feat: solution for 'Практическое задание: Агент с RAG-памятью'

src/main.py

@@ -1,42 +1,4 @@
-```python
-"""
-Script to load documents from a directory into the vector store.
-"""
-
-import argparse
-from pathlib import Path
-
-from vector_store import vector_store
-from langchain_text_splitter import RecursiveCharacterTextSplitter
-
-# Chunking configuration
-splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
-
-
-def load_documents_from_dir(directory: str):
-    """
-    Load all supported text files from the given directory into the knowledge base.
-
-    Args:
-        directory: Path to the directory containing documents.
-    """
-    dir_path = Path(directory)
-    for file_path in dir_path.rglob("*"):
-        if file_path.is_file() and file_path.suffix.lower() in {".txt", ".md"}:
-            content = file_path.read_text(encoding="utf-8")
-            title = file_path.stem
-            chunks = splitter.split_text(content)
-            vector_store.add_documents(chunks, [title] * len(chunks))
-            print(f"Loaded {file_path} into knowledge base.")
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Load documents into the knowledge base.")
-    parser.add_argument("directory", help="Path to directory with documents.")
-    args = parser.parse_args()
-    load_documents_from_dir(args.directory)
-
+from .cli import main
 
 if __name__ == "__main__":
-    main()
-```
+    main()