129 lines
4.2 KiB
Python
129 lines
4.2 KiB
Python
import asyncio
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from pathlib import Path
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from datasets import Dataset, load_dataset
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from llm_judge import LLMJudge
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from tqdm import tqdm
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from haiku.rag.client import HaikuRAG
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from haiku.rag.qa.ollama import QA
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db_path = Path(__file__).parent / "data" / "benchmark.sqlite"
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async def populate_db():
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if (db_path).exists():
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print("Benchmark database already exists. Skipping creation.")
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return
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ds: Dataset = load_dataset("ServiceNow/repliqa")["repliqa_3"] # type: ignore
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corpus = ds.filter(lambda doc: doc["document_topic"] == "News Stories")
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async with HaikuRAG(db_path) as rag:
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for i, doc in enumerate(tqdm(corpus)):
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await rag.create_document(
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content=doc["document_extracted"], # type: ignore
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uri=doc["document_id"], # type: ignore
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)
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async def run_match_benchmark():
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ds: Dataset = load_dataset("ServiceNow/repliqa")["repliqa_3"] # type: ignore
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corpus = ds.filter(lambda doc: doc["document_topic"] == "News Stories")
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correct_at_1 = 0
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correct_at_2 = 0
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correct_at_3 = 0
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total_queries = 0
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async with HaikuRAG(db_path) as rag:
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for i, doc in enumerate(tqdm(corpus)):
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doc_id = doc["document_id"] # type: ignore
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matches = await rag.search(
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query=doc["question"], # type: ignore
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limit=3,
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)
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total_queries += 1
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# Check position of correct document in results
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for position, (chunk, _) in enumerate(matches):
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retrieved = await rag.get_document_by_id(chunk.document_id)
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if retrieved and retrieved.uri == doc_id:
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if position == 0: # First position
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correct_at_1 += 1
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correct_at_2 += 1
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correct_at_3 += 1
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elif position == 1: # Second position
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correct_at_2 += 1
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correct_at_3 += 1
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elif position == 2: # Third position
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correct_at_3 += 1
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break
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# Calculate recall metrics
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recall_at_1 = correct_at_1 / total_queries
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recall_at_2 = correct_at_2 / total_queries
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recall_at_3 = correct_at_3 / total_queries
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print("\n=== Retrieval Benchmark Results ===")
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print(f"Total queries: {total_queries}")
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print(f"Recall@1: {recall_at_1:.4f}")
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print(f"Recall@2: {recall_at_2:.4f}")
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print(f"Recall@3: {recall_at_3:.4f}")
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return {"recall@1": recall_at_1, "recall@2": recall_at_2, "recall@3": recall_at_3}
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async def run_qa_benchmark(k: int | None = None):
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"""Run QA benchmarking on the corpus."""
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ds: Dataset = load_dataset("ServiceNow/repliqa")["repliqa_3"] # type: ignore
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corpus = ds.filter(lambda doc: doc["document_topic"] == "News Stories")
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if k is not None:
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corpus = corpus.select(range(min(k, len(corpus))))
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judge = LLMJudge()
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correct_answers = 0
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total_questions = 0
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async with HaikuRAG(db_path) as rag:
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qa = QA(rag)
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for i, doc in enumerate(tqdm(corpus, desc="QA Benchmarking")):
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question = doc["question"] # type: ignore
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expected_answer = doc["answer"] # type: ignore
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generated_answer = await qa.answer(question)
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is_equivalent = await judge.judge_answers(
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question, generated_answer, expected_answer
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)
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print(f"Question: {question}")
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print(f"Expected: {expected_answer}")
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print(f"Generated: {generated_answer}")
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print(f"Equivalent: {is_equivalent}\n")
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if is_equivalent:
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correct_answers += 1
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total_questions += 1
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accuracy = correct_answers / total_questions if total_questions > 0 else 0
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print("\n=== QA Benchmark Results ===")
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print(f"Total questions: {total_questions}")
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print(f"Correct answers: {correct_answers}")
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print(f"QA Accuracy: {accuracy:.4f} ({accuracy * 100:.2f}%)")
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async def main():
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await populate_db()
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print("Running retrieval benchmarks...")
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await run_match_benchmark()
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print("\nRunning QA benchmarks...")
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await run_qa_benchmark()
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if __name__ == "__main__":
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asyncio.run(main())
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