CN119783650A - A method and system for generating multimodal reports based on large language model and FreeMarker - Google Patents
A method and system for generating multimodal reports based on large language model and FreeMarker Download PDFInfo
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Abstract
本发明公开了一种基于大语言模型与FreeMarker生成多模态报告的方法及系统，属于自然语言处理技术领域，该方法的实现包括以下步骤：步骤1，专业数据预处理；步骤2，将预处理后的数据，拼接prompt利用大语言模型进行数据分析与文本生成；步骤3，解析模型输出结果得到相应的分析结论、数据统计、参考文档，借助对象存储构造出内容的多种表现形式；步骤4，在FreeMarker中设计报告模板；步骤5，将大语言模型生成的内容填充到FreeMarker模板中。本发明能够解决传统大语言模型生成报告形式单一、展现形式与信息层次差的问题，从而提高生成的报告的专业领域认可度，满足各行业对高质量、多样化报告的需求。
The present invention discloses a method and system for generating multimodal reports based on a large language model and FreeMarker, belonging to the technical field of natural language processing, and the implementation of the method includes the following steps: step 1, professional data preprocessing; step 2, splicing the preprocessed data, prompting and using the large language model to perform data analysis and text generation; step 3, parsing the output results of the model to obtain corresponding analysis conclusions, data statistics, reference documents, and constructing various forms of content with the help of object storage; step 4, designing a report template in FreeMarker; step 5, filling the content generated by the large language model into the FreeMarker template. The present invention can solve the problem that the traditional large language model generates a single report form, and the presentation form and information hierarchy are poor, thereby improving the professional field recognition of the generated report and meeting the needs of various industries for high-quality and diversified reports.
Description
Technical Field
The invention relates to the technical field of natural language processing, in particular to a method and a system for generating a multi-mode report based on a large language model and FREEMARKER.
Background
Today, large language models become a new technology place in the field of artificial intelligence and a tap technology in the field of digital economy, and play an important role in promoting the generation transformation for promoting the development of artificial intelligence and inducing the development of the next round of artificial intelligence.
Large language models (Large Language Model, LLM for short) are a class of deep learning-based artificial intelligence models that contain hundreds of billions (or more) of parameters and are trained with large amounts of text data. Not only natural language text, but also meaning of understanding language text, can be generated. And can also execute various natural language processing tasks such as translation, emotion analysis and the like. The method is widely applied to various application scenes such as text writing, question answering, dialogue and the like, and shows strong practicability and potential from automatic customer service to advanced research.
The same large language model also has some drawbacks and limitations in generating professional field reports:
1. the report outline is not fixed, and the randomness is strong, so that the large language model can obtain different content structures after analyzing and processing the same professional data due to lack of uniform structures and standards, thereby greatly compromising the standardization and the professionality of the report.
2. The report content display form and the information level are poor, namely, the output content of the large language model is often text analysis, and various content forms such as pictures, videos, tables, hyperlinks and the like are difficult to enrich, so that the report is difficult to clearly convey key information and data, and the overall quality and the readability of the report are affected.
FREEMARKER is a template engine technique, a generic tool that is used to generate output text containing various forms of content, such as pictures, videos, forms, hyperlinks, etc., based on templates and data to be changed. FREEMARKER, which is a powerful text generation tool, provides templated design functions, but has shortcomings in content intelligent analysis.
Therefore, there is a strong need for a new method that can combine the advantages of large language models and FREEMARKER to generate intelligent reports containing multi-modal content.
Disclosure of Invention
The technical task of the invention is to provide a method and a system for generating a multi-mode report based on a large language model and FREEMARKER aiming at the defects, which can solve the problems of single report generation form, poor display form and information level of the traditional large language model, thereby improving the professional field acceptance of the generated report and meeting the requirements of various industries on high-quality and diversified reports.
The technical scheme adopted for solving the technical problems is as follows:
A method of generating a multimodal report based on a large language model and FREEMARKER, the implementation of the method comprising the steps of:
Step 1, professional data preprocessing, which comprises collecting, cleaning and formatting data to be analyzed;
step 2, splicing the preprocessed data, and performing data analysis and text generation by using a large language model;
Step 3, analyzing the output result of the model to obtain corresponding analysis conclusion, data statistics, reference document and the like, and constructing various expression forms of the content including tables, pictures, videos, hyperlinks and the like by means of object storage;
Step 4, designing a report template in FREEMARKER, wherein the report template comprises a preset dynamic catalog, a table placeholder, a picture and video embedded area, a hyperlink position and the like;
And 5, filling the content generated by the large language model into FREEMARKER templates, formatting and typesetting, and outputting and displaying the report content.
