Quantify commits

Quantified Commit Activity Over 14 Days

Developer	Avatar	Branches	PRs	Commits	Files	Changes
Ashpreet		1	2/4/0	97	135	9590
Jacob Weiss		1	3/3/0	5	11	409
Yash Pratap Solanky		1	6/7/1	8	26	223
Joey Chilson		1	2/2/0	2	3	222
Fanaperana		1	1/1/0	1	2	84
kedar-1		1	1/1/0	1	1	10
Faych Chen		1	1/1/0	1	1	10
Shailen Sobhee		1	1/1/0	2	1	5
Siew Kam Onn		1	2/2/0	2	2	4
Shixian Sheng		1	1/1/0	1	1	2
Oskar Boëthius Lissheim (avocade)		0	1/0/0	0	0	0
None (hrgarber)		0	1/0/1	0	0	0

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

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Project Overview

Phidata is an open-source framework designed to build AI Assistants, also known as Agents, that possess long-term memory, contextual knowledge, and the ability to perform actions through function calling. The project is maintained by the organization phidatahq. The framework addresses the limitations of Large Language Models (LLMs) by integrating memory storage for chat history, a vector database for business context, and tools for executing actions such as API data retrieval, email sending, or database querying. The project is actively developed with a significant number of commits and contributions from various developers. It is licensed under the Mozilla Public License 2.0 and has garnered attention with 6611 stars and 912 forks on GitHub.

Recent Activities and Team Contributions

Commits in Default Branch: Main

0 days ago

• Ashpreet (ashpreetbedi)

  • Files:
  • cookbook/assistants/basic.py (+0, -4)
  • Summary: Minor adjustments in basic.py.

• Ashpreet (ashpreetbedi)

  • Files:
  • cookbook/assistants/.gitignore (added, +1)
  • cookbook/assistants/basic.py (+5, -1)
  • cookbook/assistants/data_analyst.py (+4, -0)
  • cookbook/assistants/finance.py (+3, -1)
  • cookbook/assistants/hackernews.py (+2, -3)
  • cookbook/assistants/python_assistant.py (+10, -0)
  • cookbook/assistants/research.py (+16, -9)
  • cookbook/assistants/web_search.py (+8, -2)
  • phi/llm/groq/groq.py (+1, -1)
  • phi/llm/openai/chat.py (+1, -1)
  • Summary: Added .gitignore, updated several assistant files and LLM-related scripts.

• Ashpreet (ashpreetbedi)

  • Files:
  • cookbook/llm_os/README.md (+5, -1)
  • Summary: README updates for LLM OS.

• Ashpreet (ashpreetbedi)

  • Files:
  • cookbook/llm_os/README.md (+7, -0)
  • Summary: Additional README updates for LLM OS.

• Ashpreet (ashpreetbedi)

  • Files:
  • cookbook/llm_os/README.md (+16, -17)
  • cookbook/llm_os/assistant.py (+2, -2)
  • Summary: Further modifications in README and assistant script.

• Ashpreet (ashpreetbedi)

  • Files:
  • README.md (+1, -2)
  • Summary: Minor changes in the main README file.

• Ashpreet (ashpreetbedi)

  • Files:
  • README.md (+2, -2)
  • Summary: Additional minor changes in the main README file.

• Ashpreet (ashpreetbedi)

  • Files:
  • cookbook/llm_os/README.md (+1, -1)
  • Summary: Merged branch 'main' of https://github.com/phidatahq/phidata.

• Ashpreet (ashpreetbedi)

  • Files:
  • README.md (+3, -2)
  • Summary: More updates to the main README file.

• Siew Kam Onn (kosiew)

  • Files:
  • cookbook/llm_os/README.md (+1, -1)
  • Summary: Fixed a small typo in README.md.

1 day ago

• Ashpreet (ashpreetbedi)
• Multiple commits focusing on README updates across various files including:
  • cookbook/agents
  • .gitignore
  • Requirements files across different examples and integrations.
  • Updates to assistant scripts.
  • Merge pull requests related to fixing typos and updating Azure OpenAI configurations.

Patterns and Conclusions

The recent activities indicate a strong focus on documentation updates and minor bug fixes across various assistant scripts. Ashpreet Bedi is the most active contributor with numerous commits aimed at refining documentation and ensuring code consistency across different modules. Other contributors like Siew Kam Onn have also participated in minor corrections. The team appears to be working collaboratively with frequent merges and pull requests to integrate changes from different contributors. This suggests a well-coordinated effort towards improving both the functionality and usability of the Phidata framework.

