Method and System for Multi-Level Artificial Intelligence Supercomputer Design Featuring Sequencing of Large Language Models

A system for assigning tasks to LLMs using one or more families of large language models (h-LLMs) comprising:

• a processor configured to:
• receive a received prompt at a user interface via an API;
• operate an input broker operable to:
• generate a plurality of derived prompts from the user prompt at an input broker, each derived prompt of the plurality of derived prompts corresponding to one or more specialized tasks and belonging to one or more categories;
• generate a plurality of prompt embeddings from the plurality of derived prompts by applying a plurality of embedding models;
• transmit the plurality of prompt embeddings to a vector database, the vector database comprising a database of knowledge documents, each knowledge document comprised by the database of knowledge documents having one or more embeddings associated therewith;
• receive one or more received knowledge documents from the vector database determined to be relevant to the plurality of prompt embeddings at the input broker;
• generate a plurality of context-aware tasks by the input broker responsive to at least one of the user prompt, the plurality of derived prompts, and the one or more received knowledge documents, each context-aware task of the plurality of context-aware tasks corresponding to a specialized task of the one or more specialized tasks;
• and
• transmit each context-aware task of the plurality of context-aware tasks to an h-LLM of a plurality of h-LLMs that is configured to specialize in processing tasks having a specialty corresponding to the specialty of the context-aware task;
• and
• operate an input broker operable to receive a plurality of produced results from at least one of the plurality of h-LLMs by an output broker;
• a non-transitory storage medium;
• and
• software on the storage medium containing instructions that are executable by the processor;
• wherein the plurality of h-LLMs operate as a network of communicating h-LLMs;
• wherein at least one of the received prompt or the plurality of produced results is processed and transmitted to another of the plurality of h-LLMs in the network;
• wherein new requests are created by the plurality h-LLMs as part of processing the at least one or more of the received prompt and the plurality of produced results;
• and
• wherein the processor is configured to operate at least one of the input broker and the output broker to coordinate a sequence of the plurality of context-aware tasks processed by the plurality of h-LLMs in response to at least one of the received query or the derived prompts.

A system for assigning tasks to LLMs using one or more families of large language models (h-LLMs) comprising:

• means for receiving a received prompt at a user interface via an API;
• means for generating a plurality of derived prompts from the user prompt at an input broker, each derived prompt of the plurality of derived prompts corresponding to one or more specialized tasks and belonging to one or more categories;
• means for generating a plurality of prompt embeddings from the plurality of derived prompts by applying a plurality of embedding models;
• means for transmitting the plurality of prompt embeddings to a vector database, the vector database comprising a database of knowledge documents, each knowledge document comprised by the database of knowledge documents having one or more embeddings associated therewith;
• means for receiving one or more received knowledge documents from the vector database determined to be relevant to the plurality of prompt embeddings at the input broker;
• means for generating a plurality of context-aware tasks by the input broker responsive to at least one of the user prompt, the plurality of derived prompts, and the one or more received knowledge documents, each context-aware task of the plurality of context-aware tasks corresponding to a specialized task of the one or more specialized tasks;
• means for transmitting each context-aware task of the plurality of context-aware tasks to an h-LLM of a plurality of h-LLMs that is configured to specialize in processing tasks having a specialty corresponding to the specialty of the context-aware task;
• and
• means for receiving a plurality of produced results from at least one of the plurality of h-LLMs by an output broker;
• wherein the plurality of h-LLMs operate as a network of communicating h-LLMs;
• wherein at least one of the received prompt or the plurality of produced results is processed and transmitted to another of the plurality of h-LLMs in the network;
• wherein new requests are created by the plurality h-LLMs as part of processing the at least one or more of the received prompt and the plurality of produced results;
• and
• wherein at least one of the input broker and the output broker is configured to coordinate a sequence of the plurality of context-aware tasks processed by the plurality of h-LLMs in response to at least one of the received query or the derived prompts.