option with big datasets is discouraged due to potential
memory issues.
”compute_engine” - Performant C++ implementation of the DeepLake Compute Engine. Runs on the client and can be used for
any data stored in or connected to Deep Lake. It cannot be used
with in-memory or local datasets.
”tensor_db” - Performant, fully-hosted Managed Tensor Database.Responsible for storage and query execution. Only available for
data stored in the Deep Lake Managed Database. To store datasets
in this database, specify runtime = {“db_engine”: True}
during dataset creation.
deep_memory (bool): Whether to use the Deep Memory model for improvingsearch results. Defaults to False if deep_memory is not specified
in the Vector Store initialization. If True, the distance metric
is set to “deepmemory_distance”, which represents the metric with
which the model was trained. The search is performed using the Deep
Memory model. If False, the distance metric is set to “COS” or
whatever distance metric user specifies.
Returns
List of documents most similar to the querytext with distance in float.
Return type
List[Tuple[Document, float]]
Examples using DeepLake¶
Activeloop Deep Lake
Analysis of Twitter the-algorithm source code with LangChain, GPT4 and Activeloop’s Deep Lake
Use LangChain, GPT and Activeloop’s Deep Lake to work with code base
QA using Activeloop’s DeepLake
Deep Lake