CN117993498A - Cloud fusion application service framework integration method - Google Patents
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Abstract
The invention belongs to the field of cloud fusion application service integration, and particularly relates to a cloud fusion service framework integration method. The method comprises the following steps: step S1, extracting information from development documents and website description information of a service component by using Chat Completion API of ChatGPT to form a standardized service description file; s2, analyzing two parts of service component description information and service providing description information in a service description file by using ChatGPT, and respectively extracting tag labels of the service component and the service; meanwhile, embbeding API of ChatGPT is used for calculating an embedded vector with the length of 1536 dimensions, and the embedded vector is stored in a dictionary; s3, constructing a question-answering system matched with the requirements based on ChatGPT and the constructed service information knowledge base; etc. The method can be used for one-key deployment and automatically butt-joint of various services, so that the deployment of the application is more convenient and efficient.
Description
Technical Field
The invention belongs to the field of cloud fusion application service integration, and particularly relates to a cloud fusion service framework integration method.
Background
When an enterprise deploys services, the enterprise needs to face complex application constitution and service environments, and the problems of dependency relationship among different application services, expansibility of applications and the like need to be solved. With the rapid development of internet technology, conventional application service integration methods, such as integration based on middleware, integration based on web services, and the like, have the disadvantages of high cost, low efficiency, low performance, incapability of flexible change, and the like. The cloud fusion integration deployment deploys the service on the cloud, and the characteristics of elastic resources, automatic expansion and the like of the cloud are utilized, so that a more efficient and flexible application service integration process can be realized. Currently, some application service integration frameworks exist. Then, the framework platforms are oriented to professional operation and maintenance personnel with experience, can manually process part of dependence of service, select service components according to service requirements, and have higher requirements on users.
The container is a new type of executable unit that contains the service programs that need to be run and the supporting runtime environment. The container is light and portable, is very suitable for serving as a service component in cloud service, and is suitable for many scenes. Currently, the management of service components contained in applications in a container platform is the management of containers. If the dependency relationship between services is not specified in advance, it is a very difficult thing for the user to solve the dependency at the time of deployment.
Large language models have a powerful ability to understand and generate human language, they are often trained on large amounts of text data, and can perform a wide range of tasks. Typically these models are able to master the internal laws of natural language and generate new text content based on learned knowledge. The large language model can accurately analyze natural language and make proper answers. In terms of handling user requirements, the large language model can also quickly understand the user's language and match it to the description information of the corresponding service component. And in the process of the dependency relationship of the service components, the method can help related personnel to provide corresponding support, and greatly reduces the use cost of the system.
Disclosure of Invention
The invention provides a solution for constructing a service template by a demand automatic mapping service component, supporting one-key quick deployment of the service template and automatic butt joint of the service component. By introducing a large language model, the requirements are matched with the service process templates based on the powerful language understanding capability of the large language model. And the dependency relation total set of the service template is automatically generated through the structured service process description and the dependency relation description, so that a better service template generation effect is realized. And finally, a one-key rapid deployment process from the demand to the application service is realized through deploying the generated service template.
The technical scheme is as follows:
The invention provides a cloud fusion service framework integration method, and provides a solution for automatically constructing a service template according to requirements and deploying application services by one key. By constructing the service process template, mapping from the requirements to the service components is realized by using a large language model, so that new applications can be conveniently and rapidly constructed. And deploying the service components and automatically interfacing each service while deploying the application.
A cloud fusion service framework integration method comprises the following steps:
Step S1, extracting information from the development document and the website description information of the service component by using Chat Completion API of ChatGPT to form a standardized service description file. The description file has a canonical field definition that includes: basic information of the service components, service lists provided by the service components, environment configuration data dependent on the service components and the service components. The description file specification records the functions, interfaces, deployment environments and service-dependent information of a single specific service component, and solves the problems of non-uniform field definition and information deletion in a development document. Meanwhile, during matching, the keyword matching in a large section of natural language text can be avoided, so that waste is caused, and the matching precision is reduced.
