4219 results about "SQL" patented technology

The invention discloses an interactive question intelligent agent system based on a large language model, and relates to the technical field of dialogue interaction systems. Aiming at the technical defects of the traditional BI tool, the technical scheme is as follows: a heterogeneous data management engine realizes unified access and secure access of cross-source data; the NLP semantic recognition engine converts a natural language into structured semantics through multiple steps, drives the hybrid SQL generation engine and provides visual parameters; the generation engine realizes precise generation from semantics to SQL through a two-stage architecture based on metadata and authority rules; and the visual management console is combined with multi-party configuration and parameters to visually build a number-asking agent. The method is used for realizing intelligent conversion from a natural language to a structured query language (SQL) and a data service interface, and automatically generating an interactive data visualization result.

The invention discloses a Text2SQL (Structured Query Language) implementation method and system, and relates to the field of data processing, and the method comprises the following steps: firstly, receiving a natural language query, and analyzing a query intention, field classification and a key entity through a planner; the searcher obtains domain knowledge, entity information, a database table structure and a historical query mode in a multi-path parallel mode based on the planning result; the generator constructs an SQL framework according to the retrieval result and generates an initial statement; the verifier carries out grammar, table field, authority and logic multi-dimensional verification on the SQL, and if the verification fails, iteration adjustment is carried out to generate logic; when the SQL is executed, the result is formatted and a natural language explanation containing query logic, a data source and a calculation method is generated if the SQL is executed successfully, and a diagnosis and error correction mechanism is started for correction and then rechecking is performed if the SQL is executed unsuccessfully. According to the method, through deep fusion of domain knowledge, whole-process verification error correction and interpretability enhancement, the accuracy, robustness and user interaction experience of SQL conversion in a professional scene are improved.

A user provides a question to be answered from detailed, dense or otherwise complex documents to a processing system that converts the question to a structured query language query and generates an embedding from the question, augmented by temporal data, synopses, themes, or other relevant information or data. The embedding is compared to embeddings generated from documents of a knowledge base to identify documents that are relevant to the question, and to rank such documents for their relevance. Highly ranking documents are combined with the query and provided to a language model that returns an answer to the question. A source for the answer is identified in at least one of the documents. The answer and the identified documents are presented to the user.

The invention provides a query method and system for generating an SQL statement based on a natural language, and relates to the technical field of programming, and the method comprises the steps: receiving a user query request, obtaining data source configuration information, and obtaining a multi-stage locking identifier through a distributed lock management module to control concurrency; extracting a query mode from the query mode knowledge base to generate a data template based on the data source configuration information and a user historical query record; selecting an optimal query path through query path evaluation, and calling the large model server to analyze the data template to generate an SQL statement; predicting resource occupation and setting a monitoring threshold value according to the SQL statement structure, monitoring a query state in real time in an execution process, and triggering a multi-stage fault-tolerant mechanism to process exceptions; and finally, releasing the locking identifier, returning a query result, and asynchronously updating the query mode knowledge base.

The invention discloses an NL2SQL optimization method and device based on a large model, equipment and a medium, and relates to the technical field of artificial intelligence, the method comprises the following steps: constructing a target metadata knowledge base, and obtaining an initial natural language query request; determining each target entity corresponding to the initial natural language query request, and determining missing target SQL elements in the initial natural language query request based on each target entity; generating a first cue word based on the initial natural language query request, the target SQL element and the target metadata knowledge base, and complementing the target SQL element based on the first cue word by utilizing the target large model to obtain a target natural language query request; and generating a plurality of candidate SQL statements corresponding to the target natural language query request by using the target large model, verifying each candidate SQL statement, and determining a target SQL statement from each candidate SQL statement based on a verification result. According to the method, the accuracy of the NL2SQL can be improved by utilizing a large model.

The invention discloses a database query method and device, electronic equipment and a nonvolatile storage medium. The method comprises the steps that a knowledge graph corresponding to a to-be-queried database is determined, and the knowledge graph is used for representing a logic structure and an incidence relation of data in the to-be-queried database; a similarity score between the user question text and each graph node in the knowledge graph is determined, a target node is determined in each graph node of the knowledge graph according to the similarity score, and the similarity score is used for representing a correlation degree between the graph node and the user question text; and generating database mode information corresponding to the to-be-queried database according to the target node, and generating a structured query language statement corresponding to the user question text by adopting a large language model according to the database mode information. According to the method and the device, the technical problem of poor accuracy of generating the query statement of the database by utilizing the large language model due to the fact that the field information in the database is numerous and the relationship is complex is solved.

