diff --git a/load_docs.py b/load_docs.py
new file mode 100644
index 0000000..9f98af6
--- /dev/null
+++ b/load_docs.py
@@ -0,0 +1,49 @@
+import os
+import argparse
+from dotenv import load_dotenv
+from langchain_openai import OpenAIEmbeddings
+from langchain_qdrant import QdrantVectorStore
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_core.documents import Document
+from qdrant_client import QdrantClient
+
+# Загрузка переменных окружения
+load_dotenv()
+
+# Инициализация эмбеддингов
+embeddings = OpenAIEmbeddings(
+    model="text-embedding-3-small",
+    base_url="https://openrouter.ai/api/v1",
+    api_key=os.getenv("OPENAI_API_KEY"),
+)
+
+# Инициализация Qdrant
+client = QdrantClient(url="http://localhost:6333")
+collection_name = "knowledge_base"
+vector_store = QdrantVectorStore(
+    client=client,
+    collection_name=collection_name,
+    embeddings=embeddings,
+)
+
+# Чанкинг
+splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+
+def load_directory(dir_path: str):
+    """Загружает все .txt файлы из директории в векторную базу."""
+    for root, dirs, files in os.walk(dir_path):
+        for file in files:
+            if file.lower().endswith(".txt"):
+                path = os.path.join(root, file)
+                with open(path, "r", encoding="utf-8") as f:
+                    text = f.read()
+                chunks = splitter.split_text(text)
+                docs = [Document(page_content=chunk, metadata={"title": file}) for chunk in chunks]
+                vector_store.add_documents(docs)
+                print(f"Added {len(docs)} chunks from {file}")
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Load documents into Qdrant.")
+    parser.add_argument("directory", help="Path to directory with .txt files")
+    args = parser.parse_args()
+    load_directory(args.directory)
\ No newline at end of file