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 -->
59 lines
2.4 KiB
Python
59 lines
2.4 KiB
Python
import logging
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import json
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from evals.eval_framework.evaluation.evaluation_executor import EvaluationExecutor
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from evals.eval_framework.metrics_dashboard import generate_metrics_dashboard
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from cognee.infrastructure.files.storage import LocalStorage
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from cognee.infrastructure.databases.relational.get_relational_engine import (
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get_relational_engine,
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get_relational_config,
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)
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from cognee.modules.data.models.metrics_data import Metrics
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from cognee.modules.data.models.metrics_base import MetricsBase
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async def create_and_insert_metrics_table(questions_payload):
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relational_config = get_relational_config()
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relational_engine = get_relational_engine()
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if relational_engine.engine.dialect.name == "sqlite":
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LocalStorage.ensure_directory_exists(relational_config.db_path)
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async with relational_engine.engine.begin() as connection:
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if len(MetricsBase.metadata.tables.keys()) > 0:
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await connection.run_sync(MetricsBase.metadata.create_all)
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async with relational_engine.get_async_session() as session:
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data_point = Metrics(payload=questions_payload)
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session.add(data_point)
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await session.commit()
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async def run_evaluation(params: dict) -> None:
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if params.get("evaluating_answers"):
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logging.info("Evaluation started...")
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try:
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with open(params["answers_path"], "r", encoding="utf-8") as f:
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answers = json.load(f)
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except FileNotFoundError:
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raise FileNotFoundError(f"Could not find the file: {params['answers_path']}")
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except json.JSONDecodeError as e:
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raise ValueError(f"Error decoding JSON from {params['answers_path']}: {e}")
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logging.info(f"Loaded {len(answers)} answers from {params['answers_path']}")
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evaluator = EvaluationExecutor(evaluator_engine=params["evaluation_engine"])
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metrics = await evaluator.execute(
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answers=answers, evaluator_metrics=params["evaluation_metrics"]
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)
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with open(params["metrics_path"], "w", encoding="utf-8") as f:
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json.dump(metrics, f, ensure_ascii=False, indent=4)
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await create_and_insert_metrics_table(metrics)
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logging.info("Evaluation End...")
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if params.get("dashboard"):
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generate_metrics_dashboard(
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json_data=params["metrics_path"],
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output_file=params["dashboard_path"],
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benchmark=params["benchmark"],
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)
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