Data Science Persona

[jonahjung22]

Problem

Users need a persona that can make data science-related intelligent decisions about analysis approaches based on context, understand and analyze Jupyter notebooks, provide targeted actionable insights rather than generic responses, and handle complex data science workflows with proper error handling and fallbacks.

Solution

Built an intelligent PocketFlow data science persona that uses LLM reasoning for decision-making and workflow orchestration. The solution implements a three-node architecture with DecideAction, DataAnalysis, and CompleteAnalysis nodes featuring robust YAML parsing, context integration, and adaptive routing to deliver context-aware data science analysis that intelligently chooses between focused analysis and comprehensive reviews based on user intent and available context.

Changes

• Code: Implemented PocketFlow-based agent architecture with DecideAction, DataAnalysis, and CompleteAnalysis nodes, added robust YAML decision parsing with multiple fallback strategies for LLM response handling, created intelligent notebook detection and context loading system, built comprehensive error handling and logging throughout the workflow, and integrated AWS Bedrock for LLM decision-making with graceful fallbacks.

• Tests: Added tests for YAML parsing edge cases and malformed LLM responses, created integration tests for notebook detection and context loading, implemented workflow routing tests for all decision action types, and added error handling tests for missing files and configuration issues.

• Docs: Created comprehensive README with architecture overview, usage examples, and troubleshooting guide, added API documentation for all node classes and methods, included configuration guide for AWS Bedrock setup, and documented performance characteristics and system requirements.

Testing Instructions

Set up AWS Bedrock credentials in Jupyter AI configuration, create a test notebook with data science content, create a repo_context.md file in your working directory, test basic usage with "@DataSciencePersona analyze my data", test specific notebook analysis with "@DataSciencePersona notebook: path/to/file.ipynb help me improve my model", verify error handling by testing with missing files and malformed configurations, and check logging output for decision reasoning and workflow routing to ensure the three-step architecture is working correctly.

[Zsailer]

Overall Review

This PR introduces a sophisticated data science persona with PocketFlow agent architecture. The implementation shows good architectural thinking with decision-making nodes and comprehensive error handling. However, there are several areas that need improvement before merging.

[Zsailer]

Code Quality Issues to Address:

1. Error Handling & Logging

• Lines 59-73 in agent.py: Complex nested response parsing logic should be extracted into a helper method
• Inconsistent error handling patterns - some methods use try/catch, others don't
• Logger setup needs improvement with proper formatting and levels

2. Code Structure & Maintainability

• The DecideAction class is quite large (200+ lines) and has multiple responsibilities
• YAML parsing logic (lines 119-186) should be extracted into a separate utility class
• Too many magic strings for action types - consider using an enum or constants

3. Configuration & Dependencies

• Hard-coded dependency on AWS Bedrock without proper fallback documentation
• Missing validation for required configuration parameters
• No clear dependency injection pattern for the model client

4. Performance & Resource Management

• No caching for repeated context loading
• Large notebook content might cause memory issues (only 2MB limit mentioned)
• No timeout handling for LLM calls

5. Testing & Documentation

• README is comprehensive but lacks troubleshooting for common failure scenarios
• No unit tests visible in the PR
• Missing docstrings for many private methods
  EOF < /dev/null

[Zsailer]

Playing around with AI for reviewing PRs :). Feel free to ignore the comments above for now.

[srdas]

[1] When attempting to install from scratch, either for all personas or just the data_science persona, the following install error occurs:

Note that this error is not encountered with any of the other personas, so there is some issue with the install. As of now, I cannot test the persona.

[2] The main README.md file for jupyter-ai-personas needs to be updated for the table to show this persona as well. https://github.com/jupyter-ai-contrib/jupyter-ai-personas/blob/main/README.md

[srdas]

Not sure this is the best fallback. Maybe call a LLM and let it decide? Prompt it to return the column most similar to the list in common_targets?

[jonahjung22]

I have been exploring this option of implementing an LLM but I think the process of calling the LLM may be too computationally expensive for this case. This tool is providing sample code for the user, so they should be able to change a variable to their ideal target values.