The "Opik" project by comet-ml is an open-source platform designed to manage the lifecycle of Large Language Model (LLM) applications, from development to production monitoring. It offers tools for tracing, annotation, evaluation automation, CI/CD integration, and production monitoring. The project is actively maintained and has gained significant community interest. Currently, it is focused on enhancing user experience, expanding integration capabilities, and improving robustness.
| Timespan | Opened | Closed | Comments | Labeled | Milestones |
|---|---|---|---|---|---|
| 7 Days | 14 | 19 | 16 | 1 | 1 |
| 30 Days | 49 | 38 | 93 | 5 | 1 |
| 90 Days | 72 | 58 | 137 | 5 | 1 |
| 1 Year | 76 | 62 | 146 | 8 | 1 |
| All Time | 82 | 66 | - | - | - |
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.
| Developer | Avatar | Branches | PRs | Commits | Files | Changes |
|---|---|---|---|---|---|---|
| Andres Cruz |  |
1 | 9/9/0 | 9 | 469 | 34898 |
| Jacques Verré |  |
2 | 17/16/2 | 18 | 149 | 24529 |
| Thiago dos Santos Hora |  |
1 | 7/7/0 | 7 | 42 | 8863 |
| Aliaksandr Kuzmik |  |
2 | 11/11/0 | 14 | 67 | 8487 |
| Fernando Carril |  |
1 | 3/3/0 | 3 | 9 | 4803 |
| andrii.dudar |  |
1 | 13/13/0 | 13 | 89 | 4030 |
| Ido Berkovich |  |
2 | 8/7/0 | 14 | 21 | 2378 |
| sasha |  |
1 | 8/10/1 | 10 | 59 | 1990 |
| GitHub Actions |  |
1 | 0/0/0 | 11 | 13 | 1726 |
| BorisTkachenko |  |
1 | 8/8/0 | 8 | 25 | 837 |
| AndreiCautisanu |  |
1 | 3/3/0 | 3 | 15 | 835 |
| Boris Feld |  |
1 | 1/1/0 | 1 | 11 | 405 |
| Alexander Barannikov |  |
1 | 1/1/0 | 1 | 13 | 77 |
| Liya Katz |  |
1 | 4/4/0 | 4 | 10 | 52 |
| github-actions |  |
1 | 0/0/0 | 8 | 1 | 16 |
| dependabot[bot] |  |
1 | 3/3/0 | 3 | 1 | 6 |
| Robert Lacok |  |
1 | 1/1/0 | 1 | 3 | 3 |
| Nimrod Lahav |  |
0 | 0/0/0 | 0 | 0 | 0 |
| None (LoreRizzetto) | 0 | 1/0/1 | 0 | 0 | 0 |
PRs: created by that dev and opened/merged/closed-unmerged during the period
| Risk | Level (1-5) | Rationale |
|---|---|---|
| Delivery | 4 | The project shows a consistent pattern of more issues being opened than closed, indicating potential backlog and delivery risks. Key unresolved issues, such as #746 (error handling) and #645 (Docker data loss), highlight ongoing challenges that could impact delivery timelines. Additionally, dependencies on resolving existing bugs before implementing new features, as seen in PR #765, pose risks to meeting delivery goals. |
| Velocity | 3 | While there is active engagement with issues and pull requests, the trend of opening more issues than closing them suggests a need for improved velocity. The high volume of changes by key contributors like Jacques Verré and Andres Cruz necessitates robust review processes to maintain pace without sacrificing quality. However, the team's responsiveness to user feedback and enhancements indicates a commitment to maintaining momentum. |
| Dependency | 4 | Several issues and pull requests indicate dependency risks. For instance, Issue #696 highlights the need for broader platform support (arm64 architecture), and PR #765's reliance on resolving bug #OPIK-502 underscores potential delays due to dependencies. Furthermore, integration efforts with external APIs like OpenAI require careful management to avoid disruptions. |
| Team | 2 | The team demonstrates strong collaboration and proactive engagement in addressing issues and implementing features. Contributions are well-distributed among team members, reducing the risk of burnout or communication problems. However, recurring bugs related to Docker configurations suggest possible gaps in knowledge-sharing or documentation that need addressing. |
| Code Quality | 4 | There are significant concerns regarding code quality, as evidenced by issues like potential code duplication in PR reviews and flawed error handling approaches (e.g., changing 500 errors to 400). These indicate a need for better adherence to coding standards and practices to prevent technical debt accumulation. |
| Technical Debt | 4 | The accumulation of unresolved issues and flawed code practices contribute to technical debt. Issues like repeated tags in logs (#528) and unimplemented DELETE operations in SpansResource.java suggest inefficiencies that could hinder future development if not addressed promptly. |