And S2, analyzing two parts of service component description information and service providing description information in the service description file by using ChatGPT, and respectively extracting tag labels of the service components and the services. Meanwhile, the Embbeding API of ChatGPT is used for calculating an embedded vector with the length of 1536 dimensions, and the embedded vector is stored in a dictionary. The dictionary stores a set of key-value pairs with very high lookup performance. The time key is a tag, the value is all service components or embedded vectors of the service with the tag, and the embedded vectors are stored in a linked list mode.
The dictionary and the description file set of the service component together form a service information knowledge base required by matching. Tag and embedded vectors provide both an accurate and fuzzy matching method for knowledge base information. Tags have intuitive semantic meanings and can accurately represent service characteristics. The embedded vector has an encoding of semantic information, including more dense data information.
And step S3, constructing a question-answering system matched with the requirements based on ChatGPT and the constructed service information knowledge base.
And S4, solving the component dependence according to service dependence and environment dependence fields contained in the description information of the services in the deployment platform and the confirmed service component list, constructing a dependent service set and a dependent relationship set of the service components, and forming an application template for direct deployment with parameters such as a target platform.
And S5, deploying the application template, circularly searching the service with deployed dependencies in the dependent service set, and deploying the service from bottom to top. And when the service is deployed, the interface configuration docking service of the service is automatically changed according to the dependency relationship information, so that the service can be called. And finally, the deployment of all the service components in the application template is completed, and the automatic deployment and service docking of the application are realized.
The step S3 of the question-answering system flow comprises requirement input, requirement analysis, service information matching and answer generation.
And in the requirement input stage, the original information of the user is collected through multiple rounds of interaction with the user, and the historical dialogue information is integrated and the requirement is refined at the end of the stage, so that parts such as repetition, error correction and the like in the dialogue are removed.
And in the demand analysis stage, extracting the tags of the recorded summarized demands, and calculating the embedded vectors of the tags through Embedding API. Demand analysis work is performed by a community of ChatGPT-based agents.
And in the service information matching stage, the extracted tag and the embedded vector are used for matching with a service information knowledge base to find service data of matching requirements. Tag is based on string comparison, and embedded vectors are matched based on similarity of vector distances.
And in the answer generation stage, service information data of the matched description information of the specific service components are integrated and searched for the user to examine and confirm. After the user confirms, a specific service component list corresponding to the original requirement is obtained.
The dependency resolution procedure of the service component in step S4 includes:
Step S4-1: and traversing the list by taking the service component list obtained by matching as an initial list of the to-be-solved dependence, taking out the service components in the to-be-solved dependence list, traversing the dependent service fields in the service description, if the fields are not null, acquiring the service description of each dependent service component in the fields, and adding the service description to the tail of the to-be-solved dependence list. If the current service component is empty, the current service component is directly marked that the dependent solution is completed, and the to-be-solved dependency list is removed.
Step S4-2: and generating a dependency relationship according to the dependency field and the service providing field of the service component.
The dependencies record the manner in which the services interface with each other, as well as other information. And when the dependency type is data sharing, for example, the value is shared-value, the virtual data volume is automatically allocated for mounting, and the volume character is written into the service configuration of the two parties.
Step S4-3: and removing all the dependences from the to-be-solved dependency list after all the dependences are solved by the service component.
Step S4-4: and repeating the steps S4-1 to S4-3 until the to-be-solved dependency list is empty. The dependent service components generated in the recording process obtain a set of dependent services. The dependency relationship generated in the recording process obtains a dependency relationship set.
Advantageous effects
The invention splits the application into the service components, defines the application by the mode of combining the service components, and reduces the difficulty of constructing the application and the bottleneck of expanding the application service in the traditional application definition mode. The invention takes the powerful language understanding and processing capability of the large language model into consideration, and assists in constructing the mapping relation between the requirements and the specific service components by introducing the large language model. The invention defines standardized service description and dependency relationship, abstracts the service components into standardized service description, and is easier to process and solve the dependency of the service components, so that the selection process of the applied service components is automated, and the making process of the service combination is more convenient. The framework can save a great deal of operation and maintenance cost, and the application construction is more flexible. By structuring the requirements and dependency information, an automatic mapping of the requirements of the application to the service components is achieved, as well as a service component dependent automatic processing. The user can deploy and automatically butt-joint each service by one key, so that the deployment of the application is more convenient and efficient.
