we never agree, that isn‚Äôt true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. \n\nAnd soon, we‚Äôll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \n\nSo tonight I‚Äôm offering a Unity Agenda for the Nation. Four big things we can do together.  \n\nFirst, beat the opioid epidemic.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2012})]Delete dataset‚Äãdb.delete_dataset()    and if delete fails you can also force deleteDeepLake.force_delete_by_path("./my_deeplake")    Deep Lake datasets on cloud (Activeloop, AWS, GCS, etc.) or in memory‚ÄãBy default, Deep Lake datasets are stored locally. To store them in memory, in the Deep Lake Managed DB, or in any object storage, you can provide the corresponding path and credentials when creating the vector store. Some paths require registration with Activeloop and creation of an API token that can be retrieved hereos.environ["ACTIVELOOP_TOKEN"] = activeloop_token# Embed and store the textsusername = "<USERNAME_OR_ORG>"  # your username on app.activeloop.aidataset_path = f"hub://{username}/langchain_testing_python"  # could be also ./local/path (much faster locally), s3://bucket/path/to/dataset, gcs://path/to/dataset, etc.docs = text_splitter.split_documents(documents)embedding = OpenAIEmbeddings()db = DeepLake(dataset_path=dataset_path, embedding=embeddings, overwrite=True)ids = db.add_documents(docs)    Your Deep Lake dataset has been successfully created!        Dataset(path='hub://adilkhan/langchain_testing_python', tensors=['embedding', 'id', 'metadata', 'text'])          tensor      htype      shape      dtype  compression      -------    -------    -------    -------  -------      embedding  embedding  (42, 1536)  float32   None           id
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. ->: we never agree, that isn‚Äôt true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. \n\nAnd soon, we‚Äôll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \n\nSo tonight I‚Äôm offering a Unity Agenda for the Nation. Four big things we can do together.  \n\nFirst, beat the opioid epidemic.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2012})]Delete dataset‚Äãdb.delete_dataset()    and if delete fails you can also force deleteDeepLake.force_delete_by_path("./my_deeplake")    Deep Lake datasets on cloud (Activeloop, AWS, GCS, etc.) or in memory‚ÄãBy default, Deep Lake datasets are stored locally. To store them in memory, in the Deep Lake Managed DB, or in any object storage, you can provide the corresponding path and credentials when creating the vector store. Some paths require registration with Activeloop and creation of an API token that can be retrieved hereos.environ["ACTIVELOOP_TOKEN"] = activeloop_token# Embed and store the textsusername = "<USERNAME_OR_ORG>"  # your username on app.activeloop.aidataset_path = f"hub://{username}/langchain_testing_python"  # could be also ./local/path (much faster locally), s3://bucket/path/to/dataset, gcs://path/to/dataset, etc.docs = text_splitter.split_documents(documents)embedding = OpenAIEmbeddings()db = DeepLake(dataset_path=dataset_path, embedding=embeddings, overwrite=True)ids = db.add_documents(docs)    Your Deep Lake dataset has been successfully created!        Dataset(path='hub://adilkhan/langchain_testing_python', tensors=['embedding', 'id', 'metadata', 'text'])          tensor      htype      shape      dtype  compression      -------    -------    -------    -------  -------      embedding  embedding  (42, 1536)  float32   None           id