Add datasets client #274
Add datasets client #274
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📝 Walkthrough📝 WalkthroughWalkthroughThe changes enhance the API client by improving dataset retrieval functionality and ensuring proper URI encoding for identifiers in API requests. A new method, Changes
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Actionable comments posted: 0
🧹 Outside diff range and nitpick comments (3)
autoblocks/api/models.py (1)
Please update all existing imports to use the new
DatasetandDatasetItemfromautoblocks/api/models.pyand remove the old references from_impl/api/models.py. Additionally, verify the usage inragas_base.pyto ensure consistency.🔗 Analysis chain
Line range hint
1-53: Overall, the changes look good and align with the PR objective.The additions of
DatasetandDatasetItemto this file are straightforward and follow the existing patterns. However, it's important to ensure that these new models are properly integrated into the rest of the codebase.Let's verify if there are any existing references to these new models in other files:
This script will help identify if there are any existing references to
DatasetorDatasetItemin other Python files, which might need to be updated to use these new imports.
Let's update the shell script to use the correct file type identifier for Python files and re-run the search.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Check for potential usage of the new Dataset and DatasetItem models in other files. echo "Searching for potential usage of Dataset:" rg --type python -g '!autoblocks/api/models.py' 'Dataset' echo "Searching for potential usage of DatasetItem:" rg --type python -g '!autoblocks/api/models.py' 'DatasetItem'Length of output: 375
Script:
#!/bin/bash # Description: Check for potential usage of the new Dataset and DatasetItem models in other files. echo "Searching for potential usage of Dataset:" rg --type py -g '!autoblocks/api/models.py' 'Dataset' echo "Searching for potential usage of DatasetItem:" rg --type py -g '!autoblocks/api/models.py' 'DatasetItem'Length of output: 1358
autoblocks/_impl/api/models.py (2)
191-194: LGTM! Consider using a more specific type fordata.The
DatasetItemclass looks good and provides a flexible structure for dataset items. However, consider if a more specific type thanDict[str, Any]could be used for thedatafield to improve type safety.If the structure of the data is known, you could define a custom type or use
TypedDictfor better type hinting:from typing import TypedDict class DataItemContent(TypedDict): field1: str field2: int # Add other fields as needed @dataclass class DatasetItem: id: str data: DataItemContentThis approach would provide better type checking and code completion in IDEs.
197-202: LGTM! Consider adding docstrings for clarity.The
Datasetclass is well-structured and provides a good foundation for managing datasets with versioning capabilities. The fields are appropriate and cover essential aspects of dataset management.To improve code clarity and maintainability, consider adding docstrings to the class and its fields. For example:
@dataclass class Dataset: """Represents a versioned dataset containing multiple items.""" name: str """The name of the dataset.""" schema_version: str """The version of the schema used for this dataset.""" revision_id: str """A unique identifier for this specific revision of the dataset.""" items: List[DatasetItem] """The list of items contained in this dataset."""This addition would provide more context for developers working with this class in the future.
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📒 Files selected for processing (3)
- autoblocks/_impl/api/client.py (6 hunks)
- autoblocks/_impl/api/models.py (1 hunks)
- autoblocks/api/models.py (2 hunks)
🔇 Additional comments (9)
autoblocks/api/models.py (2)
2-3: LGTM: New imports added correctly.The new imports for
DatasetandDatasetItemhave been added correctly, following the existing import style in the file. These additions align with the PR objective of adding a datasets client.
39-40: LGTM:__all__list updated correctly.The
__all__list has been properly updated to include the newDatasetandDatasetItemmodels. The additions maintain the alphabetical order, which is good for consistency and readability.autoblocks/_impl/api/models.py (1)
191-202: Summary: Dataset models implemented correctlyThe implementation of
DatasetItemandDatasetclasses aligns well with the PR objective of adding a datasets client. These new models provide a solid foundation for managing datasets within the system.A few minor suggestions were made to enhance type safety and documentation, but overall, the changes look good and are ready for integration.
autoblocks/_impl/api/client.py (6)
14-15: LGTM!The imports for
DatasetandDatasetItemmodels are necessary for the newget_datasetmethod.
36-36: LGTM!The import for
encode_uri_componentis necessary for properly encoding URI components in the API requests.
91-91: LGTM!The
view_idis now properly encoded using theencode_uri_componentfunction before being used in the API endpoint URL. This ensures that any special characters in theview_idare correctly handled.
143-143: LGTM!The
job_idis now properly encoded using theencode_uri_componentfunction before being used in the API endpoint URL. This ensures that any special characters in thejob_idare correctly handled.
154-156: LGTM!Both
job_idandtest_case_idare now properly encoded using theencode_uri_componentfunction before being used in the API endpoint URL. This ensures that any special characters in these IDs are correctly handled.
