= OpenAIEmbeddings()db = DeepLake(    dataset_path=dataset_path,    embedding=embeddings,    overwrite=True,    runtime={"tensor_db": True},)ids = db.add_documents(docs)    Your Deep Lake dataset has been successfully created!    |    Dataset(path='hub://adilkhan/langchain_testing', tensors=['embedding', 'id', 'metadata', 'text'])          tensor      htype      shape      dtype  compression      -------    -------    -------    -------  -------      embedding  embedding  (42, 1536)  float32   None           id        text      (42, 1)      str     None        metadata     json      (42, 1)      str     None          text       text      (42, 1)      str     None        TQL Search‚ÄãFurthermore, the execution of queries is also supported within the similarity_search method, whereby the query can be specified utilizing Deep Lake's Tensor Query Language (TQL).search_id = db.vectorstore.dataset.id[0].numpy()search_id[0]    '8a6ff326-3a85-11ee-b840-13905694aaaf'docs = db.similarity_search(    query=None,    tql=f"SELECT * WHERE id == '{search_id[0]}'",)db.vectorstore.summary()    Dataset(path='hub://adilkhan/langchain_testing', tensors=['embedding', 'id', 'metadata', 'text'])          tensor      htype      shape      dtype  compression      -------    -------    -------    -------  -------      embedding  embedding  (42, 1536)  float32   None           id        text      (42, 1)      str     None        metadata     json      (42, 1)      str     None          text       text      (42, 1)      str     None   Creating vector stores on AWS S3‚Äãdataset_path = f"s3://BUCKET/langchain_test"  # could be also ./local/path (much faster locally), hub://bucket/path/to/dataset, gcs://path/to/dataset, etc.embedding = OpenAIEmbeddings()db = DeepLake.from_documents(    docs,    dataset_path=dataset_path,    embedding=embeddings,    overwrite=True,    creds={        "aws_access_key_id": os.environ["AWS_ACCESS_KEY_ID"],        "aws_secret_access_key":
Activeloop Deep Lake as a Multi-Modal Vector Store that stores embeddings and their metadata including text, Jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes.
Activeloop Deep Lake as a Multi-Modal Vector Store that stores embeddings and their metadata including text, Jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes. ->: = OpenAIEmbeddings()db = DeepLake(    dataset_path=dataset_path,    embedding=embeddings,    overwrite=True,    runtime={"tensor_db": True},)ids = db.add_documents(docs)    Your Deep Lake dataset has been successfully created!    |    Dataset(path='hub://adilkhan/langchain_testing', tensors=['embedding', 'id', 'metadata', 'text'])          tensor      htype      shape      dtype  compression      -------    -------    -------    -------  -------      embedding  embedding  (42, 1536)  float32   None           id        text      (42, 1)      str     None        metadata     json      (42, 1)      str     None          text       text      (42, 1)      str     None        TQL Search‚ÄãFurthermore, the execution of queries is also supported within the similarity_search method, whereby the query can be specified utilizing Deep Lake's Tensor Query Language (TQL).search_id = db.vectorstore.dataset.id[0].numpy()search_id[0]    '8a6ff326-3a85-11ee-b840-13905694aaaf'docs = db.similarity_search(    query=None,    tql=f"SELECT * WHERE id == '{search_id[0]}'",)db.vectorstore.summary()    Dataset(path='hub://adilkhan/langchain_testing', tensors=['embedding', 'id', 'metadata', 'text'])          tensor      htype      shape      dtype  compression      -------    -------    -------    -------  -------      embedding  embedding  (42, 1536)  float32   None           id        text      (42, 1)      str     None        metadata     json      (42, 1)      str     None          text       text      (42, 1)      str     None   Creating vector stores on AWS S3‚Äãdataset_path = f"s3://BUCKET/langchain_test"  # could be also ./local/path (much faster locally), hub://bucket/path/to/dataset, gcs://path/to/dataset, etc.embedding = OpenAIEmbeddings()db = DeepLake.from_documents(    docs,    dataset_path=dataset_path,    embedding=embeddings,    overwrite=True,    creds={        "aws_access_key_id": os.environ["AWS_ACCESS_KEY_ID"],        "aws_secret_access_key":