Searching method and device based on large model, electronic equipment and storage medium

Abstract

The invention provides a search method and device based on a large model, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence, in particular to the technical field of large models, computer vision, deep learning and the like. The search method based on the large model comprises the following steps: in response to a received search request, determining a search intention indicated by the search request; the large model is used for carrying out content search on the search intention, multiple output data are obtained, and at least one piece of output data in the multiple output data is described in a specified language; the multiple pieces of output data are rendered, a first search result is obtained, and the rendering effect of the output data described by the specified language is different from the rendering effect of the remaining output data in the multiple pieces of output data.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, in particular to technical fields such as large models, computer vision and deep learning, and specifically to search methods, devices, electronic devices, intelligent agents and storage media based on large models. Background Art

[0002] With the development of computer technology and network technology, neural network models are widely used in various scenarios. For example, search engine products can use large language models (LLM) to output search results that meet users' various search needs. However, the output data style of large models is currently single and cannot clearly and accurately display search results to users. Summary of the invention

[0003] The present disclosure provides a large model-based search method, device, intelligent agent, electronic device and storage medium.

[0004] According to one aspect of the present disclosure, a search method based on a big model is provided, comprising: in response to a received search request, determining a search intent indicated by the search request; performing a content search based on the search intent using the big model to obtain a plurality of output data, at least one of the plurality of output data being described in a specified language; and rendering the plurality of output data to obtain a first search result, wherein a rendering effect of the output data described in the specified language is different from a rendering effect of the remaining output data in the plurality of output data.

[0005] According to another aspect of the present disclosure, a data display method is provided, comprising: sending a search request in response to receiving a data query request; and displaying a first search result in response to obtaining a first search result generated in response to the search request; wherein the first search result is generated using the search method provided by an embodiment of the present disclosure.

[0006] According to another aspect of the present disclosure, a search device based on a big model is provided, including: a first determination module, for determining the search intent indicated by a search request in response to a received search request; a search module, for performing content search for the search intent using the big model to obtain multiple output data, at least one of the multiple output data being described in a specified language; and a rendering module, for rendering the multiple output data to obtain a first search result, wherein the rendering effect of the output data described in the specified language is different from the rendering effect of the remaining output data in the multiple output data.

[0007] According to another aspect of the present disclosure, a data display device is provided, including: a sending module for sending a search request in response to receiving a data query request; and a display module for displaying a first search result in response to obtaining a first search result generated in response to the search request; wherein the first search result is generated using the search device provided by an embodiment of the present disclosure.

[0008] According to another aspect of the present disclosure, an artificial intelligence agent is provided, which is configured to execute the method provided by the embodiment of the present disclosure.

[0009] According to another aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method as described above.

[0010] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause the computer to execute the method as described above.

[0011] According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein the computer program implements the method described above when executed by a processor.

[0012] 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

[0013] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0014] Figure 1 A schematic diagram of an application scenario in which a large model-based search method and device according to an embodiment of the present disclosure can be applied is shown schematically;

[0015] Figure 2 The flowchart of the large model-based search method according to the embodiment of the present disclosure is schematically shown;

[0016] Figure 3 A schematic diagram schematically shows a first search result according to an embodiment of the present disclosure;

[0017] Figure 4 The schematic diagram schematically shows the principle of the large model-based search method according to an embodiment of the present disclosure;

[0018] Figure 5A A schematic diagram schematically shows a principle of updating a first search result according to an embodiment of the present disclosure;

[0019] Figure 5B A schematic diagram schematically shows a principle of updating a first search result according to another embodiment of the present disclosure;

[0020] Figure 5C A schematic diagram schematically shows a principle of updating a first search result according to another embodiment of the present disclosure;

[0021] Figure 6 The following is a schematic diagram showing a flow chart of a data display method according to an embodiment of the present disclosure;

[0022] Figure 7 The structure block diagram of the large model-based search device according to the embodiment of the present disclosure is schematically shown;

[0023] Figure 8 The structure block diagram of the data display device according to the embodiment of the present disclosure is schematically shown;

[0024] Fig. 9 A block diagram schematically shows a structure of an artificial intelligence agent according to an embodiment of the present disclosure; and

[0025] Fig.10 A block diagram of an electronic device according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0026] 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.

[0027] Figure 1 A schematic diagram of an application scenario in which a large model-based search method and device can be applied according to an embodiment of the present disclosure is schematically shown.

[0028] It should be noted that Figure 1 The examples shown are only examples of system architectures to which the embodiments of the present disclosure can be applied, in order to help those skilled in the art understand the technical content of the present disclosure, but do not mean that the embodiments of the present disclosure cannot be used in other devices, systems, environments or scenarios. For example, in another embodiment, an exemplary system architecture to which the large model-based search method and apparatus can be applied may include a terminal device, but the terminal device may implement the large model-based search method and apparatus provided by the embodiments of the present disclosure without interacting with the server.

[0029] like Figure 1 As shown, the application scenario 100 according to this embodiment may include a terminal device 101 and a server 102 .

[0030] The terminal device 101 can be connected to the server 102 via a network. The user can send a data query request 103 through the interactive interface provided by the terminal device 101, and the terminal device 101 can convert the data query request 103 into a search request 104, and send the search request 104 to the server 102, so that the server 102 calls the large model and outputs the first search result 105. The server sends the first search result 105 to the terminal device 101, so that the terminal device 101 displays the first search result 105 to the user.

