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""" WalkthroughThe changes refactor and simplify the Coralogix log fetching toolset and its integration with the rest of the system. Strongly typed Pydantic models are introduced for configuration and parameters, replacing ad-hoc dictionaries. Query construction and log formatting are streamlined, and test coverage is improved with new fixtures and parameterized integration tests. Prompt templates and fixtures are also updated for consistency. Changes
Sequence Diagram(s)sequenceDiagram
participant User
participant CoralogixLogsToolset
participant CoralogixConfig
participant Coralogix API
User->>CoralogixLogsToolset: fetch_logs(FetchPodLogsParams)
CoralogixLogsToolset->>CoralogixConfig: Access configuration
CoralogixLogsToolset->>Coralogix API: Query logs (with params)
Coralogix API-->>CoralogixLogsToolset: Return log results
CoralogixLogsToolset-->>User: StructuredToolResult (logs, status)
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Actionable comments posted: 0
🧹 Nitpick comments (1)
tests/plugins/toolsets/coralogix/test_coralogix_integration.py (1)
91-98: Consider adding additional assertions for returned dataWhile the basic query test checks for success and the presence of the search term, consider adding assertions about other expected properties of the returned data structure.
def test_basic_query(coralogix_logs_toolset): result = coralogix_logs_toolset.fetch_logs( FetchLogsParams(namespace=TEST_NAMESPACE, pod_name=TEST_POD_NAME) ) print(result.data) assert result.status == ToolResultStatus.SUCCESS, result.error assert not result.error assert TEST_SEARCH_TERM in result.data + assert result.url, "URL should be present in the result" + assert result.invocation, "Query string should be present in the invocation field" + assert "link:" in result.data, "Data should contain a link section" + assert "query:" in result.data, "Data should contain a query section"
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holmes/plugins/prompts/_fetch_logs.jinja2(1 hunks)holmes/plugins/toolsets/coralogix/api.py(4 hunks)holmes/plugins/toolsets/coralogix/toolset_coralogix_logs.py(2 hunks)holmes/plugins/toolsets/coralogix/utils.py(4 hunks)tests/plugins/toolsets/coralogix/fixtures/.gitignore(1 hunks)tests/plugins/toolsets/coralogix/fixtures/formatted_logs.txt(1 hunks)tests/plugins/toolsets/coralogix/fixtures/formatted_logs.txt.actual(0 hunks)tests/plugins/toolsets/coralogix/test_coralogix_integration.py(2 hunks)tests/plugins/toolsets/coralogix/test_coralogix_unit.py(2 hunks)
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- tests/plugins/toolsets/coralogix/fixtures/formatted_logs.txt.actual
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tests/plugins/toolsets/coralogix/test_coralogix_unit.py (3)
holmes/plugins/toolsets/logging_api.py (1)
FetchLogsParams(27-33)tests/plugins/toolsets/coralogix/test_coralogix_integration.py (1)
coralogix_config(46-57)holmes/plugins/toolsets/coralogix/api.py (1)
build_query_string(56-66)
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- GitHub Check: llm_evals
🔇 Additional comments (27)
tests/plugins/toolsets/coralogix/fixtures/.gitignore (1)
1-2: LGTM: Appropriate gitignore pattern for test artifactsAdding
.gitignoreto exclude.actualfiles from version control is a good practice. These files are generated during test execution when comparing expected vs actual output, as seen in the unit tests.holmes/plugins/prompts/_fetch_logs.jinja2 (1)
19-19: Good simplification of Coralogix log promptStandardizing the Coralogix prompt to use the same
_default_log_prompt.jinja2template as other logging systems creates consistency across different log sources. This aligns with the broader refactoring of the Coralogix toolset to use a more unified logging API.tests/plugins/toolsets/coralogix/fixtures/formatted_logs.txt (1)
1-124: Format simplification looks goodThe timestamp prefixes have been removed from the log lines while preserving the actual log content. This aligns with the changes to
stringify_flattened_logsfunction that now omits timestamp metadata for cleaner log representation.tests/plugins/toolsets/coralogix/test_coralogix_unit.py (5)
16-16: Good update to import the new Pydantic modelReplacing the import of
FetchLogswithFetchLogsParamsfrom thelogging_apimodule aligns with the architecture changes that moved to strongly typed Pydantic models for parameter handling.
