchat 在线：XXXModelWorker	本地：model_provider,支持大部分主流模型加载框架在线：oneapi 所有模型接入均兼容openai sdk
Agent	❌不稳定	✅针对ChatGLM3和QWen进行优化,Agent能力显著提升
LLM对话	✅	✅
知识库对话	✅	✅
搜索引擎对话	✅	✅
文件对话	✅仅向量检索	✅统一为File RAG功能,支持BM25+KNN等多种检索方式
数据库对话	❌	✅
多模态图片对话	❌	✅ 推荐使用 qwen-vl-chat
ARXIV文献对话	❌	✅
Wolfram对话	❌	✅
文生图	❌	✅
本地知识库管理	✅	✅
WEBUI	✅	✅更好的多会话支持,自定义系统提示词...

0.3.x 版本的核心功能由 Agent 实现,但用户也可以手动实现工具调用:

操作方式	实现的功能	适用场景
选中"启用Agent",选择多个工具	由LLM自动进行工具调用	使用ChatGLM3/Qwen或在线API等具备Agent能力的模型
选中"启用Agent",选择单个工具	LLM仅解析工具参数	使用的模型Agent能力一般,不能很好的选择工具想手动选择功能
不选中"启用Agent",选择单个工具	不使用Agent功能的情况下,手动填入参数进行工具调用	使用的模型不具备Agent能力
不选中任何工具，上传一个图片	图片对话	使用 qwen-vl-chat 等多模态模型

更多功能和更新请实际部署体验.

已支持的模型部署框架与模型

本项目中已经支持市面上主流的如 GLM-4-Chat 与 Qwen2-Instruct 等新近开源大语言模型和 Embedding 模型，这些模型需要用户自行启动模型部署框架后，通过修改配置信息接入项目，本项目已支持的本地模型部署框架如下：

模型部署框架	Xinference	LocalAI	Ollama	FastChat
OpenAI API 接口对齐	✅	✅	✅	✅
加速推理引擎	GPTQ, GGML, vLLM, TensorRT, mlx	GPTQ, GGML, vLLM, TensorRT	GGUF, GGML	vLLM
接入模型类型	LLM, Embedding, Rerank, Text-to-Image, Vision, Audio	LLM, Embedding, Rerank, Text-to-Image, Vision, Audio	LLM, Text-to-Image, Vision	LLM, Vision
Function Call	✅	✅	✅	/
更多平台支持(CPU, Metal)	✅	✅	✅	✅
异构	✅	✅	/	/
集群	✅	✅	/	/
操作文档链接	Xinference 文档	LocalAI 文档	Ollama 文档	FastChat 文档
可用模型	Xinference 已支持模型	LocalAI 已支持模型	Ollama 已支持模型	FastChat 已支持模型

除上述本地模型加载框架外，项目中也为可接入在线 API 的 One API 框架接入提供了支持，支持包括 OpenAI ChatGPT、Azure OpenAI API、Anthropic Claude、智谱清言、百川等常用在线 API 的接入使用。

[!Note] 关于 Xinference 加载本地模型: Xinference 内置模型会自动下载,如果想让它加载本机下载好的模型,可以在启动 Xinference 服务后,到项目 tools/model_loaders 目录下执行 streamlit run xinference_manager.py,按照页面提示为指定模型设置本地路径即可.

快速上手

pip 安装部署

0. 软硬件要求

💡 软件方面，本项目已支持在 Python 3.8-3.11 环境中进行使用，并已在 Windows、macOS、Linux 操作系统中进行测试。

💻 硬件方面，因 0.3.0 版本已修改为支持不同模型部署框架接入，因此可在 CPU、GPU、NPU、MPS 等不同硬件条件下使用。

1. 安装 Langchain-Chatchat

从 0.3.0 版本起，Langchain-Chatchat 提供以 Python 库形式的安装方式，具体安装请执行：

pip install langchain-chatchat -U

[!important] 为确保所使用的 Python 库为最新版，建议使用官方 Pypi 源或清华源。

[!Note] 因模型部署框架 Xinference 接入 Langchain-Chatchat 时需要额外安装对应的 Python 依赖库，因此如需搭配 Xinference 框架使用时，建议使用如下安装方式：
pip install "langchain-chatchat[xinference]" -U

