STORM (Synthesis of Topic Outlines through Retrieval and Multi-perspective Question Asking) is a sophisticated software tool developed by Stanford-OVAL, designed to automate knowledge curation by generating Wikipedia-like articles from gathered data. The project utilizes advanced AI techniques to assist experienced Wikipedia editors by providing foundational drafts for further refinement. The project is in active development, with recent updates focusing on enhancing its capabilities and user interface.
High Engagement: The project boasts over 11,000 stars and 1,000 forks on GitHub, indicating strong community interest and engagement.
Continuous Improvement: Recent updates include the addition of VectorRM for improved document grounding and a minimal UI for local development.
Active Contributions: There is an ongoing call for contributions, particularly for integrating more language models and search engines, highlighting the project's commitment to expansion and inclusivity.
Future Plans: Plans to enhance human-in-the-loop functionalities and develop more abstract representations of curated information suggest a trajectory towards more sophisticated and user-friendly interfaces.
Recent Activity
Recent activities indicate a focused effort on refining the project's functionality and addressing community feedback.
Team Members and Activities
Yijia Shao (shaoyijia)
Recent Commits: Focused on version control and package updates. Merged PR #175 to correct a typo in rm.py.
Collaborations: Actively reviews and merges pull requests, ensuring code quality and consistency.
Ikko Eltociear Ashimine (eltociear)
Recent Commits: Contributed to minor corrections in rm.py (PR #175).
Collaborations: Works closely with Yijia Shao, indicating good team coordination.
Evidencebp
Recent Pull Requests: Authored PR #181 addressing Pylint alerts, though noted challenges with project familiarity and testing.
Patterns, Themes, and Conclusions
Code Quality Focus: Recent commits and pull requests show a strong emphasis on maintaining and enhancing code quality (e.g., PR #159 integrating black for consistent formatting).
Collaborative Development: Evidence of active collaboration among team members to review and merge changes efficiently.
Responsive to Community Feedback: Issues such as multilingual support (#170) and performance improvements (#88) are being actively addressed.
Risks
Several risks could potentially impact the project's progress and reliability:
Testing and Familiarity Concerns: PR #181 reveals issues with testing due to missing API keys and the author's unfamiliarity with the project logic, which could lead to errors in production.
Delayed Reviews and Merges: Some pull requests like PR #135 have remained in draft status for extended periods, suggesting possible bottlenecks in review processes or prioritization issues.
Dependency on External APIs: The project's reliance on various external APIs (noted in knowledge_storm/rm.py) introduces risks related to third-party service stability and changes.
Of Note
Multilingual Expansion Efforts: The attempt to add a Chinese README (PR #17) despite being on hold due to upcoming major updates indicates an effort towards making the project accessible globally.
Innovative Features in Pipeline: The introduction of features like VectorRM for better grounding documents shows innovation in enhancing content generation accuracy.
Community-driven Development: The active engagement from the community in both using the system and contributing to its development suggests a healthy ecosystem around the project.
Quantified Reports
Quantify issues
Recent GitHub Issues Activity
Timespan
Opened
Closed
Comments
Labeled
Milestones
7 Days
9
9
19
9
1
30 Days
23
20
37
21
1
90 Days
83
78
168
74
1
All Time
114
92
-
-
-
Like all software activity quantification, these numbers are imperfect but sometimes useful. Comments, Labels, and Milestones refer to those issues opened in the timespan in question.
Rate pull requests
2/5
The pull request only involves a minor text correction in an error message, changing 'occured' to 'occurred'. This is a trivial change and does not impact the functionality or performance of the software. While it corrects a spelling mistake, which is positive, the overall significance of the change is minimal and does not warrant a higher rating.
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3/5
The pull request adds a Chinese version of the README file, which is beneficial for Chinese-speaking users and enhances the accessibility of the project. However, the PR is relatively straightforward, involving mostly translation and minor updates to the existing documentation. It does not introduce significant new features or complex changes to the codebase. The PR is held back due to potential upcoming changes to the main README, indicating that it might need further updates soon. Overall, this PR is useful but not exceptional, warranting an average rating.
