metadata    json     (4, 1)      str     None   
   text      text     (4, 1)      str     None   
Evaluating ingest: 100%|██████████| 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']
previous
ClickHouse Vector Search
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DocArrayHnswSearch
 Contents
  
Retrieval Question/Answering
Attribute based filtering in metadata
Choosing distance function
Maximal Marginal relevance
Delete dataset
Deep Lake datasets on cloud (Activeloop, AWS, GCS, etc.) or in memory
Creating dataset on AWS S3
Deep Lake API
Transfer local dataset to cloud
By Harrison Chase
    
      © Copyright 2023, Harrison Chase.
      
  Last updated on Jun 16, 2023.
rtdocs_stable/api.python.langchain.com/en/stable/modules/indexes/vectorstores/examples/deeplake.html