Tool using method and device based on interface document analysis, equipment and medium

Abstract

The invention relates to a tool using method and device based on interface document analysis, equipment and a medium. The method comprises the steps that a request sent by a user terminal is responded; the request comprises demand data of the user; screening out a target tool from a tool database according to the demand data; the tool database comprises a plurality of registered tools, and the registration process of the tools comprises the following steps: analyzing an interface document, and registering the tools according to analyzed interface information; the target tool comprises a plurality of tools in a tool database; calling the target tool to obtain response data corresponding to the demand data; according to the method, the tool can be automatically registered by analyzing the interface document, the tool or the tool combination can be intelligently selected based on the demand data input by the user, the user request is processed, and finally efficient and intelligent business process automation is achieved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of computer network technologies, and in particular, to a method, device, equipment, and medium for using tools based on interface document parsing. Background Art

[0002] With the development of artificial intelligence technologies, especially the popularization of large models, they play an increasingly important role in the intelligent applications of various industries. However, in order to fully unleash the potential of large models, it is often necessary to integrate existing business systems with large models and achieve seamless docking through tooling. Existing methods for tool registration and invocation are mostly manually configured, with low efficiency and prone to errors, lacking automated and intelligent support. Summary of the Invention

[0003] The following is an overview of the subject matter described in detail herein. This overview is not intended to limit the scope of protection of the claims.

[0004] The main objective of the embodiments of the present invention is to propose a method, device, equipment, and medium for using tools based on interface document parsing, which can achieve efficient and intelligent business process automation.

[0005] To achieve the above objective, a first aspect of the embodiments of the present invention provides a method for using tools based on interface document parsing, the method comprising:

[0006] Responding to a request sent by a user terminal; the request contains the user's requirement data;

[0007] Screening out target tools from a tool database according to the requirement data; the tool database contains multiple registered tools, and the registration process of the tools includes: parsing interface documents and registering them as tools according to the parsed interface information; the target tools include several of the tools in the tool database;

[0008] Invoking the target tools to obtain response data corresponding to the requirement data;

[0009] Feeding back the response data to the user terminal.

[0010] The method provided by the embodiments of the present application has at least the following beneficial effects:

[0011] This method can automatically register tools by parsing interface documents, and intelligently select tools or tool combinations based on the requirement data input by the user, process user requests, and ultimately achieve efficient and intelligent business process automation.

[0012] In some embodiments, the tool database further includes description vectors for describing the tools;

[0013] Filter out the target tool from the tool database according to the requirement data, including:

[0014] Vectorize the requirement data to obtain a requirement data vector;

[0015] Match the requirement data vector with the description vector to obtain a matching description vector;

[0016] Filter out the target tool according to the matching description vector.

[0017] In some embodiments, there are multiple matching description vectors;

[0018] The filtering out the target tool according to the matching description vector includes:

[0019] Obtain the multiple tools corresponding to the multiple matching description vectors;

[0020] Sort the multiple tools based on BCE-Rerank to obtain a sorting result;

[0021] Determine the target tool according to the sorting result.

[0022] In some embodiments, the determining the target tool according to the sorting result includes:

[0023] Input the sorting result into the first large model to determine the target tool according to the first large model.

[0024] In some embodiments, before calling the target tool to obtain the response data corresponding to the requirement data, it further includes:

[0025] Input the requirement data into the first large model to judge whether the requirement data is complete according to the first large model. If it is incomplete, send a response message indicating that the requirement is incomplete to the user terminal, so that the user terminal resends a request for requirement data containing complete information based on the response message.

[0026] In some embodiments, the matching the requirement data vector with the description vector to obtain a matching description vector includes:

[0027] Perform cosine similarity matching between the requirement data vector and the description vector to obtain a matching description vector.

[0028] In some embodiments, the feeding back the response data to the user terminal includes:

[0029] Send the response data to the user terminal for component rendering;

[0030] Alternatively, use a large model to process the response data and send the response data processed by the large model to the user terminal.

