fix: utils.py — Агент с RAG-памятью

utils.py

@@ -0,0 +1,13 @@
+from langchain_ollama import OllamaEmbeddings, Ollama
+from langchain_qdrant import QdrantVectorStore
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_core.documents import Document
+
+embeddings = OllamaEmbeddings(model="nomic-embed-text")
+llm = Ollama(model="llama3", temperature=0.0)
+vector_store = QdrantVectorStore(
+    embedding_function=embeddings,
+    url="http://localhost:6333",
+    collection_name="knowledge",
+)
+splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)