103-104: Test case updated correctly for new parameter structureThe test parameters have been properly updated to use the new Kubernetes-centric keys (
namespace,pod_name) instead of the previous resource-centric approach, matching the refactored API.
108-110: Test case properly refactored for new parameter structureThe test case has been correctly updated to use the standardized parameter names and structure, with
namespaceandpod_nameas the primary identifiers.
116-119: Matching parameter added correctly in test caseThe test case properly includes the new
matchparameter which allows filtering logs by content, a useful addition to the API.
125-129: Good test function improvementThe test function has been improved by:
- Using the new
FetchLogsParamsPydantic model to validate parameters- Adding debug print statements for easier troubleshooting when tests fail
This makes the tests more robust and easier to debug.
holmes/plugins/toolsets/coralogix/toolset_coralogix_logs.py (4)
30-42: Good implementation of BaseLoggingToolset inheritanceThe new
CoralogixLogsToolsetclass properly inherits fromBaseLoggingToolsetwith good initialization parameters. This follows a clean OOP approach and standardizes the logging interface across the application.
50-57: Well-implemented prerequisites checkGood validation approach using the Pydantic model for configuration. The method properly handles empty configs and missing API keys with appropriate error messages.
59-61: Good encapsulation of config propertyUsing a property method provides clean access to the configuration while maintaining good encapsulation principles.
63-101: Well-structured fetch_logs methodThe method provides proper error handling and uses the strongly typed
FetchLogsParamsmodel for better type safety. The use of a structured result with appropriate status codes, error messages, and data formatting enhances API consistency.holmes/plugins/toolsets/coralogix/api.py (5)
15-15: Good integration with unified logging APIImporting
FetchLogsParamsfrom the common logging API module aligns with the unified logging strategy.
22-22: Appropriate adjustment to default log countGood update of the default log count to match Coralogix's actual default value with a clear explanatory comment.
56-66: Improved query string construction with strong typingThe function now uses strongly typed parameters instead of dictionaries, which improves type safety and makes the code more maintainable. The query construction is clean with appropriate filtering logic.
69-75: Simplified timestamp handlingClean refactoring to work directly with the strongly typed parameters instead of dictionary lookups, which reduces potential errors.
78-90: Consistent use of typed parameters across API functionsAll API functions have been updated to consistently use the
FetchLogsParamsmodel andCoralogixConfigmodel, which improves code consistency and reduces the chance of errors from mismatched parameter handling.Also applies to: 94-95, 119-120
holmes/plugins/toolsets/coralogix/utils.py (5)
22-25: Good use of Pydantic for label configurationCreating a dedicated model for label keys with meaningful defaults improves configuration clarity and maintainability.
28-33: Well-defined enum for log retrieval strategiesUsing an enum for log retrieval methodologies provides type safety and self-documentation for the available options.
36-43: Comprehensive configuration modelThe
CoralogixConfigmodel properly encapsulates all necessary configuration parameters with appropriate defaults, which improves validation and usage.
57-61: Improved docstring for normalize_datetimeThe updated docstring clearly explains the function's purpose, handling of edge cases, and compatibility considerations.
102-107: Simplified log formattingThe streamlined approach to log formatting (removing timestamps and indentation) makes the output cleaner and more consistent with the unified logging strategy.
tests/plugins/toolsets/coralogix/test_coralogix_integration.py (5)
1-4: Helpful module documentationGood addition of a docstring explaining the purpose of these integration tests and how to configure them for manual execution.
18-29: Improved test skip managementWell-organized approach to handling environment variables with a centralized list and module-level skip marker. This reduces code duplication and makes test dependencies clearer.
36-42: Good test parameterizationUsing constants for test parameters improves readability and makes it easier to update test criteria. The comment explaining the expected result from the date range is particularly helpful.
45-57: Well-structured test fixturesThe fixtures provide clean test setup with appropriate error handling and type annotations. The defensive approach in
coralogix_configis a good practice for robust testing.Also applies to: 60-65
91-98: Comprehensive test coverage for log fetchingThe three log fetching tests cover different parameter combinations and verify both success conditions and content expectations. The assertions and parsing logic are clear and thorough.
Also applies to: 101-115, 117-135
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Summary by CodeRabbit
Refactor
Tests
Chores
.gitignorerule to exclude test output files in fixture directories.