From 2fb71a4d2cccfe69c58e49940b75f77b7b84a2c7 Mon Sep 17 00:00:00 2001
From: LuyaoZhuang <zhuangluyao523@gmail.com>
Date: Sun, 26 Oct 2025 05:11:12 -0400
Subject: commit

---
 run.py | 62 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 62 insertions(+)
 create mode 100644 run.py

(limited to 'run.py')

diff --git a/run.py b/run.py
new file mode 100644
index 0000000..bed9f1a
--- /dev/null
+++ b/run.py
@@ -0,0 +1,62 @@
+import argparse
+import json
+from transformers import AutoTokenizer, AutoModel
+from sentence_transformers import SentenceTransformer
+from src.config import LinearRAGConfig
+from src.LinearRAG import LinearRAG
+import os
+import warnings
+from src.evaluate import Evaluator
+from src.utils import LLM_Model
+from src.utils import setup_logging
+os.environ["CUDA_VISIBLE_DEVICES"] = "5"
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+warnings.filterwarnings('ignore')
+
+def parse_arguments():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--spacy_model", type=str, default="en_core_sci_scibert", help="The spacy model to use")
+    parser.add_argument("--embedding_model", type=str, default="model/all-mpnet-base-v2", help="The path of embedding model to use")
+    parser.add_argument("--dataset_name", type=str, default="medical", help="The dataset to use")
+    parser.add_argument("--llm_model", type=str, default="gpt-4o-mini", help="The LLM model to use")
+    parser.add_argument("--max_workers", type=int, default=16, help="The max number of workers to use")
+    return parser.parse_args()
+
+
+def load_dataset(dataset_name,tokenizer): 
+    questions_path = f"dataset/{dataset_name}/questions.json"
+    with open(questions_path, "r", encoding="utf-8") as f:
+        questions = json.load(f)
+    chunks_path = f"dataset/{dataset_name}/chunks.json"
+    with open(chunks_path, "r", encoding="utf-8") as f:
+        chunks = json.load(f)
+    passages = [f'{idx}:{chunk}' for idx, chunk in enumerate(chunks)]
+    return questions, passages
+
+def load_embedding_model(embedding_model):
+    embedding_model = SentenceTransformer(embedding_model,device="cuda")
+    return embedding_model
+
+def main():
+    args = parse_arguments()
+    embedding_model = load_embedding_model(args.embedding_model)
+    questions,passages = load_dataset(args.dataset_name)
+    setup_logging(f"results/{args.dataset_name}/log.txt")
+    llm_model = LLM_Model(args.llm_model)
+    config = LinearRAGConfig(
+        dataset_name=args.dataset_name,
+        embedding_model=embedding_model,
+        spacy_model=args.spacy_model,
+        max_workers=args.max_workers,
+        llm_model=llm_model
+    )
+    rag_model = LinearRAG(global_config=config)
+    rag_model.index(passages)
+    questions = rag_model.qa(questions)
+    os.makedirs(f"results/{args.dataset_name}", exist_ok=True)
+    with open(f"results/{args.dataset_name}/predictions.json", "w", encoding="utf-8") as f:
+        json.dump(questions, f, ensure_ascii=False, indent=4)
+    evaluator = Evaluator(llm_model=llm_model, predictions_path=f"results/{args.dataset_name}/predictions.json")
+    evaluator.evaluate(max_workers=args.max_workers)
+if __name__ == "__main__":
+    main()
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