Update concat_vector_store_정리된.py

concat_vector_store_정리된.py

@@ -5,11 +5,11 @@ from e5_embeddings import E5Embeddings
 from langchain_community.vectorstores import FAISS
 from document_processor import load_pdf_with_pymupdf, split_documents
 
-# 경로 설정
-FOLDER = "25.05.28 RAG용 2차 업무편람 취합본"
+# Path configuration
+FOLDER = "cleaned_pdfs"  # Folder containing the cleaned PDFs
 VECTOR_STORE_PATH = "vector_db"
 
-# 1. 임베딩 모델 로드
+# 1. Load the embedding model
 def get_embeddings(model_name="intfloat/multilingual-e5-large-instruct", device="cuda"):
     return E5Embeddings(
         model_name=model_name,
@@ -17,39 +17,39 @@ def get_embeddings(model_name="intfloat/multilingual-e5-large-instruct", device=
         encode_kwargs={'normalize_embeddings': True}
     )
 
-# 2. 기존 벡터 스토어 로드
+# 2. Load existing vector store
 def load_vector_store(embeddings, load_path=VECTOR_STORE_PATH):
     if not os.path.exists(load_path):
-        raise FileNotFoundError(f"벡터 스토어를 찾을 수 없습니다: {load_path}")
+        raise FileNotFoundError(f"Cannot find vector store: {load_path}")
     return FAISS.load_local(load_path, embeddings, allow_dangerous_deserialization=True)
 
-# 3. 정리된 PDF만 임베딩
+# 3. Embed only the cleaned PDFs
 def embed_cleaned_pdfs(folder, vectorstore, embeddings):
-    pattern = os.path.join(folder, "정리된*.pdf")
+    pattern = os.path.join(folder, "cleaned*.pdf")
     pdf_files = glob.glob(pattern)
-    print(f"🧾 대상 PDF 수: {len(pdf_files)}")
+    print(f"Number of target PDFs: {len(pdf_files)}")
 
     new_documents = []
     for pdf_path in pdf_files:
-        print(f"📄 처리 중: {pdf_path}")
+        print(f"Processing: {pdf_path}")
         text = load_pdf_with_pymupdf(pdf_path)
         if text.strip():
             new_documents.append(Document(page_content=text, metadata={"source": pdf_path}))
 
-    print(f"📚 문서 수: {len(new_documents)}")
+    print(f"Number of documents: {len(new_documents)}")
 
     chunks = split_documents(new_documents, chunk_size=300, chunk_overlap=50)
-    print(f"�� 청크 수: {len(chunks)}")
+    print(f"Number of chunks: {len(chunks)}")
 
-    print(f"추가 전 벡터 수: {vectorstore.index.ntotal}")
+    print(f"Vector count before addition: {vectorstore.index.ntotal}")
     vectorstore.add_documents(chunks)
-    print(f"추가 후 벡터 수: {vectorstore.index.ntotal}")
+    print(f"Vector count after addition: {vectorstore.index.ntotal}")
 
     vectorstore.save_local(VECTOR_STORE_PATH)
-    print(f"✅ 저장 완료: {VECTOR_STORE_PATH}")
+    print(f"Save completed: {VECTOR_STORE_PATH}")
 
-# 실행
+# Execution
 if __name__ == "__main__":
     embeddings = get_embeddings()
     vectorstore = load_vector_store(embeddings)
-    embed_cleaned_pdfs(FOLDER, vectorstore, embeddings)
+    embed_cleaned_pdfs(FOLDER, vectorstore, embeddings)