Data query method and device and data query agent
Through the data query agent planning and performing data positioning steps in cross-platform operation and maintenance scenarios, the problem of inefficiency of operation and maintenance personnel when querying data across platforms is solved, and more efficient and accurate data acquisition is achieved.
Patent Information
- Application Number
- CN202411910473.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-23
- Publication Date
- 2025-04-25
AI Technical Summary
In cross-platform operation and maintenance scenarios, it is difficult for operation and maintenance personnel to efficiently query cross-platform data, resulting in inefficient query.
Provide a data query method and agent, by obtaining query tasks described in natural language, using the data query agent to plan and execute data positioning steps, calling preset data positioning tools, and summarizing the query results.
It improves the efficiency of data query in cross-platform scenarios, reduces the complexity and time of manual query by operation and maintenance personnel, and achieves more accurate and efficient data acquisition.
Smart Images
Figure CN119884307A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the field of computer technology, in particular to the fields of artificial intelligence, cloud computing, and more particularly to a data query method, device, and data query intelligent agent. Background Art
[0002] In the daily operation and maintenance process, in order to effectively operate and maintain the equipment, the operation and maintenance personnel need to obtain relevant information from the operation and maintenance platform. Summary of the invention
[0003] The present disclosure provides a data query method, device and data query agent for improving the efficiency of data query.
[0004] According to a first aspect of the present disclosure, a data query method is provided, comprising:
[0005] Obtaining query description information for describing a data query task in natural language;
[0006] Using a data query agent, based on the query description information, a first data location step required to execute the data query task and a data location sequence of each of the first data location steps are planned;
[0007] Utilizing the data query agent, calling the target data locating tool corresponding to each of the first data locating steps to respectively execute each of the first data locating steps in the data locating order, and obtaining the data locating result of each of the first data locating steps, wherein the target data locating tool is a tool among a plurality of preset data locating tools that can execute the corresponding data locating step;
[0008] The data query agent is used to obtain the execution result of the data query task based on the data location result summary of each of the first data location steps.
[0009] According to a second aspect of the present disclosure, there is provided an agent training method, comprising:
[0010] Obtaining sample description information for describing a sample data query question in natural language, a second data location step required to answer the sample data query question, data location results of each of the second data location steps, an answer to the sample data query question, a first sample prompt word, and a second sample prompt word;
[0011] Based on the sample description information, the first sample prompt word and the second data location step, training a first large language model, wherein the first large language model is used to plan the data location steps required to answer the sample data query question under the guidance of the first sample prompt word;
[0012] Based on the reported data location results of each of the second data location steps, the second sample prompt words, and the answer to the sample data query question, training a second language model, wherein the second language model is used to summarize the data location results of each of the second data location steps under the guidance of the second sample prompt words to obtain the answer to the sample data query question;
[0013] The trained intelligent agent includes the first largest language model and the second largest language model.
[0014] According to a third aspect of the present disclosure, a data query device is provided, comprising:
[0015] An information acquisition module, used to acquire query description information used to describe a data query task in natural language;
[0016] A planning module, used for using a data query agent to plan, based on the query description information, the first data location steps required to execute the data query task and the data location order of each of the first data location steps;
[0017] an execution module, configured to utilize the data query agent to call the target data locating tool corresponding to each of the first data locating steps to respectively execute each of the first data locating steps in the data locating sequence to obtain a data locating result of each of the first data locating steps, wherein the target data locating tool is a tool among a plurality of preset data locating tools that can execute the corresponding data locating step;
[0018] A result module is used to utilize the data query agent to obtain the execution result of the data query task based on the data location result summary of each of the first data location steps.
[0019] According to a fourth aspect of the present disclosure, there is provided an intelligent agent training device, comprising:
[0020] A sample acquisition module, used to acquire sample description information used to describe a sample data query question in natural language, a second data location step required to answer the sample data query question, data location results of each of the second data location steps, an answer to the sample data query question, a first sample prompt word, and a second sample prompt word;
[0021] A first training module is used to train a first large language model based on the sample description information, the first sample prompt word and the second data location step, wherein the first large language model is used to plan the data location step required to answer the sample data query question under the guidance of the first sample prompt word;
[0022] a second training module, for training a second language model based on the data location results of each of the second data location steps, the second sample prompt words, and the answer to the sample data query question, wherein the second language model is used to summarize the data location results of each of the second data location steps under the guidance of the second sample prompt words to obtain the answer to the sample data query question;
[0023] The trained intelligent agent includes the first largest language model and the second largest language model.
