How can I define an S3 location in the case of langchain retrieval?

[mamonu]

Hi. You have this example for langchain

from langchain.document_loaders import TextLoader
from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from vector_lake.langchain import VectorLakeStore

loader = TextLoader("Readme.md")
documents = loader.load()

# split it into chunks
text_splitter = CharacterTextSplitter(chunk_size=100, chunk_overlap=0)
docs = text_splitter.split_documents(documents)

# create the open-source embedding function
embedding = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
db = VectorLakeStore.from_documents(documents=docs, embedding=embedding)

query = "What is Vector Lake?"
docs = db.similarity_search(query)

# print results
print(docs[0].page_content)

I would like to define the vector store gving its location and dimensions as another example

db = VectorLake(location="s3://vector-lake", dimension=5, approx_shards=243)

but also use the following in order to add documents to the vector store at the same time as you are doing above with
VectorLakeStore.from_documents

is this possible

thanks