Custom application publishing method and system based on large model enabling

Abstract

The invention discloses a custom application publishing method and system based on large model enabling, belongs to the technical field of deep learning, and aims to solve the technical problem of how to quickly develop and publish a custom application based on a large model. Comprising the steps of developing an assistant application through an assistant management system; introducing the alternative large model into an assistant application through a model access service; testing the assistant application into which the alternative large model is introduced; performing parameter adjustment on the matched large model through a dynamic configuration interface, and setting an instruction word of the large model; introducing knowledge, performing model access operation, model test operation and model optimization operation on the large model, and adding external skills for the large model; deploying the assistant application to the application scene; and performing real-time monitoring on the assistant application deployed in the application scene, performing fault processing based on a monitoring result, and performing performance optimization on the large model based on the monitoring result.

Description

Technical Field

[0001] The present invention relates to the technical field of deep learning, and specifically to a method and system for custom application publishing empowered by large models. Background Art

[0002] With the rapid development of artificial intelligence technology, large models have shown great potential in fields such as text generation and language understanding. Large models possess powerful learning and generalization abilities and can handle complex data and tasks. However, currently, the applications of large models are mainly used to provide services such as retrieval and question-and-answer generation, lacking an efficient and flexible method to empower the development and publishing of custom applications. For example, when developing, a large amount of manpower is still required to develop traditional business systems to access model services, with a long development cycle, high time and cost consumption, and many problems will occur during the process, which limits their application scope in actual business. How to effectively apply these powerful models to specific business scenarios and improve the efficiency and effectiveness of personalized applications and services will be a research topic.

[0003] The scale of software applications is increasing day by day, and the functions are becoming more and more complex. The traditional application research and development, deployment, and publishing processes often rely on manual operations, which are not only time-consuming and laborious but also prone to deployment failures or system instabilities due to human errors. However, how to effectively apply the capabilities of large models to actual software development, especially to achieve rapid customization and publishing of applications, remains a hot and difficult point in the current technical field. In the prior art, the development of applications usually relies on traditional programming methods, with a long cycle, high cost, and difficulty in quickly responding to market changes.

[0004] How to quickly develop and publish custom applications based on large models is a technical problem to be solved. Summary of the Invention

[0005] The technical task of the present invention is to address the above deficiencies and provide a method and system for custom application publishing empowered by large models to solve the technical problem of how to quickly develop and publish custom applications based on large models.

[0006] In a first aspect, a method for custom application publishing empowered by a large model according to the present invention is applied between a large model and an assistant management system, and includes the following steps:

[0007] Assistant application development: Develop an assistant application based on the requirements of the business scenario through the assistant management system;

[0008] Model access: Based on the requirements of the business scenario, select multiple large models as alternative large models, and introduce the alternative large models into the assistant application through a model access service, where the model access service is pre-built in the assistant management system;

[0009] Model testing: Test the assistant application with the alternative large model introduced. If the test result does not meet the expectation, re - execute the model access operation. If the test result meets the expectation, determine the alternative large model as the matching large model;

[0010] Model optimization: For the matching large model, determine whether parameter adjustment is required for the large model. If so, perform parameter adjustment on the matching large model through the dynamic configuration interface and conduct large model testing operations on the large model after parameter adjustment. If not, set the instruction words of the large model;

[0011] Model expansion: For the large model with instruction words set, determine whether expansion is required for the large model. If so, introduce knowledge, perform model access operations, model testing operations, and model optimization operations on the large model, add external skills to the large model, and conduct preview testing on the expanded large model. If expansion is not required, conduct preview testing on the current large model;

[0012] Application release and deployment: Release the assistant application with the large model accessed and deploy the assistant application to the application scenario;

[0013] Application monitoring: Conduct real - time monitoring on the assistant application deployed in the application scenario, perform fault handling based on the monitoring results, and optimize the performance of the large model based on the monitoring results.

[0014] Preferably, decompose the current functions of the assistant application into multiple independent modules through the assistant management system, define interfaces and parameters for each module. When developing the assistant application based on the requirements of the business scenario through the assistant management system, select and combine modules to build an assistant application adapted to the requirements of the business scenario.

