docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
Create a dataset locally at ./deeplake/, then run similiarity search. The Deeplake+LangChain integration uses Deep Lake datasets under the hood, so dataset and vector store are used interchangeably. To create a dataset in your own cloud, or in the Deep Lake storage, adjust the path accordingly.
db = DeepLake(dataset_path="./my_deeplake/", embedding_function=embeddings)
db.add_documents(docs)
# or shorter
# db = DeepLake.from_documents(docs, dataset_path="./my_deeplake/", embedding=embeddings, overwrite=True)
query = "What did the president say about Ketanji Brown Jackson"
docs = db.similarity_search(query)
/home/leo/.local/lib/python3.10/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.3.2) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.
  warnings.warn(
./my_deeplake/ loaded successfully.
Evaluating ingest: 100%|██████████████████████████████████████| 1/1 [00:07<00:00
Dataset(path='./my_deeplake/', tensors=['embedding', 'ids', 'metadata', 'text'])
  tensor     htype     shape      dtype  compression
  -------   -------   -------    -------  ------- 
 embedding  generic  (42, 1536)  float32   None   
    ids      text     (42, 1)      str     None   
 metadata    json     (42, 1)      str     None   
   text      text     (42, 1)      str     None
rtdocs_stable/api.python.langchain.com/en/stable/modules/indexes/vectorstores/examples/deeplake.html