Determining a user-specific approach for disambiguation based on an interaction recommendation machine learning model
In various embodiments, a natural language (NL) application implements functionality that enables users to more effectively access various data storage systems based on NL requests. As described, the operations of the NL application are guided by, at least in part, on one or more templates and/or machine-learning models. Advantageously, the templates and/or machine-learning models provide a flexible framework that may be readily tailored to reduce the amount of time and user effort associated with processing NL requests and to increase the overall accuracy of NL application implementations.
1. A computer-implemented method, comprising:
mapping a first natural language (NL) request to a first template of a plurality of templates stored in a database, wherein the first template is written in a first domain-specific language (DSL) associated with a first data storage system;
identifying a property field included in the first template for which a value is unknown;
generating an inquiry requesting the value for the property field included in the first template;
generating a first DSL request that is written in the first DSL based on the first template and the value for the property field produced in conjunction with one or more user inputs received in response to the inquiry; and
causing the first DSL request to be applied to the first data storage system.
2. The computer-implemented method of claim 1 , wherein generating the first DSL request comprises assigning the value to the property field included in the first template.
3. The computer-implemented method of claim 1 , wherein the first NL request comprises an audible query.
4. The computer-implemented method of claim 1 , wherein the first DSL request comprises a pipelined search language.
5. The computer-implemented method of claim 1 , wherein the first DSL request comprises a search query.
6. The computer-implemented method of claim 1 , further comprising:
receiving a search result associated with the first DSL request, wherein the search result reflects activity in an information technology environment; and
causing the search result to be provided to a user.
7. The computer-implemented method of claim 1 , further comprising generating a second DSL request associated with the first NL request and based on a second DSL associated with a second data storage system, and causing the second DSL request to be applied to the second data storage system.
8. The computer-implemented method of claim 1 , wherein the first data storage system stores data as a plurality of time-indexed events including respective segments of raw machine data.
9. The computer-implemented method of claim 1 , further comprising generating a response to the first NL request based on one or more results received in response to the first DSL request being applied to the first data storage system.
10. One or more non-transitory computer-readable storage media including instructions that, when executed by one or more processors, cause the one or more processors to perform steps of:
mapping a first natural language (NL) request to a first template of a plurality of templates stored in a database, wherein the first template is written in a first domain-specific language (DSL) associated with a first data storage system;
identifying a property field included in the first template for which a value is unknown;
generating an inquiry requesting the value for the property field included in the first template;
generating a first DSL request that is written in the first DSL based on the first template and the value for the property field produced in conjunction with one or more user inputs received in response to the inquiry; and
causing the first DSL request to be applied to the first data storage system.
11. The one or more non-transitory computer-readable storage media of claim 10 , wherein generating the first DSL request comprises assigning the value to the property field included in the first template.
12. The one or more non-transitory computer-readable storage media of claim 10 , wherein the first NL request comprises an audible query.
13. The one or more non-transitory computer-readable storage media of claim 10 , wherein the first DSL request comprises a pipelined search language.
14. The one or more non-transitory computer-readable storage media of claim 10 , wherein the first DSL request comprises a search query.
15. The one or more non-transitory computer-readable storage media of claim 10 , further comprising:
receiving a search result associated with the first DSL request, wherein the search result reflects activity in an information technology environment; and
causing the search result to be provided to a user.
16. The one or more non-transitory computer-readable storage media of claim 10 , further comprising generating a second DSL request based on a second DSL associated with a second data storage system, and causing the second DSL request to be applied to the second data storage system.
17. The one or more non-transitory computer-readable storage media of claim 10 , wherein the first data storage system stores data as a plurality of time-indexed events including respective segments of raw machine data.
18. The one or more non-transitory computer-readable storage media of claim 10 , wherein the one or more processors further perform the step of generating a response to the first NL request based on one or more results received in response to the first DSL request being applied to the first data storage system.
19. A computing device, comprising:
a memory that includes instructions; and
a processor that is coupled to the memory and, when executing the instructions, is configured to:
map a first natural language (NL) request to a first template of a plurality of templates stored in a database, wherein the first template is written in a first domain-specific language (DSL) associated with a first data storage system;
identify a property field included in the first template for which a value is unknown;
generate an inquiry requesting the value for the property field included in the first template;
generate a first DSL request that is written in the first DSL based on the first template and the value for the property field produced in conjunction with one or more user inputs received in response to the inquiry; and
cause the first DSL request to be applied to the first data storage system.
20. The computing device of claim 19 , wherein generating the first DSL request comprises assigning the value to the property field included in the first template.