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TQL Syntax

How to properly format TQL queries

Query syntax for the Tensor Query Language (TQL)

CONTAINS and ==

# Exact match, which generally requires that the sample
# has 1 value, i.e. no lists or multi-dimensional arrays
select * where tensor_name == 'text_value' # If value is numeric
select * where tensor_name == numeric_value # If values is text
​
select * where contains(tensor_name, 'text_value')
Any special characters in tensor or group names should be wrapped with double-quotes:
select * where contains("tensor-name", 'text_value')
​
select * where "tensor_name/group_name" == numeric_value

SHAPE

select * where shape(tensor_name)[dimension_index] > numeric_value
select * where shape(tensor_name)[1] > numeric_value # Second array dimension > value

LIMIT

select * where contains(tensor_name, 'text_value') limit num_samples

AND, OR, NOT

select * where contains(tensor_name, 'text_value') and NOT contains(tensor_name_2, numeric_value)
select * where contains(tensor_name, 'text_value') or tensor_name_2 == numeric_value
​
select * where (contains(tensor_name, 'text_value') and shape(tensor_name_2)[dimension_index]>numeric_value) or contains(tensor_name, 'text_value_2')

UNION and INTERSECT

(select * where contains(tensor_name, 'value')) intersect (select * where contains(tensor_name, 'value_2'))
​
(select * where contains(tensor_name, 'value') limit 100) union (select * where shape(tensor_name)[0] > numeric_value limit 100)

ORDER BY

# Order by requires that sample is numeric and has 1 value,
# i.e. no lists or multi-dimensional arrays
​
# The default order is ASCENDING (asc)
​
select * where contains(tensor_name, 'text_value') order by tensor_name asc

ANY, ALL, and ALL_STRICT

all adheres to NumPy and list logic where all(empty_sample) returns True
all_strict is more intuitive for queries so all_strict(empty_sample) returns False
select * where all(tensor_name==0) # Returns True for empty samples
​
select * where all_strict(tensor_name[:,2]>numeric_value) # Returns False for empty samples
​
select * where any(tensor_name[0:6]>numeric_value)

IN and BETWEEN

Only works for scalar numeric values and text references to class_names
select * where tensor_name in (1, 2, 6, 10)
​
select * where class_label_tensor_name in ('car', 'truck')
​
select * where tensor_name between 5 and 20
LOGICAL_AND and LOGICAL_OR
select * where any(logical_and(tensor_name_1[:,3]>numeric_value, tensor_name_2 == 'text_value'))

REFERENCING SAMPLES IN EXISTING TENORS

# Select based on index (row_number)
select * where row_number() == 10
​
# Referencing values of of a tensor at index (row_number)
select * order by l2_norm(<tensor_name> - data(<tensor_name>, index))
# Finds rows of data with embeddings most similar to index 10
select * order by l2_norm(embedding - data(embedding, 10))

SAMPLE BY

select * sample by weight_choice(expression_1: weight_1, expression_2: weight_2, ...)
replace True limit N
  • weight_choice resolves the weight that is used when multiple expressions evaluate to True for a given sample. Options are max_weight, sum_weight. For example, if weight_choice is max_weight, then the maximum weight will be chosen for that sample.
  • replace determines whether samples should be drawn with replacement. It defaults to True.
  • limit specifies the number of samples that should be returned. If unspecified, the sampler will return the number of samples corresponding to the length of the dataset
Deep Lake supports several vector operations for embedding search. Typically, vector operations are called by returning data ordered by the score based on the vector search method.
select * from (select tensor_1, tensor_2, <VECTOR_OPERATION> as score) order by score desc limit 10
​
# THE SUPPORTED VECTOR_OPERATIONS ARE:
​
l1_norm(<embedding_tensor> - ARRAY[<search_embedding>]) # Order should be asc
​
l2_norm(<embedding_tensor> - ARRAY[<search_embedding>]) # Order should be asc
​
linf_norm(<embedding_tensor> - ARRAY[<search_embedding>]) # Order should be asc
​
cosine_similarity(<embedding_tensor>, ARRAY[<search_embedding>]) # Order should be desc
​

VIRTUAL TENSORS

Virtual tensors are the result of a computation and are not tensors in the Deep Lake dataset. However, they can be treated as tensors in the API.
# "score" is a virtual tensor
select * from (select tensor_1, tensor_2, <VECTOR_OPERATION> as score) order by score desc limit 10
​
# "box_beyond_image" is a virtual tensor
select *, any(boxes[:,0])<0 as box_beyond_image where ....
​
# "tensor_sum" is a virtual tensor
select *, tensor_1 + tensor_3 as tensor_sum where ......
When combining embedding search with filtering (where conditions), the filter condition is evaluated prior to the embedding search.

GROUP BY AND UNGROUP BY

Group by creates a sequence of data based on the common properties that are being grouped (i.e. frames into videos). Ungroup by splits sequences into their individual elements (i.e. videos into images).
select * group by label, video_id # Groups all data with the same label and video_id in to the same sequence
​
select * ungroup by split # Splits sequences into their original pieces

EXPAND BY

Expand by includes samples before and after a query condition is satisfied.
select * where <condition> expand by rows_before, rows_after