Systems and Methods for Deploying a Task-Specific Machine-Learning Model
This application describes, among other things, systems and methods for deploying a task-specific machine-learning model. An example method includes receiving a prompt associated with one or more commands and a plurality of tokens. A first task-specific machine-learning model is identified based on a first command of the one or more commands. Some or all of the tokens of the plurality of tokens are applied to a node associated with the first task-specific machine-learning model, and a plurality of restricted data is received. Based on evaluating the plurality of restricted data, a correlation between the first node and a second node is determined. If the correlation satisfies a threshold condition, a plurality of text data different than the prompt is generated, otherwise the some or all of the tokens are again provided to the first node. Some or all of the tokens are then traversed and applied to the second node.
1 . A method of deploying a task-specific machine-learning model, the method comprising:
receiving a prompt from a user, wherein the prompt is associated with one or more commands and a plurality of tokens;
identifying, in accordance with a first command in the one or more commands, a first task-specific machine-learning model in a plurality of task-specific machine-learning models, wherein each task-specific machine-learning model (i) is associated with at least one node in a plurality of interconnected nodes and (ii) defines a conditional logic for performing a specific task of the task-specific machine-learning model;
applying some or all of the tokens of the plurality of tokens to a first node in the at least one node associated with the first task-specific machine-learning model, wherein the applying comprises:
communicating, via a communication network, to a remote device an access token associated with the first task-specific machine-learning model,
retrieving, via the communication network, in accordance with an authentication of the access token from a source other than the first task-specific machine-learning model, a plurality of restricted data, and
determining a correlation between the first node and a second node based on an evaluation of one or more restricted data in the plurality of restricted data, wherein:
the second node is interconnected with the first node,
when the correlation between the first node and the second node satisfies a threshold condition of the first node, generating a plurality of text data different from the prompt, and
when the correlation between the first node and the second node fails to satisfy the threshold condition of the first node, repeating the applying some or all of the tokens of the plurality of tokens to the first node;
traversing the some or all of the tokens of the plurality of tokens to the second node; and
applying the some or all of the tokens of the plurality of tokens to the second node, thereby deploying the task-specific machine-learning model.
2 . The method of claim 1 , wherein the one or more commands are determined from the prompt.
3 . The method of claim 1 , wherein the one or more commands comprise an intent of the prompt.
4 . The method of claim 1 , wherein the plurality of tokens comprises between 10 tokens and 100,000 tokens.
5 . The method of claim 1 , wherein the plurality of tokens collectively represent an entirety of the prompt.
6 . The method of claim 1 , wherein the plurality of tokens collectively represent less than all of the prompt.
7 . The method of claim 1 , wherein the plurality of tokens comprises one or more character tokens, one or more sub-word tokens, one or more word tokens, or a combination thereof.
8 . The method of claim 1 , wherein the conditional logic of a respective task-specific machine-learning model is defined, at least in part, by a different user.
9 . The method of claim 1 , wherein determining the correlation between the first node and the second node comprises determining one or more vector embeddings associated with the prompt.
10 . The method of claim 9 , wherein each vector embedding in the one or more vector embeddings is a predetermined vector embedding.
11 . The method of claim 1 , wherein determining the correlation between the first node and the second node comprises identifying one or more data sources associated with the first node and the second node.
12 . The method of claim 1 , further comprising repeating the applying some or all of the tokens of the plurality of tokens to the first node for at least 10 instances of the repeating.
13 . The method of claim 1 , wherein each respective node in the plurality of interconnected nodes is associated with a corresponding classification in a plurality of classifications.
14 . A computing system, comprising:
control circuitry;
memory; and
one or more sets of instructions stored in the memory and configured for execution by the control circuitry, the one or more sets of instructions comprising instructions for:
receiving a prompt from a user, wherein the prompt is associated with one or more commands and a plurality of tokens;
identifying, in accordance with a first command in the one or more commands, a first task-specific machine-learning model in a plurality of task-specific machine-learning models, wherein each task-specific machine-learning model (i) is associated with at least one node in a plurality of interconnected nodes and (ii) defines a conditional logic for performing a specific task of the task-specific machine-learning model;
applying some or all of the tokens of the plurality of tokens to a first node in the at least one node associated with the first task-specific machine-learning model, wherein the applying comprises:
communicating, via a communication network, to a remote device an access token associated with the first task-specific machine-learning model,
retrieving, via the communication network, in accordance with an authentication of the access token from a source other than the first task-specific machine-learning model, a plurality of restricted data, and
determining a correlation between the first node and a second node based on an evaluation of one or more restricted data in the plurality of restricted data, wherein:
the second node is interconnected with the first node,
when the correlation between the first node and the second node satisfies a threshold condition of the first node, generating a plurality of text data different from the prompt, and
when the correlation between the first node and the second node fails to satisfy the threshold condition of the first node, repeating the applying some or all of the tokens of the plurality of tokens to the first node;
traversing the some or all of the tokens of the plurality of tokens to the second node; and
applying the some or all of the tokens of the plurality of tokens to the second node.
15 . The computing system of claim 14 , wherein determining the correlation between the first node and the second node comprises determining one or more vector embeddings associated with the prompt.
16 . The computing system of claim 15 , wherein each vector embedding in the one or more vector embeddings is a predetermined vector embedding.
17 . The computing system of claim 14 , wherein determining the correlation between the first node and the second node comprises identifying one or more data sources associated with the first node and the second node.
18 . A non-transitory computer-readable storage medium storing one or more sets of instructions configured for execution by a computing device having control circuitry and memory, the one or more sets of instructions comprising instructions for:
receiving a prompt from a user, wherein the prompt is associated with one or more commands and a plurality of tokens;
identifying, in accordance with a first command in the one or more commands, a first task-specific machine-learning model in a plurality of task-specific machine-learning models, wherein each task-specific machine-learning model (i) is associated with at least one node in a plurality of interconnected nodes and (ii) defines a conditional logic for performing a specific task of the task-specific machine-learning model;
applying some or all of the tokens of the plurality of tokens to a first node in the at least one node associated with the first task-specific machine-learning model, wherein the applying comprises:
communicating, via a communication network, to a remote device an access token associated with the first task-specific machine-learning model,
retrieving, via the communication network, in accordance with an authentication of the access token from a source other than the first task-specific machine-learning model, a plurality of restricted data, and
determining a correlation between the first node and a second node based on an evaluation of one or more restricted data in the plurality of restricted data, wherein:
the second node is interconnected with the first node,
when the correlation between the first node and the second node satisfies a threshold condition of the first node, generating a plurality of text data different from the prompt, and
when the correlation between the first node and the second node fails to satisfy the threshold condition of the first node, repeating the applying some or all of the tokens of the plurality of tokens to the first node;
traversing the some or all of the tokens of the plurality of tokens to the second node; and
applying the some or all of the tokens of the plurality of tokens to the second node.
19 . The non-transitory computer-readable storage medium of claim 18 , wherein determining the correlation between the first node and the second node comprises determining one or more vector embeddings associated with the prompt.
20 . The non-transitory computer-readable storage medium of claim 18 , wherein each vector embedding in the one or more vector embeddings is a predetermined vector embedding.