[0031] Various communication client applications may be installed on the terminal device 101, such as intelligent assistant applications, knowledge reading applications, web browser applications, search applications, instant messaging tools, email clients and / or social platform software (only examples). The user may input a data query request 103 in the interactive interface of these client applications, and these client applications display the generated first search results 105 to the user.

[0032] In one embodiment, the server 102 may use a large language model to generate multimodal data and icon data, and call a rendering tool to generate a first search result 105 in a multimodal form, so as to display the first search result 105 in a multimodal form on the terminal device 101 .

[0033] The terminal device 101 can be configured with various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0034] Server 102 may be a server that provides various services, such as a background management server that provides support for the content browsed by the user using the interactive interface of the terminal device 101 (for example only). The background management server may call a large model to search for data in response to the received user instructions, and feed back the search results to the terminal device 101, and display them through the interactive interface. Server 102 may also be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short). Server 1302 may also be a server of a distributed system, or a server combined with a blockchain.

[0035] It should be noted that the search method based on the big model provided in the embodiment of the present disclosure can generally be executed by the server 102. Accordingly, the search device based on the big model provided in the embodiment of the present disclosure can also be set in the server 102. The search method based on the big model provided in the embodiment of the present disclosure can also be executed by a server or a server cluster that is different from the server 102 and can communicate with the terminal device 101 and / or the server 102. Accordingly, the search device based on the big model provided in the embodiment of the present disclosure can also be set in a server or a server cluster that is different from the server 102 and can communicate with the terminal device 101 and / or the server 102.

[0036] Alternatively, the large model-based search method provided in the embodiment of the present disclosure may also be generally executed by the terminal device 101. Accordingly, the large model-based search apparatus provided in the embodiment of the present disclosure may generally be arranged in the terminal device 101.

[0037] It should be understood that Figure 1 The number of terminal devices and servers in the embodiment is only for illustration. Any number of terminal devices and servers may be provided according to implementation requirements.

[0038] In the technical solution of the present disclosure, the collection, storage, use, processing, transmission, provision, disclosure and application of user personal information involved comply with the provisions of relevant laws and regulations, take necessary confidentiality measures, and do not violate public order and good morals.

[0039] In the technical solution of the present disclosure, the user's authorization or consent is obtained before obtaining or collecting the user's personal information.

[0040] In view of this, the embodiment of the present disclosure provides a search method based on a big model, which uses a big model to output search results displayed in a multimodal form according to the search needs of users, so as to enrich the expression form of search results and improve the expression ability of search results. In addition, the multimodal form can clearly and concisely display search results with complex content, optimize the visualization effect and readability of search results, and improve user experience.

[0041] The following will be combined Figure 2~Figure 4 , Figure 5A , Figure 5B , Figure 5C The large model-based search method provided by the present disclosure is described in detail.

[0042] Figure 2 The flowchart of the large model-based search method according to an embodiment of the present disclosure is schematically shown.

[0043] like Figure 2 As shown, the method 200 includes operations S210 to S220.

[0044] In operation S210, in response to a received search request, a search intent indicated by the search request is determined.

[0045] According to an embodiment of the present disclosure, search intent may represent the way in which a user expresses the purpose of the search and the search results. For example, the purpose of the search may be a search object, such as the weather in a certain place, the business hours of a certain park, and the life story of a certain celebrity. The way in which the search results are expressed may be the way in which the search results are displayed to the user, such as in at least one of the formats of text, audio, picture, video, chart, etc.

[0046] For example, the search intent may be obtained by parsing the search request using an intent recognition model, where the intent recognition model identifies keywords in the search request to determine the search intent.

[0047] For example, the search request may be in the format of text, audio, picture, video, chart, etc. The intent recognition model parses and recognizes the search request in the format of text, audio, picture, video, chart, etc., obtains the keywords included in the search request, and outputs the search intent. The search intent may also be expressed in at least one of the formats of text, audio, picture, video, etc.

[0048] In operation S220, a content search is performed according to the search intent using the large model to obtain a plurality of output data.

[0049] According to an embodiment of the present disclosure, the big model may be an artificial intelligence big model, which is a machine learning model with ultra-large-scale parameters and complex computing structure. The artificial intelligence big model can process massive data and complete various complex tasks, such as natural language processing, image recognition, computer vision, etc. The artificial intelligence big model may include a language big model, a visual big model, a multimodal big model, etc.

[0050] According to an embodiment of the present disclosure, at least one of the multiple output data is described in a specified language. The multiple output data may be described in multiple languages, and the data output data described in different languages ​​may be presented to the user in different expressions in the front-end terminal device.

[0051] According to an embodiment of the present disclosure, the specified language may be a language used to describe the visual effects presented by the search results, and the specified language may be a code language used to describe non-text data. For example, the specified language may be a code language used to describe audio, pictures, videos, and charts.

[0052] For example, the specified language may be a code language used to describe a chart. Since the search results are presented to the user in a single expression, the user cannot clearly and quickly obtain the effective information in the search results. Therefore, the large model needs to output output data described in a chart language so that the front-end terminal device can present the search results to the user in an image format to improve the readability of the search results.

[0053] In operation S230 , the plurality of output data are rendered to obtain a first search result.

