chore(wren-ai-service): minor updates #1219
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Actionable comments posted: 1
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wren-ai-service/src/web/v1/services/ask.py(5 hunks)wren-ai-service/tests/pytest/test_usecases.py(1 hunks)
✅ Files skipped from review due to trivial changes (1)
- wren-ai-service/tests/pytest/test_usecases.py
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- GitHub Check: pytest
- GitHub Check: pytest
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🔇 Additional comments (5)
wren-ai-service/src/web/v1/services/ask.py (5)
95-95: LGTM: Well-structured model enhancement.The addition of the optional
generation_reasoningfield is properly typed and follows the existing pattern in the response model.
289-296: LGTM: Proper integration of generation reasoning in planning phase.The
generation_reasoningis correctly populated from the SQL generation reasoning pipeline and properly integrated into the response structure.
394-395: LGTM: Consistent response handling.The
generation_reasoningfield is properly included in both success and failure response paths, maintaining consistency in the API response structure.Also applies to: 410-411
Line range hint
419-436: LGTM: Consistent error handling pattern.The omission of
generation_reasoningin the general error handler follows the existing pattern where only essential error information is included in the response.
302-303: Verify sql_generation_reasoning availability.While the propagation of
generation_reasoningis correct, we should verify thatsql_generation_reasoningis always defined before this point, especially in cases where the planning phase might be skipped.✅ Verification successful
The variable initialization is verified and safe.
The
sql_generation_reasoningvariable is properly initialized through the SQLGenerationReasoning pipeline before being used in the status update. The pipeline is a required step in the execution flow, and there's no path where the generating status can be set without this initialization.🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Search for all paths that could lead to the generating phase # without going through the planning phase where sql_generation_reasoning is defined ast-grep --pattern 'self._ask_results[query_id] = AskResultResponse( status = "generating", $$$ )'Length of output: 778
Script:
#!/bin/bash # Find the method containing this code and sql_generation_reasoning assignments ast-grep --pattern 'async def $_($$$) { $$$ sql_generation_reasoning = $_ $$$ }' # Also search for any direct references rg "sql_generation_reasoning" -A 2 -B 2Length of output: 16147
| contexts=documents, | ||
| invalid_generation_results=failed_dry_run_results, | ||
| project_id=ask_request.project_id, | ||
| generation_reasoning=sql_generation_reasoning, |
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💡 Codebase verification
Pipeline incompatibility detected: generation_reasoning parameter is not supported
The SQLCorrection pipeline's run method does not accept the generation_reasoning parameter that's being passed. The pipeline needs to be updated to handle this parameter or the parameter should be removed from the caller.
wren-ai-service/src/pipelines/generation/sql_correction.py: Update therunmethod signature to includegeneration_reasoningparameter if the reasoning context is needed for SQL correctionwren-ai-service/src/web/v1/services/ask.py: Remove the parameter if it's not required for SQL correction
🔗 Analysis chain
Verify sql_correction pipeline compatibility.
The generation_reasoning parameter has been added to the SQL correction pipeline. Ensure that the pipeline is updated to handle this new parameter.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Check if the sql_correction pipeline accepts and uses the generation_reasoning parameter
ast-grep --pattern 'class $_Pipeline:
$$$
def run(self, $$$, generation_reasoning: $_Type, $$$):
$$$
'
Length of output: 112
Script:
#!/bin/bash
# Search for SQL correction pipeline class and related files
rg -l "sql.*correction" --type py
# Then look for the specific implementation
rg "class.*(?i)sql.*correction" --type py
Length of output: 788
Script:
#!/bin/bash
# Check the SQLCorrection class implementation
cat wren-ai-service/src/pipelines/generation/sql_correction.py
Length of output: 4232
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Actionable comments posted: 0
🧹 Nitpick comments (2)
wren-ai-service/tests/pytest/test_usecases.py (2)
207-217: Consider verifying the presence of "sql" key in the response.
While the conditional logic ensures thatresult["response"][0]exists when there is no error, it's safer to confirm that"sql"is in the dictionary to prevent a possible KeyError in edge cases.
235-242: Take note of potential global representer side effects.
Adding global representers right before dumping is convenient, but can cause unexpected behavior if multiple threads or parallel tests manipulate YAML representers. For large testing suites, consider a local dumper instance for safer concurrency.
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wren-ai-service/tests/pytest/test_usecases.py(4 hunks)
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- GitHub Check: pytest
- GitHub Check: Analyze (python)
- GitHub Check: Analyze (javascript-typescript)
- GitHub Check: Analyze (go)
🔇 Additional comments (2)
wren-ai-service/tests/pytest/test_usecases.py (2)
13-13: Imports look correct and consistent.
It's good to see thatsqlparseandyamlare included. Ensure these dependencies are present in the environment or requirements to avoid runtime import errors.
148-154: Custom YAML presenter is a neat solution.
Definingstr_presenterto handle multiline strings is a clean approach and should make the YAML output more readable.
Summary by CodeRabbit
generation_reasoningfield to AI service response, providing more detailed feedback about SQL generation process.