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Analysis of Open Issues in phidatahq/phidata

Overview

The repository currently has 30 open issues. These issues range from feature requests and bug reports to enhancement suggestions and questions about usage. Below is a detailed analysis of the notable problems, uncertainties, TODOs, and anomalies among the open issues.

Notable Issues

Issue #240: Need support for PDFs with images

• Created by: Sridhar Iyer (sridharaiyer)
• Description: The user wants to read text from PDFs that contain images. They mention using Langchain with rapidocr-onnxruntime and request similar functionality in PDFReader.
• Comments: Multiple users have engaged, sharing test PDFs and expressing the critical nature of this feature. The PDFImageReader has been implemented in v2.4.8.
• Notable Points:
  • High user engagement indicates a critical need.
  • Implementation is in progress but requires documentation.

Issue #238: need enhancement for LLM response of function calling embedded in markdown

• Created by: None (1WorldCapture)
• Description: The issue describes inconsistent handling of LLM responses formatted as raw JSON or embedded in markdown.
• Comments: Ashpreet (ashpreetbedi) is working on this.
• Notable Points:
  • Inconsistent behavior needs to be standardized.
  • Active development is ongoing.

Issue #237: Feature / Suggestion

• Created by: Jonny Hinojosa (jonny7737)
• Description: Suggests adding a try-except block around the return statement in invoke and invoke_stream methods to handle rate limit errors with a backoff mechanism.
• Comments: Ashpreet (ashpreetbedi) agrees and plans to test and release soon. Discussion about using decorators for rate limiting.
• Notable Points:
  • Good community suggestion with active participation from maintainers.
  • Potential for immediate improvement in error handling.

Issue #224: Unable to use SQLLite on AutoRag Example

• Created by: None (ctur)
• Description: User encounters an error when using SQLLite as storage in the AutoRag example.
• Comments: Yash Pratap Solanky (ysolanky) is engaging with the user to troubleshoot.
• Notable Points:
  • Specific technical issue that may affect other users.
  • Requires detailed debugging and possibly documentation updates.

Issue #223: How big can the knowledge base be?

• Created by: Aurthur Musendame (aurthurm)
• Description: User queries about the scalability of the knowledge base, particularly when loading large datasets like 100+ books.
• Notable Points:
  • Scalability concerns need addressing, possibly through performance benchmarks or documentation.

Issue #220: how to replace the default LLaMA3 model with my fine-tuned LLaMA3?

• Created by: Charles Ching (win4r)
• Description: User wants to know how to replace the default LLaMA3 model with a fine-tuned version.
• Comments: Yash Pratap Solanky (ysolanky) provides guidance but requests more details about the user's setup.
• Notable Points:
  • Requires clear documentation on model customization.

Issue #225: data type error in research App (cookbook)

• Created by: Amjad Abu-Rmileh (Amjad-AbuRmileh)
• Description: User encounters a tuple attribute error in the research app example.
• Notable Points:
  • Specific bug that needs fixing to ensure example code works correctly.

General Trends from Closed Issues

Recent closed issues indicate active development and maintenance: 1. Typo fixes (#246, #243). 2. New features and enhancements (#239, #236). 3. Bug fixes (#235, #234).

Summary

The phidatahq/phidata repository shows active development with a focus on both new features and bug fixes. The community is engaged, providing valuable feedback and suggestions. Notable issues include critical feature requests like PDF image support (#240), enhancements for LLM response handling (#238), and scalability concerns for large knowledge bases (#223). Immediate attention should be given to these high-priority issues to maintain user satisfaction and project reliability.

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Analysis of Pull Requests for phidatahq/phidata

Open Pull Requests

PR #249: Add Support for Datetime Arguments

• State: Open
• Created: 0 days ago
• Summary: Adds support for datetime arguments in JSON schema and function execution.
• Comments: The author asked if tests need to be added or updated.
• Notable Changes:
  • phi/tools/function.py: +8, -1
  • phi/utils/json_schema.py: +7, -1
• Analysis: This PR seems straightforward and adds useful functionality. However, it is important to ensure that tests are added or updated to cover the new datetime handling.

PR #245: Running Oskar Changes

• State: Open
• Created: 0 days ago (Draft)
• Summary: Initial draft with minimal changes.
• Notable Changes:
  • Makefile: +5
  • OSKAR.md: +8
• Analysis: This is a draft PR with minimal changes. It is too early to evaluate its impact.

PR #200: Update README.md

• State: Open
• Created: 16 days ago
• Summary: Updates installation instructions in the README.
• Notable Changes:
  • README.md: +9, -0
• Analysis: This PR updates documentation, which is always beneficial. It should be reviewed and merged if the changes are accurate.