A computer-implemented for information processing based on a large language model (LLM) includes: obtaining a query request; determining, by the LLM and based on the query request, a query condition, a source of a query result, and a select field for generating a structured query language (SQL) statement; generating by the LLM, the SQL statement by combining the query condition, the source of the query result, and the select field; and obtaining the query result corresponding to the query request, by inputting the SQL statement into the LLM for execution

A database system generates a prompt for an LLM or other machine learning (ML) model to narrow the search space to highly relevant information about a database. A distinct instance of a classifier, a clustering algorithm, or a topic modeling model can be trained based on information from ML automation within the database system, respectively for each column or table in the database. Model instances can then be used during generative LLM inferencing to identify relevant sources of data to answer the user's question. Thus, the prompt generation combines ML automation and other ML models or an LLM for topic modeling and schema description.

The invention provides an intelligent SQL generation method and system for business query, and belongs to the technical field of artificial intelligence. Related data of query statements are acquired, and a corresponding query intention knowledge graph is constructed by semantic clustering; the method comprises the following steps: analyzing a historical SQL statement structure, extracting a natural language template and an SQL template, and expanding through a large language model to generate a feed-shot example set; and constructing a composite cue word template by combining task setting guidance, a feed-shot example and CoT chain thinking reasoning guidance. An intention completion module is arranged in a large language model, a natural language query statement of a user is combined with a composite cue word template context, a structured query statement is generated through entity recognition, semantic completion, parameter filling and fuzzy intention training, and the structured query statement is converted into a standard SQL statement through a knowledge graph and a template. According to the method, the use threshold of business personnel is remarkably reduced, and efficient conversion from natural language questions to SQL statements is realized.

The invention discloses an RAG enhanced Text-to-SQL (Structured Query Language) query method and system for a large-scale database environment. The method comprises the following steps: constructing a vector database; based on the original query of the user, generating query enhancement description aiming at Schema recall and query enhancement description aiming at SQL (Structured Query Language) by utilizing LLM (Logistics Language Model); carrying out Schema recall and historical question and answer pair recall operations in a double-way parallel manner by utilizing an RAG technology and relying on the constructed vector database; based on query enhancement description, recalled Schema and historical question and answer pairs, generating an SQL query statement by using LLM in combination with RAG, and realizing Text-to-SQL conversion; the generated SQL query statement is subjected to post-processing optimization through a database interface, the post-processing optimization comprises grammar verification, performance optimization and error correction, the SQL query statement is combined for execution and feedback, efficient, accurate and reliable natural language query is achieved, and continuous optimization can be conducted through user feedback.

The invention relates to the technical field of automatic report generation, and provides a test analysis and report generation method and system based on large model retrieval enhancement, and the method comprises the steps: firstly carrying out the semantic task decomposition of user query, and meanwhile, achieving the multi-dimensional information extraction through the butt joint of a knowledge vector library and a structured image-text knowledge system established by a private knowledge base module. And three strategies of RAG, Self-RAG and Graph-RAG are combined to enhance the generation capability of the large model so as to accurately obtain background knowledge. And constructing a standard SQL statement according to a structured query requirement, and performing data filling according to template prompt by an output result fusion module in combination with a query background and an SQL execution result to form a test report. By optimizing a retrieval enhancement generation method, professional knowledge supplement related to query is realized, and the reasoning ability of a large model in a professional scene is effectively enhanced, so that the accuracy and the intelligent level of data analysis are improved.

The invention discloses a low-code platform based on a component library and a form engine, and relates to the field of data processing. The platform comprises a design system, an application system and a service system. The design system is provided with a form designer and a template manager, the form designer can configure a form component to generate a description file, and the template manager supports separate storage of a page template; the application system comprises a form analysis engine, a UI rendering engine and a reusable resource management component, the analysis engine analyzes configuration generation logic, the rendering engine loads a resource rendering page, and the reusable resource management component stores resources and generates configuration; the service system comprises a metadata analysis engine and an SQL assembler, the metadata analysis engine analyzes business rules to generate SQL and filtering strategies, and the SQL assembler generates operation statements adaptive to multiple data sources. Through the modular architecture design and the dynamic analysis mechanism, the problem that a low-code platform is insufficient in flexibility and usability is solved.

The invention discloses a structured query language generation method and device based on a large model, equipment and a medium, and relates to the technical field of artificial intelligence. The method comprises the steps of performing unified index definition and dimension definition for different business scenes, and combining indexes and dimensions in the business scenes according to the index definition and the dimension definition to obtain different structured query language templates; obtaining a natural language query statement input by a user in the current service scene, and performing semantic analysis on the natural language query statement by using a preset large model to obtain a semantic analysis result; and determining a target structured query language template matched with the natural language query statement from the structured query language templates according to the semantic analysis result, and filling the target structured query language template to generate a structured query language statement result. According to the technical scheme, the natural language query of the user can be efficiently and accurately converted into the structured query language statement.

