fix(output_type): handle errors for wrong output type

[ArslanSaleem]

Summary by CodeRabbit

• New Features

  • Introduced a template for auto-correcting Python code snippets to match specified type hints.
  • Added a new exception class for handling errors related to incorrect output types from queries.

• Enhancements

  • Updated the validation logic to include a list of default types for output verification.

• Bug Fixes

  • Adjusted the FakeLLM class to ensure proper semantic meaning of the output attribute.

• Testing

  • Expanded test suite with new cases for handling and validating output types.
  • Added new fixtures and mocks to improve test reliability.

[coderabbitai]

Walkthrough

The recent updates focus on enhancing type validation and error handling in the pandasai project. A new template and class have been introduced to correct Python code to match specified type hints. Additionally, an exception class has been added for handling errors from malicious queries. The DefaultOutputType class now includes a validation method for checking result types against a predefined list. Changes in execution logic and testing ensure these features integrate smoothly, improving the robustness of code execution and output type verification.

Changes

File Path	Change Summary
.../prompt_templates/correct_output_type_error_prompt.tmpl	Added template for generating Python code to fix type hint mismatches.
pandasai/exceptions.py pandasai/helpers/output_types/_output_types.py	Added InvalidLLMOutputType exception class. Modified DefaultOutputType class to include default_types list and updated validate method.
pandasai/llm/fake.py	Modified _output attribute type from 'text' to 'string'.
.../smart_datalake_chat/code_execution.py .../prompts/correct_output_type_error_prompt.py	Added imports and methods for output validation and error handling. Introduced CorrectOutputTypeErrorPrompt class for error correction prompts.
.../smart_datalake/test_code_execution.py tests/test_smartdataframe.py	Added new test methods and imports related to error prompts. Added llm_result_mocks fixture and updated a test method for output type verification.

🐇✨
To code, we hop, with ears perked high,
Through bugs and errors, our spirits fly.
For every type mismatch that we find,
We fix, we patch, with an AI's mind. 🌟🎩

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[coderabbitai]

Review Status

Actionable comments generated: 1

Configuration used: CodeRabbit UI

Commits

Files that changed from the base of the PR and between 5336ef1 and 78f315f.

Files selected for processing (8)

• pandasai/assets/prompt_templates/correct_output_type_error_prompt.tmpl (1 hunks)
• pandasai/exceptions.py (1 hunks)
• pandasai/helpers/output_types/_output_types.py (2 hunks)
• pandasai/llm/fake.py (1 hunks)
• pandasai/pipelines/smart_datalake_chat/code_execution.py (5 hunks)
• pandasai/prompts/correct_output_type_error_prompt.py (1 hunks)
• tests/pipelines/smart_datalake/test_code_execution.py (2 hunks)
• tests/test_smartdataframe.py (3 hunks)

Additional comments: 13

pandasai/assets/prompt_templates/correct_output_type_error_prompt.tmpl (1)

• 1-9: The template structure looks good and follows the intended design to prompt for correcting Python code with the specified output type hint.

pandasai/llm/fake.py (1)

• 12-12: The change from 'text' to 'string' in the _output attribute aligns with the PR's objective to standardize output types.

pandasai/prompts/correct_output_type_error_prompt.py (1)

• 19-22: The CorrectOutputTypeErrorPrompt class is correctly implemented and references the new template file for generating prompts.

pandasai/helpers/output_types/_output_types.py (1)

• 118-125: > Note: This review was outside the patches, so it was mapped to the patch with the greatest overlap. Original lines [121-145]

The DefaultOutputType class has been updated with a default_types list and the validate method now checks if the result type is in this list. This change is consistent with the PR's objectives to enhance output type validation.

pandasai/pipelines/smart_datalake_chat/code_execution.py (4)

• 5-9: The new imports for InvalidLLMOutputType, AbstractPrompt, and CorrectOutputTypeErrorPrompt are correctly added to support the new error handling functionality.

• 60-70: The updated execute method includes new logic for output type validation and raises the new InvalidLLMOutputType exception if the validation fails, which aligns with the PR's objectives.

• 103-107: The _get_error_prompt method is implemented to return the appropriate prompt based on the exception type, which is a good use of polymorphism and consistent with the PR's error handling strategy.

• 86-118: > Note: This review was outside the patches, so it was mapped to the patch with the greatest overlap. Original lines [110-141]

The _retry_run_code method has been updated to include an error_prompt parameter, which allows for dynamic error handling based on the type of error encountered. This is a robust enhancement to the error correction framework.

tests/pipelines/smart_datalake/test_code_execution.py (3)

• 2-14: The updated imports are correct and necessary for the new test cases that cover the error handling functionality introduced in the PR.

• 203-213: The new test method test_get_error_prompt_invalid_llm_output_type is correctly implemented to ensure that the CorrectOutputTypeErrorPrompt is returned when an InvalidLLMOutputType exception is encountered.