PR #196: Chromadb Implementation WIP

• State: Open
• Created: 17 days ago, edited 14 days ago
• Summary: Work in progress for implementing ChromaDB.
• Comments:
  • Discussion about type hinting issues and collaboration.
• Notable Changes:
  • New files added for ChromaDB implementation.
• Analysis: This PR is still in progress but shows active collaboration and problem-solving. It should be monitored for completion.

PR #141: v2.3.53.dev0

• State: Open
• Created: 63 days ago
• Summary: Tests the break_after_run functionality.
• Notable Changes:
  • Multiple files updated with new functionality.
• Analysis: This PR has been open for a while. It might need a review to determine if it can be merged or if further work is required.

PR #111: WIP: TTS Tool PHI 329

• State: Open
• Created: 89 days ago
• Summary: Work in progress for a TTS tool.
• Notable Changes:
  • New file phi/tools/tts.py added.
• Analysis: This PR has been open for a long time without updates. It might need attention to either complete it or close it if it's no longer relevant.

PR #49: Chromadb Update

• State: Open
• Created: 121 days ago, edited 99 days ago
• Summary: Updates related to ChromaDB.
• Notable Changes:
  • Multiple files updated with ChromaDB-related changes.
• Analysis: This PR has been open for a very long time. It needs a review to determine its current status and whether it can be merged or needs further work.

Closed Pull Requests

Notable Closed PRs

PR #246: Fix Typo in README.md (llm_os)

• State: Closed (Merged)
• Created & Closed: Same day
• Summary: Fixes a small typo in the README.
• Analysis: A minor but important fix that was promptly merged.

PR #244: Merge Changes

• State: Closed (Not Merged)
• Created & Closed: Same day
• Summary & Comments:
  • Extensive conversation about integrating CSV tools into Streamlit app.
  • The changes were not merged, possibly due to issues during implementation.
• Analysis:
  • The detailed conversation indicates significant effort but also highlights potential issues that prevented merging. This might need revisiting if the functionality is still required.

Summary

1. Several open PRs (#141, #111, #49) have been open for an extended period and may need attention to either move them forward or close them if they are no longer relevant.
2. Recent closed PRs like #246 show prompt action on minor fixes, which is good practice.
3. The issue with exceeding the maximum allowed length of tools (#244) indicates a deeper problem that needs addressing rather than just patching over it.

Recommendations

1. Review and merge documentation updates like #200 promptly as they improve usability.
2. Ensure that tests are added or updated for new functionalities like those in #249.
3. Monitor ongoing collaborative efforts like #196 closely to ensure they reach completion.
4. Revisit long-standing open PRs to decide their fate—either push them towards completion or close them if they are no longer needed.
5. Investigate the root cause of tool duplication issues thoroughly as seen in the errors reported from closed PRs like #244.

By addressing these points, the project can maintain a healthy and manageable codebase while ensuring new features and fixes are integrated smoothly.

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Source Code Assessment

1. phi/assistant/assistant.py

URL: phi/assistant/assistant.py Reason: Core implementation of the Assistant class, which is central to the framework's functionality.

Analysis:

• Structure and Organization: The file is quite large (1522 lines), indicating it contains significant logic and functionality. It would be beneficial to break down this file into smaller, more manageable modules if possible.
• Code Quality: Without seeing the actual content, it's hard to assess the code quality. However, given its size, ensuring proper documentation and comments throughout the code is crucial for maintainability.
• Potential Improvements:
  • Modularity: Consider breaking down the file into smaller modules based on functionality.
  • Documentation: Ensure comprehensive docstrings and inline comments are present to explain complex logic.
  • Testing: Given its importance, thorough unit tests should be in place to cover all functionalities.

2. phi/api/assistant.py

URL: phi/api/assistant.py Reason: API endpoints related to the Assistant, important for understanding how the Assistant interacts with external systems.

Analysis:

• Structure and Organization: The file is well-structured with clear separation of functions.
• Code Quality: The code is concise and uses type hints effectively. The use of environment variables for configuration is a good practice.
• Potential Improvements:
  • Error Handling: While exceptions are caught, consider adding more specific exception handling and logging for different error scenarios.
  • Logging: Ensure that all critical operations have appropriate logging levels (info, warning, error).

3. cookbook/examples/auto_rag/app.py

URL: cookbook/examples/auto_rag/app.py Reason: Example application demonstrating the use of Autonomous Retrieval-Augmented Generation (RAG) with an Assistant.

Analysis:

• Structure and Organization: The file is organized into functions that handle different parts of the application logic.
• Code Quality: The use of Streamlit for UI components is well-integrated. The code is readable with appropriate use of comments.
• Potential Improvements:
  • State Management: Ensure that session state management in Streamlit is robust, especially when dealing with multiple user inputs and interactions.
  • Error Handling: Add more detailed error messages and handling for different failure points (e.g., database connection issues).