[0015] Preferably, a development toolkit is customized in the assistant management system, and the development toolkit provides data processing, large model training, and user interface design services;

[0016] When developing the assistant application based on the requirements of the business scenario through the assistant management system, perform the following operations through the development toolkit:

[0017] According to the requirements of the business scenario, select functions and combine the functions to develop an assistant application adapted to the business scenario;

[0018] Optimize the user interface of the developed assistant application, and set up an assistant command setting area, an assistant module configuration area, and an assistant preview and test area for the user interface of the assistant application. The assistant command setting area provides a command word input box and gives reference examples for command word input. The assistant module configuration area provides the configuration of the assistant application process, including knowledge input and the setting of TopK and Score thresholds. The assistant preview and test area provides preview and test, and tests the application effect of the assistant application according to the configured commands and module processes;

[0019] For the large model accessed by the assistant application, process the sample data used for training the large model, and perform model training on the large model based on the processed sample data;

[0020] For the assistant application accessing the trained large model, initialize the assistant application, generate a conversation ID. When executing a conversation, the assistant application calls the large model for a reply, and records the conversation content, recording the user's questions and the assistant application's own replies;

[0021] Provide the download address of the assistant application.

[0022] Preferably, introduce knowledge, and perform model access, model testing, and model optimization operations on the large model, including the following operations:

[0023] Collect relevant knowledge based on the requirements of the business scenario, and store the knowledge in the constructed knowledge base;

[0024] Set an index indicating the position of the knowledge in the knowledge base, and vectorize the knowledge;

[0025] Instruct the large model currently accessed by the assistant application to learn the vectorized knowledge, and perform model selection based on the learning results of the large model to screen out a suitable large model;

[0026] For the suitable large model, perform model testing and model optimization operations.

[0027] Preferably, publish the application assistant accessing the large model through the constructed release management tool, and update, monitor, and maintain the published application assistant. Among them, the release management tool supports publishing the application assistant to the cloud or deploying it on a local server.

[0028] Preferably, when expanding the model, if the business scenario involves the field of government affairs Q&A, the expansion of the large model includes enterprise business approval processes, instruction queries, and common question answers. If the business scenario involves the personal field, the expansion of the large model involves language translation.

[0029] Preferably, after the assistant application is deployed to the application scenario, the assistant application provides session interaction services by calling the large model, performs artificial customer service and customer service records through the session interaction services, manages customer service for the session records generated during the session interaction service process, and returns the session records to the assistant management system;

[0030] Monitor the execution process of the session interaction service in real time through application monitoring, and optimize the performance of the large model based on the session records.

[0031] In a second aspect, a custom application publishing system empowered by a large model according to the present invention is characterized by including a large model and an assistant management system. The large model and the assistant management system cooperate to develop and publish an assistant application by executing a custom application publishing method empowered by a large model as described in any item of the first aspect.

[0032] The custom application publishing method and system empowered by the large model of the present invention have the following advantages:

[0033] 1. Users can efficiently utilize the large model to build personalized intelligent applications, greatly shortening the time from application development to release, and at the same time reducing the technical threshold;

[0034] 2. Improve the maintainability and scalability of the application, enabling the business to quickly adjust according to market changes; 3. Ensure the security and privacy of user data, and enhance users' trust in intelligent applications;

[0035] 4. Ensure the usage effect of the application, monitor the running data of users in real time, analyze the running situation based on the data, and give guiding opinions. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0037] The present invention will be further described below with reference to the drawings.

[0038] Figure 1 It is a principle block diagram of a custom application publishing method empowered by a large model for Embodiment 1;

[0039] Figure 2 It is a block diagram of the large model integration process in a custom application publishing method empowered by a large model for Embodiment 1;

[0040] Figure 3It is the flow chart of the assistant application release process in a custom application release method empowered by a large model in Embodiment 1;

[0041] Figure 4 It is the principle block diagram of the modular design in a custom application release method empowered by a large model in Embodiment 1;

[0042] Figure 5 It is the method for designing the sdk process in a custom application release method empowered by a large model in Embodiment 1;

[0043] Figure 6 It is the schematic diagram of the functional partition setting of the user interface in a custom application release method empowered by a large model in Embodiment 1. Detailed implementation manners

[0044] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, so that those skilled in the art can better understand the present invention and be able to implement it. However, the specific embodiments cited are not intended to limit the present invention. Without conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0045] The embodiments of the present invention provide a custom application release method and system empowered by a large model, which are used to solve the technical problem of how to quickly develop and release a custom application based on a large model.