[+] Read More
3/5
The pull request introduces significant functionality by allowing multiple retrievers to be used simultaneously, which is a substantial improvement for the system's flexibility and capability. However, the PR has several issues such as lack of detailed documentation on changes, potential redundancy in code (e.g., repeated 'search_top_k' without clear usage), and minor bugs like unused parameters. These flaws make the PR average as it stands, balancing its functional advancements with the need for further refinement.
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3/5
The PR addresses a significant number of pylint alerts, which improves code quality by adhering to standard practices and reducing potential bugs. However, the changes are mostly minor refactors like method extractions and exception narrowing, which, while beneficial, do not fundamentally alter or enhance the functionality of the project. The author's unfamiliarity with the project and inability to test changes comprehensively due to environment issues also introduce risks of unintended side effects. Therefore, despite the good intentions and some positive impact on code maintainability, the overall significance and execution warrant an average rating.
[+] Read More
4/5
The pull request introduces a significant enhancement to the existing application by allowing users to select themes and search engines, which improves user experience and customization. The addition of fallback options for both search and LLM (language learning models) increases robustness. The code includes comprehensive changes across multiple files, indicating thorough development work. However, there are some unresolved comments and suggestions for improvements in the review, suggesting that while the PR is quite good, it could benefit from minor refinements.
PRs: created by that dev and opened/merged/closed-unmerged during the period
Quantify risks
Project Risk Ratings
Risk
Level (1-5)
Rationale
Delivery
2
The project shows a structured approach with clear stages of operation and active issue resolution, as seen in the consistent opening and closing of issues (e.g., #179, #176). However, the absence of the 'init.py' file (critical for package initialization) presents a significant risk to delivery, potentially halting development until resolved.
Velocity
3
Recent commits and pull requests indicate moderate activity (e.g., PR#135, PR#181), but delays like the long-open PR#135 suggest potential bottlenecks. The steady but not rapid pace of commits from contributors like Yijia Shao and Ikko Eltociear Ashimine shows ongoing but not accelerated development.
Dependency
4
The project heavily relies on external systems such as various language models and search engines (e.g., Google Scholar for #176). Changes or failures in these dependencies could severely impact functionality, evidenced by integration challenges in issues like #97 and #86.
Team
3
The project benefits from an open and collaborative approach, encouraging external contributions. However, this also poses risks of reliance on external contributors for critical integrations, which could affect continuity if not managed properly.
Code Quality
3
Efforts are made to maintain code quality through minor refactors and corrections (e.g., PR#181 addressing pylint alerts). However, frequent changes to critical files and the presence of unresolved comments in PR reviews suggest areas for improvement.
Technical Debt
4
While there are proactive measures like addressing pylint alerts (PR#181), the project exhibits signs of accumulating technical debt through frequent updates to critical files without thorough testing, potentially introducing new errors.
Test Coverage
3
There is no explicit evidence of comprehensive test coverage. The presence of bugs and feature enhancements in issues suggests that while some testing is happening, it may not be sufficiently thorough to catch all regressions or errors before deployment.
Error Handling
3
The project includes mechanisms to manage failures gracefully in modules like 'rm.py', but the varying configurations in 'run_storm_wiki_gpt.py' and other scripts indicate a need for more robust error handling practices to ensure reliability across different operational scenarios.
Detailed Reports
Report On: Fetch issues
Recent Activity Analysis
Recent GitHub issue activity for the STORM project shows a mix of bug reports, feature requests, and discussions on enhancements. Notably, there are several issues related to language support, integration with various APIs, and performance improvements. A recurring theme is the desire for multilingual capabilities and better integration with open-source language models. Some issues highlight technical challenges like citation generation inconsistencies and integration with specific retrieval models.
Notable Anomalies and Themes
Multilingual Support: Issues #170 and #169 focus on expanding language support, indicating a community interest in making STORM more inclusive.
Integration Challenges: Several issues (#97, #86) discuss integrating alternative APIs and models, reflecting ongoing efforts to enhance flexibility.
Citation and Output Quality: Issues like #168 highlight problems with citation generation, which could affect the reliability of generated content.
Performance and Usability: There are concerns about performance (#88) and usability of the demo interface (#141), suggesting areas for improvement in user experience.
Summary: This PR addresses Pylint alerts as part of an intervention experiment. The changes involve minor refactors and some method extractions to reduce complexity.