[0054] According to an embodiment of the present disclosure, the rendering effect of the output data described in the specified language is different from the rendering effect of the remaining output data in the plurality of output data. For example, the specified language describes the chart data, and the remaining output data are all described in the language describing the text data. The chart data described in the specified language is rendered as a chart, and the remaining output data is rendered as text.

[0055] For example, the display object rendered by the output data described in the specified language can be editable. The display object is the content displayed by the terminal device, for example, the display object can be a picture, audio, chart, video, etc. For example, the style of the rendered chart is editable, and the color and size of the chart can be modified.

[0056] For example, the specified language may be mermaid. The output data described in mermaid can be rendered to display the first search result to the user in the form of a flowchart, a mind map, a timeline diagram, a user journey diagram, a block diagram, a C4 model diagram, an XY chart, etc. This allows the output data of the large model to support the display requirements of different scenarios.

[0057] According to an embodiment of the present disclosure, the big model can perform content search and content generation according to the search intent, and directly output multiple output data described in multiple languages, so that the terminal device can display the first search result to the user in a centralized expression.

[0058] According to an embodiment of the present disclosure, the big model can also perform content search and content generation for search intent to obtain a code language describing text data. The big model can then analyze the data content of the text data, convert the text data into image data, chart data, audio data, etc., and output multiple output data described in multiple languages. The big model can also perform content search and content generation on the data content of the text data, obtain image data, chart data, audio data, etc. related to the text data, and output multiple output data described in multiple languages.

[0059] For example, the large model can analyze the obtained text data to convert the text data into data in other formats according to the data features included in the text data, so that the data content can be more accurately and clearly reflected in other formats. In the case where it is determined that the text data is difficult to understand or difficult to accurately express the actual meaning, the large model can also perform content search and content generation again based on the data content of the text data to obtain data in other formats related to the text data, so that the data in other formats can be used to illustrate and supplement the text data.

[0060] According to the embodiments of the present disclosure, by using a large model to output output data described in different languages ​​according to the user's search needs, the output data can be displayed to the user in different expressions on the front end, so as to enrich the expression form of the search results and improve the expression ability of the search results. For search results with complex content, displaying them in different expressions can clearly and concisely reflect the effective information of the search results, optimize the visualization effect and readability of the search results, and improve the user experience.

[0061] The following will be combined Figure 3 A schematic description is given of the first search result displayed by the terminal device. Figure 3 A schematic diagram schematically shows a first search result according to an embodiment of the present disclosure.

[0062] like Figure 3 As shown, in the display page 300 of the terminal device, the expression of the first search result includes text object 301 , picture object 302 , chart object 302 , table object 340 , audio object 305 , video object 306 , and link object 307 .

[0063] According to an embodiment of the present disclosure, the text object 301 , the picture object 302 , the chart object 302 , the table object 340 , the audio object 305 , the video object 306 , and the link object 307 may be rendered from output data described in different languages.

[0064] For example, the search request output by the user is "Search for the career of athlete A". Text object 301 can describe the personal profile of athlete A, picture object 302 can be related photos of athlete A, chart object 303 can describe the types of events that athlete A participates in, and table object 304 can describe the awards won by athlete A. Audio object 304 can be the audio of an interview with athlete A. The user can listen to the interview content by clicking on audio object 304. Video object 305 can be a game video of athlete A. The user can watch the game content by clicking on video object 305. Link object 307 can be the personal homepage website of athlete A on a social networking site. The user can view the personal homepage website of athlete A by clicking on link object 307.

[0065] It should be noted that the data sources of the various display objects shown on the display page 300 are all in compliance with relevant regulations and legal, and the display method and display content can comply with relevant regulations.

[0066] According to an embodiment of the present disclosure, search results are displayed to users in a variety of expressions in response to their search needs, thereby increasing the readability of the search results.

[0067] The following will be combined Figure 4 The principle of the large model-based search method is schematically described.

[0068] Figure 4 A schematic diagram of the principle of a large model-based search method according to an embodiment of the present disclosure is schematically shown.

[0069] like Figure 4 As shown, the intent recognition model 410 performs intent recognition on the search request 410 to obtain the search intent. For example, the intent recognition model 410 can be a deep learning model running in the server.

[0070] According to an embodiment of the present disclosure, the search intent output by the intent recognition model 410 is input to the big model 420, and the search intent can indicate the description language of the output data output by the big model 420 to indicate that the output data output by the big model 420 can be displayed in multiple expressions.

[0071] For example, the intent recognition model 410 can determine whether the terminal device needs to display the first search result 406 in multiple expressions based on the search request 401. For example, the intent recognition model 410 determines whether expressions such as charts, text, audio, video, and pictures are needed.

[0072] For example, the search request may be "sportsman A's game video", and the intent recognition model 410 may recognize that the first search result 406 needs to be displayed in the form of a video. For example, the search request may be "weather changes displayed in the form of a chart", and the intent recognition model 410 may recognize that the first search result 406 needs to be displayed in the form of a chart. For example, the search request may be "a certain scenic spot introduced in a diversified form", and the intent recognition model 410 may recognize that the first search result 406 needs to be displayed in a variety of different expressions.

[0073] When the search request does not explicitly indicate that the first search result 406 should be displayed in only a single expression, the intent recognition model 410 may instruct the large model 420 to preferentially display the first search result 406 in multiple different expressions.