Drawings
FIG. 1 is an overall block diagram of a frame of the present invention;
FIG. 2 is a flow chart of a solution for one-touch quick generation deployment of the present invention;
FIG. 3 is a dependency resolution process for a service component list of the present invention;
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings and examples. The specific embodiments described herein are to be considered in an illustrative sense only and are not intended to limit the invention.
The invention aims to solve the technical problem of providing a cloud fusion service integration framework for realizing one-key rapid matching generation and deployment of demand to application service. The user demand can be rapidly understood through the large language model, and the automatic matching of the service assembly and the automatic generation of the application template are realized based on a pre-constructed service information knowledge base. The deployment application template automatically deploys the service components and interfaces each service. Under the condition of insufficient professional technology, a user can also quickly construct and deploy application services according to the application field and the needs, and the deployment operation and the operation and maintenance cost are greatly simplified.
Fig. 1 is a diagram showing the overall structure of the frame of the present invention.
Fig. 2 is a flowchart of a cloud fusion service framework integration method, which includes the following steps:
Step S1, extracting information from the development document and the website description information of the service component by using Chat Completion API of ChatGPT to form a standardized service description file. The description file has a canonical field definition that includes: the basic information of the service component, the service list provided by the service component, and the environment configuration data depending on the service component and the service component are as follows:
ServiceBase basic information of the service components.
Name, service component Name, or unique ID.
Description-Description of the overall functionality of the service component.
Image: service component installation mirror name.
ServiceProvided: the service component provides a list of services.
Index: index value of service.
Description: description of the service.
InteractionMethod: the service type includes http, https, message _queue and shared_volume.
Advance: additional configuration data for different service types.
Dependencies：
Name: rely on service component names.
Required: whether it is necessary.
InterfaceIndex: service index of dependent service components.
Advance: dependent supplemental configuration data.
SERVICEENV environmental configuration data for the service components.
EnvValues: environmental variable data.
Resources: a resource inventory of the service component.
The description file specification records the functions, interfaces, deployment environments and service-dependent information of a single specific service component, and solves the problems of non-uniform field definition and information deletion in a development document. Meanwhile, during matching, the keyword matching in a large section of natural language text can be avoided, so that waste is caused, and the matching precision is reduced.
And S2, analyzing Service/Description and ServiceProvided/Description parts in the Service Description file by using ChatGPT to respectively extract tag labels of the Service components and the services. Meanwhile, the Embbeding API of ChatGPT is used for calculating an embedded vector with the length of 1536 dimensions, and the embedded vector is stored in a dictionary. The dictionary stores a set of key-value pairs with very high lookup performance. The time key is a tag, the value is all service components or embedded vectors of the service with the tag, and the embedded vectors are stored in a linked list mode.
The dictionary and the description file set of the service component together form a service information knowledge base required by matching. Tag and embedded vectors provide both an accurate and fuzzy matching method for knowledge base information. Tags have intuitive semantic meanings and can accurately represent service characteristics. The embedded vector has an encoding of semantic information, including more dense data information.
And step S3, constructing a question-answering system matched with the requirements based on ChatGPT and the constructed service information knowledge base. The question-answering system flow comprises requirement input, requirement analysis, service information matching and answer generation.
And in the requirement input stage, the original information of the user is collected through multiple rounds of interaction with the user, and the historical dialogue information is integrated and the requirement is refined at the end of the stage, so that parts such as repetition, error correction and the like in the dialogue are removed. Summarized promts are shown below:
"you are a summarizer, your task is to summarize the user's needs in the chat log.
The following is a chat log:
$chat_history。
# output format
1. (Requirement 1) (user requirement entry, a sentence)
2. (Requirement 2) (user requirement entry, a sentence)
…
Please combine all the repeated or similar requirements into one. If there is a change in the demand at a later time, the changes are merged. If the demand is subsequently deleted, it is not output in the final result. "
The "$chat_history" field is chat information for multiple interactions with the user.