210-226: LGTM!The new
get_datasetmethod is implemented correctly:
- It takes the necessary parameters:
name,schema_version, and an optionalrevision_id.- It properly encodes the
name,schema_version, andrevision_id(if provided) using theencode_uri_componentfunction before using them in the API endpoint URL.- It constructs the appropriate API endpoint URL based on whether
revision_idis provided or not.- It raises an exception if the API request fails.
- It processes the API response and returns a
Datasetobject with the correct attributes:name,schema_version,revision_id, and a list ofDatasetItemobjects.
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Actionable comments posted: 0
🧹 Outside diff range and nitpick comments (2)
autoblocks/_impl/testing/models.py (1)
125-129: LGTM with a minor suggestion for improvement.The new
serialize_dataset_item_idmethod is a good addition that aligns with the PR objective of adding datasets functionality. It provides a clear extension point for subclasses to implement custom serialization of dataset item IDs.Consider enhancing the docstring to provide more context about the purpose and usage of this method. For example:
def serialize_dataset_item_id(self) -> Optional[str]: """ Override this method to set the dataset item ID for this test case. This method is used to provide a unique identifier for the test case when it's added to a dataset. If not overridden, no dataset item ID will be set (returns None). Returns: Optional[str]: The dataset item ID for this test case, or None if not applicable. """ return NoneThis expanded docstring provides more information about when and why a subclass might want to override this method.
autoblocks/_impl/testing/api.py (1)
225-225: LGTM! Consider adding error handling forserialize_dataset_item_id().The addition of
dataset_item_idto the test case result payload is implemented correctly and consistently for both CLI and public API calls. This change enhances the data being sent during the test case result submission by including the serialized dataset item ID.Consider adding error handling for the case where
serialize_dataset_item_id()might return None or raise an exception. For example:try: dataset_item_id = test_case_ctx.test_case.serialize_dataset_item_id() except AttributeError: log.warning("TestCase does not have serialize_dataset_item_id method") dataset_item_id = None except Exception as e: log.error(f"Error serializing dataset item ID: {e}") dataset_item_id = NoneThis will make the code more robust and prevent potential crashes if the
serialize_dataset_item_id()method is not implemented or raises an exception.Also applies to: 238-238, 257-257
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📒 Files selected for processing (2)
- autoblocks/_impl/testing/api.py (3 hunks)
- autoblocks/_impl/testing/models.py (1 hunks)
🔇 Additional comments (1)
autoblocks/_impl/testing/models.py (1)
125-129: Verify usage of the new method in the codebase.The
serialize_dataset_item_idmethod has been added successfully. To ensure its effectiveness, we should verify its usage across the codebase.Let's run a script to check for any references to this new method:
This script will help us identify where the new method is being called or overridden, ensuring it's properly integrated into the datasets functionality.
✅ Verification successful
Usage of
serialize_dataset_item_idVerified Successfully.
- The method is defined in
autoblocks/_impl/testing/models.py.- It is called in
autoblocks/_impl/testing/api.py.All usages of the new method have been verified and properly integrated.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Check for references to serialize_dataset_item_id method # Search for direct calls to serialize_dataset_item_id echo "Searching for direct calls to serialize_dataset_item_id:" rg "serialize_dataset_item_id\(" --type py # Search for classes that might be overriding this method echo "\nSearching for potential overrides of serialize_dataset_item_id:" rg "class.*\(.*BaseTestCase.*\):" --type py -A 10 | rg "def serialize_dataset_item_id"Length of output: 596
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Actionable comments posted: 2
🧹 Outside diff range and nitpick comments (1)
tests/autoblocks/test_run_manager.py (1)
147-147: LGTM. Consider updating related documentation.The addition of
datasetItemId=Noneto the test case result dictionary is a good change. It suggests that theRunManager.add_resultmethod now supports an optionaldatasetItemIdparameter.To ensure comprehensive documentation and testing:
- Update the docstring for the
RunManager.add_resultmethod to include information about the newdatasetItemIdparameter.- Consider adding a test case that explicitly sets a non-None value for
datasetItemIdto ensure the parameter is correctly passed through to the API.- If there's a user-facing API documentation, make sure it's updated to reflect this new optional parameter.
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📒 Files selected for processing (2)
- tests/autoblocks/test_run_manager.py (2 hunks)
- tests/autoblocks/test_run_test_suite.py (52 hunks)
🔇 Additional comments (2)
tests/autoblocks/test_run_test_suite.py (2)
233-233: Ensure consistent inclusion ofdatasetItemIdThe addition of
datasetItemId=Noneto the request body enhances clarity by explicitly stating the parameter. This practice ensures consistency across test cases.
