in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/davitbun/langchain_test_copy    Your Deep Lake dataset has been successfully created!    The dataset is private so make sure you are logged in!    Dataset(path='hub://davitbun/langchain_test_copy', tensors=['embedding', 'ids', 'metadata', 'text'])db = DeepLake(dataset_path=destination, embedding=embeddings)db.add_documents(docs)         This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/davitbun/langchain_test_copy        /    hub://davitbun/langchain_test_copy loaded successfully.        Deep Lake Dataset in hub://davitbun/langchain_test_copy already exists, loading from the storage    Dataset(path='hub://davitbun/langchain_test_copy', 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       Evaluating ingest: 100%|‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà| 1/1 [00:31<00:00    -    Dataset(path='hub://davitbun/langchain_test_copy', tensors=['embedding', 'ids', 'metadata', 'text'])          tensor     htype     shape     dtype  compression      -------   -------   -------   -------  -------      embedding  generic  (8, 1536)  float32   None           ids      text     (8, 1)      str     None        metadata    json     (8, 1)      str     None          text      text     (8, 1)      str     None            ['ad42f3fe-e188-11ed-b66d-41c5f7b85421',     'ad42f3ff-e188-11ed-b66d-41c5f7b85421',     'ad42f400-e188-11ed-b66d-41c5f7b85421',     'ad42f401-e188-11ed-b66d-41c5f7b85421']PreviousVector storesNextAlibaba Cloud OpenSearchSetting upExample provided by ActiveloopDeep Lake locallyCreate a local datasetQuery datasetRetrieval
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. ->: in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/davitbun/langchain_test_copy    Your Deep Lake dataset has been successfully created!    The dataset is private so make sure you are logged in!    Dataset(path='hub://davitbun/langchain_test_copy', tensors=['embedding', 'ids', 'metadata', 'text'])db = DeepLake(dataset_path=destination, embedding=embeddings)db.add_documents(docs)         This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/davitbun/langchain_test_copy        /    hub://davitbun/langchain_test_copy loaded successfully.        Deep Lake Dataset in hub://davitbun/langchain_test_copy already exists, loading from the storage    Dataset(path='hub://davitbun/langchain_test_copy', 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       Evaluating ingest: 100%|‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà| 1/1 [00:31<00:00    -    Dataset(path='hub://davitbun/langchain_test_copy', tensors=['embedding', 'ids', 'metadata', 'text'])          tensor     htype     shape     dtype  compression      -------   -------   -------   -------  -------      embedding  generic  (8, 1536)  float32   None           ids      text     (8, 1)      str     None        metadata    json     (8, 1)      str     None          text      text     (8, 1)      str     None            ['ad42f3fe-e188-11ed-b66d-41c5f7b85421',     'ad42f3ff-e188-11ed-b66d-41c5f7b85421',     'ad42f400-e188-11ed-b66d-41c5f7b85421',     'ad42f401-e188-11ed-b66d-41c5f7b85421']PreviousVector storesNextAlibaba Cloud OpenSearchSetting upExample provided by ActiveloopDeep Lake locallyCreate a local datasetQuery datasetRetrieval