[0024] According to a fifth aspect of the present disclosure, there is provided a data query agent, comprising:
[0025] An input module, used for receiving input information;
[0026] a processing module, configured to determine a target task based on the input information received by the input module, determine a large language model based on the target task, and obtain output information by calling the large language model to execute any one of the methods described in the first aspect above;
[0027] An output module is used to output the output information obtained by the processing module.
[0028] According to a sixth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute any one of the methods described in the first aspect.
[0029] According to a seventh aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the method according to any one of the above first aspects.
[0030] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS
[0031] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.
[0032] Figure 1 It is a flow chart of the data query method provided by the present disclosure;
[0033] Figure 2a It is a diagram of a user entering descriptive information through a conversational front end;
[0034] Figure 2b It is a schematic diagram of a user inputting description information through a service number;
[0035] Figure 2c It is a schematic diagram of a user inputting description information through an intelligent assistant;
[0036] Figure 3 It is a flow chart of a training method for a data query agent provided by the present disclosure;
[0037] Figure 4 It is a flow chart of the intelligent agent training method provided by the present disclosure;
[0038] Figure 5 It is a structural schematic diagram of a data query device provided by the present disclosure;
[0039] Figure 6 is a structural schematic diagram of an intelligent agent training device provided by the present disclosure;
[0040] Figure 7 It is a block diagram of an electronic device used to implement the data query method of the embodiment of the present disclosure. DETAILED DESCRIPTION
[0041] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.
[0042] First, the relevant terms or concepts in the embodiments of the present disclosure are explained:
[0043] AI Agent: It is an intelligent agent system with a large language model as the core controller. Its essence is an agent system that controls the large language model to solve user problems. It relies on the large language model as its core decision-making and processing unit, and can use the logical reasoning ability of the large language model to solve user problems. In practical applications, the agent can be a software or hardware entity that can act autonomously.
[0044] LLM (Large Language Model) refers to a deep learning model trained on massive text data, which has strong expressive and generalization capabilities. It can not only generate natural language text, but also deeply understand the meaning of the text and handle various natural language tasks such as text summarization, question answering, translation, etc.
[0045] It is understandable that in some application scenarios, operation and maintenance personnel want to query specific data. In this case, the operation and maintenance personnel need to access a specific page of a specific operation and maintenance platform to query the data. However, in cross-platform operation scenarios, there are many operation and maintenance platforms and pages in each operation and maintenance platform. It is difficult for operation and maintenance personnel to know which page of which operation and maintenance platform each data is located in, which leads to low efficiency of data query by operation and maintenance personnel.
[0046] Based on this, the present application provides a data query agent, so that the data query agent can be used to execute data query tasks and obtain the execution results of the data query tasks.
[0047] The data query agent can perform data query tasks in the following ways: Figure 1 As shown, including:
[0048] Step S101, obtaining query description information for describing a data query task in natural language.
[0049] Step S102, using a data query agent, based on the query description information, plans to obtain the first data location steps required to execute the data query task and the data location sequence of each first data location step.
[0050] Step S103, using the data query agent, calling the target data locating tool corresponding to each first data locating step to execute each first data locating step in a data locating sequence to obtain the data locating result of each first data locating step.
[0051] The target data locating tool is a tool among a plurality of preset data locating tools that can execute corresponding data locating steps.
[0052] Step S104, using the data query agent, summarizes the data locating results of each first data locating step to obtain the execution result of the data query task.
[0053] This embodiment is selected to directly use the pre-trained data query agent to plan the query description information for the query description information, and then call each data location tool to respectively execute each first data location step obtained by the plan, and summarize the data location results of each first data location step to obtain the execution result of the data query task. During the whole process, the operation and maintenance personnel can realize data query without manually performing data query based on their own accumulated experience, which effectively improves the efficiency of data query in cross-platform scenarios.
