6a0c0e3ef8
<!-- .github/pull_request_template.md --> This PR contains the evaluation framework development for cognee ## 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** - Expanded evaluation framework now integrates asynchronous corpus building, question answering, and performance evaluation with adaptive benchmarks for improved metrics (correctness, exact match, and F1 score). - **Infrastructure** - Added database integration for persistent storage of questions, answers, and metrics. - Launched an interactive metrics dashboard featuring advanced visualizations. - Introduced an automated testing workflow for continuous quality assurance. - **Documentation** - Updated guidelines for generating concise, clear answers. <!-- end of auto-generated comment: release notes by coderabbit.ai -->
56 lines
1.9 KiB
Python
56 lines
1.9 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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# 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"
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evaluation_metrics: List[str] = ["correctness", "EM", "f1"]
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deepeval_model: str = "gpt-4o-mini"
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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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dashboard_path: str = "dashboard.html"
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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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"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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"dashboard_path": self.dashboard_path,
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"deepeval_model": self.deepeval_model,
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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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