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)