Report on issues

The recently opened issues for the software project are primarily related to bugs and feature requests. The bugs are often related to installation methods, versions, and operating systems. For instance, issues #1298 and #1290 are about bugs in the software, with the former being about the GPT4 environment still using GPT3 and the latter about loading local weight files. Feature requests, on the other hand, are more varied. For example, issue #1299 is a request for quota information, issue #1296 is about using multiple GPUs, and issue #1295 provides UI suggestions. These issues indicate that the software project has some problems with its installation and operation, and users are actively seeking improvements in its functionality and user interface.

The older open issues are also a mix of bugs and feature requests. For instance, issue #648 is a request for support for chatGPT web version's accessToken connection, while issue #653 reports a bug about NewBing not working properly. These issues have remained open for over 200 days, suggesting that they may be complex or not a priority for the project maintainers. Recently closed issues include #714, which was about missing dependencies for running chatGLM on Windows, and #728, which was a bug report about failed updates. These closed issues indicate that the project maintainers are actively addressing bugs and improving the software. The common theme among all open and recently closed issues is the need for improvements in the software's functionality, usability, and compatibility.

Report on pull requests

Open Pull Requests Analysis

Notable Themes

• There is a focus on improving the functionality of the software, with several pull requests (#1285, #1282, #1273) introducing new features or improvements to existing ones.
• There is also a focus on improving the user experience, with pull requests (#1285, #1282) improving the user interface and making the software easier to use.
• There is a concern with security, with pull request #1273 introducing user management to the Dockerfile to avoid running the image as root.

Commonalities

• Most of the pull requests involve modifications to existing files, with very few new files being added.
• The pull requests are generally well-documented, with detailed descriptions of the changes made.

Concerns

• There is a potential issue with pull request #1285, as the author mentions that their changes may have negatively impacted the parameter judgement logic for the dall-e-3 plugin.
• Pull request #1282 introduces image content recognition when using sparkv2 or sparkv3, but it's unclear how this feature will impact the overall performance of the software.

Significant Problems

• No significant problems were identified in the open pull requests.

Major Uncertainties

• It's unclear how the changes in pull request #1285 will impact the overall functionality of the dall-e-3 plugin.
• The impact of the image content recognition feature introduced in pull request #1282 is uncertain.

Worrying Anomalies

• No worrying anomalies were identified in the open pull requests.

Closed Pull Requests Analysis

Notable Themes

• There is a focus on improving the documentation of the software, with several pull requests (#1293, #1279) updating the README and other documentation files.
• There is also a focus on fixing bugs and improving the functionality of the software, with pull requests (#1274, #1268, #1244, #1241, #1238) introducing bug fixes and new features.

Commonalities

• Most of the pull requests involve modifications to existing files, with very few new files being added.
• The pull requests are generally well-documented, with detailed descriptions of the changes made.

Concerns

• No major concerns were identified in the closed pull requests.

Significant Problems

• No significant problems were identified in the closed pull requests.

Major Uncertainties

• No major uncertainties were identified in the closed pull requests.

Worrying Anomalies

• No worrying anomalies were identified in the closed pull requests.

Report on README and metadata

The binary-husky/gpt_academic software project is a Python-based application designed to provide a practical interactive interface for ChatGPT/GLM, optimized for academic reading, polishing, and writing. The modular design supports custom shortcut buttons and function plugins, and it can analyze and self-translate Python and C++ projects. It also includes features for translating and summarizing PDF/LaTex papers, parallel inquiries to various LLM models, and support for local models like chatglm2. The software is licensed under the GNU General Public License v3.0.

The repository is quite popular and active, with 46780 stars, 6045 forks, and 259 open issues. It has a large size of 70430 kB and has seen 1739 commits across 22 branches, indicating a high level of development activity. The software is primarily written in Python. The README provides detailed instructions for installation and usage, including how to add custom shortcut buttons and function plugins. It also includes a list of features and their descriptions, as well as a variety of screenshots demonstrating the software in action.

The repository has a few notable aspects. For one, it supports a wide range of models and features, making it highly versatile. However, some dependencies are not yet compatible with Python 3.12, so Python 3.11 is recommended. There have also been instances of individuals violating the open-source license and using the project for illicit profit. The README also mentions that the project is compatible with a variety of other models and tools, including 文心一言, moss, llama2, rwkv, claude2, 通义千问, 书生, and 讯飞星火.