diff --git a/knowledge_base.py b/knowledge_base.py
deleted file mode 100644
index 7890407..0000000
--- a/knowledge_base.py
+++ /dev/null
@@ -1,90 +0,0 @@
-"""Векторное хранилище знаний: Qdrant + Ollama embeddings + чанкинг."""
-from __future__ import annotations
-
-import os
-from pathlib import Path
-from typing import Any
-
-from langchain_core.documents import Document
-from langchain_ollama import OllamaEmbeddings
-from langchain_qdrant import QdrantVectorStore
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-from qdrant_client import QdrantClient
-from qdrant_client.http.models import Distance, VectorParams
-
-COLLECTION_NAME = "knowledge_base"
-DEFAULT_QDRANT_PATH = os.getenv("QDRANT_PATH", "./qdrant_storage")
-OLLAMA_BASE_URL = os.getenv("OLLAMA_BASE_URL", "http://localhost:11434")
-EMBED_MODEL = os.getenv("OLLAMA_EMBED_MODEL", "nomic-embed-text")
-
-
-class KnowledgeBase:
-    """Локальная RAG-база на Qdrant с эмбеддингами Ollama."""
-
-    def __init__(
-        self,
-        qdrant_path: str | Path = DEFAULT_QDRANT_PATH,
-        collection_name: str = COLLECTION_NAME,
-    ) -> None:
-        self.collection_name = collection_name
-        self.qdrant_path = Path(qdrant_path)
-        self.qdrant_path.mkdir(parents=True, exist_ok=True)
-        self.embeddings = OllamaEmbeddings(
-            model=EMBED_MODEL,
-            base_url=OLLAMA_BASE_URL,
-        )
-        self.client = QdrantClient(path=str(self.qdrant_path))
-        self.splitter = RecursiveCharacterTextSplitter(
-            chunk_size=500,
-            chunk_overlap=50,
-        )
-        self._ensure_collection()
-        self.vector_store = QdrantVectorStore(
-            client=self.client,
-            collection_name=self.collection_name,
-            embedding=self.embeddings,
-        )
-
-    def _ensure_collection(self) -> None:
-        if self.client.collection_exists(self.collection_name):
-            return
-        sample = self.embeddings.embed_query("init")
-        self.client.create_collection(
-            collection_name=self.collection_name,
-            vectors_config=VectorParams(size=len(sample), distance=Distance.COSINE),
-        )
-
-    def add_document(self, content: str, title: str) -> int:
-        """Добавляет документ (с чанкингом) в базу. Возвращает число чанков."""
-        chunks = self.splitter.create_documents(
-            texts=[content],
-            metadatas=[{"title": title}],
-        )
-        ids = self.vector_store.add_documents(chunks)
-        return len(ids)
-
-    def search(self, query: str, max_results: int = 5) -> list[dict[str, Any]]:
-        """Семантический поиск с оценкой релевантности (score)."""
-        hits = self.vector_store.similarity_search_with_score(query, k=max_results)
-        results: list[dict[str, Any]] = []
-        for doc, score in hits:
-            results.append(
-                {
-                    "title": doc.metadata.get("title", "без названия"),
-                    "content": doc.page_content,
-                    "score": round(float(score), 4),
-                }
-            )
-        return results
-
-    def add_file(self, file_path: Path) -> int:
-        text = file_path.read_text(encoding="utf-8")
-        title = file_path.stem
-        return self.add_document(text, title)
-
-
-def build_knowledge_base(
-    qdrant_path: str | Path = DEFAULT_QDRANT_PATH,
-) -> KnowledgeBase:
-    """Сборка RAG-хранилища — точка входа для автопроверки."""
-    return KnowledgeBase(qdrant_path=qdrant_path)