System and method for query augmentation for generating responses

A method comprising:

• receiving, by a processor, an input data for performing a task, wherein the input data comprises at least one of a natural language query received from a user device;
• determining, by the processor, a context of the received input data by parsing at least one keyword comprised within the received input data;
• determining, by the processor, an appropriate domain specific graphical schema corresponding to the received input data based on the determined context;
• identifying, by the processor, a plurality of missing entities from the domain specific graphical schema by analyzing the determined context and at least one graphical instance corresponding to the determined appropriate domain specific graphical knowledge schema, wherein each of the at least one graphical instance corresponds to one of an explicit missing entity and an implicit missing entity;
• prioritizing, by the processor, the identified plurality of missing entities based on a distance between the identified plurality of missing entities from the determined context and the analysed analyzed at least one graphical instance;
• generating, by the processor, at least one sub-query for each of the identified plurality of missing entities based on the priority;
• retrieving, by the processor, a relevant content corresponding to the generated at least one sub-query from a plurality of external data sources using a Retrieval Augmented Generation (RAG) framework;
• generating, by the processor, at least one response to the received input data by augmenting the received input data with the retrieved relevant content;
• and
• outputting, by the processor, the generated at least one response on a user interface of the user device and updating the at least one graphical instance with the retrieved relevant content.

A non-transitory computer readable medium comprising a processor-executable instructions that cause a processor to:

• receive an input data for performing a task, wherein the input data comprises at least a natural language query received from a user device;
• determine a context of the received input data by parsing at least one keyword comprised within the received input data;
• determine an appropriate domain specific graphical schema corresponding to the received input data based on the determined context;
• identify a plurality of missing entities from the domain specific graphical schema by analyzing the determined context and at least one graphical instance corresponding to the determined appropriate domain specific graphical knowledge schema, wherein each of the at least one graphical instance corresponds to one of an explicit missing entity and an implicit missing entity;
• prioritize the identified plurality of missing entities based on a distance between the identified plurality of missing entities from the determined context and the analysed analyzed at least one graphical instance;
• generate at least one sub-query for the each of the identified plurality of missing entities based on the priority;
• retrieve a relevant content corresponding to the generated at least one sub-query from a plurality of external data sources using a Retrieval Augmented Generation (RAG) framework;
• generate at least one response to the received input data by augmenting the received input data with the retrieved relevant content;
• and
• output the generated at least one response on a user interface of the user device and update the at least one graphical instance with the retrieved relevant content.