text: = getpass.getpass("OpenAI API Key:")    OpenAI API Key: Â·Â·Â·Â·Â·Â·Â·Â·from langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import Qdrantfrom langchain.document_loaders import TextLoaderloader = TextLoader("../../../state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)embeddings = OpenAIEmbeddings()Connecting to Qdrant from LangChainâ€‹Local modeâ€‹Python client allows you to run the same code in local mode without running the Qdrant server. That's great for testing things out and debugging or if you plan to store just a small amount of vectors. The embeddings might be fully kepy in memory or persisted on disk.In-memoryâ€‹For some testing scenarios and quick experiments, you may prefer to keep all the data in memory only, so it gets lost when the client is destroyed - usually at the end of your script/notebook.qdrant = Qdrant.from_documents(    docs,    embeddings,    location=":memory:",  # Local mode with in-memory storage only    collection_name="my_documents",)On-disk storageâ€‹Local mode, without using the Qdrant server, may also store your vectors on disk so they're persisted between runs.qdrant = Qdrant.from_documents(    docs,    embeddings,    path="/tmp/local_qdrant",    collection_name="my_documents",)On-premise server deploymentâ€‹No matter if you choose to launch Qdrant locally with a Docker container, or select a Kubernetes deployment with