wehub-resource-sync
94 lines
2.8 KiB
Python
94 lines
2.8 KiB
Python
import os
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import sys
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from pathlib import Path
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from openai import OpenAI
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from ragas import Dataset, experiment
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from ragas.llms import llm_factory
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from ragas.metrics import DiscreteMetric
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# Add the current directory to the path so we can import rag module when run as a script
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sys.path.insert(0, str(Path(__file__).parent))
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from rag import default_rag_client
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openai_client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
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rag_client = default_rag_client(llm_client=openai_client, logdir="evals/logs")
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llm = llm_factory("gpt-4o", client=openai_client)
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def load_dataset():
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dataset = Dataset(
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name="test_dataset",
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backend="local/csv",
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root_dir="evals",
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)
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data_samples = [
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{
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"question": "What is ragas 0.3",
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"grading_notes": "- experimentation as the central pillar - provides abstraction for datasets, experiments and metrics - supports evals for RAG, LLM workflows and Agents",
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},
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{
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"question": "how are experiment results stored in ragas 0.3?",
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"grading_notes": "- configured using different backends like local, gdrive, etc - stored under experiments/ folder in the backend storage",
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},
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{
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"question": "What metrics are supported in ragas 0.3?",
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"grading_notes": "- provides abstraction for discrete, numerical and ranking metrics",
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},
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]
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for sample in data_samples:
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row = {"question": sample["question"], "grading_notes": sample["grading_notes"]}
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dataset.append(row)
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# make sure to save it
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dataset.save()
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return dataset
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my_metric = DiscreteMetric(
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name="correctness",
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prompt="Check if the response contains points mentioned from the grading notes and return 'pass' or 'fail'.\nResponse: {response} Grading Notes: {grading_notes}",
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allowed_values=["pass", "fail"],
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)
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@experiment()
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async def run_experiment(row):
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response = rag_client.query(row["question"])
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score = my_metric.score(
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llm=llm,
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response=response.get("answer", " "),
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grading_notes=row["grading_notes"],
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)
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experiment_view = {
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**row,
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"response": response.get("answer", ""),
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"score": score.value,
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"log_file": response.get("logs", " "),
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}
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return experiment_view
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async def main():
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dataset = load_dataset()
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print("dataset loaded successfully", dataset)
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experiment_results = await run_experiment.arun(dataset)
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print("Experiment completed successfully!")
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print("Experiment results:", experiment_results)
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# Save experiment results to CSV
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experiment_results.save()
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csv_path = Path(".") / "experiments" / f"{experiment_results.name}.csv"
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print(f"\nExperiment results saved to: {csv_path.resolve()}")
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if __name__ == "__main__":
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import asyncio
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asyncio.run(main())
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