[0074] When the server calls the large model 420 , the server may obtain prompt information 402 related to the search request 401 , concatenate the prompt information Prompt 402 with the search request 401 , and input the prompt information Prompt 402 into the large model 420 .

[0075] According to an embodiment of the present disclosure, the prompt information 402 may be preset. For example, the prompt information 402 may be obtained during the training process of the large model 420. The prompt information 402 may describe the role, workflow, and precautions of the large model 420.

[0076] For example, the prompt information 402 may be an instruction sent to the large model 420, or may be a text description, or may be a parameter description in a certain format.

[0077] For example, the user can adjust the large model by editing the prompt information 402 during the training process of the large model 420, and pre-store the edited prompt information 402 in the memory of the server. During the use of the large model 420, the server can call the prompt information 402 from the memory and splice it with the search request 401 to input the large model 420.

[0078] For example, the user may also adjust the prompt information 402 according to actual operation requirements and operation results during the use of the large model 420 to improve the flexibility of the large model.

[0079] For example, the prompt information 402 may describe the role of the large model 420, for example, the role of the large model is a model that can write mermaid code. The brief introduction of the role is that a diagram with a simple structure and clear process can be drawn using the mermaid diagram language according to the user's search request.

[0080] For example, if prompt information 402 instructs the big model 420 to generate a flowchart, prompt information 402 describing the workflow of the big model 420 may include: 1. Please describe the relevant conceptual information and knowledge involved in the search request in detail, comprehensively and accurately. 2. Clarify the various prerequisites, possible accidents, potential actions and processes for the occurrence of the event. 3. Use diamond symbols for each decision point, and clarify the branch path of each decision. 4. The logical structure must be clear, the wording must be concise, and the amount of information must be appropriate. 5. The examples provide a variety of grammatical formats and organizational structures for flowcharts. Please refer to but do not stick to the examples given. 6. Output mermaid code.

[0081] For example, the precautions described in the prompt information 402 may include requirements on the format of the mermaid code output by the large model 420 and suggestions on writing the mermaid code.

[0082] The large model 420 performs content search based on the prompt information 420 and the search intent, and outputs a plurality of output data. For example, at least one of the plurality of output data may be described in mermaid code to be rendered as a chart in the terminal device.

[0083] In some embodiments, the large model 402 can obtain real-time data related to the search intent and process the real-time data to obtain multiple output data. For example, the large model 420 performs data search and data generation based on the data resource 403 and outputs multiple output data.

[0084] For example, data resources 403 may include pre-built databases and resource libraries, and may also include currently available real-time resources on the entire network. The large model 420 may search the entire network for real-time data related to the search intent to obtain the latest information related to the search intent, thereby ensuring that the output data is factual, timely, and accurate.

[0085] For example, the large model 420 can query the real-time data for data that meets the search intent and the search request, and output the data that meets the requirements. The large model 420 can also generate and output data that meets the search intent and the search request based on the real-time data.

[0086] According to an embodiment of the present disclosure, the output data output by the large model 420 includes chart data 404 and multimodal data 405. The chart data 404 may be described in mermaid code. The multimodal data 405 may include text data, image data, audio data, video data, web page link data, presentation files (PowerPoint PPT data), etc.

[0087] According to an embodiment of the present disclosure, a renderer 430 is used to render the chart data 404 and the multimodal data 405 so that a plurality of output data are displayed on the front end in a corresponding expression manner.

[0088] In some embodiments, the renderer 430 may determine a display type of at least one output data described in a specified language, and render the at least one output data based on a display control of the display type to obtain the first search result 406 .

[0089] For example, based on the display type of the output data, a display control of the display type is called, and the output data is inserted into the display control. The renderer 420 renders the display space after the output data is inserted, so that the front end displays it in the display type. The display type can be an expression of the output data. For example, the display type can include text, image, audio, video, web link, PPT, etc. The display control is used to display a specific type of output data. The style of the output data can be edited and modified in the display control.

[0090] For example, the renderer 430 may insert the chart data 404 described in the mermaid code into a presentation control for presenting a chart, and render the presentation control to display the chart data 404 in a chart form on the front end.

[0091] In some embodiments, the large model 420 may also output layout information and style information based on the search intent. The search intent indicates the display method and display content required by the user. The large model 420 may analyze the search intent and determine the layout information and style information of the multiple output data in the first search result 406 based on the correlation between the display contents.

[0092] For example, the layout information may include position information of the plurality of output data in the display page described above. For example, the layout information may indicate that the text data and the image data are arranged in adjacent positions so as to describe the image data through the text data. The layout information may also indicate that the video data and the chart data are arranged in adjacent positions so as to intuitively reflect the content in the video data through the chart data.

[0093] For example, the style information may include size information and color information of each of the plurality of output data, etc. For example, the style information may indicate the font size and font of the text data, and the style information may also indicate the resolution and size of the image data.

[0094] The renderer 430 renders the multiple output data based on the layout information and the style information to obtain the first search result 460. This allows the multiple output data in the first search result 406 to be displayed in a corresponding layout and style, and the content of the first search result 406 is selectively mixed in a clear and appropriate manner to improve the readability and effectiveness of the output content.

[0095] According to an embodiment of the present disclosure, the first search result 406 is sent to the terminal device at the front end to be displayed to the user. The user can view the first search result 406 through the display screen of the terminal device. The user can send an update request 407 for the first search result 406, so that the large model 402 updates the first search result 406 to obtain a second search result.

