Quantify commits

Quantified Commit Activity Over 14 Days

Developer	Avatar	Branches	PRs	Commits	Files	Changes
binary-husky		3	3/2/0	29	29	2071
Menghuan1918		2	2/2/0	2	4	311
iluem		1	0/0/0	2	2	13
owo		1	2/2/0	2	2	4
WFS		1	1/1/0	1	1	3
jiangfy-ihep		1	2/1/0	1	1	2
XIao (Kilig947)		0	1/0/0	0	0	0
None (awwaawwa)		0	1/0/0	0	0	0
Yuxiang Zhang (lin-qian123)		0	1/0/0	0	0	0

_{PRs: created by that dev and opened/merged/closed-unmerged during the period}

Report On: Fetch issues

Analysis of Open Issues

Notable Problems

• Issue #1748: Feature request for improving the alignment of translated bilingual texts in a Latex plugin. This is a significant usability issue for users relying on accurate translations for academic purposes.

• Issue #1747: A bug where the software prompts for dependency installation method selection every time it's opened, indicating a potential issue with dependency management or user settings persistence.

• Issue #1746: A quota exceeded error with OpenAI's API, which is a critical service limitation that users need to address by checking their OpenAI account or adjusting their usage patterns.

• Issue #1745: A feature request to add multi-threaded requests and retry parameters for certain APIs, suggesting an enhancement for robustness and efficiency in API interactions.

• Issue #1744: Similar to #1746, another user experiencing quota issues with OpenAI's API. This indicates a trend where users are hitting service limits, which could suggest either heavy usage patterns or a lack of awareness about quota management.

• Issue #1742: Request for long document parsing functionality, which is a notable feature gap for users needing to work with extensive texts.

• Issue #1741: Request to support the Coze platform, indicating user interest in integrating diverse language models into the academic tool.

• Issue #1737: A network error related to proxy settings, which could indicate either user configuration problems or issues with the software's network management capabilities.

• Issue #1735: An issue with an API interface update causing connection failures, suggesting potential problems with maintaining compatibility with third-party services.

• Issue #1734: Support for GROQ models indicates an interest in expanding the range of supported language models and potentially enhancing the tool's capabilities.

Uncertainties and TODOs

• The resolution of dependency installation issues as reported in #1747 needs to be investigated further to ensure a smoother user experience.

• The handling of service quotas (#1746 and #1744) needs clarification, possibly by providing better guidance on managing API usage or exploring alternative solutions for users hitting these limits.

• The implementation details for multi-threaded requests and retry parameters (#1745) are not fully clear and may require careful design to avoid introducing complexity or instability.

• The feasibility and implications of supporting long document parsing (#1742) and Coze platform integration (#1741) need to be evaluated, as they could significantly affect the tool's architecture and performance.

• Network-related errors (#1737) should be addressed by improving error handling and possibly offering more detailed troubleshooting guidance for users.

Anomalies

• Multiple reports of quota issues with OpenAI's API suggest either a common misunderstanding among users about how quotas work or that the software might benefit from better quota management features or documentation.

Recent Closures

• Several issues have been recently closed, including fixes for bugs related to NOUGAT PDF translation (#1738), OpenAI project API key pattern (#1721), and illegal temperature parameter errors with zhipuai (#1684).

Overall Trends

The current state of open issues suggests that while there are some feature requests that could significantly enhance the tool's capabilities, there are also recurring themes around dependency management, service quotas, and network errors that need attention. Addressing these issues could improve user satisfaction and reduce the volume of similar reports in the future.

Report On: Fetch pull requests

Analysis of Pull Requests for binary-husky/gpt_academic

Open Pull Requests

Notable PRs

• PR #1745: This PR adds multi-threaded requests for qianfan, gemini-, and moonshot- and introduces a RETRY_TIMES_AT_UNKNOWN_ERROR setting in config.py. The reduction of LaTeX compilation retries from 32 to 12 is significant as it optimizes performance by avoiding unnecessary compilations. This PR is recent and should be reviewed promptly due to its potential impact on performance.

• PR #1734: Adds support for several GROQ models, which is a significant enhancement. The PR is based on the frontier branch, which suggests it's intended for a future version (3.75). Given the scale of changes (over 400 lines), this PR requires thorough review and testing.

• PR #1727: This PR introduces text-to-speech (TTS) functionality but has a security concern flagged by GitGuardian regarding hardcoded Alibaba Cloud Keys. Immediate action is required to address the potential security issue before merging.

