System for managing agent use scene in large language model

Abstract

The invention discloses a system for managing an agent use scene in a large language model, which comprises but is not limited to an agent plug-in module, an agent context management module, an agent business process module, an agent scene matching module and an agent application feedback module, the data processing and analysis of the system cover but are not limited to statistics, analysis, mining and prediction operations. Multiple technical means are adopted to guarantee the accuracy, integrity and real-time performance of data, data collection is conducted in an asynchronous queue mode, and the influence of data collection on the response performance of a system is reduced; and meanwhile, a Maven project management tool, a JUnit test tool and the like are fused to improve the performance and the stability of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of large language models, and in particular to a system for managing usage scenarios of intelligent agents in large language models. Background Art

[0002] With the progress of large language models, intelligent agents based on large language models that use universal natural language as an interface have shown strong generalization capabilities in various applications. However, large language models themselves cannot meet the increasingly rich scenario requirements.

[0003] Therefore, it is necessary to design a system to manage the usage scenarios of agents in large language models. Summary of the invention

[0004] The purpose of the present invention is to provide a system for managing usage scenarios of intelligent agents in a large language model to solve the problems raised in the above-mentioned background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] A system for managing agent usage scenarios in a large language model, including but not limited to an agent plug-in module, an agent context management module, an agent business process module, an agent scenario matching module, and an agent application feedback module:

[0007] Agent application feedback module: collects question and answer data in real time through the system's tracking interface, and performs preliminary screening, cleaning and standardization on the data. The generated data includes but is not limited to text, pictures, videos, charts and tables;

[0008] Agent plug-in module: including plug-in definition, plug-in modification and plug-in deletion;

[0009] Agent context management module: Based on historical questions, load historically generated data for understanding and analysis of current questions;

[0010] Agent business process module: defines the step positions of the above-mentioned agents in the process of processing;

[0011] Agent scene matching module: selects from the above agent modules according to the context of the question and determines the most suitable agent to handle the current question;

[0012] Agent application feedback module: Incorporates the collected data into the context of the question to improve the accuracy of the configuration in the above agent module.

[0013] According to the above technical solution, the intelligent agent plug-in module also loads and compiles the dynamic code defined by the plug-in.

[0014] According to the above technical solution, the agent context management module makes answers based on the understanding and analysis results of the current question and the historically generated data.

[0015] According to the above technical solution, the intelligent agent business process module also includes an interactive interface module for interactive operations in which users ask questions and obtain answers.

[0016] According to the above technical solution, the intelligent agent application feedback module can use the merged data as the result data required for output.

[0017] According to the above technical solution, the intelligent agent scene matching module is used to merge the collected data into the question context to improve the accuracy of the intelligent agent configuration.

[0018] According to the above technical solution, the system also includes Maven project management tools, JUnit testing tools, fault tolerance mechanism and Web API.

[0019] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0020] The present invention adopts a variety of technical means to ensure the rapid definition, deployment and optimization of the intelligent body; at the same time, it also integrates queue technology, Maven project management tools, JUnit testing tools, etc. to improve the performance and stability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flowchart of the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0023] like Figure 1 As shown, the present invention provides a technical solution: a system for managing agent usage scenarios in a large language model, including but not limited to an agent plug-in module, an agent context management module, an agent business process module, an agent scenario matching module and an agent application feedback module. The data processing and analysis of the system include but are not limited to statistics, analysis, mining, and prediction operations. A variety of technical means are used to ensure the accuracy, integrity and real-time performance of the data, and asynchronous queues are used for data collection to reduce the impact of data collection on system response performance;

[0024] Agent application feedback module: collects question and answer data in real time through the system's tracking interface, and performs preliminary screening, cleaning and standardization on the data. The generated data includes but is not limited to text, pictures, videos, charts and tables;

[0025] Agent plug-in module: including plug-in definition, plug-in modification and plug-in deletion;

[0026] Agent context management module: Based on historical questions, load historically generated data for understanding and analysis of current questions;

[0027] Agent business process module: defines the step positions of the above-mentioned agents in the process of processing;

[0028] Agent scene matching module: selects from the above agent modules according to the context of the question and determines the most suitable agent to handle the current question;

[0029] Agent application feedback module: Incorporates the collected data into the context of the question to improve the accuracy of the configuration in the above agent module.

[0030] Specifically, the agent plug-in module also loads and compiles the dynamic code defined by the plug-in.

[0031] Specifically, the agent context management module makes responses based on the understanding and analysis results of the current question and the historically generated data.

[0032] Specifically, the intelligent agent business process module also includes an interactive interface module for interactive operations in which users ask questions and obtain answers.

[0033] Specifically, the agent applies a feedback module and can use the merged data as the result data required for output.

[0034] Specifically, the agent scene matching module is used to merge the collected data into the question context to improve the accuracy of the agent configuration.

[0035] Specifically, the system also includes Maven project management tools, JUnit testing tools, fault tolerance mechanisms and WebAPI:

[0036] The Maven project management tool and JUnit testing tool are used to improve the performance and stability of the system;

[0037] The fault-tolerant mechanism is used to detect and handle system failures and ensure the effectiveness of the agent;

[0038] The Web API is used to integrate this system with other systems.

[0039] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0040] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A system for managing agent usage scenarios in a large language model, including but not limited to an agent plug-in module, an agent context management module, an agent business process module, an agent scenario matching module and an agent application feedback module, characterized in that: Agent application feedback module: collects question and answer data in real time through the system's tracking interface, and performs preliminary screening, cleaning and standardization on the data. The generated data includes but is not limited to text, pictures, videos, charts and tables; Agent plug-in module: including plug-in definition, plug-in modification and plug-in deletion; Agent context management module: Based on historical questions, load historically generated data for understanding and analysis of current questions; Agent business process module: defines the step positions of the above-mentioned agents in the process of processing; Agent scene matching module: selects from the above agent modules according to the context of the question and determines the most suitable agent to handle the current question; Agent application feedback module: Incorporates the collected data into the context of the question to improve the accuracy of the configuration in the above agent module.

2. A system for managing agent usage scenarios in a large language model according to claim 1, characterized in that: The intelligent agent plug-in module also loads and compiles the dynamic code defined by the plug-in.

3. A system for managing agent usage scenarios in a large language model according to claim 1, characterized in that: The agent context management module makes responses based on the understanding and analysis results of the current question and the historically generated data.

4. A system for managing agent usage scenarios in a large language model according to claim 1, characterized in that: The intelligent agent business process module also includes an interactive interface module for interactive operations in which users ask questions and obtain answers.

5. A system for managing agent usage scenarios in a large language model according to claim 1, characterized in that: The agent application feedback module can use the merged data as the result data required for output.

6. A system for managing agent usage scenarios in a large language model according to claim 1, characterized in that: The intelligent agent scene matching module is used to merge the collected data into the question context to improve the accuracy of the intelligent agent configuration.

7. A system for managing agent usage scenarios in a large language model according to claim 1, characterized in that: The system also includes Maven project management tools, JUnit testing tools, fault tolerance mechanisms and Web APIs.

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