The invention provides a natural language SQL (Structured Query Language) conversion method based on a data platform and a large language model, which relates to the technical field of data processing and comprises the following steps of: performing vectorization storage on metadata, blood relationship, indexes and historical query information, performing intention judgment and vector matching on a user query request, and determining a query condition based on a matching result. An association path is constructed according to data consanguinity, and an optimal SQL statement is generated by utilizing a historical query optimization table connection sequence and combining index matching. According to the method, intelligent conversion from the natural language to the efficient SQL can be realized, the query efficiency is improved, and the use threshold of a user is reduced.

The invention discloses a method and a system for realizing data synchronization between heterogeneous databases based on logs. The method comprises the following steps: acquiring connection information of a source database and a target database; querying a table structure of the source end database and generating an SQL statement to establish a data mapping rule; matching a corresponding rule model from a mapping rule model library; if the matching is not successful, the system abnormally quits and prompts the user to create a new mapping model and add the new mapping model to the mapping rule model base; converting a table structure of the source end database into a JSON character string based on a standard field type; an SQL statement required by the target database is output through the data mapping engine module; the message monitoring program reads the data in the temporary storage area and converts the data into data meeting the requirements of the target database; formatting the converted data into SQL statements supported by a target database; and sending the generated SQL statement to a target database to complete data migration. Accurate and controllable synchronization of the data and the model in a heterogeneous environment is realized.

The invention relates to the technical field of natural language processing, and provides an LLM-based complex language question SQL query generation method and system, and the method comprises the steps: responding to a target language question request; splitting a requested target language question sentence into one or more core clauses through a language model LLM, converting the core clauses into semantic vectors, calculating a matching score of each core clause and a question normal vector of each table in the schema knowledge document, performing sorting and screening according to the score size and a score threshold, and returning a recall result of the core clauses; combining and sorting the recall results of all the core clauses, and screening to generate a recall result of the target language question; and generating a Prompt through a Prompt script, and calling an LLM (Language Model) to automatically generate an SQL (Structured Query Language) query statement. The method does not depend on field fine tuning, does not need to reconstruct a wide table, and effectively solves the problem that a target table is difficult to position when a large language model processes coded value mapping, multiple tables and complex business logic by constructing a virtual table and splitting a question sentence into clauses containing complete semantics and matching the clauses.

The invention belongs to the technical field of data query in the electric power field, and particularly relates to an electric power field SQL intelligent agent construction method based on a KMDI chain. According to the method, a plurality of field knowledge bases are constructed based on professional knowledge such as a data structure, data coding and a data relation in the electric power field, and a chain type process composed of three links of knowledge matching and decision making, key knowledge distillation and key knowledge injection is designed and an SQL intelligent agent is constructed; under the support of a large language model, the data coding conversion of the electric power technical terms in the natural language questions is realized, and the data query SQL statements matched with the requirements are accurately generated, so that the business personnel in the electric power field can directly realize the access and operation of the data through the natural language.

A text-to-structured query language conversion method based on a large language model comprises the steps that database outline information is cut, and tables and columns needed by query of a non-target structured query language SQL are eliminated through correlation scoring and Steiner tree problem solving; screening context samples, predicting a similarity score from a text to a structured query language Text-to-SQL by using a cross encoder language model, and selecting a sample most related to a query problem; and injecting the cut database table and column information and the screened context sample into a cue word template, and generating an SQL query statement by using a large language model. The method can be used for optimizing the automatic conversion process from the natural language to the SQL and improving the conversion accuracy, and is used for providing a quick and convenient database retrieval mechanism for a non-SQL user and providing applications such as a data access mode in a wider form for mechanisms such as big language model retrieval enhancement generation RAG and the like.

The invention relates to a task planning and scheduling method which is quicker, lighter and finer in task splitting based on a large model, and aims to improve the capability of processing complex tasks by calling a tool through the large model. According to the method, on the basis of a FastAPI framework, a Text-to-SQL technology, a local knowledge base, Function Calling and other core technologies are integrated, user intention understanding is completed through a large model, dynamic calling and fusion of an API interface, the knowledge base and a database are achieved on the basis of intention understanding, and the task planning, tool calling and decision support capacity of the large model in a complex scene is remarkably enhanced. By introducing a thinking chain technology of multi-level deep task decomposition, the system can decompose a complex task into a plurality of sub-tasks, an execution path is optimized, and the accuracy and execution efficiency of a multi-step reasoning task are improved. A user query tool calling module is researched and developed, user requirements can be accurately recognized and decomposed, proper tools are automatically selected and called for task processing, and efficient solving of complex problems and optimal configuration of resources are achieved.

