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yotam319-sparkbeyond
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| test_dataset_with_strategy, | ||
| ) | ||
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| __all__ = ['problem', 'real_llm_with_spec', 'test_dataset_with_strategy'] |
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are we sure we want to share ['problem', 'real_llm_with_spec', 'test_dataset_with_strategy'] between all generators?
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No, we'll definitely want a separate, non conversation tabular data, I just didn't want to decide on it in this PR, so this is a placeholder, for now
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then define an placeholder ['problem', 'real_llm_with_spec', 'test_dataset_with_strategy'] in 'semantic_insights_generator/test_e2e.py' and remove this conftest.py.
| generation_model: LLMWithSpec | ||
| repair_model: LLMWithSpec | ||
| seed: int | None = None | ||
| validator: FeatureValidator = field(factory=LawAndOrderValidator) |
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should be a list
Create minimal SemanticInsightsGenerator module with validation integration: - SemanticInsightsGenerator: Main orchestrator implementing FeatureGenerator - BasicFeatureGenerator: Core generation logic with validate_and_retry loop - LLMSqlFeatureCorrector: LLM-based SQL repair stub (TODO implementation) - GeneratedFeatureSpec: Minimal schema for LLM-generated features - Configurable validator and retry budgets with module defaults - Uses official validation infrastructure (LawAndOrderValidator, validate_and_retry) - Streaming feature generation (yields immediately after validation) - Seed parameter for reproducible generation
- Created nested test folder: tests/agentune/analyze/feature/gen/semantic_insights_generator/ - test_e2e.py: Validates generator instantiation and API contract - Expects empty results until LLM generation is implemented - refactor: Move InsightfulTextGenerator tests into a nested folder structure, matching the SemanticInsightsGenerator test structure
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What does this PR do?
Sets up the SemanticInsightsGenerator module structure with validation framework integration. LLM generation logic is stubbed out and ready for implementation.
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Related Issues
Closes SparkBeyond/ao-core#113