The method utilizes a large language model to carry out data analysis and text generation, and simultaneously realizes the multi-mode content organization and display of the report by virtue of the FREEMARKER template function.
Further, the method for preprocessing professional data comprises the following steps:
Data preprocessing, namely cleaning, mid-culture and the like of original professional data to reduce understanding deviation of a large language model and convert a data set into a form which can be processed by a computer;
Keyword recognition, namely recognizing the key values/keywords in the data collection by utilizing a Natural Language Processing (NLP) technology, wherein the key values/keywords comprise related names, categories, degrees, areas and the like, and recognizing meanings corresponding to the data to form a data dictionary;
And converting the data set subjected to formatting into a computer-processable form, such as JSON and other formats, constructing a project, and inputting the project into a LLM model for analysis and processing.
Further, the FREEMARKER template design allows the user to customize the layout and style of the report to meet the requirements of different industries and application scenarios.
Further, the FREEMARKER template design further includes:
designing a template layout, including defining the size, margin, font style, size and the like of a page, and setting the title, subtitle and footer of a report;
Setting a dynamic catalog including links of chapter titles and subtitle so that a user can quickly jump to a corresponding report part by clicking a catalog item;
Configuration tables, pictures, videos, hyperlinks, etc. for multimodal content placeholders.
Further, the method for presetting the module dynamic catalogue comprises the following steps:
The dynamic catalog algorithm can automatically identify chapters and sub-chapters in the report, and a structured catalog is generated, so that a user can conveniently and quickly navigate to different parts of the report;
WIN32 implements updating the directory field for the generated report, ensuring that the directory page number is consistent with the dynamic report content page number.
Furthermore, the setting of the dynamic catalog, in FREEMARKER templates, uses specific FREEMARKER grammar to load the dynamic catalog by Servlet context loading or Spring Boot integration, and automatically generates the catalog according to the content in the report, wherein the catalog comprises links of chapter titles and sub-titles, so that a user can quickly jump to a corresponding report part by clicking catalog items;
Configuring table placeholders, namely reserving positions of tables in a report template, dynamically generating the tables by using a FREEMARKER list and a circulating instruction, and automatically filling table data according to a data analysis result generated by a large language model;
The method comprises the steps of embedding pictures and videos, reserving placeholders of the pictures and videos in a template, storing link addresses of related pictures and videos output by a model into an object, acquiring remote URL (uniform resource locator) of the object, and embedding the remote URL into a report through URL processing instructions of FREEMARKER;
Adding hyperlinks-in the text of the report, hyperlinks are added using the FREEMARKER link instructions.
Further, the text content generated by the large language model is filled according to the appointed position of FREEMARKER templates, the FREEMARKER template engine replaces the analysis template file with the actual content by placeholders, and a preset formatting rule is applied to generate a final report document;
the method for outputting and displaying the report content is as follows:
The object storage such as Minio is utilized to store pictures, videos, reference documents and the like in the report, so that the report has high expandability and reliability, and the remote quick access and efficient storage of the report are ensured.
The invention also claims a system for generating a multimodal report based on a large language model and FREEMARKER, comprising:
The professional data preprocessing module is used for collecting, cleaning and formatting data to be analyzed;
The large language model processing module is used for carrying out data analysis and text generation on the preprocessed data by splicing the template by using a large language model;
The content analysis module analyzes the output result of the model to obtain corresponding analysis conclusion, data statistics, reference document and the like, and constructs various expression forms of the content including tables, pictures, videos, hyperlinks and the like by means of object storage;
The FREEMARKER template custom module designs a report template in FREEMARKER, wherein the report template comprises a preset dynamic catalog, a table placeholder, a picture and video embedded area, a hyperlink position and the like;
The output and display module is used for filling the content generated by the large language model into a FREEMARKER template, formatting and typesetting the content, and outputting and displaying the report content;
the system realizes the generation of the multi-modal report by the method for generating the multi-modal report based on the large language model and FREEMARKER.
The invention also claims an apparatus for generating a multimodal report based on a large language model and FREEMARKER, comprising at least one memory and at least one processor;
the at least one memory for storing a machine readable program;
The at least one processor is configured to invoke the machine-readable program to implement the method described above.
The invention also claims a computer readable medium having stored thereon computer instructions which, when executed by a processor, cause the processor to perform the above-described method.
