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6dfa658 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 | from __future__ import annotations
import argparse
import sys
from pathlib import Path
ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT / "src"))
from legal_rag.data import load_corpus, load_gold_benchmark, write_json
from legal_rag.metrics import (
citation_label_accuracy,
exact_match,
lexical_faithfulness_proxy,
retrieved_source_hit,
rouge_l,
token_f1,
)
from legal_rag.rag import GenerationConfig, build_answer_generator, build_context
from legal_rag.retrievers import BM25Retriever, ChromaDBRetriever, HybridRetriever
def build_retriever(args: argparse.Namespace, corpus):
if args.retriever == "bm25":
return BM25Retriever(corpus)
collection_name = "baseline_rag_db"
if args.embedding_model and ("triplet" in args.embedding_model.lower() or "finetuned" in args.embedding_model.lower()):
collection_name = "finetuned_rag_db"
dense = ChromaDBRetriever(
corpus,
model_name=args.embedding_model,
persist_dir=args.index_dir.parent / "chroma_db",
collection_name=collection_name,
batch_size=args.batch_size,
)
if args.retriever == "dense":
return dense
bm25 = BM25Retriever(corpus)
return HybridRetriever(dense=dense, bm25=bm25, dense_weight=args.dense_weight, candidate_k=args.candidate_k)
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--data-dir", type=Path, default=Path("data"))
parser.add_argument("--output", type=Path, default=Path("outputs/qa_eval.json"))
parser.add_argument("--index-dir", type=Path, default=Path("outputs/index"))
parser.add_argument("--retriever", choices=["dense", "bm25", "hybrid"], default="bm25")
parser.add_argument("--embedding-model", default="intfloat/multilingual-e5-base")
parser.add_argument("--top-k", type=int, default=5)
parser.add_argument("--candidate-k", type=int, default=50)
parser.add_argument("--dense-weight", type=float, default=0.5)
parser.add_argument("--batch-size", type=int, default=64)
parser.add_argument("--limit", type=int, default=None)
parser.add_argument("--generation-mode", choices=["extractive", "local_hf", "ollama"], default="extractive")
parser.add_argument("--generation-model", default=None)
parser.add_argument("--max-new-tokens", type=int, default=192)
parser.add_argument("--temperature", type=float, default=0.0)
parser.add_argument("--reranker-model", default=None, help="Path or ID of CrossEncoder reranker model")
parser.add_argument("--rerank-top-n", type=int, default=15, help="Number of candidates to rerank")
args = parser.parse_args()
corpus = load_corpus(args.data_dir)
doc_id_to_citation = {doc.id: doc.citation_label for doc in corpus}
gold = load_gold_benchmark(args.data_dir)
if args.limit:
gold = gold[: args.limit]
retriever = build_retriever(args, corpus)
generator = build_answer_generator(
GenerationConfig(
mode=args.generation_mode,
model_name=args.generation_model,
max_new_tokens=args.max_new_tokens,
temperature=args.temperature,
)
)
reranker = None
if args.reranker_model:
from legal_rag.rerankers import CrossEncoderReranker, RerankerConfig
reranker = CrossEncoderReranker(RerankerConfig(model_name=args.reranker_model))
from tqdm import tqdm
rows = []
totals = {
"exact_match": 0.0,
"token_f1": 0.0,
"rouge_l": 0.0,
"top1_source_hit": 0.0,
"top5_source_hit": 0.0,
"citation_label_accuracy": 0.0,
"faithfulness_proxy": 0.0,
}
for item in tqdm(gold, desc="Evaluating QA"):
retrieve_k = args.rerank_top_n if reranker else args.top_k
results = retriever.search(item["question"], top_k=retrieve_k)
if reranker:
results = reranker.rerank(item["question"], results, top_k=args.top_k)
answer = generator.generate(item["question"], results)
reference = item["verified_answer"]
retrieved_ids = [result.doc.id for result in results]
contexts = [result.doc.text for result in results]
gold_source_ids = {source["corpus_row_id"] for source in item["gold_sources"]}
gold_citation_labels = {source.get("citation_label") or doc_id_to_citation.get(source["corpus_row_id"], source["corpus_row_id"]) for source in item["gold_sources"]}
metrics = {
"exact_match": exact_match(answer, reference),
"token_f1": token_f1(answer, reference),
"rouge_l": rouge_l(answer, reference),
"top1_source_hit": retrieved_source_hit(retrieved_ids, gold_source_ids, 1),
"top5_source_hit": retrieved_source_hit(retrieved_ids, gold_source_ids, min(5, args.top_k)),
"citation_label_accuracy": citation_label_accuracy(answer, gold_citation_labels),
"faithfulness_proxy": lexical_faithfulness_proxy(answer, contexts),
}
for key, value in metrics.items():
totals[key] += value
rows.append(
{
"question_id": item["question_id"],
"question": item["question"],
"answer": answer,
"reference": reference,
"retrieved_ids": retrieved_ids,
"gold_source_ids": sorted(gold_source_ids),
"gold_citation_labels": sorted(gold_citation_labels),
"metrics": metrics,
"context": build_context(results),
}
)
n = len(gold)
output = {
"config": {
"retriever": args.retriever,
"embedding_model": args.embedding_model if args.retriever != "bm25" else None,
"top_k": args.top_k,
"dense_weight": args.dense_weight if args.retriever == "hybrid" else None,
"generation_mode": args.generation_mode,
"generation_model": args.generation_model,
"num_questions": n,
},
"summary": {key: value / n for key, value in totals.items()},
"answers": rows,
}
write_json(args.output, output)
print("QA evaluation complete")
print(output["config"])
print(output["summary"])
print(f"Wrote {args.output}")
if __name__ == "__main__":
main()
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