Method and system for multi-level artificial intelligence supercomputer design

1. A system for enhancing large language models (LLMs), comprising:

an input component configured to:

receive an input text from an input data stream; and

split the input text into one or more tokens;

a batch processing component comprising one or more LLMs, the one or more LLMs being fine-tuned based on an aggregation of input data received from the input data stream over a predetermined time duration and configured to generate an output responsive to receiving a token of the one or more tokens, the output being based upon the token and associated with the token;

a ranking component configured to:

score outputs from the one or more LLMs of the batch processing component; and

rank the one or more tokens responsive to the scores of the outputs from the one or more LLMs of the batch processing component;

a clustering component configured to:

select a subset of highest-ranked tokens; and

consolidate the subset of the highest-ranked tokens into refined context token batches;

a control component configured to iteratively provide the refined context token batches to the one or more LLMs comprised by the batch processing component for further training of the one or more LLMs; and

a query component configured to:

receive input queries via a user interface;

generate derived queries responsive to the input queries;

transmit the derived queries to the batch processing component;

receive responses from the batch processing component responsive to the derived queries;

at least one of score the responses and rank the responses from the batch processing component; and

transmit one or more responses responsive to at least one of scoring the responses and ranking the responses via the user interface.

2. The system of claim 1 wherein the query component is further configured to:

combine two or more responses into a single combined response; and

transmit the combined response via the user interface.

3. The system of claim 1 wherein the query component is further configured to filter the responses.

4. The system of claim 1 further comprising a real-time processing component comprising one or more LLMs that are comparatively smaller than the one or more LLMs comprised by the batch processing component and updated in real-time with input data received from the input data stream and configured to generate a real-time output responsive to receiving a token of the one or more tokens, the real-time output being based upon the token and associated with the token; wherein the ranking component is further configured to:

score real-time outputs from the one or more LLMs of the real-time processing component and the outputs from the one or more LLMs of the batch processing component; and

rank the one or more tokens responsive to the scores of the real-time outputs from the one or more LLMs of the real-time processing component and the scores of the outputs from the one or more LLMs of the batch processing component; and

the query component is further configured to:

transmit the derived queries to each of the batch processing component and the real-time processing component; and

receive responses from each of the batch processing component and the real-time processing component responsive to the derived queries.

5. The system of claim 4 wherein the real-time processing component utilizes Map Reduce type functions in routing tokens received by the real-time processing component to the one or more LLMs comprised by the real-time processing component.

6. A system for enhancing large language models (LLMs), comprising:

an input component configured to:

receive an input text from an input data stream; and

split the input text into one or more tokens;

a real-time processing component comprising one or more LLMs that are updated in real-time with input data received from the input data stream and configured to generate an output responsive to receiving a token of the one or more tokens, the output being based upon the token and associated with the token;

a ranking component configured to:

score outputs from the one or more LLMs of the real-time processing component; and

rank the one or more tokens responsive to the scores of the outputs from the one or more LLMs of the real-time processing component;

a clustering component configured to:

select a subset of highest-ranked tokens; and

consolidate the subset of the highest-ranked tokens into refined context token batches;

a control component configured to iteratively provide the refined context token batches to the one or more LLMs comprised by the real-time processing component for further training of the one or more LLMs; and

a query component configured to:

receive input queries via a user interface;

generate derived queries responsive to the input queries;

transmit the derived queries to the real-time processing component;

receive responses from the real-time processing component responsive to the derived queries;

at least one of score the responses and rank the responses from the real-time processing component; and

transmit one or more responses responsive to at least one of scoring the responses and ranking the responses via the user interface.

7. The system of claim 6 wherein the query component is further configured to:

combine two or more responses into a single combined response; and

transmit the combined response via the user interface.

8. The system of claim 6 wherein the query component is further configured to filter the responses.

9. The system of claim 6 wherein the real-time processing component utilizes Map Reduce type functions in routing tokens received by the real-time processing component to the one or more LLMs comprised by the real-time processing component.

10. The system of claim 6 further comprising a batch processing component comprising one or more LLMs, the one or more LLMs of the batch processing component being fine-tuned based on an aggregation of input data received from the input data stream over a predetermined time duration and configured to generate a batch output responsive to receiving a token of the one or more tokens, the batch output being based upon the token and associated with the token, and being comparatively larger than the one or more LLMs comprised by the real-time processing component;

wherein the ranking component is further configured to:

score the outputs from the one or more LLMs of the real-time processing component and outputs from the one or more LLMs of the batch processing component; and

rank the one or more tokens responsive to the scores of the outputs from the one or more LLMs of the real-time processing component and the scores of the outputs from the one or more LLMs of the batch processing component; and

the query component is further configured to:

transmit the derived queries to each of the batch processing component and the real-time processing component; and

receive responses from each of the batch processing component and the real-time processing component responsive to the derived queries.

11. A method for enhancing large language models (LLMs), comprising:

receiving an input text from an input data stream at an input component;

splitting by the input component the input text into one or more tokens;

receiving a token of the one or more tokens at a batch processing component comprising one or more LLMs, the one or more LLMs being fine-tuned based on an aggregation of input data received from the input data stream over a predetermined time duration;

generating by the batch processing component an output responsive to receiving a token of the one or more tokens, the output being based upon the token and associated with the token;

scoring by a ranking component outputs from the one or more LLMs of the batch processing component;

ranking by the ranking component the one or more tokens responsive to the scores of the outputs from the one or more LLMs of the batch processing component;

selecting by a clustering component a subset of highest-ranked tokens;

consolidating by the clustering component the subset of the highest-ranked tokens into refined context token batches;

iteratively providing by a control component the refined context token batches to the one or more LLMs comprised by the batch processing component for further training of the one or more LLMs;

receiving by a query component input queries via a user interface;

generating by the query component derived queries responsive to the input queries;

transmitting by the query component the derived queries to the batch processing component;

receiving by the query component responses from the batch processing component responsive to the derived queries;

at least one of scoring the responses and ranking the responses from the batch processing component by the query component; and

transmitting by the query component one or more responses responsive to at least one of scoring the responses and ranking the responses via the user interface.

12. The method of claim 11 further comprising:

combining by the query component two or more responses into a single combined response; and

transmitting by the query component the combined response via the user interface.

13. The method of claim 11 further comprising filtering the responses by the query component.

14. The method of claim 11 further comprising

receiving the token of the one or more tokens at a real-time processing component comprising one or more LLMs that are updated in real-time with input data received from the input data stream;

generating by the real-time processing component a real-time output responsive to receiving the token, the real-time output being based upon the token and associated with the token;

scoring by the ranking component real-time outputs from the one or more LLMs of the real-time processing component and the outputs from the one or more LLMs of the batch processing component;

ranking by the ranking component the one or more tokens responsive to the scores of the real-time outputs from the one or more LLMs of the real-time processing component and the scores of the outputs from the one or more LLMs of the batch processing component;

transmitting by the query component the derived queries to each of the batch processing component and the real-time processing component; and

receiving by the query component responses from each of the batch processing component and the real-time processing component responsive to the derived queries.

15. The method of claim 14 further comprising routing tokens received by the real-time processing component to the one or more LLMs comprised by the real-time processing component using Map Reduce type functions.