And in the demand analysis stage, extracting the tags of the recorded summarized demands, and calculating the embedded vectors of the tags through Embedding API. Demand analysis work is performed by a community of ChatGPT-based agents. The Prompt for demand analysis is as follows:
"you are $roll_description. You are in a discussion group, aiming at analyzing and listing the tag information of the given user needs:
$requirements
Is you agreeing to the tags contained in this proposal based on the knowledge of the field you are in? Or do you have any improved opinion?
Your proposal is proposed according to the following dimensions:
1. The functions are as follows: operations performed by the system or application and services provided.
2. Disaster recovery: the ability to remain stable upon unexpected failure or disaster event.
3. Performance: efficiency and performance level of a system, application, or service when executing.
4. Safety: measures designed and implemented for protecting user resources.
# Output format
-If you consider the proposal perfect, use the following output format:
the actions are as follows: agree A
Input: agree (reason for not outputting consent)
-If you give a supplementary opinion or raise an counteropinion, use the following output format:
the actions are as follows: disagree A
Input: (any reason you want to say, list of labels you propose)
If no proposal is made, you can give your opinion arbitrarily according to your role.
”
The "$role_description" field is dynamically generated agent role information, and is based on the domain included in the input summary requirement. Through role assignment, the agent can give out answers and suggestions with more specific fields.
Wherein, "$ requirements" is the summary requirement of the input.
And in the service information matching stage, the extracted tag and the embedded vector are used for matching with a service information knowledge base to find service data of matching requirements. Tag is based on string comparison, and embedded vectors are matched based on similarity of vector distances.
And in the answer generation stage, service information data of the matched description information of the specific service components are integrated and searched for the user to examine and confirm. After the user confirms, a specific service component list corresponding to the original requirement is obtained.
And S4, solving the component dependence according to service dependence and environment dependence fields contained in the description information of the services in the deployment platform and the confirmed service component list, constructing a dependent service set and a dependent relationship set of the service components, and forming an application template for direct deployment with parameters such as a target platform.
And S5, deploying the application template, circularly searching the service with deployed dependencies in the dependent service set, and deploying the service from bottom to top. And when the service is deployed, the interface configuration docking service of the service is automatically changed according to the dependency relationship information, so that the service can be called. And finally, the deployment of all the service components in the application template is completed, and the automatic deployment and service docking of the application are realized.
As shown in fig. 3, the dependency resolution process of the service component in step S4 includes:
Step S4-1: and traversing the list, taking the service component list obtained by matching as an initial list of the to-be-solved dependence, taking out the service components in the to-be-solved dependence list, traversing DEPENDENCIES fields of the service description, if the fields are not null, obtaining the service description of each dependent service component in the fields, and adding the service description to the end of the to-be-solved dependence list. If the current service component is empty, the current service component is directly marked that the dependent solution is completed, and the to-be-solved dependency list is removed.
Step S4-2: a dependency is generated based on the service component's dependency field DEPENDENCIES and the interface provide field ServiceProvided.
The dependencies record the manner in which the services interface with each other, as well as other information. And when the dependency type is data sharing, for example, the value is shared-value, the virtual data volume is automatically allocated for mounting, and the volume character is written into the service configuration of the two parties. The dependency specific field contains:
DependencyID, a unique index of dependencies.
InteractionMethod: the service type is defined in the description information of the service component.
Description: description information of the service.
Client: relying on information of the service party.
Name: service component name or unique index.
Configuration: some configuration data under the service type.
Provider: information of the service provider.
Name: service component name or unique index.
Configuration: some configuration data under the service type.
Step S4-3: and removing all the dependences from the to-be-solved dependency list after all the dependences are solved by the service component.
Step S4-4: and repeating the steps S4-1 to S4-3 until the to-be-solved dependency list is empty. The dependent service components generated in the recording process obtain a set of dependent services. The dependency relationship generated in the recording process obtains a dependency relationship set.
The above description is only illustrative of the preferred embodiments of the application and is not intended to limit the scope of the application in any way. Any alterations or modifications of the application, which are obvious to those skilled in the art based on the teachings disclosed above, are intended to be equally effective embodiments, and are intended to be within the scope of the appended claims.