• 215-225: The new test method test_get_error_prompt_other_exception is correctly implemented to ensure that the CorrectErrorPrompt is returned for exceptions other than InvalidLLMOutputType.

tests/test_smartdataframe.py (2)

• 136-145: The new fixture llm_result_mocks is well-structured and uses a template for generating mock results. It's a good practice to centralize mock data for reuse across multiple tests.

• 236-245: > Note: This review was outside the patches, so it was mapped to the patch with the greatest overlap. Original lines [239-274]

The test_run_passing_output_type method has been updated to include a new patch and the use of llm_result_mocks. It's important to ensure that the patched method ResponseParser.parse is being used correctly within the test and that the llm_result_mocks fixture is providing the expected mock data for the test scenarios.

Verification successful

---

The extended output from the script provides a more complete view of the test_run_passing_output_type method. It shows that the parser_mock is set to return a Mock object, and the llm_result_mocks fixture is used to set the _output attribute of the llm object based on the type_ variable, which is determined by the output_type parameter of the test.

The test then proceeds to call the chat method on the SmartDataframe instance and asserts that the last_prompt matches the expected_prompt. This indicates that the ResponseParser.parse method is likely being used within the chat method to parse the response from the llm object, and the llm_result_mocks is providing the mock data for the llm object's output.

However, the actual call to ResponseParser.parse is not directly shown in the test method itself, which suggests that it is being used internally within the chat method or another method called by chat. The test is structured to ensure that the last_prompt generated by the chat method is as expected, which indirectly tests the parsing functionality.

Given the information provided, the review comment is consistent with the codebase. The patched ResponseParser.parse method is being used correctly within the test, albeit indirectly, and the llm_result_mocks fixture is providing the expected mock data for the test scenarios.

Scripts Executed

The following scripts were executed for the analysis:

---

Script:

#!/bin/bash
# Verify that the ResponseParser.parse method is being used correctly.
ast-grep --lang python --pattern $'ResponseParser.parse($_, $_)'
# Verify that the llm_result_mocks fixture is providing the expected mock data.
ast-grep --lang python --pattern $'llm_result_mocks[$_]'

Length of output: 204

---

---

Script:

#!/bin/bash
# View the entire test_run_passing_output_type method to verify the usage of ResponseParser.parse and the context of llm_result_mocks.
rg "def test_run_passing_output_type" -A 30 ./tests/test_smartdataframe.py

Length of output: 739

---

---

Script:

#!/bin/bash
# View more lines following the test_run_passing_output_type method to capture the entire context of its implementation.
rg "def test_run_passing_output_type" -A 50 ./tests/test_smartdataframe.py

Length of output: 1466

[coderabbitai]

The docstring for the InvalidLLMOutputType exception class incorrectly mentions malicious query generation, which seems unrelated to output type validation. This should be corrected to accurately reflect the purpose of the exception.

- Raise error if malicious query is generated
+ Raise error if the output type is invalid

---

Committable suggestion

❗ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.

Suggested change

	class InvalidLLMOutputType(Exception):
	"""
	Raise error if malicious query is generated
	Args:
	Exception (Exception): MaliciousQueryError
	"""
	class InvalidLLMOutputType(Exception):
	"""
	Raise error if the output type is invalid
	Args:
	Exception (Exception): MaliciousQueryError
	"""

[gventuri]

@ArslanSaleem leftover here!

[coderabbitai]

Seems like the humans are having a chat. I'll hop back into my burrow for now. If you need me again, just tag @coderabbitai in a new comment, and I'll come hopping out!

[gventuri]

Some tests are failing, but overall great improvement!

[gventuri]

@ArslanSaleem leftover here!

[coderabbitai]

Review Status

Actionable comments generated: 0

Configuration used: CodeRabbit UI

Commits

Files that changed from the base of the PR and between 78f315f and 8e53bf8.

Files selected for processing (2)

• pandasai/exceptions.py (1 hunks)
• pandasai/pipelines/smart_datalake_chat/code_execution.py (5 hunks)

Files skipped from review as they are similar to previous changes (2)

• pandasai/exceptions.py
• pandasai/pipelines/smart_datalake_chat/code_execution.py

[coderabbitai]

Review Status

Actionable comments generated: 0

Configuration used: CodeRabbit UI

Commits

Files that changed from the base of the PR and between 8e53bf8 and 2a34983.

Files selected for processing (1)

• tests/test_smartdataframe.py (3 hunks)

Files skipped from review as they are similar to previous changes (1)

• tests/test_smartdataframe.py

[codecov-commenter]

Codecov Report

All modified and coverable lines are covered by tests ✅

Comparison is base (36f3f23) 85.08% compared to head (2a34983) 85.15%.
Report is 1 commits behind head on main.

❗ Your organization needs to install the Codecov GitHub app to enable full functionality.

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #866      +/-   ##
==========================================
+ Coverage   85.08%   85.15%   +0.06%     
==========================================
  Files          88       89       +1     
  Lines        3849     3866      +17     
==========================================
+ Hits         3275     3292      +17     
  Misses        574      574              

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