[0054] The above steps S101 to S104 will be described below respectively:
[0055] Among them, in step S101, the resource management description information can be input by the operation and maintenance personnel in text form or in voice form. For example, the operation and maintenance personnel can input the text "how to view operation and maintenance data A" or input the voice "how to view operation and maintenance data A".
[0056] When the operation and maintenance personnel input text, the text can be directly used as the problem description information. When the operation and maintenance personnel input voice, the voice can be recognized and the recognized text can be used as the problem description information.
[0057] The execution subjects of the cross-platform operation method provided in this application include but are not limited to mobile phones, desktop computers, tablet computers, smart watches, smart speakers, etc. For mobile phones, desktop computers, platform computers and devices that support text input, the operation and maintenance personnel can input text or voice, while for devices such as smart speakers that do not support text input, the operation and maintenance personnel can only input voice.
[0058] The operation and maintenance personnel may input text or voice by opening a specific page and inputting text or voice on the page. For example, Figure 2a It is also possible to open a specific service number through a specific application and input text or voice in the dialog window with the service number, for example, Figure 2b It is also possible to input text or voice by interacting with a specific group chat robot in the group chat window of the social software, for example, Figure 2c shown.
[0059] It is understandable that although Figure 2a , Figure 2b as well as Figure 2c The inputs of the operation and maintenance personnel are shown to be text, but this is only for the convenience of showing the problem description information input by the operation and maintenance personnel. The principle is the same for the case of voice input, which will not be repeated here.
[0060] In step S102, the data query agent may perform planning based on pre-learned knowledge about how to perform planning. During planning, the data query agent may be implemented based on any algorithm model with planning capabilities.
[0061] Exemplarily, in a possible embodiment, step S102 includes:
[0062] Step S1021, using the data query agent, calling the first language model, and inputting the query description information and the first prompt word into the first language model, obtaining the first data locating step output and executed by the first language model under the guidance of the first prompt word, and the data locating order of each first data locating step.
[0063] The first prompt word is used to guide the first language model to perform step planning. The first prompt word may be pre-set or obtained during training of the first language model.
[0064] This embodiment is selected to utilize a pre-trained data query agent, and planning is performed based on the query description information through the trained first language model combined with the first prompt word. Due to the guidance of the first prompt word, the ability of the first language model planning step can be fully utilized, thereby improving the accuracy of the data locating step obtained by planning, and further making the subsequent execution results obtained based on the data locating step more accurate.
[0065] The first prompt word can be set according to the needs of the user. In order to enable the first prompt word to better guide the first language model, the first prompt word can be used to describe the process of planning the data location steps required to perform the example data query task and the data location order of each data location step based on the example query description information, and the example data query task is the data query task described by the example query description information.
[0066] In step S103, the number and type of preset data location tools may vary according to different application scenarios. In addition, in a possible embodiment, the target data location tools corresponding to each first data location step may be obtained in the following manner:
[0067] The query description information, the first prompt word, and the description information of each of the plurality of preset data locating tools are input into the first large language model, and the first data locating steps required to perform the data locating task and the data locating sequence of each first data locating step output by the first large language model under the guidance of the first prompt word are obtained, as well as the target data locating tools corresponding to each first data locating step output by the first large language model under the guidance of the first prompt word.
[0068] By selecting this embodiment, the data query agent can grasp the capabilities of each data location tool based on the description information of each data location tool, thereby more accurately calling the data location tool with corresponding capabilities to execute the first data location step, thereby improving the accuracy of the data location results, and further improving the accuracy of the subsequent execution results obtained based on the summary of the data location results.
[0069] Correspondingly, in this embodiment, in order to enable the first prompt word to better guide the first large language model to accurately output the target data locating tool corresponding to each first data locating step, the first prompt word can be used to describe the precautions when calling each data locating tool.