[0031] In some embodiments, the registration process of the tool further includes:

[0032] Configure fixed pass-through parameters for the tool;

[0033] Before invoking the target tool to obtain the response data corresponding to the requirement data, it further includes:

[0034] Assemble the fixed pass-through parameters for the target tool.

[0035] To achieve the above object, a second aspect of the embodiments of the present invention provides a tool usage device based on interface document parsing, and the device includes:

[0036] A request acquisition module, configured to respond to a request sent by a user terminal; the request contains requirement data of a user;

[0037] A tool screening module, configured to screen out a target tool from a tool database according to the requirement data; the tool database contains multiple registered tools, and the registration process of the tools includes: parsing an interface document and registering it as a tool according to the parsed interface information; the target tool includes several of the tools in the tool database;

[0038] A response acquisition module, configured to invoke the target tool to obtain the response data corresponding to the requirement data;

[0039] A data feedback module, configured to feedback the response data to the user terminal.

[0040] To achieve the above object, a third aspect of the embodiments of the present invention provides an electronic device, including: at least one control processor and a memory for communicatively connecting with the at least one control processor; the memory stores instructions executable by the at least one control processor, and the instructions are executed by the at least one control processor so that the at least one control processor can execute the above-mentioned tool usage method based on interface document parsing.

[0041] To achieve the above object, a fourth aspect of the embodiments of the present invention provides a computer-readable storage medium, and the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to cause a computer to execute the above-mentioned tool usage method based on interface document parsing.

[0042] It should be understood that the beneficial effects of the second to fourth aspects compared with the related art are the same as those of the first aspect compared with the related art. For the relevant descriptions, reference can be made to the relevant descriptions in the first aspect, which will not be elaborated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments or the related art descriptions. Obviously, the drawings in the following descriptions are only some embodiments of the embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0044] Figure 1 is a schematic diagram of a method for using a tool based on interface document parsing provided by an embodiment of the present application;

[0045] Figure 2 is a schematic diagram of tool registration provided by an embodiment of the present application;

[0046] Figure 3 is a schematic diagram of tool invocation provided by an embodiment of the present application;

[0047] Figure 4 is a schematic diagram of a device for using a tool based on interface document parsing provided by an embodiment of the present application;

[0048] Figure 5 is a schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] In order to make the purpose, technical solutions and advantages of the present application clearer, the following will further describe the present application in detail with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0050] As Figure 1 , an embodiment of the present application provides a method for using a tool based on interface document parsing. The method includes the following steps S110 to S140:

[0051] Step S110, responding to a request sent by a user terminal; the request contains the user's demand data;

[0052] Step S120, screening out a target tool from a tool database according to the demand data; the tool database contains multiple registered tools, and the registration process of the tool includes: parsing an interface document and registering it as a tool according to the parsed interface information; the target tool includes several tools in the tool database;

[0053] Step S130: Invoke the target tool to obtain the response data corresponding to the requirement data;

[0054] Step S140: Feed back the response data to the user terminal.

[0055] In this embodiment, the user terminal can be a computer device linked to the interface. The user can input the requirement data in the user terminal, and then the user terminal generates a request.

[0056] It should be noted that the requirement data can be the user's question or user requirement.

[0057] In step S120 of this example, the following technical terms are first explained:

[0058] The interface document is a document used to describe the interfaces between different modules, components, or applications in a software system during the software development process. Its main purpose is to help developers understand and use the system interfaces to ensure the effective integration and data communication between systems. Taking the Swagger interface document as an example, it is an API description specification, usually defined in JSON or YAML format, used to describe the request method, parameters, and return results of the interface. The Swagger interface document contains key information such as the API path, parameters, and return values.

[0059] The tool refers to a business API or functional module encapsulated for the large model call.