• PR #1711: Introduces a large set of features related to testing plugins, GUI knowledge base construction, cloud document link parsing, and more. However, GitGuardian has uncovered 11 secrets, indicating serious security concerns that must be addressed. The extensive list of commits and files changed suggests this PR significantly alters the project's functionality.

• PR #1708: Allows core functions to specify models, which can streamline workflows when using domain-specific models like deepseek-math-7b-rl. This feature could enhance user experience by reducing manual model switching.

Other PRs

The remaining open PRs seem to introduce various features or enhancements but do not stand out as particularly notable at this time. They should still be reviewed in due course.

Recently Closed Pull Requests

Notable PRs

• PR #1743: A small fix that adds chat history recording when using Google Gemini Pro. It was merged quickly, indicating its importance for user experience.

• PR #1728: Addresses multiple security issues related to pickle usage by migrating to JSON. This is a critical security improvement.

• PR #1721 & PR #1720: Small fixes that address bugs in key patterns and function arguments. These were merged quickly, suggesting they were important for maintaining functionality.

Other Closed PRs

Several closed PRs were merged without issues, such as PR #1700, which added support for the glm-4v model, and PR #1698, which integrated the gpt-4-turbo-2024-04-09 model. These additions likely enhance the project's capabilities with new models.

Summary

The project has a healthy number of open and recently closed pull requests, indicating active development and maintenance. Security issues detected by GitGuardian in open PRs are particularly concerning and should be addressed immediately. The addition of new models and functionalities suggests ongoing efforts to improve and expand the project's capabilities. It is recommended to prioritize reviewing and testing significant changes like those in PR #1734 and PR #1745 while also resolving any security vulnerabilities identified by automated scanning tools.

Report On: Fetch commits

Project Analysis: GPT Academic Optimization (GPT 学术优化)

The project in question, known as GPT Academic Optimization or "GPT 学术优化" (binary-husky/gpt_academic), is a software initiative designed to provide practical interaction interfaces for large language models (LLMs) like GPT and GLM, with a particular focus on enhancing the experience of reading, polishing, and writing academic papers. It features a modular design that supports custom shortcut buttons and function plugins, and it offers functionalities such as project analysis and self-translation for programming languages like Python and C++. Additionally, it includes PDF/LaTeX paper translation and summarization features. The project is open-source under the GNU General Public License v3.0 and is managed by the organization or individual under the GitHub username binary-husky. As of the latest information, the project boasts a substantial number of forks (7,131), stars (56,479), and a significant volume of commits (2,028), indicating active development and community interest.

Development Team Members and Recent Activities

ycwfs

• Recent Commit Activity: 1 commit in the past 14 days.
• Collaboration: Collaborated with binary-husky on fixing an issue related to chat history when using Google Gemini Pro.
• Files & Branches Worked On: Worked on request_llms/bridge_google_gemini.py in the master branch.

Menghuan1918

• Recent Commit Activity: 2 commits in the past 14 days.
• Collaboration: Collaborated with binary-husky on adding support for Ollama.
• Files & Branches Worked On: Modified files such as config.py and added new files like request_llms/bridge_ollama.py in both master and frontier branches.

binary-husky

• Recent Commit Activity: 29 commits in the past 14 days across multiple branches.
• Collaboration: Collaborated with various team members including ycwfs, Menghuan1918, Keycatowo, jiangfy-ihep, and others.
• Files & Branches Worked On: Worked extensively across numerous files such as main.py, shared_utils/fastapi_server.py, themes/common.js, and many more. Active in branches like master, frontier, and sovits.

Keycatowo

• Recent Commit Activity: 2 commits in the past 14 days.
• Collaboration: Addressed issues related to function parameters and variable definitions.
• Files & Branches Worked On: Made changes to files like crazy_functions/Latex输出PDF.py and crazy_functions/json_fns/pydantic_io.py in the master branch.

jiangfy-ihep

• Recent Commit Activity: 1 commit in the past 14 days.
• Collaboration: Co-authored a commit with Fayu Jiang to fix an API key pattern issue.
• Files & Branches Worked On: Updated shared_utils/key_pattern_manager.py in the master branch.

Qhaoduoyu

• Recent Commit Activity: 2 commits in the past 14 days.
• Collaboration: Co-authored commits addressing security improvements.
• Files & Branches Worked On: Improved security in files like crazy_functions/crazy_utils.py.