[0070] In step S104, the data query agent may summarize based on the pre-learned knowledge on how to summarize the data location results of each data location step. When summarizing, the data query agent may be implemented based on any algorithm model with summarizing capabilities.
[0071] Exemplarily, in a possible embodiment, step S104 includes:
[0072] Step S1041, using the data query agent, calling the second largest language model, and inputting the data location results of each first data location step and the second prompt word into the second largest language model, to obtain the execution result of the data query task output by the second largest language model under the guidance of the second prompt word.
[0073] The second prompt word is used to guide the second language model to summarize. The second prompt word can be pre-set or obtained when training the second language model. The summary in this article refers to the process of refining the results of the task to obtain the answer to the question.
[0074] This embodiment is selected to utilize the pre-trained data query agent, and summarizes through the trained second language model combined with the second prompt word. Due to the guidance of the second prompt word, the summarizing ability of the second language model can be fully utilized, thereby improving the accuracy of the execution result obtained by the summary.
[0075] The execution result obtained in step S104 can be used to represent a page that can display the data indicated by the data query task, for example, it can be a link to the page, so that the operation and maintenance personnel can directly jump to the corresponding page according to the execution result to obtain the required query data.
[0076] The following will explain how to train a data query agent. Figure 3 , Figure 3 The figure is a flow chart of the training method of the data query agent provided by the present application, including:
[0077] Step S301, obtaining sample description information used to describe the sample data query question in natural language, the second data locating step required to answer the sample data query question, the data locating results of each second data locating step, the answer to the sample data query question, the first sample prompt word and the second sample prompt word.
[0078] Step S302: training a first language model based on the sample description information, the first sample prompt word and the second data positioning step.
[0079] The first language model is used to plan the data location steps required to answer the sample data query question under the guidance of the first sample prompt word.
[0080] Step S303: training a second language model based on the reported data location results of each second data location step, the second sample prompt words, and the answers to the sample data query questions.
[0081] The second largest language model is used to summarize the data location results of each second data location step under the guidance of the second sample prompt word to obtain the answer to the sample data query question.
[0082] The trained data query agent includes the first largest language model and the second largest language model.
[0083] Among them, in step S302, the sample description information and the first sample prompt word can be used as the input of the first large language model to obtain the output of the first large language model, and the second data positioning step can be used as the true value to calculate the loss between the output of the first large language model and the true value, and then the first large language model is adjusted according to the loss.
[0084] In step S303, the data location results of each second data location step and the second sample prompt word may be used as inputs of the second largest language model to obtain the output of the second largest language model, and the answer to the sample data query question may be used as the true value to calculate the loss between the output of the second largest language model and the true value, and then the second largest language model may be adjusted according to the loss.
[0085] By selecting this embodiment, the data query process of the operation and maintenance personnel can be integrated into the function of the data query agent by training the first language model and the second language model, so that alarm analysis can be performed through the data query agent, thereby improving the efficiency of data query.
[0086] The alarm analysis agent provided in this application can achieve the following functions:
[0087] Natural Language Processing (NLP): The intelligent agent can understand the user's natural language input and translate it into database query statements or platform interface parameters, thereby lowering the user's technical threshold.
[0088] Semantic understanding and parsing: Through semantic analysis, the agent can accurately understand the user's query intent, even in complex or ambiguous query requests.
[0089] Cross-platform data access: The agent supports queries from multiple data sources, including relational databases, NoSQL databases, data lakes, etc., and provides a unified query interface.
[0090] Data aggregation and integration: Intelligent agents can integrate data from different data sources, perform aggregation and calculations, and provide a global view and comprehensive analysis results.
[0091] Query optimization: The agent uses intelligent algorithms to optimize query paths and execution plans to improve query speed and efficiency.
[0092] Result caching: The agent can cache the results of frequently queried results to reduce repeated calculations and database accesses.
[0093] Query habit learning: The intelligent agent provides personalized query suggestions and auto-completion functions by learning the user's query habits and history.
[0094] Intelligent recommendation: Based on the user's query history and preferences, the agent can recommend relevant data sets and analysis dimensions to improve the user experience.
