bee04cad86
<!-- .github/pull_request_template.md --> ## Description - Split metrics dashboard into two modules: calculator (statistics) and generator (visualization) - Added aggregate metrics as a new phase in evaluation pipeline - Created modal example to run multiple evaluations in parallel and collect results into a single combined output ## DCO Affirmation I affirm that all code in every commit of this pull request conforms to the terms of the Topoteretes Developer Certificate of Origin <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **New Features** - Enhanced metrics reporting with improved visualizations, including histogram and confidence interval plots. - Introduced an asynchronous evaluation process that supports parallel execution and streamlined result aggregation. - Added new configuration options to control metrics calculation and aggregated output storage. - **Refactor** - Restructured dashboard generation and evaluation workflows into a more modular, maintainable design. - Improved error handling and logging for better feedback during evaluation processes. - **Bug Fixes** - Updated test cases to ensure accurate validation of the new dashboard generation and metrics calculation functionalities. <!-- end of auto-generated comment: release notes by coderabbit.ai -->
72 lines
2.7 KiB
Python
72 lines
2.7 KiB
Python
from functools import lru_cache
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from pydantic_settings import BaseSettings, SettingsConfigDict
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from typing import List
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class EvalConfig(BaseSettings):
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# Corpus builder params
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building_corpus_from_scratch: bool = True
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number_of_samples_in_corpus: int = 1
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benchmark: str = "Dummy" # Options: 'HotPotQA', 'Dummy', 'TwoWikiMultiHop'
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task_getter_type: str = "Default" # Options: 'Default', 'CascadeGraph'
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# Question answering params
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answering_questions: bool = True
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qa_engine: str = (
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"cognee_completion" # Options: 'cognee_completion' or 'cognee_graph_completion'
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)
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# Evaluation params
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evaluating_answers: bool = True
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evaluation_engine: str = "DeepEval" # Options: 'DeepEval' (uses deepeval_model), 'DirectLLM' (uses default llm from .env)
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evaluation_metrics: List[str] = [
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"correctness",
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"EM",
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"f1",
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] # Use only 'correctness' for DirectLLM
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deepeval_model: str = "gpt-4o-mini"
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# Metrics params
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calculate_metrics: bool = True
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# Visualization
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dashboard: bool = True
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# file paths
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questions_path: str = "questions_output.json"
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answers_path: str = "answers_output.json"
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metrics_path: str = "metrics_output.json"
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aggregate_metrics_path: str = "aggregate_metrics.json"
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dashboard_path: str = "dashboard.html"
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direct_llm_system_prompt: str = "direct_llm_eval_system.txt"
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direct_llm_eval_prompt: str = "direct_llm_eval_prompt.txt"
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model_config = SettingsConfigDict(env_file=".env", extra="allow")
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def to_dict(self) -> dict:
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return {
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"building_corpus_from_scratch": self.building_corpus_from_scratch,
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"number_of_samples_in_corpus": self.number_of_samples_in_corpus,
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"benchmark": self.benchmark,
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"answering_questions": self.answering_questions,
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"qa_engine": self.qa_engine,
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"evaluating_answers": self.evaluating_answers,
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"evaluation_engine": self.evaluation_engine,
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"evaluation_metrics": self.evaluation_metrics,
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"calculate_metrics": self.calculate_metrics,
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"dashboard": self.dashboard,
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"questions_path": self.questions_path,
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"answers_path": self.answers_path,
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"metrics_path": self.metrics_path,
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"aggregate_metrics_path": self.aggregate_metrics_path,
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"dashboard_path": self.dashboard_path,
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"deepeval_model": self.deepeval_model,
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"task_getter_type": self.task_getter_type,
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"direct_llm_system_prompt": self.direct_llm_system_prompt,
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"direct_llm_eval_prompt": self.direct_llm_eval_prompt,
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}
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@lru_cache
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def get_llm_config():
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return EvalConfig()
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