Notable Issues:
The author mentions unfamiliarity with the project logic, which could introduce errors if not thoroughly reviewed.
The inability to fully test due to missing API keys and installation issues with fastchat raises concerns about the reliability of the changes.
PR #135: [Demo Enhancement] Added Storm Wiki Frontend with Themes
State: Open (Draft)
Created by: Jaigouk Kim
Created: 45 days ago
Summary: Introduces theme options and fallback mechanisms for search engines and language models in the frontend.
Notable Issues:
The PR has been in draft status for a long time, indicating potential delays or issues in development.
Several review comments suggest improvements, such as using st.secrets for API keys and enhancing configurability.
Summary: Integrates black for consistent code formatting across the repository. Enhances code quality and maintainability.
PR #158 & #157: Use black as the Python Code Formatter
State: Closed (Not Merged)
Summary: These appear to be earlier attempts or duplicates related to integrating black. They were closed without merging, likely superseded by #159.
Notable Trends and Observations
Open PRs with Long Duration:
PRs like #135 being open for extended periods suggest possible challenges in implementation or prioritization issues.
Closed Without Merge:
Several PRs (#158, #157, #156) were closed without merging, indicating possible redundancies or alternative solutions being preferred.
Focus on Code Quality:
Recent efforts like adopting black (#159) highlight a focus on improving code quality and consistency.
Enhancements in Flexibility:
Open PRs like #155 aim to enhance system flexibility, reflecting ongoing efforts to make STORM more adaptable to various use cases.
Documentation and Localization Efforts:
Initiatives like adding a Chinese README (#17) show an effort to make the project accessible to a broader audience, although it's currently on hold.
Overall, the STORM project is actively evolving with a focus on improving code quality, expanding functionality, and enhancing user accessibility. However, some open pull requests indicate areas where progress could be accelerated or where additional resources might be needed.
Structure & Purpose: This file initializes the knowledge_storm package by importing key classes from the storm_wiki.engine module. It also defines the package version.
Quality: The imports are clear and specific, which is good for maintainability. The versioning is explicit, aiding in version control.
Improvements: None needed; the file is concise and serves its purpose well.
Structure & Purpose: Demonstrates how to run the STORM system using GPT models with various retrieval modules.
Quality:
Provides a comprehensive example of setting up and executing a STORM pipeline.
Uses environment variables for configuration, promoting flexibility and security.
Argument parsing allows customization of execution parameters.
Improvements:
Add comments explaining each major block of code for clarity to new users.
Consider adding error handling around API calls or file operations to improve robustness.
Overall Assessment
The codebase demonstrates strong organization and adherence to best practices in Python development. Key areas such as dependency management, CI/CD integration, and modular design are well-addressed. Some minor improvements could be made in terms of code reuse and error handling granularity. Overall, it reflects a mature project structure suitable for both development and production environments.
Report On: Fetch commits
## Development Team and Recent Activity
### Team Members and Activities
#### Yijia Shao (shaoyijia)
- Recent commits focused on version control and package updates:
- Enforced checking of Python package version.
- Bumped up PyPI package version.
- Merged a pull request to update [`rm.py`](https://github.com/stanford-oval/storm/blob/main/rm.py).
- Collaborated with Ikko Eltociear Ashimine on a pull request.
- Active in maintaining code quality and package management.
#### Ikko Eltociear Ashimine (eltociear)
- Made a minor correction in [`rm.py`](https://github.com/stanford-oval/storm/blob/main/rm.py) to fix a typo.
- Collaborated with Yijia Shao on a pull request.
#### Evidencebp
- No recent commits.
- Has an open pull request but no merges or changes.
### Patterns, Themes, and Conclusions
- **Active Maintenance**: The project is actively maintained with frequent updates to package versions and code formatting.
- **Collaboration**: There is evidence of collaboration between team members, particularly in reviewing and merging pull requests.
- **Focus on Quality**: Recent activities include enforcing version checks and correcting typos, indicating attention to detail and code quality.
- **Ongoing Work**: Some work appears to be ongoing, with open pull requests suggesting future updates.
Overall, the development team is engaged in routine maintenance, quality assurance, and collaborative efforts to enhance the STORM project.
Summary
Quantified Reports
Detailed Reports
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