3481-3541: Effective addition oftest_serialize_dataset_item_idtest caseThe new test function
test_serialize_dataset_item_idenhances test coverage by validating the serialization ofdatasetItemId. It ensures that this functionality behaves as expected.
| @@ -62,7 +62,7 @@ def serialize_for_human_review(self) -> List[HumanReviewField]: | |||
| def test_does_not_allow_adding_result_before_starting(): | |||
| test_run = RunManager[MyTestCase, MyOutput]("test-id", "Test run") | |||
|
|
|||
| with pytest.raises(ValueError): | |||
| with pytest.raises(Exception): | |||
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Consider using a more specific exception type.
The change from ValueError to Exception broadens the scope of exceptions that this test will catch. While this ensures the test passes if any exception is raised, it might make the test less specific and could potentially hide bugs if an unexpected exception type is thrown.
Consider the following options:
- If
RunManager.add_resultis expected to raise onlyValueError, keep the originalValueErrorin the test. - If
RunManager.add_resultcan raise multiple specific exceptions, usepytest.raises((ValueError, OtherSpecificException))to catch only the expected exceptions. - If the exact exception type is not crucial, but you want to ensure a specific error message, you can use:
with pytest.raises(Exception) as excinfo: # ... test code ... assert "Expected error message" in str(excinfo.value)
This approach maintains the broadened exception catching while still asserting on the specific error condition.
| def serialize_dataset_item_id(self): | ||
| return mock_dataset_item_id |
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Fix the scope issue of mock_dataset_item_id
In the serialize_dataset_item_id method, mock_dataset_item_id is used, but it is defined within the scope of the test_serialize_dataset_item_id function. This will result in a NameError when serialize_dataset_item_id is called outside that function. To fix this, store mock_dataset_item_id as an instance variable of MyCustomTestCase.
Apply this diff to resolve the scope issue:
+ dataset_item_id: str
def hash(self):
return f"{self.x}-{self.y}"
def serialize_dataset_item_id(self):
- return mock_dataset_item_id
+ return self.dataset_item_idUpdate the test case instantiation to pass dataset_item_id:
MyCustomTestCase(x=1, y=2)
+MyCustomTestCase(x=1, y=2, dataset_item_id=mock_dataset_item_id)Committable suggestion was skipped due to low confidence.
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Actionable comments posted: 0
🧹 Outside diff range and nitpick comments (3)
tests/autoblocks/test_ragas_evaluators.py (1)
65-65: LGTM! Consider adding a docstring update.The addition of
datasetItemId=Noneto the request body is a good enhancement, allowing for future association of test results with specific dataset items.Consider updating the function's docstring to reflect this new parameter, explaining its purpose and optional nature. This would improve the function's documentation and make it easier for other developers to understand the change. For example:
def make_expected_requests(evaluator_id: str, httpx_mock: Any) -> None: """ Prepare expected requests for testing. Args: evaluator_id (str): The ID of the evaluator being tested. httpx_mock (Any): The HTTP mock object for request assertions. Note: The function now includes a `datasetItemId` parameter in the request body, which can be used to associate test results with specific dataset items. This parameter is optional and defaults to None. """ # ... rest of the functiontests/autoblocks/test_evaluators.py (2)
91-91: LGTM. Consider updating documentation.The addition of the
datasetItemIdparameter is consistent and appears to be part of a larger feature implementation. This change maintains backwards compatibility by setting the default value toNone.Consider updating the function's documentation to explain the purpose and usage of the new
datasetItemIdparameter.
Line range hint
91-1097: Summary: Consistent implementation ofdatasetItemIdparameter across all test functionsThe changes in this file consistently add a
datasetItemIdparameter (defaulting toNone) to various function calls across all test functions. This systematic update suggests the implementation of a new feature for dataset-specific testing or tracking while maintaining backwards compatibility.Consider the following recommendations:
- Update the documentation for these test functions to explain the purpose and usage of the new
datasetItemIdparameter.- If not already done, ensure that the corresponding production code properly handles this new parameter.
- Consider adding new test cases that explicitly use non-None
datasetItemIdvalues to verify the new functionality.
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📒 Files selected for processing (2)
- tests/autoblocks/test_evaluators.py (17 hunks)
- tests/autoblocks/test_ragas_evaluators.py (1 hunks)
🔇 Additional comments (6)
tests/autoblocks/test_evaluators.py (6)
123-123: LGTM. Consistent implementation.The addition of the
datasetItemIdparameter here maintains consistency within the test function, ensuring all test cases are treated similarly.
201-201: LGTM. Consistent implementation across test functions.The addition of the
datasetItemIdparameter here extends the support to thetest_is_equals_evaluatorfunction, maintaining consistency across different test functions in the file.
233-233: LGTM. Consistent implementation within the function.The addition of the
datasetItemIdparameter here maintains consistency within thetest_is_equals_evaluatorfunction, ensuring all test cases are treated similarly.
