docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
Connecting to Qdrant from LangChain#
Local mode#
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-memory#
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 storage#
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 deployment#
No matter if you choose to launch Qdrant locally with a Docker container, or select a Kubernetes deployment with the official Helm chart, the way you’re going to connect to such an instance will be identical. You’ll need to provide a URL pointing to the service.
url = "<---qdrant url here --->"
qdrant = Qdrant.from_documents(
    docs, embeddings, 
    url, prefer_grpc=True, 
    collection_name="my_documents",
)
Qdrant Cloud#
rtdocs_stable/api.python.langchain.com/en/stable/modules/indexes/vectorstores/examples/qdrant.html