Method and system for information extraction and aggregation

A computing apparatus for using large language models (LLMs) to extract and aggregate information in order to generate a structured response to a user query, the computing apparatus comprising:

• a processor;
• a memory;
• and
• a communication interface coupled to each of the processor and the memory,
• wherein the processor is configured to:
• receive, from a user via the communication interface, a query;
• use a first LLM to act as a query replication LLM agent to transform the query into a plurality of action items;
• use a second LLM to act as a plan generator LLM agent to generate at least one plan for responding to each of the plurality of action items;
• use a third LLM to act as a plan selection LLM agent to select an optimum plan from among the generated at least one plan for executing each of the plurality of action items;
• use a fourth LLM to act as a plan orchestrator LLM agent to assemble a plurality of fifth LLMs to act as expert LLM sub-agents to perform respective tasks based on the selected optimum plan;
• obtain, from each of the expert LLM sub-agents, a respective output that results from a performance of the corresponding task;
• and
• use the plan orchestrator LLM agent to aggregate the respective outputs into a single response to the query,
• wherein the processor is further configured to use the plan orchestrator LLM agent to notify the user of a progress status when the respective output is obtained from each of the expert LLM sub-agents, wherein when a particular one of the expert LLM sub-agents is unable to successfully execute the corresponding one of the respective tasks, the progress status includes information that relates to the inability of the particular one of the expert LLM sub-agents to successfully execute the corresponding one of the respective tasks.

A non-transitory computer readable storage medium storing instructions for using large language models (LLMs) to extract and aggregate information in order to generate a structured response to a user query, the storage medium comprising executable code which, when executed by a processor, causes the processor to:

• receive a query from a user;
• use a first LLM to act as a query replication LLM agent to transform the query into a plurality of action items;
• use a second LLM to act as a plan generator LLM agent to generate at least one plan for responding to each of the plurality of action items;
• use a third LLM to act as a plan selection LLM agent to select an optimum plan from among the generated at least one plan for executing each of the plurality of action items;
• use a fourth LLM to act as a plan orchestrator LLM agent to assemble a plurality of fifth LLMs to act as expert LLM sub-agents to perform respective tasks based on the selected optimum plan;
• obtain, from each of the expert LLM sub-agents, a respective output that results from a performance of the corresponding task;
• and
• use the plan orchestrator LLM agent to aggregate the respective outputs into a single response to the query,
• wherein when executed, the executable code further causes the processor to use the plan orchestrator LLM agent to notify the user of a progress status when the respective output is obtained from each of the expert LLM sub-agents, wherein when a particular one of the expert LLM sub-agents is unable to successfully execute the corresponding one of the respective tasks, the progress status includes information that relates to the inability of the particular one of the expert LLM sub-agents to successfully execute the corresponding one of the respective tasks.