| Test Coverage | 3 | While there is a focus on enhancing test coverage, as seen in PRs like #763 for OpenAI API support, there are concerns about the alignment of tests with code changes. Some tests have been questioned for their necessity, indicating potential misalignment that could affect overall test effectiveness. |
| Error Handling | 4 | Error handling remains a significant challenge, with ongoing issues like the misclassification of HTTP status codes in PR #754. This reflects a need for improved understanding and implementation of error handling mechanisms to ensure accurate reporting and resolution. |
Recent GitHub issue activity for the Opik project shows a mix of enhancements, bug reports, and feature requests. There is a notable emphasis on improving user experience through enhancements like better UI features, support for additional architectures, and integration with other tools. Several issues have been closed recently, indicating active maintenance and development.
Summary
Quantified Reports
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prompt.format is called with the wrong arguments" - Enhancement; Created 1 day ago; Status: OpenOverall, the Opik project is actively addressing both enhancements and bugs, with a clear focus on improving usability and expanding its integration capabilities.
InternalServerErrorException instead of IllegalArgumentException, removing code duplication, and considering the use of WireMock for testing.Efficient Handling of PRs: Many pull requests are being closed or merged within a day of their creation, indicating an active development team with quick turnaround times.
Focus on Cost Tracking Features: Several recent pull requests (#765, #761) focus on adding or improving cost tracking features, reflecting an emphasis on resource management capabilities in the Opik platform.
Robust Review Process: The open pull request (#754) showcases a thorough review process with multiple reviewers providing detailed feedback, ensuring high-quality code before merging.
Frequent Documentation Updates: There are multiple instances of documentation updates (e.g., #745, #744), which is crucial for maintaining clarity and usability for contributors and users alike.
Closed Without Merge Instances: Some pull requests like #761 were closed without merging, which could indicate either redundancy or integration into other branches, highlighting the importance of clear communication in version control practices.
Overall, the Opik project demonstrates strong project management with rapid development cycles, attention to detail in code reviews, and continuous integration of new features aligned with user needs.
Imports: The file imports a wide range of components, hooks, and utilities, indicating a modular approach. The use of libraries like lodash for utility functions is appropriate for handling complex operations.
Constants: Constants such as REFETCH_INTERVAL and TRACES_PAGE_COLUMNS are well-defined, enhancing readability and maintainability. The use of constants for configuration values is a good practice.
Component Definition: The TracesSpansTab component is defined as a functional component using React's hooks (useState, useEffect, etc.). This modern approach leverages React's capabilities effectively.
State Management: The component uses both local state and query parameters to manage its state. This is particularly useful for maintaining state across page reloads or URL sharing.
Logic Separation: The logic for fetching data (useTracesOrSpansList) and statistics (useTracesOrSpansStatistic) is separated into custom hooks, promoting code reusability and separation of concerns.
Data Handling: The component handles data fetching with appropriate loading states and error handling (though not visible in the snippet). This ensures a smooth user experience.
UI Components: A variety of UI components are used (e.g., SearchInput, DataTable, Loader). This indicates a well-thought-out UI structure that likely provides a rich user interface.
