Removed pip install deeplake[enterprise] references in docs

deeplake/core/dataset/dataset.py

@@ -2137,7 +2137,7 @@ def pytorch(
         return dataloader
 
     def dataloader(self, ignore_errors: bool = False, verbose: bool = False):
-        """Returns a :class:`~deeplake.enterprise.DeepLakeDataLoader` object. To use this, install deeplake with ``pip install deeplake[enterprise]``.
+        """Returns a :class:`~deeplake.enterprise.DeepLakeDataLoader` object.
 
         Args:
             ignore_errors (bool): If ``True``, the data loader will ignore errors appeared during data iteration otherwise it will collect the statistics and report appeared errors. Default value is ``False``
@@ -2271,7 +2271,7 @@ def query(
         runtime: Optional[Dict] = None,
         return_data: bool = False,
     ):
-        """Returns a sliced :class:`~deeplake.core.dataset.Dataset` with given query results. To use this, install deeplake with ``pip install deeplake[enterprise]``.
+        """Returns a sliced :class:`~deeplake.core.dataset.Dataset` with given query results.
 
         It allows to run SQL like queries on dataset and extract results. See supported keywords and the Tensor Query Language documentation
         :ref:`here <tql>`.
@@ -2349,7 +2349,6 @@ def sample_by(
         size: Optional[int] = None,
     ):
         """Returns a sliced :class:`~deeplake.core.dataset.Dataset` with given weighted sampler applied.
-        To use this, install deeplake with ``pip install deeplake[enterprise]``.
 
         Args:
             weights: (Union[str, list, tuple]): If it's string then tql will be run to calculate the weights based on the expression. list and tuple will be treated as the list of the weights per sample.
@@ -4764,7 +4763,6 @@ def max_view(self):
     def random_split(self, lengths: Sequence[Union[int, float]]):
         """Splits the dataset into non-overlapping :class:`~deeplake.core.dataset.Dataset` objects of given lengths.
         If a list of fractions that sum up to 1 is given, the lengths will be computed automatically as floor(frac * len(dataset)) for each fraction provided.
-        The split generated is only performant with enterprise dataloader which can be installed with ``pip install deeplake[enterprise]``.
 
         After computing the lengths, if there are any remainders, 1 count will be distributed in round-robin fashion to the lengths until there are no remainders left.
 

deeplake/core/dataset/deeplake_query_dataset.py

@@ -199,7 +199,7 @@ def __len__(self):
 
     @deeplake_reporter.record_call
     def dataloader(self, ignore_errors: bool = False, verbose: bool = False):
-        """Returns a :class:`~deeplake.enterprise.DeepLakeDataLoader` object. To use this, install deeplake with ``pip install deeplake[enterprise]``.
+        """Returns a :class:`~deeplake.enterprise.DeepLakeDataLoader` object.
 
         Args:
             ignore_errors (bool): If ``True``, the data loader will ignore errors appeared during data iteration otherwise it will collect the statistics and report appeared errors. Default value is ``False``

deeplake/core/vectorstore/deep_memory.py

@@ -398,7 +398,7 @@ def evaluate(
 
         if not INDRA_INSTALLED:
             raise ImportError(
-                "indra is not installed. Please install indra to use this functionality with: pip install `deeplake[enterprise]`"
+                "The C++ library is not installed. The library should be installed using `pip install deeplake`, but if you want to install it separately, you may run `pip install libdeeplake`"
             )
 
         from indra import api  # type: ignore
@@ -421,7 +421,7 @@ def evaluate(
 
             if embedding_function is None:
                 raise ValueError(
-                    "Embedding function should be specifed either during initialization or during evaluation."
+                    "Embedding function should be specified either during initialization or during evaluation."
                 )
             query_embs = embedding_function(queries)
 

deeplake/integrations/pytorch/pytorch.py

@@ -99,7 +99,7 @@ def dataset_to_pytorch(
     for t in tensors:
         if dataset[t].is_sequence:
             raise NotImplementedError(
-                f"Deep Lake’s OSS pure-python dataloader is not compatible with tensor `{t}` with htype = sequence[…]. Please use the C++ dataloader via ds.dataloader(…), which can be installed using ‘pip install deeplake[enterprise]’."
+                f"Deep Lake’s OSS pure-python dataloader is not compatible with tensor `{t}` with htype = sequence[…]. Please use the high-performance dataloader via ds.dataloader(…)"
             )
 
     tensors = map_tensor_keys(dataset, tensors)

docs/source/Tensor-Query-Language.rst

@@ -7,7 +7,7 @@ Tensor Query Language
     :language: sql
 
 This page describes the Tensor Query Language (TQL), an SQL-like language used for `Querying in Activeloop Platform <https://docs.activeloop.ai/enterprise-features/querying-datasets>`_
-as well as in :meth:`ds.query <deeplake.core.dataset.Dataset.query>` in our Python API. To use queries, install deeplake with ``pip install deeplake[enterprise]``.
+as well as in :meth:`ds.query <deeplake.core.dataset.Dataset.query>` in our Python API.
 
 Syntax
 ~~~~~~~~