[0046] Embodiment 1:

[0047] A custom application release method empowered by a large model according to the present invention is applied between a large model and an assistant management system, and includes seven steps: assistant application development, model access, model testing, model optimization, model extension, application release and deployment, and application monitoring.

[0048] Step S100 assistant application development: Develop an assistant application based on the requirements of the business scenario through the assistant management system.

[0049] In this embodiment, when developing the assistant application, the current functions of the assistant application are decomposed into multiple independent modules through the assistant management system, interfaces and parameters are defined for each module, and when developing the assistant application based on the requirements of the business scenario through the assistant management system, modules are selected and combined to construct an assistant application adapted to the requirements of the business scenario.

[0050] Step S200 model access: Based on the requirements of the business scenario, select multiple large models as alternative large models, and introduce the alternative large models into the assistant application through the model access service, and the model access service is pre-built in the assistant management system.

[0051] Step S300 Model Testing: Test the assistant application with the alternative large model integrated. If the test result does not meet the expectation, re - execute the model access operation. If the test result meets the expectation, determine the alternative large model as the matching large model.

[0052] Step S400 Model Optimization: For the matching large model, determine whether parameter adjustment is required. If so, perform parameter adjustment on the matching large model through the dynamic configuration interface and conduct large model testing operations on the large model after parameter adjustment. If not, set the instruction words of the large model.

[0053] Step S500 Model Expansion: For the large model with instruction words set, determine whether expansion is required. If so, introduce knowledge, perform model access operations, model testing operations, and model optimization operations on the large model, add external skills to the large model, and conduct preview testing on the expanded large model. If expansion is not required, conduct preview testing on the current large model.

[0054] Step S600 Application Release and Deployment: Release the assistant application integrated with the large model and deploy the assistant application to the application scenario.

[0055] In this embodiment, the release management tool is constructed to release the application assistant integrated with the large model and update, monitor, and maintain the released application assistant. Among them, the release management tool supports releasing the application assistant to the cloud or deploying it on the local server.

[0056] Step S700 Application Monitoring: Conduct real - time monitoring on the assistant application deployed in the application scenario, perform fault handling based on the monitoring results, and optimize the performance of the large model based on the monitoring results.

[0057] As a specific implementation, a development toolkit is customized in the assistant management system. The development toolkit provides data processing, large model training, and user interface design services. When developing an assistant application through the assistant management system based on the requirements of the business scenario, the following operations are performed through the development toolkit:

[0058] (1) Select functions according to the requirements of the business scenario and combine the functions to develop an assistant application adapted to the business scenario;

[0059] (2) Optimize the user interface of the developed assistant application, and set up an assistant command setting area, an assistant module configuration area, and an assistant preview and test area for the user interface of the assistant application. The assistant command setting area provides a command word input box and gives reference examples for entering command words. The assistant module configuration area provides the configuration of the assistant application process, including knowledge input and the setting of TopK and Score thresholds. The assistant preview and test area provides preview and test, and tests the application effect of the assistant application according to the configured commands and module processes;

[0060] (3) For the large model accessed by the assistant application, process the sample data used for training the large model, and perform model training on the large model based on the processed sample data;

[0061] (4) For the assistant application accessed with the trained large model, initialize the assistant application, generate a conversation ID. When executing a conversation, the assistant application calls the large model for a reply, and records the conversation content, recording the user's questions and the assistant application's own replies;

[0062] (5) Provide the download address of the assistant application.

[0063] During the model expansion process, introduce knowledge, and perform model access, model testing, and model optimization operations on the large model, including the following operations:

[0064] (1) Collect relevant knowledge based on the requirements of the business scenario, and store the knowledge in the already constructed knowledge base;

[0065] (2) Set an index indicating the location of the knowledge in the knowledge base, and vectorize the knowledge;

[0066] (3) Instruct the large model currently accessed by the assistant application to learn the vectorized knowledge, and perform model selection based on the learning results of the large model to screen out a suitable large model;

[0067] (4) For the suitable large model, perform model testing and model optimization operations.