[0096] In some embodiments, the large model 402 may determine the update intent of the update request in response to the received update request 407 for the first search result, and update the first search result 406 based on the update intent to obtain the second search result.

[0097] According to an embodiment of the present disclosure, after browsing the first search result 406 , the user may make another request for the content of the first search result 406 , thereby satisfying the user's extended request for the first search result 406 .

[0098] The following will be combined Figure 5A , Figure 5B and Figure 5C The principle of updating the first search result is schematically described.

[0099] Figure 5A The schematic diagram schematically shows the principle of updating the first search result according to an embodiment of the present disclosure.

[0100] like Figure 5A As shown, in the display page 500a of the terminal device, the user can input a data query request through the search bar and click the "Search" button. The terminal device converts the data query request into a search request and sends it to the server. The server calls the large model to output the first search result 502. The first search result 502 is displayed in the display page 500a.

[0101] The display page 500a may also display the information source 503 of the first search result 502. The user may modify the search scope of the first search result 502 by clicking a “directional search” button 504 or a “information source preference setting” button 505.

[0102] According to an embodiment of the present disclosure, when the user believes that the first search result 502 is not accurate enough, the user can input an update intention for the first search result 502 to narrow the search scope. For example, the target object indicated by the update intention in the first search result 502 is determined, and the update output data of the target object is output using a large model. The update output data is rendered using a renderer to obtain an update object, and the target object in the first search result 502 is updated using the update object to obtain a second search result.

[0103] For example, the user clicks the "Directed Search" button 504, and a window 506 is displayed in the display page 500a. The user can enter the desired search scope and search content in the window 506. The large model can determine the target object in the first search result 502 based on the search content, and the target object is the content that the user wants to update in the first search result 502. For example, the large model can determine that the content in the first search result 502 that is related to or has a high similarity to the search content is the target object.

[0104] The search scope and search content input by the user are update requests. The large model determines the update intention based on the search scope and search content as re-searching or regenerating content within a specific search scope for the target object in the first search result 502, and renders the output updated output data. The updated object obtained by rendering can replace the target object in the first search result 502 to obtain the second search result.

[0105] The representation form of the updated object is not limited, as long as the representation form of the updated object is compatible with the representation form of the target object.

[0106] For example, the user can modify the information source of the first search result 502 by clicking the "Information Source Preferences" button 505. For example, the user can set the web page from which the large model obtains resources to modify the information source.

[0107] Figure 5B A schematic diagram schematically shows a principle of updating a first search result according to another embodiment of the present disclosure.

[0108] like Figure 5B As shown, in the display page 500b of the terminal device, the first search result 502 includes a text object 521 and a chart object 522. The target object 5211 is the text content that the user can select in the display page 500b. After the user selects the target object 5211, a window 509 can be displayed in the display page 500b. The user can enter an update request for the target object 5211 in the window 509.

[0109] The large model can perform content search and / or content generation in combination with the update request input by the user and the target object 5211 selected by the user, and output associated output data. For example, the associated output data is feedback to the user's update request, and the associated output data is rendered to obtain the associated object. The associated object can be used as supplementary content of the target object, and the first search result 502 is merged with the target object to obtain the second search result. For example, the target object 5211 describes the relevant content about the rental contract. The user can enter an update request in window 509: "Is this item valid for shop rental?" The associated object can describe whether the target object 5211 is applicable to shop rental, as well as the relevant content of the shop rental contract.

[0110] The presentation form of the associated object is not limited, as long as the presentation form of the associated object matches the presentation form of the target object.

[0111] For example, the user can also click the "adjust expression" button 507 to display a window 508 in the page 500b. The user can enter an expression requirement for the first search result 502 in the window 508. For example, the user can enter an expression requirement for the chart object 522 in the window 508 to modify the style of the chart object.

[0112] For example, the user may directly input a color update request and a size update request, so that the large model outputs new icon data that meets the color update request and the size update request, and replaces the chart object 522 with the rendered new icon object to display the second search result.

[0113] For example, the user may also input modification suggestions for the chart object 522. For example, the user inputs an update request in window 508, "I am a junior high school student, please explain this chart to me in a way that I can understand." The large model determines the chart object 522 as the target object based on the update request, and may generate text data according to the update request, so as to describe the chart object 522 through the rendered text object. At this time, the first search result 502 and the text object are merged into the second search result.

[0114] The large model may also simplify the content of the chart object 522 based on the update request, or split the content described by the chart object 522 into multiple chart objects, and replace the image object 522 with a new image object to obtain a second search result.

[0115] Figure 5C A schematic diagram schematically shows a principle of updating a first search result according to another embodiment of the present disclosure.

[0116] like Figure 5C As shown, the display page 500c of the terminal device displays the first search result 502 and evaluation information 510. The evaluation information 510 may be a confidence score output by the large model regarding the first search result 502. The evaluation information 510 may also include an explanation of the confidence score and the information source of the first search result 502.

[0117] The user clicks the "Information Verification" button 511, and a window 512 is displayed in the display page 500c. The user can enter an update request in the window 512: the content that needs to be verified. Based on the content entered by the user, a target object is determined in the first search result 502, and the target object is the content that the user needs to verify. The target object is verified for confidence using the large model to obtain the target confidence, and the target confidence is combined with the first search result and output to obtain the second search result.