[0095] Corresponding to the aforementioned data query method, the present application also provides an agent training method, such as Figure 4 As shown, including:
[0096] Step S401, obtaining sample description information used to describe the sample data query question in natural language, the second data locating step required to answer the sample data query question, the data locating results of each second data locating step, the answer to the sample data query question, the first sample prompt word and the second sample prompt word.
[0097] Step S402: training a first language model based on the sample description information, the first sample prompt word and the second data positioning step.
[0098] The first large language model is used to plan the data location steps required to answer the sample data query question under the guidance of the first sample prompt word.
[0099] Step S403: training a second language model based on the reported data location results of each second data location step, the second sample prompt words, and the answers to the sample data query questions.
[0100] The second large language model is used to summarize the data location results of each of the second data location steps under the guidance of the second sample prompt word to obtain the answer to the sample data query question.
[0101] The trained intelligent agent includes the first largest language model and the second largest language model.
[0102] Corresponding to the aforementioned data query method, the present application also provides a data query device, such as Figure 5 As shown, including:
[0103] An information acquisition module 501 is used to acquire query description information for describing a data query task in natural language;
[0104] A planning module 502, configured to utilize a data query agent to plan, based on the query description information, the first data location steps required to execute the data query task and the data location sequence of each of the first data location steps;
[0105] An execution module 503 is used to use the data query agent to call the target data locating tool corresponding to each of the first data locating steps to respectively execute each of the first data locating steps in the data locating order to obtain the data locating result of each of the first data locating steps, wherein the target data locating tool is a tool that can execute the corresponding data locating step among a plurality of preset data locating tools;
[0106] The result module 504 is used to utilize the data query agent to obtain the execution result of the data query task based on the data location result summary of each of the first data location steps.
[0107] In a possible embodiment, the execution result is used to represent a page that can display the data indicated by the data query task.
[0108] In a possible embodiment, the planning module uses the data query agent to plan, based on the query description information, the first data location steps required to execute the data query task and the data location order of each of the first data location steps, including:
[0109] The data query agent is used to call a first large language model, and the query description information and a first prompt word are input into the first large language model to obtain a first data location step output and executed by the first large language model under the guidance of the first prompt word, as well as a data location order of each of the first data location steps.
[0110] In a possible embodiment, the first prompt word is used to describe the process of planning the data location steps required to execute an example data query task and the data location order of each data location step based on the example query description information, and the example data query task is the data query task described by the example query description information.
[0111] In a possible embodiment, the planning module inputs the query description information and the first prompt word into the first large language model, and obtains the first data location step output by the first large language model under the guidance of the first prompt word and the data location order of each of the first data location steps, including:
[0112] The query description information, the first prompt word, and the description information of each of the plurality of preset data locating tools are input into the first large language model to obtain the first report data locating steps required to execute the data locating task and the data locating order of each of the first data locating steps, which are output by the first large language model under the guidance of the first prompt word, and the target data locating tools corresponding to each of the first data locating steps, which are output by the first large language model under the guidance of the first prompt word.
[0113] In a possible embodiment, the using the data query agent to obtain the execution result of the data query task based on the data location result summary of each of the first data location steps includes:
[0114] The data query agent is used to call the second largest language model, and the data location results of each of the first data location steps and the second prompt word are input into the second largest language model to obtain the execution result of the data query task output by the second largest language model under the guidance of the second prompt word.
[0115] In a possible embodiment, the training process of the data query agent includes:
[0116] Obtaining sample description information for describing a sample data query question in natural language, a second data location step required to answer the sample data query question, data location results of each of the second data location steps, an answer to the sample data query question, a first sample prompt word, and a second sample prompt word;
[0117] Based on the sample description information, the first sample prompt word and the second data location step, training a first large language model, wherein the first large language model is used to plan the data location steps required to answer the sample data query question under the guidance of the first sample prompt word;
[0118] Based on the reported data location results of each of the second data location steps, the second sample prompt words, and the answer to the sample data query question, training a second language model, wherein the second language model is used to summarize the data location results of each of the second data location steps under the guidance of the second sample prompt words to obtain the answer to the sample data query question;
[0119] The trained data query agent includes the first largest language model and the second largest language model.