4. cookbook/assistants/python_assistant.py

URL: cookbook/assistants/python_assistant.py Reason: Example of an Assistant that can write and run Python code, showcasing practical usage.

Analysis:

• Structure and Organization: The file is compact and focuses on setting up a PythonAssistant instance.
• Code Quality: The code is straightforward and uses type hints effectively. It also handles directory creation gracefully.
• Potential Improvements:
  • Error Handling: Add try-except blocks around critical operations like directory creation and API calls to handle potential failures gracefully.
  • Documentation: Include more detailed docstrings explaining the purpose of each configuration parameter.

5. phi/aws/resource/ec2/security_group.py

URL: phi/aws/resource/ec2/security_group.py Reason: Handles security configurations for AWS EC2 instances, relevant for deployment and security aspects.

Analysis:

• Structure and Organization: The file defines multiple classes related to security group rules and configurations, which are well-organized.
• Code Quality: The code uses type hints extensively and follows good practices for defining AWS resources.
• Potential Improvements:
  • Validation: Ensure that input parameters are validated before using them in AWS API calls to prevent runtime errors.
  • Logging: Enhance logging to provide more context about operations being performed, especially during create/update/delete actions.

6. cookbook/examples/research/app.py

URL: cookbook/examples/research/app.py Reason: Example application for research purposes, demonstrating advanced features of the framework.

Analysis:

• Structure and Organization: The file is structured around a main function that orchestrates the research workflow using various assistants.
• Code Quality: The code is modular with functions dedicated to specific tasks like generating search terms and searching databases.
• Potential Improvements:
  • Error Handling: Add more detailed error handling for different stages of the research workflow (e.g., API failures).
  • User Feedback: Provide more user feedback in the UI to indicate progress or errors during long-running operations.

7. phi/api/routes.py

URL: phi/api/routes.py Reason: Defines API routes, crucial for understanding how different parts of the system communicate.

Analysis:

• Structure and Organization: The file uses a dataclass to define API routes in a structured manner.
• Code Quality: The use of a dataclass ensures that routes are defined in a consistent manner. This approach also makes it easy to update routes centrally.
• Potential Improvements:
  • None specific; the current implementation is clean and effective.

8. phi/aws/app/base.py

URL: phi/aws/app/base.py Reason: Base class for AWS applications, important for understanding cloud deployment and integration.

Analysis:

• Structure and Organization: The file defines an AwsApp class with various configurations related to AWS deployments. It’s well-organized with clear separation of concerns.
• Code Quality: The use of Pydantic models for validation is a good practice. Type hints are used extensively.
• Potential Improvements:
  • Complexity Management: Given the size (762 lines), consider breaking down some methods into smaller helper functions to improve readability.
  • Documentation: Ensure comprehensive documentation for each configuration option to aid users in understanding their purpose.

9. phi/docker/app/base.py

URL: phi/docker/app/base.py Reason: Base class for Docker applications, relevant for containerized deployment and management.

Analysis:

• Structure and Organization: Similar to AwsApp, this file defines a DockerApp class with various configurations related to Docker deployments.
• Code Quality: The code follows good practices with extensive use of type hints and Pydantic models for validation.
• Potential Improvements:
  • Complexity Management: As with AwsApp, consider breaking down larger methods into smaller functions to improve readability.
  • Documentation & Examples: Provide examples or templates for common Docker configurations to help users get started quickly.

10. cookbook/integrations/singlestore/ai_apps/pages/1_Research_Assistant.py

URL: cookbook/integrations/singlestore/ai_apps/pages/1_Research_Assistant.py Reason: Example page for a Research Assistant using SingleStore integration, showcasing database interactions.

Analysis:

• Structure and Organization: The file integrates Streamlit components with backend logic effectively. It’s structured around main functions handling different parts of the research assistant workflow.
• Code Quality: The code is modular with clear separation between UI components and backend logic. It uses type hints effectively.
• Potential Improvements:
  • Error Handling & User Feedback: Enhance error handling around database interactions and provide user feedback in case of failures or long-running operations.
  • State Management & Performance Optimization:
  • Ensure session state management in Streamlit is robust, especially when dealing with multiple user inputs and interactions.
  • Optimize performance by caching results where appropriate to reduce redundant computations or database queries.

Overall, the codebase demonstrates good practices such as modularity, use of type hints, Pydantic models for validation, and effective use of logging. However, there are areas where improvements can be made in terms of error handling, documentation, complexity management through modularization, and providing better user feedback during operations.