SYSTEM AND METHOD FOR RESPONDING TO QUERIES
One or more computing devices and/or methods are provided. In an example, a feature-sensitive query may be received. A first language model may be used to generate an executable feature constraint determination command based upon a set of information including the feature-sensitive query. The executable feature constraint determination command may be executed to determine a feature constraint associated with the feature-sensitive query. The data structure may be analyzed based upon the feature constraint to identify a subset of data, of the data structure, relevant to the feature constraint. A response to the feature-sensitive query may be generated based upon the subset of data.
1 . A method, comprising:
receiving a time-sensitive query;
using a first language model to generate an executable time constraint determination command, comprising a first function corresponding to a data management system language, based upon a set of information comprising the time-sensitive query;
executing the executable time constraint determination command to determine a time constraint associated with the time-sensitive query;
analyzing a data structure based upon the time constraint to identify a subset of data, of the data structure, relevant to the time constraint; and
generating a response to the time-sensitive query based upon the subset of data.
2 . The method of claim 1 , comprising:
generating, based upon one or more characteristics of the data structure, a data structure template, wherein at least one of:
the set of information comprises the data structure template; or
the method comprises training a language model using the data structure template to generate the first language model.
3 . The method of claim 1 , wherein generating the response comprises:
using a second language model to generate the response based upon:
the subset of data; and
the time-sensitive query.
4 . The method of claim 3 , wherein:
the second language model is the same as the first language model.
5 . The method of claim 3 , wherein:
the second language model is different than the first language model.
6 . The method of claim 1 , comprising:
displaying the response via a client device.
7 . The method of claim 1 , wherein:
the set of information comprises a current date.
8 . The method of claim 1 , wherein:
the data structure comprises a relational database.
9 . A non-transitory machine-readable medium having stored thereon processor-executable instructions that when executed cause performance of operations, the operations comprising:
receiving a feature-sensitive query;
using a first language model to generate an executable feature constraint determination command, comprising a first function corresponding to a data management system language, based upon a set of information comprising the feature-sensitive query;
executing the executable feature constraint determination command to determine a feature constraint associated with the feature-sensitive query;
analyzing a data structure based upon the feature constraint to identify a subset of data, of the data structure, relevant to the feature constraint; and
generating a response to the feature-sensitive query based upon the subset of data.
10 . The non-transitory machine-readable medium of claim 9 , the operations comprising:
generating, based upon one or more characteristics of the data structure, a data structure template, wherein at least one of:
the set of information comprises the data structure template; or
the operations comprise training a language model using the data structure template to generate the first language model.
11 . The non-transitory machine-readable medium of claim 9 , wherein generating the response comprises:
using a second language model to generate the response based upon:
the subset of data; and
the feature-sensitive query.
12 . The non-transitory machine-readable medium of claim 11 , wherein:
the second language model is the same as the first language model.
13 . The non-transitory machine-readable medium of claim 11 , wherein:
the second language model is different than the first language model.
14 . The non-transitory machine-readable medium of claim 9 , the operations comprising:
displaying the response via a client device.
15 . The non-transitory machine-readable medium of claim 9 , wherein:
the set of information comprises a current date.
16 . The non-transitory machine-readable medium of claim 9 , wherein:
the data structure comprises a relational database.
17 . A computing device comprising:
a processor; and
memory comprising processor-executable instructions that when executed by the processor cause performance of operations, the operations comprising:
receiving a feature-sensitive query;
using a first language model to generate an executable feature constraint determination command, comprising a first function corresponding to a data management system language, based upon a set of information comprising the feature-sensitive query;
executing the executable feature constraint determination command to determine a feature constraint associated with the feature-sensitive query;
analyzing a data structure based upon the feature constraint to identify a subset of data, of the data structure, relevant to the feature constraint; and
generating a response to the feature-sensitive query based upon the subset of data.
18 . The computing device of claim 17 , the operations comprising:
generating, based upon one or more characteristics of the data structure, a data structure template, wherein at least one of:
the set of information comprises the data structure template; or
the operations comprise training a language model using the data structure template to generate the first language model.
19 . The computing device of claim 17 , wherein generating the response comprises:
using a second language model to generate the response based upon:
the subset of data; and
the feature-sensitive query.
20 . The computing device of claim 17 , the operations comprising:
displaying the response via a client device.