[0120] Corresponding to the aforementioned intelligent agent training method, the present application also provides an intelligent agent training device, such as Figure 6 As shown, including:
[0121] The sample acquisition module 601 is used to acquire sample description information for describing a sample data query question in natural language, a second data location step required to answer the sample data query question, data location results of each of the second data location steps, an answer to the sample data query question, a first sample prompt word, and a second sample prompt word;
[0122] A first training module 602 is used to train a first large language model based on the sample description information, the first sample prompt word and the second data location step, wherein the first large language model is used to plan the data location step required to answer the sample data query question under the guidance of the first sample prompt word;
[0123] A second training module 603 is used to train a second language model based on the data location results of each of the second data location steps, the second sample prompt words, and the answer to the sample data query question, wherein the second language model is used to summarize the data location results of each of the second data location steps under the guidance of the second sample prompt words to obtain the answer to the sample data query question;
[0124] The trained intelligent agent includes the first largest language model and the second largest language model.
[0125] The present application also provides a data query agent, including:
[0126] An input module, used for receiving input information;
[0127] a processing module, configured to determine a target task based on the input information received by the input module, determine a large language model based on the target task, and obtain output information by executing any of the above-mentioned data query methods by calling the large language model;
[0128] An output module is used to output the output information obtained by the processing module.
[0129] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.
[0130] Figure 7A schematic block diagram of an example electronic device 700 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.
[0131] like Figure 7 As shown, the device 700 includes a computing unit 701, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 702 or a computer program loaded from a storage unit 708 into a random access memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the device 700 can also be stored. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.
[0132] A number of components in the device 700 are connected to the I / O interface 705, including: an input unit 706, such as a keyboard, a mouse, etc.; an output unit 707, such as various types of displays, speakers, etc.; a storage unit 708, such as a disk, an optical disk, etc.; and a communication unit 709, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 709 allows the device 700 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.
[0133] The computing unit 701 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 701 performs the various methods and processes described above, such as a data query method or an agent training method. For example, in some embodiments, the data query method or the agent training method may be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 708. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 700 via the ROM 702 and / or the communication unit 709. When the computer program is loaded into the RAM 703 and executed by the computing unit 701, one or more steps of the data query method or the agent training method described above may be performed. Alternatively, in other embodiments, the computing unit 701 may be configured to execute the data query method or the agent training method in any other appropriate manner (eg, by means of firmware).
[0134] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.
[0135] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.
[0136] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
[0137] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).
[0138] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.
[0139] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.
[0140] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.
[0141] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.
Claims
1. A data query method, comprising: Obtaining query description information for describing a data query task in natural language; Using a data query agent, based on the query description information, a first data location step required to execute the data query task and a data location sequence of each of the first data location steps are planned; Utilizing the data query agent, calling the target data locating tool corresponding to each of the first data locating steps to respectively execute each of the first data locating steps in the data locating order, and obtaining the data locating result of each of the first data locating steps, wherein the target data locating tool is a tool among a plurality of preset data locating tools that can execute the corresponding data locating step; The data query agent is used to obtain the execution result of the data query task based on the data location result summary of each of the first data location steps.
2. The method according to claim 1, wherein: The execution result is used to represent a page that can display the data indicated by the data query task.
3. The method according to claim 1, wherein: The method of using the data query agent to plan and obtain the first data location steps required to execute the data query task and the data location sequence of each of the first data location steps based on the query description information includes: The data query agent is used to call a first large language model, and the query description information and a first prompt word are input into the first large language model to obtain a first data location step output and executed by the first large language model under the guidance of the first prompt word, as well as a data location order of each of the first data location steps.
4. The method according to claim 3, wherein: The first prompt word is used to describe the process of planning the data location steps required to execute an example data query task and the data location order of each data location step based on the example query description information. The example data query task is the data query task described by the example query description information.