[0118] For example, the confidence level can be determined by the score of the information source by the big model, for example, the confidence level is determined based on the reputation of the information source and whether the information provided by multiple information sources is consistent.

[0119] According to an embodiment of the present disclosure, during the update of the first search result 502, the large model may only update part of the content in the first search result 502, thereby achieving a partial update of the first search result 502 to reduce the amount of calculation of the large model during the update process.

[0120] Based on the large model-based search method provided by the present disclosure, the present disclosure also provides a data display method. Figure 6 The data display method is described in detail.

[0121] like Figure 6 As shown, the data display method 600 of this embodiment may include operations S610 to S620.

[0122] In operation S610, in response to receiving a data query request, a search request is sent.

[0123] According to an embodiment of the present disclosure, a data query request may be input to a terminal device by a user through an interactive interface provided by the terminal device. After receiving the input data query request, the terminal device may generate the search request and send the search request to the server.

[0124] For example, the terminal device can convert the data query request into a language recognizable by the large model, or add prompt information to the data query request to generate a search request.

[0125] For example, the prompt information added by the terminal device can be used to instruct the gesture recognition model to perform intent recognition.

[0126] In operation S620, in response to acquiring a first search result generated in response to the search request, the first search result is displayed.

[0127] According to an embodiment of the present disclosure, the first search result is generated by the large model-based search method described above.

[0128] For example, after receiving the search request, the server may output the first search result through the large model-based search method described above, and feed the first search result back to the terminal device. The terminal device may display the first search result after acquiring the first search result.

[0129] Through the data display method provided by the present invention, the first search result can be displayed to the user in a variety of ways based only on the user's data query request, thereby improving the expressiveness and readability of the search results output by the large model, which is conducive to improving the user experience.

[0130] Based on the large model-based search method provided by the present disclosure, the present disclosure also provides a large model-based search device. Figure 7 The device is described in detail.

[0131] Figure 7 A block diagram of a large model-based search device according to an embodiment of the present disclosure is schematically shown.

[0132] like Figure 7 As shown, the large model-based search device 700 may include: a first determination module 710 , a search module 720 and a rendering module 730 .

[0133] The first determination module 710 is used to determine the search intent indicated by the search request in response to the received search request.

[0134] The search module 720 is used to perform content search according to the search intent using the large model to obtain multiple output data, at least one of the multiple output data is described in a specified language.

[0135] The rendering module 730 is configured to render the plurality of output data to obtain a first search result, wherein the rendering effect of the output data described in the specified language is different from the rendering effect of the remaining output data in the plurality of output data.

[0136] According to an embodiment of the present disclosure, the search device 700 based on the large model further includes a second determination module and an update module. The second determination module is used to determine the update intention of the update request in response to the update request received for the first search result. The update module is used to update the first search result based on the update intention using the large model to obtain a second search result.

[0137] According to an embodiment of the present disclosure, the update module includes: a first determination submodule, used to determine the target object indicated by the update intention in the first search result; a first output submodule, used to output the update output data of the target object using a large model; a first rendering submodule, used to render the update output data to obtain an updated object; and an update submodule, used to update the target object in the first search result using the update object to obtain a second search result.

[0138] According to an embodiment of the present disclosure, the update module includes: a first determination submodule, used to determine the target object indicated by the update intention in the first search result; a first search submodule, used to use a large model to perform content search for the update intention, and obtain associated output data associated with the target object; a second rendering submodule, used to render the associated output data and obtain an associated object; and a second output submodule, used to output a second search result based on the associated object and the first search result.

[0139] According to an embodiment of the present disclosure, the update module includes: a first determination submodule, used to determine the target object indicated by the update intention in the first search result; a verification submodule, used to perform confidence verification on the target object using a large model to obtain a target confidence; and a third output submodule, used to output a second search result based on the first search result and the target confidence.

[0140] According to an embodiment of the present disclosure, the rendering module 730 includes: a second determination submodule, used to determine the display type of at least one output data described in a specified language; and a third rendering submodule, used to render at least one output data based on a display control of the display type to obtain a first search result.

[0141] According to an embodiment of the present disclosure, the search module 720 includes: a first acquisition submodule, used to acquire real-time data related to the search intent using a large model; and a processing submodule, used to process the real-time data to obtain multiple output data.

[0142] According to an embodiment of the present disclosure, the search module 720 includes: a second acquisition submodule, used to obtain prompt information related to the search intent, where the prompt information is pre-set; and a second search submodule, used to use a large model to perform content search based on the prompt information and the search intent to obtain multiple output data.

[0143] According to an embodiment of the present disclosure, the rendering module 730 includes: a third determination submodule, used to determine the layout information and style information indicated by the search intent; and a fourth rendering submodule, used to render multiple output data based on the layout information and style information to obtain a first search result.

[0144] Based on the data display method provided by the present disclosure, the present disclosure also provides a data display device. Figure 8 The device is described in detail.

[0145] Figure 8 It is a structural block diagram of a data display device according to an embodiment of the present disclosure.

[0146] like Figure 8 As shown, the data display device 800 of this embodiment may include an information sending module 810 and a display module 820 .

[0147] The sending module 810 is used to send a search request in response to receiving a data query request. In one embodiment, the sending module 810 can be used to perform the operation S610 described above, which will not be described in detail here.

[0148] The display module 820 is used for displaying the first search result in response to obtaining the first search result generated in response to the search request.