[0068] When the model is expanded, if the business scenario involves the field of government affairs Q&A, the expansion of the large model includes enterprise business approval processes, instruction queries, and common question answers. If the business scenario involves the personal field, the expansion of the large model involves language translation.

[0069] In this embodiment, after the assistant application is deployed to the application scenario, the assistant application provides a session interaction service by calling the large model, performs artificial customer service and customer service records through the session interaction service, and performs customer service management on the session records generated during the session interaction service process, and returns the session records to the assistant management system. Correspondingly, the execution process of the session interaction service is monitored in real time through application monitoring, and the performance of the large model is optimized based on the session records.

[0070] The method of this embodiment realizes the rapid customization of applications and the rapid release of applications by integrating the intelligent generation, understanding, and analysis capabilities of large models. By this method, the cycle from the concept of the application to the market is greatly shortened, endowing developers with flexibility and creativity in operation, and generating the results of configuration and application generation. The large model provides model services for the assistant management system. The assistant management system publishes assistant applications, can access customer service management through session interaction, and the generated interaction data will be recorded in the assistant management and fed back to the model for fine-tuning training. The overall working principle is as Figure 1 shown.

[0071] Based on the method disclosed in this embodiment, when developing and publishing a large model based on a large model and an assistant management system, the following eight technical points are involved.

[0072] Technology 1: Provide model access services, enabling developers to easily integrate large models independently developed by themselves or released by other manufacturers directly as services into their custom applications. Build a large model integration service in the assistant management system to provide model access services, enabling developers to easily integrate large models independently developed by themselves or released by other manufacturers directly as services into their custom applications. This system enables users to directly and quickly integrate into their custom application frameworks and enjoy the powerful capabilities brought by large models without cumbersome underlying development. The large model integration system is as Figure 2 shown.

[0073] Technology 2: Dynamic configuration and optimization mechanism. Provide a dynamic configuration interface that allows developers to adjust the parameters and strategies of the large model in real time according to the application scenario to achieve optimal performance. In actual applications, the requirements for model performance vary in different scenarios. This embodiment allows developers to adjust the parameters and operation strategies of the large model in real time according to specific application scenarios. The overall process of dynamic configuration and effect testing is as Figure 3 shown.

[0074] Technology 3: Design a modular structure. In order to further improve development efficiency and application flexibility, this example adopts a modular design concept. By splitting it into multiple independent and clearly defined modules, developers can flexibly select and combine these modules according to the specific needs of their own projects, quickly build an application system that meets the business scenario, reduces the development difficulty compared with traditional systems, and greatly improves the maintainability and scalability of the application. Adopt a modular design concept, split the functions of the large model into multiple modules, which is convenient for developers to select and combine according to their needs. The modular design is as Figure 4 shown.

[0075] Technology Four: Building Release and Management Tools. To simplify the complex release process and lower the operation threshold for non-technical personnel, this embodiment develops an integrated release and management tool. This tool not only supports the generation of release-ready applications but also provides powerful functions such as release updates, monitoring, and maintenance, enabling developers to easily manage their applications and ensuring the stable operation and continuous optimization of the applications. It supports users to release their applications to the cloud or deploy them to local servers.

[0076] Technology Five: Custom Application Development Kit (SDK). To support developers in more in-depth customization of applications, this embodiment provides a comprehensive and easy-to-use development kit (SDK). This SDK covers tools and methods for multiple key aspects such as data processing, model training, and interface design, helping developers quickly build high-quality applications that meet business requirements. The overall SDK provides methods as Figure 5 shown.

[0077] Technology Six: Optimizing User Interface Design. By dividing the functional areas, setting up the assistant instruction setting area, assistant module configuration area, and assistant preview and test area. The user interface is made intuitive, concise, and easy to operate. Using large models for application development and management, configuring while applying, and the release can be completed immediately after configuration. The functional area settings are as Figure 6 shown.

