Is there a way to save the weaviate vectorstore in disk, and then reuse it for further querying?

We have this functionality in Chroma DB: Chroma | 🦜️🔗 Langchain

But I can’t find this functionality in Weaviate’s documentation despite a lot of searching.

Here’s my full code for a local document querying app I’m building:

	import os, weaviate
	from langchain.text_splitter import CharacterTextSplitter
	from langchain.document_loaders import DirectoryLoader
	from langchain.vectorstores.weaviate import Weaviate
	from langchain.embeddings import OpenAIEmbeddings
	from langchain.chains import RetrievalQA
	from langchain.chat_models import ChatOpenAI

	# get environment variables
	OPENAI_API_KEY = os.environ.get["OPENAI_API_KEY"]

	# load documents
	doc_loader = DirectoryLoader(
	    './Docs', # the relative directory address
	    glob='**/*.pdf',      # load all pdf files in every subdirectory
	docs = doc_loader.load()

	# split documents
	splitter = CharacterTextSplitter(
	splitted_docs_list = splitter.split_documents(docs)

	auth_config = weaviate.auth.AuthApiKey(api_key=os.environ.get('WEAVIATE_API_KEY'))
	client = weaviate.Client(
	        "X-OpenAI-Api-Key": os.environ.get('OPENAI_API_KEY')

	# set index_name and vectorizer for the database
	class_obj = {
	    "class": "LangChain",
	    "vectorizer": "text2vec-openai",

	  # Add the class to the schema
	  print("Class already exists")

	embeddings = OpenAIEmbeddings()
	# I use 'LangChain' for index_name and 'text' for text_key
	vectorstore = Weaviate(client, "LangChain", "text", embedding=embeddings)

	# Add text chunks' embeddings to the Weaviate vector database

	texts = [d.page_content for d in splitted_docs_list]
	metadatas = [d.metadata for d in splitted_docs_list]
	# vectorstore.add_texts(texts, metadatas=metadatas, embedding=embeddings)

	vectorstore = Weaviate.from_texts(

	# Query the vectorstore with the LLM
	llm = ChatOpenAI()
	retrieval_qa = RetrievalQA.from_chain_type(


Everytime I run a query, all the docs are read, split into chunks, and the vectorstore is created, again and again. So, is it possible to save the vector store in disk instead and resuse it for further querying?

Hi @Kristada673 :wave:

I can’t speak to the LangChain specifics, but whenever you run code to ingest objects to WCS, the created objects will persist unless you specifically delete them.

If you just run the query part of that code snippet what happens?

Thanks for your prompt response, @jphwang

Do you mean if I delete everything before the llm = ChatOpenAI() and run the script? I get the following error:

NameError: name 'vectorstore' is not defined

If the vectorstore is persistent within the weaviate cluster, could you please show a snippet of just how to access or use it for querying? When I go to and try to open my cluster, it can’t be opened and I can’t see its contents. So I don’t even know what name are my vectorstores saved by internally.

X-posting here from Slack.

As far as Weaviate goes:

You can get a list of available classes like this: Manage classes (object collections) | Weaviate - vector database

And I think from your code snippet it looks like it’s getting saved under the class name “LangChain”.

You should be able to retrieve objects as shown here. Read all objects | Weaviate - vector database

And you can search the data using any of these methods: Search | Weaviate - vector database

I don’t use LangChain myself so I can’t answer exactly what functions to use to query data that’s already in Weaviate unfortunately.