5. The method according to claim 3, wherein: The step of inputting the query description information and the first prompt word into the first large language model to obtain the first data locating steps output by the first large language model under the guidance of the first prompt word and the data locating sequence of each of the first data locating steps includes: The query description information, the first prompt word, and the description information of each of the plurality of preset data locating tools are input into the first large language model to obtain the first report data locating steps required to execute the data locating task and the data locating order of each of the first data locating steps, which are output by the first large language model under the guidance of the first prompt word, and the target data locating tools corresponding to each of the first data locating steps, which are output by the first large language model under the guidance of the first prompt word.
6. The method according to claim 1, wherein: The step of obtaining the execution result of the data query task by using the data query agent based on the data location result of each of the first data location steps includes: The data query agent is used to call the second largest language model, and the data location results of each of the first data location steps and the second prompt word are input into the second largest language model to obtain the execution result of the data query task output by the second largest language model under the guidance of the second prompt word.
7. The method according to any one of claims 1 to 6, wherein: The training process of the data query agent includes: Obtaining sample description information for describing a sample data query question in natural language, a second data location step required to answer the sample data query question, data location results of each of the second data location steps, an answer to the sample data query question, a first sample prompt word, and a second sample prompt word; Based on the sample description information, the first sample prompt word and the second data location step, training a first large language model, wherein the first large language model is used to plan the data location steps required to answer the sample data query question under the guidance of the first sample prompt word; Based on the data location results of each of the second data location steps, the second sample prompt words, and the answer to the sample data query question, training a second language model, wherein the second language model is used to summarize the data location results of each of the second data location steps under the guidance of the second sample prompt words to obtain the answer to the sample data query question; The trained data query agent includes the first largest language model and the second largest language model.
8. A method for training an intelligent agent, comprising: Obtaining sample description information for describing a sample data query question in natural language, a second data location step required to answer the sample data query question, data location results of each of the second data location steps, an answer to the sample data query question, a first sample prompt word, and a second sample prompt word; Based on the sample description information, the first sample prompt word and the second data location step, training a first large language model, wherein the first large language model is used to plan the data location steps required to answer the sample data query question under the guidance of the first sample prompt word; Based on the data location results of each of the second data location steps, the second sample prompt words, and the answer to the sample data query question, training a second language model, wherein the second language model is used to summarize the data location results of each of the second data location steps under the guidance of the second sample prompt words to obtain the answer to the sample data query question; The trained intelligent agent includes the first largest language model and the second largest language model.
9. A data query device, comprising: An information acquisition module, used to acquire query description information used to describe a data query task in natural language; A planning module, used for using a data query agent to plan, based on the query description information, the first data location steps required to execute the data query task and the data location order of each of the first data location steps; an execution module, configured to utilize the data query agent to call the target data locating tool corresponding to each of the first data locating steps to respectively execute each of the first data locating steps in the data locating sequence to obtain a data locating result of each of the first data locating steps, wherein the target data locating tool is a tool among a plurality of preset data locating tools that can execute the corresponding data locating step; A result module is used to utilize the data query agent to obtain the execution result of the data query task based on the data location result summary of each of the first data location steps.
10. An intelligent agent training device, comprising: A sample acquisition module, used to acquire sample description information used to describe a sample data query question in natural language, a second data location step required to answer the sample data query question, data location results of each of the second data location steps, an answer to the sample data query question, a first sample prompt word, and a second sample prompt word; A first training module is used to train a first large language model based on the sample description information, the first sample prompt word and the second data location step, wherein the first large language model is used to plan the data location step required to answer the sample data query question under the guidance of the first sample prompt word; a second training module, for training a second language model based on the data location results of each of the second data location steps, the second sample prompt words, and the answer to the sample data query question, wherein the second language model is used to summarize the data location results of each of the second data location steps under the guidance of the second sample prompt words to obtain the answer to the sample data query question; The trained intelligent agent includes the first largest language model and the second largest language model.
11. A data query agent, comprising: An input module, used for receiving input information; a processing module, configured to determine a target task based on the input information received by the input module, determine a large language model based on the target task, and obtain output information by executing any one of the methods of claims 1 to 7 by calling the large language model; An output module is used to output the output information obtained by the processing module.
12. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-7.
13. A computer program product, comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 7.