[0060] In this step, by parsing the interface document, the key information in it is obtained, and then the key information is registered as a tool and stored in the database for future invocation. When in use, the target tool can be screened out from the tool database according to the requirement data. The target tool here includes several tools in the tool database, which can be a single tool or a combination of tools.

[0061] In this embodiment, invoking the target tool can obtain the response data corresponding to the requirement data and feed back the response data to the user terminal, and the terminal can obtain the response corresponding to the requirement;

[0062] The method provided in this embodiment has at least the following effects:

[0063] This method can automatically register tools by parsing the interface document, and can intelligently select a tool or a combination of tools based on the requirement data input by the user to process the user request, and finally realize an efficient and intelligent business process automation.

[0064] In some embodiments of this application, the tool database also includes a description vector for describing the tool;

[0065] Filtering out the target tool from the tool database according to the requirement data in step S120 includes the following steps S210 to S230:

[0066] Step S210, vectorize the requirement data to obtain a requirement data vector;

[0067] Step S220, match the requirement data vector with the description vector to obtain a matched description vector;

[0068] Step S230, filter out the target tool according to the matched description vector.

[0069] In step S210, BCE-Embedding can be used to vectorize the requirement data, and then the requirement data vector can be matched with the description vector until a matched description vector is obtained, and the corresponding tool can be found based on the matched description vector.

[0070] BCE-Embedding is a Chinese-English bilingual and cross-lingual semantic representation algorithm model library, mainly used to generate semantic vectors, and plays a key role in semantic search and question answering.

[0071] In some embodiments, the matching method can be a similarity calculation method, for example:

[0072] Perform cosine similarity matching on the requirement data vector and the description vector to obtain a matched description vector.

[0073] This embodiment can improve the accuracy and relevance of computing tool selection based on the vectorized matching method.

[0074] In some embodiments of the present application, there are multiple matched description vectors;

[0075] Filtering out the target tool according to the matched description vector in step S230 includes the following steps S310 to S330:

[0076] Step S310, obtain multiple tools corresponding to multiple matched description vectors;

[0077] Step S320, sort the multiple tools based on BCE-Rerank to obtain a sorting result;

[0078] Step S330, determine the target tool according to the sorting result.

[0079] In this embodiment, an additional filtering process is added on the basis of step S220, that is, sorting the multiple tools again through BCE-Rerank to obtain a sorting result. The present application can ensure the accuracy and relevance of tool selection based on vectorized calculation and sorting models.

[0080] It should be noted that BCE-Rerank does not directly select the target tool. The process of determining the target tool according to the sorting result in step S330 includes:

[0081] Input the sorting result into the first large model to determine the target tool according to the first large model.

[0082] The first large model here can be a model trained based on deep learning and neural networks, which will not be elaborated here.

[0083] In some embodiments of the present application, the process of feeding back the response data to the user terminal in step S130 includes the following steps:

[0084] Send the response data to the user terminal for component rendering;

[0085] Alternatively, use the second large model to process the response data and send the response data processed by the second large model to the user terminal.

[0086] In some embodiments, the second large model is a pre-trained language model specifically based on deep learning, such as Glm4-plus, qwen-max, for understanding and generating natural language.

[0087] This embodiment uses a large model to process user requests, which can achieve efficient process automation; it is worth explaining that the specific type of the large model is not limited here.

[0088] In some embodiments of the present application, the registration process of the tool further includes:

[0089] Configure fixed pass-through parameters for the tool;

[0090] Before calling the target tool to obtain the response data corresponding to the demand data, it further includes:

[0091] Assemble fixed pass-through parameters for the target tool.

[0092] The fixed pass-through parameters in this embodiment refer to sensitive parameters that are directly passed during the interface call but not modified, such as authentication tokens, etc.

[0093] This embodiment can support custom pass-through parameters and can meet various business requirements.