Performance Considerations: The use of useMemo and useCallback hooks suggests an awareness of performance optimization by memoizing expensive calculations and functions.
Code Readability: Overall, the code is readable with descriptive variable names and consistent formatting. However, the file is quite long (536 lines), which might benefit from further modularization.
The file demonstrates good practices in React development with effective state management, modularity, and performance considerations. Further improvements could include breaking down the component into smaller sub-components to enhance readability and maintainability.
Imports: The file imports necessary modules from both internal (opik) and external (openai) libraries. The use of type hints indicates an effort to improve code clarity and type safety.
Functionality: The primary functionality appears to be patching methods of OpenAI's streaming classes to integrate additional behavior. This is a common pattern when extending or modifying third-party library behavior.
Decorator Usage: The use of decorators (@functools.wraps) helps maintain the original function's metadata, which is crucial for debugging and introspection.
Error Handling: There is minimal error handling visible in the snippet. Adding logging or exception handling could improve robustness.
Code Readability: The code is concise and follows Python conventions. Function names are descriptive, making it clear what each function aims to achieve.
The file effectively extends OpenAI's streaming functionality with custom behavior while maintaining code clarity through type hints and decorators. Enhancing error handling could further improve the robustness of this integration.
Class Design: The class follows a DAO pattern, encapsulating database operations related to spans. This separation aligns with best practices in Java application architecture.
Query Management: SQL queries are embedded within the class as static strings. While this approach works, using an external query management system or ORM could enhance maintainability.
Transaction Management: Reactive programming constructs (Mono, Flux) are used for asynchronous database operations. This modern approach supports non-blocking I/O operations, improving scalability.
Logging: Logging is integrated using SLF4J, which is essential for monitoring and debugging in production environments.
Code Complexity: The class is quite large (1229 lines), indicating potential complexity. Breaking it down into smaller classes or methods could improve readability and maintainability.
The class implements a robust DAO pattern with modern reactive programming techniques. However, its size suggests that further refactoring could be beneficial to manage complexity better.
Test Coverage: The file contains numerous test cases (7610 lines), indicating comprehensive test coverage for span-related API endpoints.
Use of Test Frameworks: It leverages JUnit 5 for structuring tests, which is standard practice for Java applications. Parameterized tests are used effectively to test multiple scenarios with varying inputs.
Mocking Dependencies: External dependencies are mocked using WireMock, allowing isolated testing of API logic without relying on actual external services.
Test Organization: Tests are organized into nested classes based on functionality (e.g., authentication tests), improving readability and organization.
The test file provides extensive coverage for span-related functionalities using modern testing practices. Its organization into nested classes enhances readability despite its large size.
Documentation: The notebook includes markdown cells that provide context and explanations for each step, which is crucial for user understanding in educational materials.
Code Organization: Code cells are logically organized to guide users through installation, configuration, implementation, and evaluation processes step-by-step.
Integration Demonstration: It demonstrates Opik's integration with OpenAI effectively through practical examples, making it easier for users to understand how to apply these tools in real-world scenarios.
User Guidance: Instructions on setting up environments (e.g., installing packages) are clear, reducing potential setup issues for users following along.
The notebook serves as an effective educational tool by combining clear documentation with practical examples. It successfully guides users through the initial setup and usage of Opik in conjunction with OpenAI integrations.
Sasha (aadereiko)
Aliaksandr Kuzmik (alexkuzmik)
Boris Tkachenko
Jacques Verré (jverre)
Andrii Dudar (andriidudar)
Thiago dos Santos Hora (thiagohora)
Ido Berkovich (idoberko2)
Liya Katz (liyaka)
Andres Cruz (andrescrz)
Fernando Carril (ferc)
Overall, the development team is actively engaged in enhancing both the functionality and usability of the Opik platform while maintaining a strong focus on collaboration and documentation.