Other Members

Other members such as lin-qian123, Kilig947, and awwaawwa have also been mentioned in pull requests or co-authored commits but have not directly committed to the repository within the last 14 days.

Patterns and Conclusions

From this information, we can draw several conclusions:

1. The development team is actively working on integrating new models (e.g., Ollama) and improving existing functionalities (e.g., Google Gemini Pro integration).
2. There is a strong collaborative effort among team members, with multiple individuals contributing to single issues or features.
3. The project's focus remains on enhancing user experience through interface improvements, new feature additions, and addressing security concerns.
4. The team seems to be responsive to community feedback, as evidenced by their work on fixing bugs reported by users.
5. The project maintains multiple active branches (master, frontier, etc.), indicating a structured approach to development where new features are tested before being merged into the stable release.

Overall, GPT Academic Optimization appears to be a dynamic project with a dedicated team working towards making large language models more accessible and useful for academic purposes.

Report On: Fetch Files For Assessment

Source Code Analysis Report

General Overview

The repository binary-husky/gpt_academic is a highly active and popular project with a significant number of stars (56,479) and forks (7,131), indicating a strong community interest and engagement. The project is updated frequently, as seen from the total commits (2,028) and the recent push. It supports multiple languages and integrates various large language models (LLMs) for academic purposes, such as translating documents, generating summaries, and enhancing academic writing.

Specific Files Analysis

1. crazy_functions/批量Markdown翻译.py

• Purpose: This Python script is designed to handle batch translation of Markdown files. It appears to be part of a larger system that interacts with language models to translate content without altering Markdown syntax or code blocks.
• Structure: The file defines a class PaperFileGroup for managing file operations and translations. It includes methods for splitting files into manageable parts, merging results, and writing outputs. The main function orchestrates reading files, invoking translation through threads, and handling outputs.
• Quality:
  • Readability: The code is moderately readable with some comments explaining critical sections. However, the mixture of English and Chinese in variable names and comments might confuse non-Chinese speakers.
  • Error Handling: There is basic error handling, but it could be improved to handle specific exceptions more gracefully.
  • Efficiency: The use of multithreading suggests an attempt to improve performance. However, the actual efficiency would depend on the implementation details of the functions called within threads.

2. crazy_functions/PDF批量翻译.py

• Purpose: This script translates PDF documents in batches using different methods depending on available APIs and services like DOC2X or GROBID.
• Structure: The file provides multiple approaches for PDF translation, including using external services or local utilities to parse and translate content.
• Quality:
  • Modularity: The code is well-structured into functions that handle specific tasks (e.g., 解析PDF_DOC2X, 解析PDF_基于GROBID), which improves modularity.
  • Robustness: There is an evident effort to fallback to different methods if one fails, which enhances the robustness of the application.
  • Dependencies: The script checks for necessary packages dynamically, which is good practice, but it also implies heavy dependency on external services.

3. request_llms/bridge_google_gemini.py

• Purpose: Acts as a bridge to interact with Google's Gemini API for processing requests using Google's models.
• Structure: Includes functions to send requests to the Gemini API and handle responses appropriately.
• Quality:
  • Error Handling: Implements retry mechanisms and error checks which are crucial for network communication.
  • Security: Uses API keys securely by fetching them from configuration, avoiding hard-coded sensitive information.

4. shared_utils/advanced_markdown_format.py

• Purpose: Provides utilities for converting Markdown content into HTML, handling special formatting cases like mathematical expressions.
• Structure: Utilizes several helper functions and regular expressions to process text efficiently.
• Quality:
  • Functionality: Rich in features for handling complex Markdown transformations including code blocks and math equations.
  • Maintainability: While functional, the complexity of regex operations might make maintenance challenging.

Recommendations

1. Internationalization: Given the bilingual nature of comments and documentation, it would be beneficial to standardize on one language (preferably English) for global accessibility or provide full translations.
2. Error Handling Improvement: Enhance error handling by covering more specific cases and potentially using more sophisticated error recovery strategies.
3. Performance Optimization: For scripts dealing with large files or data sets (like PDF processing), consider profiling and optimizing critical sections of code.
4. Documentation: Improve inline documentation and possibly add more detailed function headers to describe parameters, expected outputs, and exceptions.

Overall, the examined files show a robust development approach with attention to functionality and user experience. However, improvements in documentation, error handling, and internationalization could further enhance the project's quality and maintainability.