Compared with the prior art, the method and the system for generating the multi-mode report based on the large language model and FREEMARKER have the following beneficial effects:
The invention provides an innovative report generation method, which combines an advanced large language model and a flexible FREEMARKER template technology to realize the multi-modeling of report contents. Compared with the prior method for generating the report by means of the large language model, the method enables the generated report to not only contain text information, but also embed various modal contents such as dynamic catalogues, tables, pictures, videos and hyperlinks, and greatly enriches the display form and information level of the report. Compared with manual writing, the working time is greatly shortened. The invention realizes the intelligent generation and the customized display of the report content through the dynamic analysis capability of the large language model and the fixed template structure of FREEMARKER, and is suitable for various industries and application scenes.
Drawings
FIG. 1 is a flow chart of a method for generating a multimodal report based on a large language model and FREEMARKER provided by an embodiment of the present invention;
FIG. 2 is a flow diagram of a build FREEMARKER stencil provided by an embodiment of the present invention.
Detailed Description
The invention will be further described with reference to the drawings and the specific examples.
The embodiment of the invention provides a method for generating a multi-modal report based on a large language model and FREEMARKER, which comprises the following steps:
step 1, professional data preprocessing, which comprises collecting, cleaning and formatting data to be analyzed;
Step 2, splicing the preprocessed data, and performing data analysis and text generation by using a large language model;
Analyzing the output result of the model to obtain corresponding analysis conclusion, data statistics, reference document and the like, and constructing various expression forms of the content including tables, pictures, videos, hyperlinks and the like by means of object storage;
Step 4, designing a report template in FREEMARKER, wherein the report template comprises a preset dynamic catalog, a table placeholder, a picture and video embedded area, a hyperlink position and the like;
And 5, filling the content generated by the large language model into FREEMARKER templates, formatting and typesetting, and outputting and displaying the report content.
The report template is designed in FREEMARKER, and FREEMARKER template design allows users to customize the layout and style of the report to meet the requirements of different industries and application scenarios. The FREEMARKER template design further includes:
designing a template layout, including defining the size, margin, font style, size and the like of a page, and setting the title, subtitle and footer of a report;
Setting a dynamic catalog including links of chapter titles and subtitle so that a user can quickly jump to a corresponding report part by clicking a catalog item;
Configuration tables, pictures, videos, hyperlinks, etc. for multimodal content placeholders.
The dynamic catalog algorithm can automatically identify chapters and sub-chapters in the report, and a structured catalog is generated, so that a user can conveniently and quickly navigate to different parts of the report;
WIN32 implements updating the directory field for the generated report, ensuring that the directory page number is consistent with the dynamic report content page number.
The report content is output and displayed, and object storage such as Minio is utilized to store pictures, videos, reference documents and the like in the report, so that the report has high expandability and reliability, and the remote quick access and high-efficiency storage of the report are ensured.
The method utilizes a large language model to carry out data analysis and text generation, and simultaneously realizes the multi-mode content organization and display of the report by virtue of the FREEMARKER template function. The method is described in further detail below with reference to fig. 1-2.
As shown in FIG. 1, a report generation flow chart for generating a multimodal report based on a large language model and FREEMARKER is shown:
s1, collecting professional data to be analyzed, formatting the data, and ensuring that the data is suitable for the input requirement of a large language model.
S2, constructing a prompt project, and carrying out data analysis and text generation on the preprocessed data by using a large language model.
And S3, analyzing the output result of the model to obtain corresponding analysis conclusion, data statistics, reference document and the like, and constructing various expression forms of the content including tables, pictures, videos, hyperlinks and the like by means of object storage.
S4, a user self-defines a outline template of the report, FREEMARKER a template engine analyzes and generates the report template, presets a dynamic catalog, a table placeholder, a picture and video embedded area, a hyperlink position and the like, and ensures reasonable logic structure and layout of the content.
And S5, filling the content generated by the large language model according to the structure of the FREEMARKER template, automatically generating report content containing multiple modes, and displaying the finally generated multi-mode report.
The method for formatting professional data comprises the following steps:
And data preprocessing, namely cleaning, mid-culture and the like of the original professional data to reduce understanding deviation of a large language model, and converting the data set into a computer-processable form.
Keyword recognition, namely recognizing the key values/keywords in the data collection by utilizing a Natural Language Processing (NLP) technology, wherein the key values/keywords are related to names, categories, degrees, areas and the like, and recognizing the meanings corresponding to the data to form a data dictionary.
