Computing tool retrieval using sequence processing models

A computer-implemented method for improving efficiency of computing tool retrieval for a sequence processing model, comprising:

• providing, by a computing system to one or more machine-learned sequence processing models for each of a plurality of computing tools, at least one synthetic query generation request based at least in part on tool documentation associated with said each computing tool;
• obtaining, by the computing system from the one or more machine-learned sequence processing models for each of the plurality of computing tools, at least one synthetic query that is configured for processing by said each computing tool of the plurality of computing tools, wherein the at least one synthetic query represents a potential use case for said each computing tool;
• storing, by the computing system in an embedding database, a plurality of embeddings corresponding to an expanded tool documentation data encoding for the plurality of computing tools, the expanded tool documentation data encoding comprising an encoding of the at least one synthetic query into a multidimensional embedding space;
• and
• determining, by the computing system, a subset of the plurality of computing tools that are relevant to a particular user query by comparing an embedding of the particular user query to the plurality of stored embeddings, wherein the subset includes less than all of the plurality of computing tools.

A computing system, comprising:

• one or more processors;
• and
• one or more computer-readable storage media that store instructions that, when executed by the one or more processors, cause the one or more processors to perform operations, the operations comprising:
• for each of a plurality of computing tools, creating an expanded tool documentation data encoding by encoding, via one or more machine-learned sequence processing models, at least one synthetic query into a multidimensional embedding space based on tool documentation for said each computing tool, wherein the at least one synthetic query represents a potential use case for said each computing tool;
• storing in an embedding database, a plurality of embeddings corresponding to the expanded tool documentation encodings for the plurality of computing tools;
• receiving a particular user query;
• determining a subset of the of the plurality of computing tools that are relevant to the particular user query by comparing an embedding of the particular user query to the plurality of stored embeddings, where the subset includes less than all of the plurality of computing tools;
• generating at least one prompt for at least one machine-learned sequence processing model, the at least one prompt including the particular user query and a processing result from each of the subset of the plurality of computing tools in response to the particular user query;
• and
• generating a response to the particular user query based at least in part on an output of the at least one machine-learned sequence processing model in response to the at least one prompt.