75bc7f67eb
* QA eval dataset as argument, with hotpot and 2wikimultihop as options. Json schema validation for datasets. * Load dataset file by filename, outsource utilities * restructure metric selection * Add comprehensiveness, diversity and empowerment metrics * add promptfoo as an option * refactor RAG solution in eval;2C * LLM as a judge metrics implemented in a uniform way * Use requests.get instead of wget * clean up promptfoo config template * minor fixes * get promptfoo path instead of hardcoding * minor fixes * Add LLM as a judge prompts * Support 4 different rag options in eval * Minor refactor and logger usage * feat: make tasks a configurable argument in the cognify function * Run eval on a set of parameters and save results as json and png * fix: add data points task * script for running all param combinations * enable context provider to get tasks as param * bugfix in simple rag * Incremental eval of cognee pipeline * potential fix: single asyncio run * temp fix: exclude insights * Remove insights, have single asyncio run, refactor * Include incremental eval in accepted paramsets * minor fixes * handle pipeline slices in utils * Handle insights and customize search types * Handle retrieved edges more safely * bugfix * fix simple rag --------- Co-authored-by: lxobr <122801072+lxobr@users.noreply.github.com> Co-authored-by: hajdul88 <52442977+hajdul88@users.noreply.github.com>
64 lines
1.9 KiB
Python
64 lines
1.9 KiB
Python
import asyncio
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from evals.eval_on_hotpot import eval_on_QA_dataset, incremental_eval_on_QA_dataset
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from evals.qa_eval_utils import get_combinations, save_results_as_image
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import argparse
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from pathlib import Path
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import json
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async def run_evals_on_paramset(paramset: dict, out_path: str):
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combinations = get_combinations(paramset)
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json_path = Path(out_path) / Path("results.json")
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results = {}
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for params in combinations:
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dataset = params["dataset"]
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num_samples = params["num_samples"]
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rag_option = params["rag_option"]
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if dataset not in results:
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results[dataset] = {}
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if num_samples not in results[dataset]:
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results[dataset][num_samples] = {}
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if rag_option == "cognee_incremental":
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result = await incremental_eval_on_QA_dataset(
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dataset,
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num_samples,
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paramset["metric_names"],
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)
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results[dataset][num_samples] |= result
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else:
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result = await eval_on_QA_dataset(
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dataset,
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rag_option,
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num_samples,
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paramset["metric_names"],
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)
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results[dataset][num_samples][rag_option] = result
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with open(json_path, "w") as file:
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json.dump(results, file, indent=1)
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save_results_as_image(results, out_path)
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return results
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async def main():
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--params_file", type=str, required=True, help="Which dataset to evaluate on"
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)
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parser.add_argument("--out_dir", type=str, help="Dir to save eval results")
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args = parser.parse_args()
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with open(args.params_file, "r") as file:
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parameters = json.load(file)
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await run_evals_on_paramset(parameters, args.out_dir)
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if __name__ == "__main__":
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asyncio.run(main())
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