And converting the data set subjected to formatting into a computer-processable form, such as JSON and other formats, constructing a project, and inputting the project into a LLM model for analysis and processing.
Prompt is a technique based on Artificial Intelligence (AI) instructions by explicitly and specifically directing the output of a language model. In Prompt word engineering, the definition of Prompt encompasses three main elements of task, instruction and role to ensure that the model generates text that meets the needs of the user. For example, the road disease professional data is taken as an example, and the following Prompt is spliced, wherein the corresponding relation of the data dictionary is that the name of the data field is the Chinese meaning of the data field, the following content is needed to be analyzed according to the following data, the following content is described by a section of natural language (task) +data set+what all disease types are in the whole road section in the data, what the total sum of all disease areas is, and what the typical structural disease type is (instruction) "is used for standardizing the output of a large language model, so that the text with correlation, accuracy and high quality is generated.
The large language model described above may be a model of public cloud deployment. Such as the caretaker, GPT series, or a professional model of the local deployment training.
As a detailed embodiment of constructing FREEMARKER templates, the steps are as shown in fig. 2:
s41, designing FREEMARKER template layout, namely designing the layout and structure of the report by using the FREEMARKER template engine according to the requirements of the report. This includes defining the page size, margins, font style and size, etc., and setting the headings, subheadings, and footers of the report.
S42, setting the dynamic catalogue, namely loading the dynamic catalogue in FREEMARKER templates by using specific FREEMARKER grammar such as Servlet context loading or Spring Boot integration and the like. The catalog will be automatically generated based on the content in the report, including links to chapter titles and sub-titles, enabling the user to quickly jump to the corresponding report section by clicking on the catalog entry.
S43, configuring a table placeholder, namely reserving the position of a table in a report template, and dynamically generating the table by using the list of FREEMARKER and a circulating instruction. And automatically filling table data, such as statistical information of disease types, areas, frequencies and the like, according to data analysis results generated by the large language model.
S44, embedding the pictures and the videos, namely reserving placeholders of the pictures and the videos in the template. And storing the link addresses of the related pictures and videos output by the model into an object for storage, acquiring remote URL thereof, and embedding the remote URL into a report through URL processing instructions of FREEMARKER. The media content may be live photographs of disease, video recordings, or other related visual material.
S45, adding hyperlinks, namely adding hyperlinks in the text of the report by using FREEMARKER link instructions. These hyperlinks may provide the user with more information and context for some of the generated content reference documents provided by the large model, or for externally directed reference documents, related studies, or further resources.
As an optimized implementation mode, after report contents are generated, page numbers of the dynamic catalogue are automatically updated by means of the WIN32, and algorithm codes are as follows:
doc=word.Documents.Open(file_path)
doc.Fields.Update()
And filling the text content generated by the large language model according to the appointed position of the FREEMARKER template. And FREEMARKER, the template engine analyzes the template file, replaces placeholders with actual contents, and applies preset formatting rules to generate a final report document.
The method realizes the following steps:
and the multi-mode fusion is realized by combining the text generation capacity of the large language model with the template design function of FREEMARKER for the first time.
Dynamic content generation, namely dynamically generating report content by utilizing the data analysis capability of a large language model, thereby greatly reducing manual intervention, reducing subjectivity and saving labor cost.
The embodiment of the invention also provides a system for generating the multi-modal report based on the large language model and FREEMARKER, which realizes the generation of the multi-modal report by the method for generating the multi-modal report based on the large language model and FREEMARKER.
The system comprises:
The professional data preprocessing module is used for collecting, cleaning and formatting data to be analyzed;
The large language model processing module is used for carrying out data analysis and text generation on the preprocessed data by splicing the template by using a large language model;
The content analysis module analyzes the output result of the model to obtain corresponding analysis conclusion, data statistics, reference document and the like, and constructs various expression forms of the content including tables, pictures, videos, hyperlinks and the like by means of object storage;
The FREEMARKER template custom module designs a report template in FREEMARKER, wherein the report template comprises a preset dynamic catalog, a table placeholder, a picture and video embedded area, a hyperlink position and the like;
And the output and display module is used for filling the content generated by the large language model into the FREEMARKER template, formatting and typesetting the content, and outputting and displaying the report content.