[0094] In some embodiments of the present application, before calling the target tool to obtain the response data corresponding to the demand data in step S130, it further includes:

[0095] Input the requirement data into the first large model to determine whether the requirement data is complete according to the first large model. If it is incomplete, send a response message indicating that the requirement is incomplete to the user terminal so that the user terminal can resend a request for requirement data containing complete information based on the response message.

[0096] This embodiment introduces a dynamic questioning mechanism for the large model to ensure the integrity of the call parameters and improve the call success rate.

[0097] Such as Figure 2 and Figure 3 For the convenience of understanding, an embodiment of the present application provides a method for using a tool based on interface document parsing. This method includes the following steps:

[0098] The first part, tool registration;

[0099] (1) Parse the Swagger document.

[0100] Automatically read and parse the Swagger interface document through the program, and extract key information such as the path, parameters, and return values of the API.

[0101] (2) Generate a tool identifier;

[0102] Generate a unique tool identifier for each interface and register it in the tool database.

[0103] (3) Configure pass-through parameters;

[0104] The user can configure fixed pass-through parameters for the tool, such as authentication tokens, specific HTTP header information, etc.

[0105] (4) Configure the processing method for the returned response data;

[0106] Allow the user to select the processing method for the response data returned by the tool, including:

[0107] 1) Direct return: Directly pass the response data returned by the interface to the front end.

[0108] 2) Secondary processing by the large model: Pass the returned response data to the large model for further processing.

[0109] (5) Store the configuration in the database;

[0110] Store the configuration information of the tool in the tool database for subsequent calls.

[0111] The second part, tool call;

[0112] (1) User input;

[0113] The user poses a question or issues a requirement at the user terminal.

[0114] (2) Vectorization processing;

[0115] Vectorize the user input through the BCE-Embedding model, calculate the cosine similarity with the description vector of the tool, and filter out relevant tools.

[0116] (3) Tool sorting;

[0117] Sort the preliminarily filtered tools through the BCE-Rerank model to determine the optimal tool list.

[0118] The following briefly introduces the differences between the Embedding and Rerank models:

[0119]

[0120]

[0121] (4) Parameter verification;

[0122] Check whether the parameters required for tool invocation are complete according to the large model.

[0123] If not enough, the large model will ask the user to supplement information until the invocation conditions are met.

[0124] If satisfied, directly enter the tool invocation process.

[0125] (5) Tool invocation;

[0126] Query the tool configuration and interface documentation in the tool database.

[0127] Assemble the user dynamic parameters and fixed pass-through parameters into an HTTP request.

[0128] Invoke the tool interface and obtain the return result.

[0129] (6) Return data processing;

[0130] If the response data returned by the tool needs to be processed by the large model for a second time, it will be passed to the large model for processing and then returned to the user terminal.

[0131] If no processing is required, the response data will be directly returned to the user terminal for component rendering.

[0132] (7) Complete response;

[0133] Return the final result to the user.

[0134] The method for using a tool based on interface document parsing provided in this embodiment has at least the following beneficial effects:

[0135] 1) Automatically complete tool registration by parsing Swagger documents, reducing the complexity of manual configuration;

[0136] 2) Support users to customize pass-through parameters and data processing methods to meet various business requirements;

[0137] 3) Based on vectorized computing and sorting models, ensure the accuracy and relevance of tool selection;

[0138] 4) Introduce a dynamic questioning mechanism for the input by the large model to ensure the integrity of call parameters and improve the call success rate;

[0139] 5) The entire process of tool call and data processing is automated without manual intervention, significantly improving efficiency;

[0140] 6) Applicable to a variety of business scenarios, supporting complex business logics and tool integrations;

[0141] 7) Provide users with a seamless interaction experience through intelligent tool selection and dynamic feedback mechanisms.

