fix: pandas import error fix

[shyam-cb]

No description provided.

[gemini-code-assist]

Hello @shyam-cb, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

Summary of Changes

This pull request addresses a pandas import error within the couchbase_haystack library. The primary change involves making the pandas library an optional dependency and implementing a fallback mechanism if it's not installed. Additionally, some code related to DataFrame handling was commented out, and tests were added to verify the behavior with DataFrames.

Highlights

• Optional pandas dependency: The code now handles cases where pandas is not installed by providing a dummy DataFrame class that raises an ImportError when instantiated.
• Conditional DataFrame handling: The code that converts DataFrames to JSON has been commented out, suggesting a temporary removal or a change in how DataFrames are processed.
• Test case for DataFrame: Added test cases to verify the behavior of filters when using DataFrames.

Changelog

Click here to see the changelog

• pyproject.toml
  • Added pandas as an extra dependency for the default environment.
• src/couchbase_haystack/document_stores/filters.py
  • Implemented optional import for pandas.DataFrame with a fallback class if pandas is not installed (lines 12-21).
  • Commented out code that converts DataFrames to JSON in _equal function (lines 113-114).
  • Commented out code that converts DataFrames to JSON in _not_equal function (lines 125-126).
  • Removed the _normalize_ranges function (lines 276-314).
• tests/test_filter.py
  • Imported the pandas library (line 4).
  • Added test case test_filter_eq_condition_dataframe to verify DataFrame filtering with the == operator (lines 79-83).
  • Added test case test_filter_neq_condition_dataframe to verify DataFrame filtering with the != operator (lines 169-178).

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A library's need,
For data's structured grace,
Pandas, if it's there.

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[gemini-code-assist]

Code Review

This pull request addresses a pandas import error by adding pandas as an explicit dependency and providing a fallback DataFrame class when pandas is not available. The changes seem reasonable and address the issue described in the title. However, there are a few points that need clarification and improvement.

Summary of Findings

• Conditional pandas import: The approach of conditionally importing pandas and defining a fallback DataFrame class is a good way to handle the missing dependency. However, the fallback class raises an ImportError upon instantiation, which might not be the most user-friendly approach. Consider providing a more informative error message or a way to avoid the error altogether if the DataFrame functionality is not used.
• DataFrame handling in filters: The code that handles DataFrame objects in the filters has been commented out. This might indicate that the functionality is not fully implemented or that there are unresolved issues. It's important to either fully implement the functionality or remove the commented-out code to avoid confusion.
• Test coverage for pandas functionality: The tests added for DataFrame functionality are good, but they might need to be updated to reflect the conditional import and the fallback DataFrame class. Consider adding tests that check the behavior of the code when pandas is not available.

Merge Readiness

The pull request addresses the pandas import error, but there are some areas that need further attention before merging. Specifically, the handling of DataFrame objects in the filters and the test coverage for the conditional import need to be improved. I would recommend addressing these issues before merging. I am unable to directly approve this pull request, and other reviewers should review and approve this code before merging.

[shyam-cb]

/gemini review

[gemini-code-assist]

Code Review

The pull request addresses a pandas import error by making the import optional and providing a fallback DataFrame class that raises an ImportError if pandas is not installed. This prevents the application from crashing if pandas is not a dependency. The change also comments out the dataframe conversion to json in the filter functions. Additionally, the pull request adds pandas as an extra dependency and adds tests for dataframe filtering.

Summary of Findings

• Conditional pandas usage: The approach of conditionally importing pandas and providing a fallback class is a good way to handle optional dependencies. However, ensure that all code paths that rely on pandas are properly guarded and tested.
• Commenting out dataframe conversion: Commenting out the dataframe conversion to JSON might affect functionality. Ensure that this change is intentional and that there is a plan to handle dataframes correctly in the future.
• Test coverage: The addition of tests for dataframe filtering is good. Ensure that the tests cover all relevant scenarios and edge cases.

Merge Readiness

The pull request addresses a specific issue and includes tests. However, the commenting out of the dataframe conversion raises concerns about potential functionality loss. I recommend ensuring that the commented-out code is addressed before merging. I am unable to approve this pull request, and others should review and approve this code before merging.