The professional data preprocessing module is used for preprocessing the professional data, and the method for preprocessing the professional data comprises the following steps:
Data preprocessing, namely cleaning, mid-culture and the like of original professional data to reduce understanding deviation of a large language model and convert a data set into a form which can be processed by a computer;
Keyword recognition, namely recognizing the key values/keywords in the data collection by utilizing a Natural Language Processing (NLP) technology, wherein the key values/keywords comprise related names, categories, degrees, areas and the like, and recognizing meanings corresponding to the data to form a data dictionary;
And converting the data set subjected to formatting into a computer-processable form, such as JSON and other formats, constructing a project, and inputting the project into a LLM model for analysis and processing.
The FREEMARKER template customization module allows a user to customize the layout and style of the report so as to meet the requirements of different industries and application scenes. FREEMARKER the template design further includes:
designing a template layout, including defining the size, margin, font style, size and the like of a page, and setting the title, subtitle and footer of a report;
Setting a dynamic catalog including links of chapter titles and subtitle so that a user can quickly jump to a corresponding report part by clicking a catalog item;
Configuration tables, pictures, videos, hyperlinks, etc. for multimodal content placeholders.
The dynamic catalog algorithm can automatically identify chapters and sub-chapters in the report, and a structured catalog is generated, so that a user can conveniently and quickly navigate to different parts of the report;
WIN32 implements updating the directory field for the generated report, ensuring that the directory page number is consistent with the dynamic report content page number.
The setting of the dynamic catalogue, in FREEMARKER templates, uses specific FREEMARKER grammar to load Servlet context or Spring Boot integration, loads the dynamic catalogue, automatically generates the catalogue according to the content in the report, and comprises links of chapter titles and sub-titles, so that a user can quickly jump to a corresponding report part by clicking a catalogue item;
Configuring table placeholders, namely reserving positions of tables in a report template, dynamically generating the tables by using a FREEMARKER list and a circulating instruction, and automatically filling table data according to a data analysis result generated by a large language model;
The method comprises the steps of embedding pictures and videos, reserving placeholders of the pictures and videos in a template, storing link addresses of related pictures and videos output by a model into an object, acquiring remote URL (uniform resource locator) of the object, and embedding the remote URL into a report through URL processing instructions of FREEMARKER;
Adding hyperlinks-in the text of the report, hyperlinks are added using the FREEMARKER link instructions.
The FREEMARKER template engine replaces the analysis template file with the actual content and applies the preset formatting rule to generate the final report document;
The output and display module utilizes object storage such as Minio to store pictures, videos, reference documents and the like in the report, has high expandability and reliability, and ensures remote quick access and efficient storage of the report.
The embodiment of the invention also provides a device for generating the multi-modal report based on the large language model and FREEMARKER, which comprises at least one memory and at least one processor;
the at least one memory for storing a machine readable program;
the at least one processor is configured to invoke the machine-readable program to implement the method for generating a multimodal report based on the large language model and FREEMARKER as described in the above embodiments.
Embodiments of the present invention also provide a computer readable medium having stored thereon computer instructions that, when executed by a processor, cause the processor to perform the method of generating a multimodal report based on a large language model and FREEMARKER as described in the above embodiments. Specifically, a system or apparatus provided with a storage medium on which a software program code realizing the functions of any of the above embodiments is stored, and a computer (or CPU or MPU) of the system or apparatus may be caused to read out and execute the program code stored in the storage medium.
In this case, the program code itself read from the storage medium may realize the functions of any of the above-described embodiments, and thus the program code and the storage medium storing the program code form part of the present invention.
Examples of storage media for providing program code include floppy disks, hard disks, magneto-optical disks, optical disks (e.g., CD-ROMs, CD-R, CD-RWs, DVD-ROMs, DVD-RAMs, DVD-RWs, DVD+RWs), magnetic tapes, nonvolatile memory cards, and ROMs. Alternatively, the program code may be downloaded from a server computer by a communication network.
Further, it should be apparent that the functions of any of the above-described embodiments may be implemented not only by executing the program code read out by the computer, but also by causing an operating system or the like operating on the computer to perform part or all of the actual operations based on the instructions of the program code.
Further, it is understood that the program code read out by the storage medium is written into a memory provided in an expansion board inserted into a computer or into a memory provided in an expansion unit connected to the computer, and then a CPU or the like mounted on the expansion board or the expansion unit is caused to perform part and all of actual operations based on instructions of the program code, thereby realizing the functions of any of the above embodiments.
While the invention has been illustrated and described in detail in the drawings and in the preferred embodiments, the invention is not limited to the disclosed embodiments, and it will be appreciated by those skilled in the art that the code audits of the various embodiments described above may be combined to produce further embodiments of the invention, which are also within the scope of the invention.
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