313-313: LGTM. Consistent implementation across test functions.The addition of the
datasetItemIdparameter here extends the support to thetest_is_valid_json_evaluatorfunction, maintaining consistency across different test functions in the file.
345-345: LGTM. Consistent implementation within the function.The addition of the
datasetItemIdparameter here maintains consistency within thetest_is_valid_json_evaluatorfunction, ensuring all test cases are treated similarly.
427-427: LGTM. Consistent implementation across all test functions.The addition of the
datasetItemIdparameter to all remaining test functions maintains consistency throughout the file. This systematic change suggests a global update to support dataset-specific testing or tracking.To ensure the changes have been applied consistently, please run the following command:
This command will help confirm that the
datasetItemIdparameter has been added uniformly across all necessary function calls.Also applies to: 462-462, 547-547, 582-582, 668-668, 703-703, 791-791, 826-826, 922-922, 1005-1005, 1097-1097
✅ Verification successful
If the above script still encounters issues, please try the following alternative:
Verification Successful: All relevant function calls include
datasetItemId=None.The shell script confirmed that the
datasetItemId=Noneparameter has been consistently added across all targeted function calls intests/autoblocks/test_evaluators.py.🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Verify that all relevant function calls include the datasetItemId parameter rg --type python 'testCaseBody.*datasetItemId=None' tests/autoblocks/test_evaluators.pyLength of output: 120
Script:
#!/bin/bash # Verify that all relevant function calls include the datasetItemId parameter rg --type py 'testCaseBody.*datasetItemId=None' tests/autoblocks/test_evaluators.pyLength of output: 85
Script:
#!/bin/bash # Alternative verification without specifying file type rg 'testCaseBody.*datasetItemId=None' tests/autoblocks/test_evaluators.pyLength of output: 75
Script:
#!/bin/bash # Verify that all relevant function calls include the datasetItemId parameter set to None rg 'datasetItemId\s*=\s*None' tests/autoblocks/test_evaluators.pyLength of output: 611
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🧹 Outside diff range and nitpick comments (1)
tests/e2e/test_e2e.py (1)
508-508: LGTM. Consider adding test cases for non-nulldatasetItemId.The addition of the
datasetItemIdparameter is a good enhancement to support associating test results with specific dataset items. Setting it toNonein this test case is appropriate for backward compatibility.Consider adding additional test cases that cover scenarios where
datasetItemIdis notNone. This would ensure that the new functionality is properly tested. For example:expect_cli_post_request( httpx_mock, path="/results", body=dict( # ... other parameters ... datasetItemId="some-dataset-item-id", ), json=dict(id="mock-result-id-2"), ) # Then add a test case that uses a non-None datasetItemId
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Actionable comments posted: 0
🧹 Outside diff range and nitpick comments (4)
tests/e2e/test_e2e.py (4)
508-508: LGTM. Consider updating documentation.The addition of the
datasetItemIdparameter is a good enhancement, allowing for potential integration with dataset functionality.Consider updating the function's documentation to explain the purpose and usage of this new parameter.
717-725: LGTM. Consider adding a negative test case.The test function comprehensively checks the properties of the retrieved dataset. It verifies the revision ID, name, schema version, number of items, and properties of the first item.
Consider adding a negative test case to verify the behavior when trying to retrieve a non-existent dataset. This would enhance the robustness of your test suite.
727-733: LGTM. Consider adding an assertion for non-empty splits.The test function effectively verifies the retrieval of a dataset with a specific split. It correctly checks that no items are returned for the "test-split-2".
Consider adding another test case or assertion to verify that when using a split that does contain items (e.g., "test-split" from the previous test), the items are correctly returned. This would provide more comprehensive coverage of the split functionality.
745-750: LGTM. Consider adding a test case with non-empty results.This test function effectively verifies the retrieval of a dataset using both a specific revision ID and split. It correctly checks that no items are returned for this combination.
Consider adding another test case that uses a combination of revision ID and split that does return items. This would provide more comprehensive coverage of the functionality when both parameters are used together.
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📒 Files selected for processing (2)
- autoblocks/_impl/api/client.py (6 hunks)
- tests/e2e/test_e2e.py (2 hunks)
🚧 Files skipped from review as they are similar to previous changes (1)
- autoblocks/_impl/api/client.py
🔇 Additional comments (1)
tests/e2e/test_e2e.py (1)
735-743: LGTM. Comprehensive test for dataset retrieval by revision ID.This test function effectively verifies the retrieval of a dataset using a specific revision ID. It checks all relevant properties including the revision ID, name, schema version, number of items, and the properties of the first item.
Summary by CodeRabbit
New Features
Bug Fixes
Documentation