"aws_secret_access_key": os.environ["AWS_SECRET_ACCESS_KEY"],        "aws_session_token": os.environ["AWS_SESSION_TOKEN"],  # Optional    },)    s3://hub-2.0-datasets-n/langchain_test loaded successfully.    Evaluating ingest: 100%|‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà| 1/1 [00:10<00:00    \    Dataset(path='s3://hub-2.0-datasets-n/langchain_test', tensors=['embedding', 'ids', 'metadata', 'text'])          tensor     htype     shape     dtype  compression      -------   -------   -------   -------  -------      embedding  generic  (4, 1536)  float32   None           ids      text     (4, 1)      str     None        metadata    json     (4, 1)      str     None          text      text     (4, 1)      str     None        Deep Lake API‚Äãyou can access the Deep Lake  dataset at db.vectorstore# get structure of the datasetdb.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   # get embeddings numpy arrayembeds = db.vectorstore.dataset.embedding.numpy()Transfer local dataset to cloud‚ÄãCopy already created dataset to the cloud. You can also transfer from cloud to local.import deeplakeusername = "davitbun"  # your username on app.activeloop.aisource = f"hub://{username}/langchain_testing"  # could be local, s3, gcs, etc.destination = f"hub://{username}/langchain_test_copy"  # could be local, s3, gcs, etc.deeplake.deepcopy(src=source, dest=destination, overwrite=True)    Copying dataset: 100%|‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà| 56/56 [00:38<00:00    This dataset can be visualized in Jupyter Notebook by ds.visualize() or at
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. ->: "aws_secret_access_key": os.environ["AWS_SECRET_ACCESS_KEY"],        "aws_session_token": os.environ["AWS_SESSION_TOKEN"],  # Optional    },)    s3://hub-2.0-datasets-n/langchain_test loaded successfully.    Evaluating ingest: 100%|‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà| 1/1 [00:10<00:00    \    Dataset(path='s3://hub-2.0-datasets-n/langchain_test', tensors=['embedding', 'ids', 'metadata', 'text'])          tensor     htype     shape     dtype  compression      -------   -------   -------   -------  -------      embedding  generic  (4, 1536)  float32   None           ids      text     (4, 1)      str     None        metadata    json     (4, 1)      str     None          text      text     (4, 1)      str     None        Deep Lake API‚Äãyou can access the Deep Lake  dataset at db.vectorstore# get structure of the datasetdb.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   # get embeddings numpy arrayembeds = db.vectorstore.dataset.embedding.numpy()Transfer local dataset to cloud‚ÄãCopy already created dataset to the cloud. You can also transfer from cloud to local.import deeplakeusername = "davitbun"  # your username on app.activeloop.aisource = f"hub://{username}/langchain_testing"  # could be local, s3, gcs, etc.destination = f"hub://{username}/langchain_test_copy"  # could be local, s3, gcs, etc.deeplake.deepcopy(src=source, dest=destination, overwrite=True)    Copying dataset: 100%|‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà| 56/56 [00:38<00:00    This dataset can be visualized in Jupyter Notebook by ds.visualize() or at