[0149] The first search result is generated by the large model-based search device described above. In one embodiment, the display module 820 can be used to perform the operation S620 described above, which will not be described in detail here.

[0150] Fig. 9 The structural block diagram of an artificial intelligence agent according to an embodiment of the present disclosure is schematically shown.

[0151] In the embodiments of the present disclosure, inspired by the von Neumann structure in modern computer theory, such as Fig. 9As shown, the AI ​​agent 900 may include five core modules: an input module 910 , a control module 920 , a storage module 930 , a calculation module 940 and an output module 950 .

[0152] The input module 910 is responsible for receiving or sensing information such as queries, requests, instructions, signals or data from the outside world (such as users or external environment), and converting them into a format that can be understood and processed by the AI ​​agent 900. The input module 910 is the primary link for the AI ​​agent 900 to interact with the outside world. It enables the AI ​​agent 900 to efficiently and accurately obtain the necessary "sensory" information from the outside world and respond to this information.

[0153] In an example, the input module 910 may input the search request and update request described above.

[0154] In the example, the control module 920 is the core support for the AI ​​agent 900 to handle complex tasks. The control module 920 can execute the large model-based search method described above.

[0155] In the example, the control module 920 will continuously interact with the storage module 930, the computing module 940 and / or the output module 950 during operation. However, it should be noted that in the embodiment of the present disclosure, the control module 920 acts as a single initiator to initiate communication with the storage module 930, the computing module 940 and / or the output module 950, and there is no communication coupling between the storage module 930, the computing module 940 and the output module 950.

[0156] In the example, the performance of the control module 920 may be closely related to the large model on which the AI ​​agent 900 is based. In order to give full play to the capabilities of the large language model, the internal structure of the control module 920 may be designed to be highly configurable and scalable to cope with various types of tasks and requirements in real scenarios.

[0157] The storage module 930 may be responsible for memorizing information such as historical conversations, event streams, etc. The prompt information and data resources mentioned above may be included in the storage module 930 .

[0158] In the example, after the AI ​​agent 900 obtains the search request, the AI ​​agent 900 can use the intent recognition model to determine the search intent from the search request. The search intent can be stored in the storage module 930. The AI ​​agent 900 can retrieve the relevant search intent, prompt information and data resources from the storage module 930 and feed them back to the control module 920. Then, the control module 920 can use the feedback prompt information and data resources to obtain the first search result corresponding to the search request, and pass the first search result to the output module 950.

[0159] The operation module 940 can be regarded as a predefined tool library. The renderer and display control mentioned above can be included in the operation module 940.

[0160] In the example, when the AI ​​agent 900 needs to render multiple output data, the relevant renderer and display control can be called from the operation module 940 and fed back to the control module 920. Then, the control module 920 can use the feedback renderer and display control to render the first search result and pass the first search result to the output module 950. It can be understood that although the large language model has excellent language understanding and generation capabilities, it is the same as a human being. Without the help of any tools, the tasks that can be solved are very limited. When the AI ​​agent 900 is given the ability to call tools, it can achieve tasks such as completing mathematical operations with the help of a calculator, completing data analysis with the help of python, and completing weather forecasts with the help of search engines.

[0161] In an example, the output module 950 may output the first search result and the second search result described above.

[0162] The AI ​​agent 900 according to the embodiment of the present disclosure can simply and effectively improve the level of intelligence, and improve flexibility and versatility.

[0163] 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.

[0164] According to an embodiment of the present disclosure, an electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method as described above.

[0165] According to an embodiment of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause a computer to execute the method as described above.

[0166] According to an embodiment of the present disclosure, a computer program product includes a computer program, and when the computer program is executed by a processor, the computer program implements the method as described above.

[0167] Fig.10A schematic block diagram of an example electronic device 1000 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.

[0168] like Fig.10 As shown, the electronic device 1000 includes a computing unit 1001, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1002 or a computer program loaded from a storage unit 1008 into a random access memory (RAM) 1003. In the RAM 1003, various programs and data required for the operation of the device 800 can also be stored. The computing unit 1001, the ROM 1002, and the RAM 1003 are connected to each other via a bus 804. An input / output (I / O) interface 1005 is also connected to the bus 1004.

[0169] Multiple components in the electronic device 1000 are connected to the I / O interface 1005, including: an input unit 1006, such as a keyboard, a mouse, etc.; an output unit 1007, such as various types of displays, speakers, etc.; a storage unit 1008, such as a disk, an optical disk, etc.; and a communication unit 1009, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 1009 allows the electronic device 1000 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0170] The computing unit 1001 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 1001 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 801 performs the various methods and processes described above, such as a search method based on a large model. For example, in some embodiments, the search method based on a large model may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the search method based on the large model described above may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to execute the large model-based search method in any other appropriate manner (eg, by means of firmware).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] A computer system may include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises through computer programs running on respective computers and having a client-server relationship to each other. The server may be a cloud server, a server in a distributed system, or a server combined with a blockchain.

[0177] 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.

[0178] 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 search method based on a large model, comprising: In response to the received search request, determining a search intent indicated by the search request; Performing a content search for the search intent using the large model to obtain a plurality of output data, at least one of the plurality of output data being described in a specified language; as well as The plurality of output data are rendered to obtain a first search result, wherein a rendering effect of the output data described in the specified language is different from a rendering effect of the remaining output data in the plurality of output data.