[0142] For example Figure 4 , an embodiment of the present application provides a tool usage device based on interface document parsing. The device includes:

[0143] A request acquisition module 1100 is used to respond to a request sent by a user terminal; the request contains the user's demand data;

[0144] A tool screening module 1200 is used to screen out target tools from a tool database according to the demand data; the tool database contains multiple registered tools, and the registration process of the tools includes: parsing interface documents and registering them as tools according to the parsed interface information; the target tools include several tools in the tool database;

[0145] A response acquisition module 1300 is used to call the target tool to obtain response data corresponding to the demand data;

[0146] A data feedback module 1400 is used to feedback the response data to the user terminal.

[0147] It should be noted that the tool usage device based on interface document parsing provided in this embodiment and the above-mentioned tool usage method based on interface document parsing are based on the same inventive concept. Therefore, the relevant content of the above-mentioned tool usage method based on interface document parsing also applies to the content of the tool usage device based on interface document parsing. Therefore, it will not be elaborated here.

[0148] For example Figure 5 , an embodiment of the present application further provides an electronic device. This electronic device includes:

[0149] At least one memory;

[0150] At least one processor;

[0151] At least one program;

[0152] The program is stored in the memory, and the processor executes at least one program to implement the method for using the tool based on interface document parsing described above in the present disclosure.

[0153] The electronic device can be any intelligent terminal including a mobile phone, a tablet computer, a personal digital assistant (PDA), an in-vehicle computer, etc.

[0154] The electronic device of the embodiments of the present application will be introduced in detail below.

[0155] The processor 1600 can be implemented by using a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is used to execute relevant programs to implement the technical solutions provided by the embodiments of the present invention;

[0156] The memory 1700 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM), etc. The memory 1700 can store an operating system and other application programs. When implementing the technical solutions provided by the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 1700, and are called by the processor 1600 to execute the method for using the tool based on interface document parsing of the embodiments of the present invention.

[0157] The input / output interface 1800 is used to implement information input and output;

[0158] The communication interface 1900 is used to implement communication interaction between this device and other devices, and can implement communication through a wired method (such as USB, network cable, etc.) or through a wireless method (such as a mobile network, WIFI, Bluetooth, etc.);

[0159] The bus 2000 transmits information between the various components of the device (such as the processor 1600, the memory 1700, the input / output interface 1800, and the communication interface 1900);

[0160] Among them, the processor 1600, the memory 1700, the input / output interface 1800, and the communication interface 1900 are communicatively connected to each other inside the device through the bus 2000.

[0161] An embodiment of the present invention also provides a storage medium, which is a computer-readable storage medium. The computer-readable storage medium stores computer-executable instructions for causing a computer to execute the above-mentioned method for using a tool based on interface document parsing.

[0162] As a non-transitory computer-readable storage medium, the memory can be used to store non-transitory software programs and non-transitory computer-executable programs. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices.

[0163] In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0164] The embodiments described in the present invention are for more clearly illustrating the technical solutions of the embodiments of the present invention, and do not constitute a limitation on the technical solutions provided by the embodiments of the present invention. Those skilled in the art will know that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of the present invention are equally applicable to similar technical problems.

[0165] Those skilled in the art can understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present invention, and may include more or fewer steps than those shown, or combine certain steps, or different steps.

[0166] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0167] Those of ordinary skill in the art can understand that all or some of the steps in the above-disclosed methods, and the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, and appropriate combinations thereof.

[0168] In the description of this application and the above-mentioned drawings, terms such as "first", "second", "third", "fourth", etc. (if any) are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances so that the embodiments of this application described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that comprises a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0169] It should be understood that in this application, "at least one (item)" means one or more, and "a plurality" means two or more. "And / or" is used to describe the association relationship of associated objects and indicates that three relationships can exist. For example, "A and / or B" can mean: only A exists, only B exists, and both A and B exist at the same time. Among them, A and B can be singular or plural. The character " / " generally means that the associated objects before and after are in an "or" relationship. "At least one (one) of the following" or its similar expression refers to any combination of these items, including any combination of single items (ones) or plural items (ones). For example, at least one (one) of a, b, or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0170] In several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces, and the indirect coupling or communication connection of devices or units can be in an electrical, mechanical, or other form.