2. 模型推理框架并加载模型

从 0.3.0 版本起，Langchain-Chatchat 不再根据用户输入的本地模型路径直接进行模型加载，涉及到的模型种类包括 LLM、Embedding、Reranker 及后续会提供支持的多模态模型等，均改为支持市面常见的各大模型推理框架接入，如 Xinference、Ollama、LocalAI、FastChat、One API 等。

因此，请确认在启动 Langchain-Chatchat 项目前，首先进行模型推理框架的运行，并加载所需使用的模型。

这里以 Xinference 举例, 请参考 Xinference文档进行框架部署与模型加载。

[!WARNING]
为避免依赖冲突，请将 Langchain-Chatchat 和模型部署框架如 Xinference 等放在不同的 Python 虚拟环境中, 比如 conda, venv, virtualenv 等。

3. 初始化项目配置与数据目录

从 0.3.1 版本起，Langchain-Chatchat 使用本地 yaml 文件的方式进行配置，用户可以直接查看并修改其中的内容，服务器会自动更新无需重启。

设置 Chatchat 存储配置文件和数据文件的根目录（可选）

# on linux or macos
export CHATCHAT_ROOT=/path/to/chatchat_data

# on windows
set CHATCHAT_ROOT=/path/to/chatchat_data

若不设置该环境变量，则自动使用当前目录。

执行初始化

chatchat init

该命令会执行以下操作：

创建所有需要的数据目录
复制 samples 知识库内容
生成默认 yaml 配置文件

修改配置文件

配置模型（model_settings.yaml）
需要根据步骤 2. 模型推理框架并加载模型 中选用的模型推理框架与加载的模型进行模型接入配置，具体参考 model_settings.yaml 中的注释。主要修改以下内容：

# 默认选用的 LLM 名称
 DEFAULT_LLM_MODEL: qwen1.5-chat

 # 默认选用的 Embedding 名称
 DEFAULT_EMBEDDING_MODEL: bge-large-zh-v1.5

# 将 `LLM_MODEL_CONFIG` 中 `llm_model, action_model` 的键改成对应的 LLM 模型
# 在 `MODEL_PLATFORMS` 中修改对应模型平台信息

配置知识库路径（basic_settings.yaml）（可选）
默认知识库位于 CHATCHAT_ROOT/data/knowledge_base，如果你想把知识库放在不同的位置，或者想连接现有的知识库，可以在这里修改对应目录即可。

# 知识库默认存储路径
 KB_ROOT_PATH: D:\chatchat-test\data\knowledge_base

 # 数据库默认存储路径。如果使用sqlite，可以直接修改DB_ROOT_PATH；如果使用其它数据库，请直接修改SQLALCHEMY_DATABASE_URI。
 DB_ROOT_PATH: D:\chatchat-test\data\knowledge_base\info.db

 # 知识库信息数据库连接URI
 SQLALCHEMY_DATABASE_URI: sqlite:///D:\chatchat-test\data\knowledge_base\info.db

配置知识库（kb_settings.yaml）（可选）

默认使用 FAISS 知识库，如果想连接其它类型的知识库，可以修改 DEFAULT_VS_TYPE 和 kbs_config。

4. 初始化知识库

[!WARNING]
进行知识库初始化前，请确保已经启动模型推理框架及对应 embedding 模型，且已按照上述步骤3完成模型接入配置。

chatchat kb -r

更多功能可以查看 chatchat kb --help

出现以下日志即为成功:


----------------------------------------------------------------------------------------------------
知识库名称      ：samples
知识库类型      ：faiss
向量模型：      ：bge-large-zh-v1.5
知识库路径      ：/root/anaconda3/envs/chatchat/lib/python3.11/site-packages/chatchat/data/knowledge_base/samples
文件总数量      ：47
入库文件数      ：42
知识条目数      ：740
用时            ：0:02:29.701002
----------------------------------------------------------------------------------------------------

总计用时        ：0:02:33.414425

[!Note] 知识库初始化的常见问题
1. Windows 下重建知识库或添加知识文件时卡住不动

此问题常出现于新建虚拟环境中，可以通过以下方式确认：

from unstructured.partition.auto import partition

如果该语句卡住无法执行，可以执行以下命令：
pip uninstall python-magic-bin
# check the version of the uninstalled package
pip install 'python-magic-bin=={version}'
然后按照本节指引重新创建知识库即可。