Claims (3)
1. The cloud fusion service framework integration method is characterized by comprising the following steps of:
step S1, extracting information from development documents and website description information of a service component by using Chat Completion API of ChatGPT to form a standardized service description file;
The description file has a canonical field definition that includes: basic information of the service component, a service list provided by the service component, and environment configuration data depending on the service component and the service component; the description file specification records the information of functions, interfaces, deployment environments and service dependence of a single specific service component, and solves the problems of non-uniform field definition and information deletion in a development document; meanwhile, when matching, the keyword matching in a large section of natural language text can be avoided, so that waste is caused and the matching precision is reduced;
S2, analyzing two parts of service component description information and service providing description information in a service description file by using ChatGPT, and respectively extracting tag labels of the service component and the service; meanwhile, embbeding API of ChatGPT is used for calculating an embedded vector with the length of 1536 dimensions, and the embedded vector is stored in a dictionary;
the dictionary stores a set of key-value pairs with very high lookup performance. The key is a tag when stored, the value is all service components or embedded vectors of the service with the tag, and the embedded vectors are stored in a linked list form;
the dictionary which is built and the description file set of the service component together form a service information knowledge base which is needed by matching; the Tag and the embedded vector provide two matching methods of accuracy and ambiguity of knowledge base information, the Tag has visual semantic meaning, can accurately represent service characteristics, and the embedded vector has semantic information coding and contains denser data information;
s3, constructing a question-answering system matched with the requirements based on ChatGPT and the constructed service information knowledge base;
s4, solving component dependence according to service dependence and environment dependence fields contained in the description information of the services in the deployment platform and the confirmed service component list, constructing a dependent service set and a dependent relationship set of the service components, and forming an application template for direct deployment with parameters such as a target platform;
S5, deploying an application template, circularly searching the service with deployed dependencies in the dependent service set, and deploying the service from bottom to top; when the service is deployed, the interface configuration docking service of the service is automatically changed according to the dependency relationship information, so that the service can be called; and finally, the deployment of all the service components in the application template is completed, and the automatic deployment and service docking of the application are realized.
2. The cloud fusion service framework integration method according to claim 1, wherein the step S3 of question-answering system flow comprises requirement input, requirement analysis, service information matching and answer generation;
In the requirement input stage, original information of a user is collected through multiple rounds of interaction with the user, historical dialogue information is integrated and requirements are refined at the end of the stage, and parts such as repetition, error correction and the like in the dialogue are removed;
And in the demand analysis stage, extracting the tags of the recorded summarized demands, and calculating the embedded vectors of the tags through Embedding API. The demand analysis work is performed by a plurality of groups of ChatGPT-based agents;
And in the service information matching stage, the extracted tag and the embedded vector are used for matching with a service information knowledge base to find service data of matching requirements. Tag is based on character string comparison, and embedded vectors are matched based on similarity of vector distances;
And in the answer generation stage, service information data of the matched description information of the specific service components are integrated and searched for the user to examine and confirm. After the user confirms, a specific service component list corresponding to the original requirement is obtained.
3. The cloud fusion service framework integration method according to claim 1, wherein the service component dependency resolution process in step S4 includes:
Step S4-1: taking the service component list obtained by matching as an initial list of the to-be-solved dependence, traversing the list, taking out the service components in the to-be-solved dependence list, traversing the dependent service fields in the service description, if the fields are not null, obtaining the service description of each dependent service component in the fields, and adding the service description to the tail of the to-be-solved dependence list; if the service component is empty, directly marking the completion of the current service component dependency resolution, and removing a to-be-resolved dependency list;
step S4-2: generating a dependency relationship according to the dependency field of the service component and the service providing field;
The dependency relationship records the docking mode between services and other information, the deployment configuration of the service components is dynamically adjusted according to the dependency relationship in actual deployment, when the dependency type is inter-service communication, ports are automatically allocated and both sides of service configuration are written, when the dependency type is data sharing, virtual data volumes are automatically allocated for mounting, and volume symbols are written into both sides of service configuration;
Step S4-3: the service component removes all the dependencies from the to-be-solved dependency list after all the dependencies are solved;
step S4-4: repeating the steps S4-1 to S4-3 until the to-be-solved dependency list is empty; the dependent service component generated in the recording process obtains a dependent service set; the dependency relationship generated in the recording process obtains a dependency relationship set.
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