[0171] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0172] In addition, in each embodiment of the present application, the functional units can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0173] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes multiple instructions for causing an electronic device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in the various embodiments of the present application. The foregoing storage medium includes: various media that can store programs, such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0174] The above is a specific description of the preferred implementation of the embodiments of the present application. However, the embodiments of the present application are not limited to the above implementation manners. Those skilled in the art can make various equivalent deformations or substitutions without departing from the spirit of the embodiments of the present application. These equivalent deformations or substitutions are all included within the scope defined by the claims of the embodiments of the present application.

Claims

1. A method for using a tool based on interface document parsing, characterized in that: The method comprises: Responding to a request sent by a user terminal; the request includes user demand data; Filtering a target tool from a tool database according to the demand data; the tool database contains a plurality of registered tools, and the tool registration process includes: parsing an interface document, and registering the tool according to the parsed interface information; the target tool includes a plurality of the tools in the tool database; Calling the target tool to obtain response data corresponding to the demand data; The response data is fed back to the user terminal.

2. The method for using a tool based on interface document parsing according to claim 1, characterized in that: The tool database also includes a description vector describing the tool; Filtering target tools from the tool database according to the demand data includes: Vectorizing the demand data to obtain a demand data vector; Matching the demand data vector with the description vector to obtain a matching description vector; Filter out target tools based on the matching description vectors.

3. The method for using a tool based on interface document parsing according to claim 2, characterized in that: There are multiple matched description vectors; The step of screening out target tools according to the matched description vectors includes: Acquire the multiple tools corresponding to the matched multiple description vectors; Sorting the plurality of tools based on BCE-Rerank to obtain a ranking result; The target tool is determined according to the sorting result.

4. The method for using a tool based on interface document parsing according to claim 2, characterized in that: The matching the demand data vector with the description vector to obtain a matching description vector includes: The demand data vector is matched with the description vector by cosine similarity to obtain a matching description vector.

5. The method for using a tool based on interface document parsing according to claim 3, characterized in that: Determining the target tool according to the sorting result includes: The ranking results are input into a first large model to determine the target tool according to the first large model.

6. The method for using a tool based on interface document parsing according to claim 5, characterized in that: Before calling the target tool to obtain response data corresponding to the demand data, the method further includes: The demand data is input into the first large model to determine whether the demand data is complete according to the first large model. If incomplete, a response message indicating incomplete demand is sent to the user terminal so that the user terminal resends a request for demand data containing complete information based on the response message.

7. The method for using a tool based on interface document parsing according to claim 1, characterized in that: Feeding back the response data to the user terminal includes: Sending the response data to the user terminal for component rendering; Alternatively, the response data is processed using a second large model, and the response data processed by the second large model is sent to the user terminal.

8. The method for using a tool based on interface document parsing according to claim 1, characterized in that: The registration process for the tool also includes: Configuring fixed transparent transmission parameters for the tool; Before calling the target tool to obtain response data corresponding to the demand data, the method further includes: Assemble the fixed transparent transmission parameters for the target tool.

9. A tool using device based on interface document parsing, characterized in that: The device comprises: A request acquisition module, used to respond to a request sent by a user terminal; the request includes user demand data; A tool screening module is used to screen out a target tool from a tool database according to the demand data; the tool database contains a plurality of registered tools, and the registration process of the tool includes: parsing an interface document and registering the tool according to the parsed interface information; the target tool includes a plurality of the tools in the tool database; A response acquisition module, used for calling the target tool to acquire response data corresponding to the demand data; A data feedback module is used to feed back the response data to the user terminal.

10. An electronic device, characterized in that: include: at least one control processor and a memory for communicatively coupling with the at least one control processor; The memory stores instructions that can be executed by the at least one control processor, and the instructions are executed by the at least one control processor so that the at least one control processor can execute the tool usage method based on interface document parsing as described in any one of claims 1 to 8.

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