5. 启动项目

chatchat start -a

出现以下界面即为启动成功:

[!WARNING]
由于 chatchat 配置默认监听地址 DEFAULT_BIND_HOST 为 127.0.0.1, 所以无法通过其他 ip 进行访问。

如需通过机器ip 进行访问(如 Linux 系统), 需要到 basic_settings.yaml 中将监听地址修改为 0.0.0.0。

其它配置

数据库对话配置请移步这里数据库对话配置说明

源码安装部署/开发部署

源码安装部署请参考开发指南

Docker 部署

docker pull chatimage/chatchat:0.3.0-2024-0624

[!important] 强烈建议: 使用 docker-compose 部署, 具体参考 README_docker

旧版本迁移

0.3.x 结构改变很大,强烈建议您按照文档重新部署. 以下指南不保证100%兼容和成功. 记得提前备份重要数据!

首先按照 安装部署 中的步骤配置运行环境，修改配置文件
将 0.2.x 项目的 knowledge_base 目录拷贝到配置的 DATA 目录下

项目里程碑

2023年4月: Langchain-ChatGLM 0.1.0 发布，支持基于 ChatGLM-6B 模型的本地知识库问答。
2023年8月: Langchain-ChatGLM 改名为 Langchain-Chatchat，发布 0.2.0 版本，使用 fastchat 作为模型加载方案，支持更多的模型和数据库。
2023年10月: Langchain-Chatchat 0.2.5 发布，推出 Agent 内容，开源项目在Founder Park & Zhipu AI & Zilliz 举办的黑客马拉松获得三等奖。
2023年12月: Langchain-Chatchat 开源项目获得超过 20K stars.
2024年6月: Langchain-Chatchat 0.3.0 发布，带来全新项目架构。
🔥 让我们一起期待未来 Chatchat 的故事 ···

协议

本项目代码遵循 Apache-2.0 协议。

联系我们

项目交流群

🎉 Langchain-Chatchat 项目微信交流群，如果你也对本项目感兴趣，欢迎加入群聊参与讨论交流。

公众号

🎉 Langchain-Chatchat 项目官方公众号，欢迎扫码关注。

引用

如果本项目有帮助到您的研究，请引用我们：

@software{langchain_chatchat,
    title        = {{langchain-chatchat}},
    author       = {Liu, Qian and Song, Jinke, and Huang, Zhiguo, and Zhang, Yuxuan, and glide-the, and liunux4odoo},
    year         = 2024,
    journal      = {GitHub repository},
    publisher    = {GitHub},
    howpublished = {\url{https://github.com/chatchat-space/Langchain-Chatchat}}
}

InternLM/Tutorial

2024-07-17T01:28:47Z

沉浸式双语网页翻译扩展 , 支持输入框翻译，鼠标悬停翻译， PDF, Epub, 字幕文件, TXT 文件翻译 - Immersive Dual Web Page Translation Extension

keith/swift.vim

2024-07-17T01:35:48Z

Vim runtime files for Swift

ikskuh/zig-network

2024-07-18T01:36:15Z

A smallest-common-subset of socket functions for crossplatform networking, TCP & UDP

GitHub 1C Enterprise Daily Trending

MervinPraison/PraisonAI

Praison AI

TL;DR

Table of Contents

Installation

Initialise

To initialse with a specific agent framework (Optional):

Run

Specify the agent framework (Optional):

Full Automatic Mode

User Interface

PraisonAI User Interfaces:

Praison AI Chat

Create Custom Tools

Step 1: Pre-requisite to Create a Custom Tool

Step 2: to Create a Custom Tool

Step 3: to Create a Custom Tool

Agents Playbook

Simple Playbook Example

Use 100+ Models

Include praisonai package in your project

Option 1: Using RAW YAML

Option 2: Using separate agents.yaml file

Commands to Install Dependencies:

Contributing

Star History

PaddlePaddle/Paddle

AI4Animation: Deep Learning for Character Control

SIGGRAPH 2024 Categorical Codebook Matching for Embodied Character Controllers Sebastian Starke, Paul Starke, Nicky He, Taku Komura, Yuting Ye, ACM Trans. Graph. 43, 4, Article 142.