Systems and methods for translating natural language to SQL on a custom enterprise data warehouse powered by Generative AI. With an embodiment of the present invention, a natural language question may be converted to a meaningful and accurate database query, e.g., SQL query, relevant to tables existing in an enterprise data warehouse. An embodiment of the present invention is directed to a comprehensive approach of transforming a natural language query to a focused SQL query using domain specific data models across firmwide metadata systems and data systems. In response to a user query, an embodiment of the present invention performs metadata analysis, targeted data retrieval and then SQL generation. An embodiment of the present invention may apply data warehousing standards and guidelines followed in the enterprise and provide a plug-and-play type architecture and solution that is scalable to large warehousing and other systems.

The invention discloses an SQL (Structured Query Language) statement generation method and device based on a large language model, equipment and a medium. The method comprises the following steps: obtaining a user query text; inputting the user query text into a pre-trained Text2SQL (Structured Query Language) model to obtain an SQL statement output by the Text2SQL model; the SQL statement is input into a pre-trained SQL2Text model, so that a target query text output by the SQL2Text model is obtained; calculating semantic similarity between the user query text and the target query text; and if the semantic similarity is greater than or equal to a preset target value, determining that the SQL statement is the SQL statement corresponding to the user query text. By means of the method, the problem that the accuracy rate of SQL statement generation is low is solved, and the accuracy rate and efficiency of SQL statement generation are improved.

The invention provides a method for generating an SQL (Structured Query Language) by a natural language based on bidirectional mapping and semantic parsing, which relates to the technical field of database query and comprises the following steps of: extracting natural language query elements and packaging the natural language query elements into structured data, and establishing a mapping relationship from a query field to a service attribute and a physical data table by adopting a bidirectional Hash index technology; and automatically identifying multi-table association keys, performing semantic extension and compliance verification, generating an abstract syntax tree, performing processing according to user permission, and finally converting the abstract syntax tree into an SQL statement conforming to a target database syntax specification. According to the method, the accuracy and the efficiency of converting the natural language into the SQL are improved, and the flexibility and the safety of the system are enhanced.

The invention provides an SQL (Structured Query Language) statement generation method and system based on a large language model. Target database metadata are collected, a structured dictionary is constructed, cue words adaptive to the large language model are generated in combination with a user query strategy, and accurate SQL generation is realized through a closed-loop execution optimization mechanism after model reasoning and SQL analysis. Through a large language model intelligent analysis technology, a multi-source heterogeneous database metadata dynamic adaptation technology and a closed-loop optimization mechanism for SQL statement generation, execution and feedback, the efficient and accurate SQL statement automatic generation function is achieved through the characteristics of cross-database grammar automatic adaptation, complex service requirement accurate analysis, intelligent error correction and the like. The system not only improves the working efficiency of developers, but also reduces the resource occupation and operation and maintenance cost of the system, and improves the accuracy and safety of data query and processing.

The invention relates to a knowledge-enhanced large model data analysis agent visualization method, which comprises the following steps of: performing syntactic analysis by using a PCFG algorithm, identifying syntactic components and relationships of sentences, and splitting the sentences into sub-sentences with complete logic; extracting keywords and entity words of the sentences by combining a word segmentation technology with a business word segmentation library; recommending and summarizing candidate segmented words through a recall strategy, carrying out rough arrangement, fine arrangement and fine arrangement on the candidate segmented words, and correcting and complementing a segmented word result; splitting and analyzing the natural language problem based on the fine-tuned large model, and generating SQL statements meeting business requirements; analyzing the data set generated by the SQL, extracting field information and business meanings, and identifying an association relationship between fields; and automatically recommending chart styles according to data features and user preferences, assembling an analysis report and distributing the analysis report according to a specified frequency. According to the method, knowledge enhancement and large model technologies are combined, so that the efficiency and effect of data analysis and visualization are remarkably improved.

The invention relates to the cross technical field of business intelligence and natural language processing, in particular to an interactive AI report generation method and system based on intelligent semantic driving, and the method comprises the steps: (1) a large language model analyzes user question semantics, calculates the matching degree with a report subject, and dynamically selects a question correction or report generation process; (2) the fuzzy query is converted into a deterministic request based on the domain knowledge base, and semantic ambiguity is eliminated; (3) analyzing field association in combination with database metadata, and generating an executable SQL (Structured Query Language); (4) dynamically rewriting SQL (Structured Query Language) according to user roles, adding permission filtering conditions, executing query and desensitizing sensitive data; (5) analyzing result features, automatically matching a visual form and generating natural language interpretation; and (6) collecting user feedback, and optimizing a semantic model and a query strategy. According to the scheme, the accuracy of natural language query and the adaptive capacity of the system can be remarkably improved, and the security of data access is guaranteed.