[0078] Technology Seven: Multi-Scene Adaptation and High Scalability. The diversity of market demands requires attention to the multi-scene adaptation ability and scalability of large models, which can ensure that large models can flexibly adapt to various complex business scenarios and provide necessary model fine-tuning and extension interfaces to support the rapid development and changes of the business.

[0079] Technology Eight: Real-Time Monitoring of Application Operation. Using advanced monitoring technologies and data analysis means to comprehensively and round-the-clock monitor the running status of application programs on servers, ensuring the stability, performance optimization, and rapid response to faults of the applications.

[0080] Based on the method disclosed in this embodiment and the eight technical points briefly introduced, screenshot examples of developing and releasing applications are given, including the following eight steps.

[0081] Step S1: Construction of the Large Model Integration Framework. Develop a general large model integration framework that can be compatible with existing large model APIs, such as Wenxin Yiyan, Tongyi Qianwen, Zhipu Large Model, etc. Design an abstraction layer so that different large models can be called through a unified interface, facilitating developers' integration.

[0082] Step S2, Modular Implementation: Decompose the current functions of the application into independent modules, such as input, output, model, tool node and other modules, and define clear interfaces and parameters for each module to support interoperability and replaceability between modules;

[0083] Step S3, Dynamic Configuration and Optimization: Through dynamic configuration, by setting parameters, allow developers to dynamically adjust the parameters of the large model according to application requirements, and adjust the model parameters in real time according to the performance of the application to improve performance and accuracy:

[0084] Step S4, Publishing and Management: After configuring the application, it can be published and managed. After publishing, the access address can be provided, and at the same time, the API interface is also published, which can be accessed and displayed in a custom application, realizing that the application can be used immediately after publishing, without secondary development and deployment;

[0085] Step S5, SDK Usage: The assistant is hooked with the SDK for download. The specific process is as follows:

[0086] (1) Initialize the assistant. According to the incoming assistant code and secret key, initialize the assistant;

[0087] (2) Generate an ID. After initializing the assistant, generate a conversation ID;

[0088] (3) The assistant executes the conversation. Call the assistant to execute the conversation;

[0089] (4) Record the conversation content. Record the user's questions and the assistant's answers;

[0090] Step S6, User Interface Design: The assistant instruction setting area provides an instruction word input box and gives a reference example for inputting instruction words. The assistant module configuration area provides assistant process configuration, provides knowledge input and TopK, Score threshold settings. The assistant preview test area provides preview tests to test the application effect of the assistant according to the configured instructions and module processes;

[0091] Step S7, Multi-Scene Adaptation and High Scalability: Support adaptation and expansion for the requirements of multiple scenarios. The system provides a multi-scene configuration function and classifies and displays the running status of the assistant. For example, the assistant can provide support in the government affairs intelligent question and answer scenario, and can also provide instruction queries and common question and answer in the enterprise business approval process, and can provide a translation assistant in the personal field, etc.

[0092] Step S8, Real-Time Monitoring: Real-time monitor the running status of the assistant, collect, analyze and display data immediately, and allow managers or operators to respond immediately to any abnormal situations or problems.

[0093] The method of this embodiment realizes the rapid customized development and efficient release of applications through the powerful capabilities of large models, reduces development costs, enables users to quickly build applications without understanding code, and improves application quality and user experience.

[0094] Embodiment 2:

[0095] A custom application publishing system based on large model empowerment according to the present invention includes a large model and an assistant management system. The large model and the assistant management system cooperate to develop and publish assistant applications by executing the method disclosed in Embodiment 1.