SIGGRAPH 2022 DeepPhase: Periodic Autoencoders for Learning Motion Phase Manifolds Sebastian Starke, Ian Mason, Taku Komura, ACM Trans. Graph. 41, 4, Article 136.

SIGGRAPH 2021 Neural Animation Layering for Synthesizing Martial Arts Movements Sebastian Starke, Yiwei Zhao, Fabio Zinno, Taku Komura, ACM Trans. Graph. 40, 4, Article 92.

SIGGRAPH 2020 Local Motion Phases for Learning Multi-Contact Character Movements Sebastian Starke, Yiwei Zhao, Taku Komura, Kazi Zaman. ACM Trans. Graph. 39, 4, Article 54.

SIGGRAPH Asia 2019 Neural State Machine for Character-Scene Interactions Sebastian Starke+, He Zhang+, Taku Komura, Jun Saito. ACM Trans. Graph. 38, 6, Article 178. (+Joint First Authors)

SIGGRAPH 2018 Mode-Adaptive Neural Networks for Quadruped Motion Control He Zhang+, Sebastian Starke+, Taku Komura, Jun Saito. ACM Trans. Graph. 37, 4, Article 145. (+Joint First Authors)

SIGGRAPH 2017 Phase-Functioned Neural Networks for Character Control Daniel Holden, Taku Komura, Jun Saito. ACM Trans. Graph. 36, 4, Article 42.

Thesis Fast Forward Presentation from SIGGRAPH 2020

Copyright Information

free-educa/books

Git Credential Manager

Install

Current status

Supported Git versions

How to use

Configuring a proxy

Additional Resources

Experimental Features

Future features

Contributing

License

thedevdojo/auth

ROCm/HIP

What is this repository for?

DISCLAIMER

Repository branches

Release tagging

More Info

How do I get set up?

Simple Example

HIP Portability and Compiler Technology

Examples and Getting Started

More Examples

Tour of the HIP Directories

Reporting an issue

strawberrymusicplayer/strawberry

🍓 Strawberry Music Player

‼ Opening an issue

💰 Sponsoring

✔ Features

❗ Requirements

🔧 Compiling from source

Get the code:

Compile and install:

🐧 Packaging status

IvorySQL/IvorySQL

We are committed to following the principles of the open source way

Installation

Contributing to the IvorySQL

Documentation

Omkar-Sonawane-23/Maa-Vimala

SIGGRAPH 2024
Categorical Codebook Matching for Embodied Character Controllers
_{Sebastian Starke, Paul Starke, Nicky He, Taku Komura, Yuting Ye, ACM Trans. Graph. 43, 4, Article 142.}

SIGGRAPH 2022
DeepPhase: Periodic Autoencoders for Learning Motion Phase Manifolds
_{Sebastian Starke, Ian Mason, Taku Komura, ACM Trans. Graph. 41, 4, Article 136.}

SIGGRAPH 2021
Neural Animation Layering for Synthesizing Martial Arts Movements
_{Sebastian Starke, Yiwei Zhao, Fabio Zinno, Taku Komura, ACM Trans. Graph. 40, 4, Article 92.}

SIGGRAPH 2020
Local Motion Phases for Learning Multi-Contact Character Movements
_{Sebastian Starke, Yiwei Zhao, Taku Komura, Kazi Zaman. ACM Trans. Graph. 39, 4, Article 54.}

SIGGRAPH Asia 2019
Neural State Machine for Character-Scene Interactions
_{Sebastian Starke⁺, He Zhang⁺, Taku Komura, Jun Saito. ACM Trans. Graph. 38, 6, Article 178.}
_{^{(+Joint First Authors)}}

SIGGRAPH 2018
Mode-Adaptive Neural Networks for Quadruped Motion Control
_{He Zhang⁺, Sebastian Starke⁺, Taku Komura, Jun Saito. ACM Trans. Graph. 37, 4, Article 145.}
_{^{(+Joint First Authors)}}

SIGGRAPH 2017
Phase-Functioned Neural Networks for Character Control
_{Daniel Holden, Taku Komura, Jun Saito. ACM Trans. Graph. 36, 4, Article 42.}