2. The method according to claim 1, further comprising: In response to the received update request for the first search result, determining an update intent of the update request; as well as The first search result is updated based on the update intention using the large model to obtain a second search result.

3. The method according to claim 2, wherein: The step of updating the first search result based on the update intention by using the large model to obtain a second search result includes: Determine a target object indicated by the update intention in the first search result; outputting updated output data of the target object using the large model; Rendering the updated output data to obtain an updated object; and The target object in the first search result is updated using the update object to obtain the second search result.

4. The method according to claim 2, wherein: The step of updating the first search result based on the update intention by using the large model to obtain a second search result includes: Determine a target object indicated by the update intention in the first search result; Using the large model to perform content search for the update intention, and obtain associated output data associated with the target object; Rendering the associated output data to obtain an associated object; and Based on the associated object and the first search result, the second search result is output.

5. The method according to claim 2, wherein: The step of updating the first search result based on the update intention by using the large model to obtain a second search result includes: Determine a target object indicated by the update intention in the first search result; Using the large model to perform confidence verification on the target object to obtain target confidence; and Based on the first search result and the target confidence, the second search result is output.

6. The method according to claim 1, wherein: The rendering of the plurality of output data to obtain a first search result includes: determining a display type of at least one output data described in the specified language; and Based on the display control of the display type, the at least one output data is rendered to obtain the first search result.

7. The method according to claim 1, wherein: The large model is used to perform content search according to the search intent, and multiple output data are obtained, including: Using the large model to obtain real-time data related to the search intent; and The real-time data is processed to obtain the multiple output data.

8. The method according to claim 1, wherein: The large model is used to perform content search according to the search intent, and multiple output data are obtained, including: Acquire prompt information related to the search request, where the prompt information is preset; and The large model is used to perform content search based on the prompt information and the search intent to obtain the multiple output data.

9. The method according to claim 1, wherein: The rendering of the plurality of output data to obtain a first search result includes: Determining layout information and style information indicated by the search intent; and The plurality of output data are rendered based on the layout information and the style information to obtain the first search result.

10. A data display method, comprising: In response to receiving the data query request, sending the search request; as well as In response to acquiring the first search result generated in response to the search request, displaying the first search result; Wherein, the first search result is generated by using the search method described in any one of claims 1 to 9.

11. A search device based on a large model, comprising: A first determination module, configured to determine, in response to a received search request, a search intent indicated by the search request; A search module, configured to use the large model to perform content search for the search intent, and obtain a plurality of output data, at least one of the plurality of output data being described in a specified language; as well as A rendering module is used to render the multiple output data to obtain a first search result, wherein a rendering effect of the output data described in the specified language is different from a rendering effect of the remaining output data in the multiple output data.

12. The device according to claim 11, further comprising: A second determination module, configured to determine, in response to the received update request for the first search result, an update intention of the update request; as well as An updating module is used to update the first search result based on the update intention using the large model to obtain a second search result.

13. The device according to claim 12, wherein: The update module includes: A first determination submodule, configured to determine a target object indicated by the update intention in the first search result; A first output submodule, configured to output updated output data of the target object using the large model; A first rendering submodule, configured to render the updated output data to obtain an updated object; and An updating submodule is used to update the target object in the first search result by using the update object to obtain the second search result.

14. The device according to claim 12, wherein: The update module includes: A first determination submodule, configured to determine a target object indicated by the update intention in the first search result; A first search submodule, configured to use the large model to perform content search for the update intention, and obtain associated output data associated with the target object; A second rendering submodule, configured to render the associated output data to obtain an associated object; and The second output submodule is configured to output the second search result based on the associated object and the first search result.

15. The device according to claim 12, wherein: The update module includes: A first determination submodule, configured to determine a target object indicated by the update intention in the first search result; A verification submodule, used to perform confidence verification on the target object using the large model to obtain target confidence; and The third output submodule is used to output the second search result based on the first search result and the target confidence.

16. The device according to claim 11, wherein the rendering module comprises: A second determining submodule, configured to determine a display type of at least one output data described in the specified language; as well as The third rendering submodule is used to render the at least one output data based on the display control of the display type to obtain the first search result.

17. The device according to claim 11, wherein: The search module includes: A first acquisition submodule is used to acquire real-time data related to the search intent using the large model; and The processing submodule is used to process the real-time data to obtain the multiple output data.

18. The device according to claim 11, wherein The search module includes: A second acquisition submodule is used to acquire prompt information related to the search request, where the prompt information is preset; and The second search submodule is used to use the large model to perform content search based on prompt information and search intent to obtain multiple output data.

19. The device according to claim 11, wherein: The rendering module includes: A third determination submodule is used to determine the layout information and style information indicated by the search intention; and The fourth rendering submodule is used to render the multiple output data based on the layout information and the style information to obtain the first search result.

20. A data display device, comprising: A sending module, configured to send the search request in response to receiving the data query request; as well as a display module, configured to display the first search result in response to obtaining the first search result generated in response to the search request; Wherein, the first search result is generated by using the search device described in any one of claims 11 to 19.

21. An artificial intelligence agent configured to execute the method according to any one of claims 1 to 10.

22. An electronic device, comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any one of claims 1 to 10.

23. 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 to 10.

24. A computer program product, 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 claims 1 to 10.

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