[0096] The above has introduced in detail the custom application publishing method and system based on large model empowerment provided by the present invention. Specific examples are used in this article to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A method for publishing custom applications based on large model empowerment, characterized in that: Applied between the large model and the assistant management system, it includes the following steps: Assistant application development: Develop assistant applications based on business scenario requirements and through the assistant management system; Model access: Based on the needs of business scenarios, multiple large models are selected as candidate large models, and the candidate large models are introduced into the assistant application through the model access service, which is pre-built in the assistant management system; Model testing: Test the assistant application that has introduced the candidate large model. If the test result does not meet expectations, re-execute the model access operation. If the test result meets expectations, the candidate large model is determined to be the matching large model. Model optimization: for the matched large model, determine whether the large model needs parameter adjustment. If necessary, adjust the parameters of the matched large model through the dynamic configuration interface, and perform large model test operations on the large model after parameter adjustment. If not, set the instruction word of the large model. Model extension: For the large model with set instruction words, determine whether the large model needs to be extended. If necessary, introduce knowledge, perform model access operations, model testing operations, and model optimization operations on the large model, add external skills to the large model, and preview and test the expanded large model. If no extension is required, preview and test the current large model; Application release and deployment: Release the assistant application that is connected to the large model and deploy the assistant application to the application scenario; Application monitoring: Real-time monitoring of assistant applications deployed in application scenarios, troubleshooting based on monitoring results, and performance optimization of large models based on monitoring results.

2. The method for publishing a custom application based on large model empowerment according to claim 1 is characterized in that: The assistant management system is used to decompose the current functions of the assistant application into multiple independent modules, and interfaces and parameters are defined for each module. When developing assistant applications through the assistant management system based on the needs of business scenarios, modules are selected and combined to build assistant applications that meet the needs of business scenarios.

3. The method for publishing a custom application based on large model empowerment according to claim 1 or 2, characterized in that: A development toolkit is customized in the assistant management system, and the development toolkit provides data processing, large model training and user interface design services; When developing assistant applications through the assistant management system based on business scenario requirements, the following operations are performed through the development toolkit: According to the needs of business scenarios, select and combine functions to develop assistant applications that are adapted to business scenarios; Optimize the user interface of the developed assistant application, set the assistant instruction setting partition, assistant module configuration area and assistant preview test area for the user interface of the assistant application. The assistant instruction setting partition provides an instruction word input box and gives a reference example for instruction word input. The assistant module configuration area provides assistant application process configuration, knowledge input and TopK and Score threshold settings. The assistant preview test area provides preview testing to test the application effect of the assistant application according to the configured instructions and module processes. For the large model of the access assistant application, data processing is performed on sample data used for training the large model, and model training is performed on the large model based on the processed sample data; For the assistant application that is connected to the trained big model, the assistant application is initialized and a conversation ID is generated. When the conversation is executed, the assistant application calls the big model to answer and records the conversation content, including the user's questions and the assistant application's own answers. Provide the download address of the assistant application.

4. The method for publishing a custom application based on large model empowerment according to claim 1, characterized in that: Introduce knowledge, and perform model access, model testing, and model optimization operations on large models, including the following operations: Collect relevant knowledge based on business scenario requirements and store the knowledge in the constructed knowledge base; Set the index used to represent the location of knowledge in the knowledge base and vectorize the knowledge; Instruct the big model currently accessing the assistant application to learn vectorized knowledge, and select a model based on the big model learning result to select an appropriate big model; For the adapted large model, perform model testing and model optimization operations.

5. The method for publishing a custom application based on large model empowerment according to claim 1, characterized in that: The release management tool is used to release application assistants connected to large models, and to update, monitor and maintain the released application assistants. The release management tool supports publishing application assistants to the cloud or deploying them on local servers.

6. The method for publishing a custom application based on large model empowerment according to claim 1, characterized in that: When the model is expanded, if the business scenario involves the field of government affairs Q&A, the expansion of the big model includes enterprise business approval process, instruction query and frequently asked questions; if the business scenario involves the personal field, the expansion of the big model involves language translation.

7. The method for publishing a custom application based on large model empowerment according to claim 1, characterized in that: After the assistant application is deployed to the application scenario, it provides conversation interaction services by calling the big model, performs manual customer service and customer service records through the conversation interaction service, and performs customer service management on the conversation records generated during the conversation interaction service, and returns the conversation records to the assistant management system; Application monitoring can be used to monitor the execution process of conversation interaction services in real time, and performance of large models can be optimized based on conversation records.

8. A custom application publishing system based on large model empowerment, characterized in that: It includes a big model and an assistant management system. The big model and the assistant management system are used together to execute a custom application publishing method based on big model empowerment as described in any one of claims 1-7 to develop and publish assistant applications.

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