The invention provides a low-code dynamic configuration method based on a natural language, and relates to the technical field of natural language processing, and the method comprises the steps: carrying out semantic analysis on a natural language description input by a user through an intelligent agent, extracting a page type, a field attribute, a verification rule and interaction logic, and generating structured configuration data; dynamically generating JSON metadata for describing page layout, field components, verification rules and interaction logic according to a preset template on the basis of the structured configuration data; according to page function requirements, interface metadata matched with the JSON metadata is generated, and the interface metadata comprises a request address, a parameter list and corresponding SQL (Structured Query Language) operation logic; and analyzing the JSON metadata by utilizing a low-code configuration rendering engine, dynamically generating an interactive management function page, and modifying the JSON metadata in real time and re-rendering the page through natural language interaction between a user and an intelligent agent.

The invention relates to the technical field of artificial intelligence, and particularly provides an intelligent question asking method and device, based on a large model and semantic modeling, the method comprises the following steps: S1, a user interaction module receives a natural language question input by a user; s2, the semantic modeling module collects and integrates related data sources; s3, an analysis rule is built in the rule analysis module; s4, the large model processing module understands and answers the natural language question input by the user; s5, the SQL conversion module generates a universal logic SQL for the natural language problem input by the user through rules and natural language analysis; s6, the data processing and indexing module establishes a data indexing system; s7, establishing a dynamic feedback and learning mechanism; and S8, establishing a security and privacy protection mechanism. Compared with the prior art, the accuracy and timeliness of intelligent question answering of the user questions can be improved, and accurate answering of specific big data scene tasks is achieved.

The invention relates to the technical field of database operation and maintenance, in particular to a database operation and maintenance decision-making method and device based on a large language model.The method comprises the steps that real-time operation indexes and historical log data of a database are collected, and multi-dimensional time sequence features are extracted; and analyzing the characteristics by using a large language model, judging whether a performance bottleneck or an abnormal risk exists or not, and determining the relevance between the performance bottleneck or the abnormal risk and a database performance problem. If the risk exists, acquiring a system load sudden change trend and a resource request rate, quantifying a system pressure degree, and inputting a performance optimization model to generate an adaptive tuning strategy; calculating statement complexity through an SQL audit log, and selecting an index adjustment strategy in combination with an index optimization model; evaluating the influence of the tuning strategy and the index strategy on the throughput, the response delay and the resource utilization rate, and determining a target high-performance strategy; according to the scheme, accurate optimization and efficient operation and maintenance of database performance are realized through intelligent analysis and dynamic adjustment and optimization.

The invention relates to the technical field of data optimization, and discloses a financial data warehouse intelligent SQL (Structured Query Language) optimization method based on a large model, which comprises the following steps: S1, SQL statement analysis and preprocessing: carrying out word segmentation, grammatical analysis and logic structure analysis on an SQL statement submitted by a user, and identifying keywords, table structures and query conditions in query; s2, large model semantic understanding and optimization suggestion: performing semantic understanding and logical reasoning on the analyzed SQL statements by using a large model, and automatically generating optimization suggestions, the optimization suggestions including index recommendation, query rewriting and execution plan optimization; and S3, generating and selecting a dynamic execution plan. According to the method, the advantages of a large model in the aspects of natural language processing and intelligent decision making are fully utilized and combined with the specific requirements of the financial data warehouse, and the execution efficiency of SQL query and the overall performance of the system are remarkably improved; the query response time of the financial data warehouse can be effectively shortened, and the resource utilization rate is improved.

The invention provides an interactive data query and display method and system based on a large language model. The method comprises the steps that a user query request and a service database used for query are obtained; based on the user query request, obtaining a query intention classification result of the user query request and a key entity in the user query request; generating a structured query parameter based on the obtained query intention classification result and the key entity; based on the generated structured query parameters, through interactive processing, rewriting a user query request to conform to a standard query grammar; constructing a query cue word based on the rewritten user query, and generating an SQL statement through a pre-trained large language model based on the constructed query cue word; obtaining a data query result from a business database for query based on the generated SQL statement; based on the query result, a chart is automatically generated to display the query result, and the chart type is determined according to the chart display type of the historical query result of the user.