557 results about "Query language" patented technology

According to the artificial intelligence-based data analysis system and method provided by the invention, a set of novel data analysis system architecture is designed, and key technical components such as natural language processing, a large language model, database query optimization, multi-modal visualization and a semantic knowledge graph are fused; data transmission between modules is realized through unified intermediate data objects such as a structured query intention, an analytic tree and a structured query language template object, and asynchronous collaboration is realized through event driving and a message queue mechanism, so that a non-professional user can input an analysis request through a natural language; semantic analysis, structured query language query statement generation, data query and result visualization presentation are automatically completed, the method can be widely applied to data analysis scenes in the industries of government affairs, traffic, finance, education and the like, the data use efficiency is improved, the technical threshold is reduced, and the digital decision-making ability is enhanced.

The invention provides a data query method and system for converting a natural language into a database query language, and the method comprises the steps: analyzing the natural language input of a user through a multi-modal understanding agent on the basis of constructing a dynamic knowledge graph based on metadata, combining a historical session with a business term table, eliminating ambiguity, and generating a standardized Query, a retrieval routing agent selects a query strategy according to Query complexity, simple query directly matches a cache template, complex query traverses a knowledge graph, and related tables, fields and service constraints are returned; then, an expert committee agent generates an SQL (Structured Query Language) by adopting multi-stage collaboration, executes plan pre-evaluation, and selects a version with the highest comprehensive score; the test agent simulates and executes the SQL in the isolation environment, and verifies the grammar legality and the field permission; and finally, executing the detected SQL, and processing a result. According to the method, the accuracy of converting the natural language into the SQL (NL2SQL) in a complex database scene can be effectively improved.

The invention discloses an SQL statement structure verification system based on large model and knowledge graph fusion enhancement. The SQL statement structure verification system comprises a knowledge graph construction module, an input design module, a verifier design module and an output design module. According to the method, a knowledge graph is introduced as a structured semantic support, and a closed-loop enhancement processing flow of SQL statements from generation, verification to optimization is realized in combination with a structure perception cue word design, a rule verification mechanism and a Function Call feedback interface; the defects that an existing Text2SQL method is insufficient in structural understanding, lack of semantic reasoning, weak in generalization ability, dependent on computing power and the like are overcome. According to the method, triple guarantee of semantic perception, rule driving and model assistance is realized, the accuracy, robustness and interpretability of SQL generation are remarkably improved, and the cognitive load and operation risk of non-professional users in database interaction are effectively reduced.

The invention discloses a natural language-to-SQL (Structured Query Language) method and system based on RAG (Random Access Gateway) enhancement, and the method comprises the steps: constructing a query intention graph through dependency syntax analysis, recognizing and complementing semantic missing components, and forming complete semantic representation; vector representation is carried out on the business term segments by adopting vectorization coding, accurate definitions of business terms are obtained from a factory structured knowledge base, and an enhanced context set is formed through expansion retrieval in a low-confidence region; an SQL template mapping network is established based on historical query records, a mapping relation matrix is generated through field candidate matching, mapping conflict positions are identified, and multiple SQL candidate sequences are generated; grammar verification is carried out on the candidate sequence to identify grammar errors, semantic consistency verification is carried out to calculate the intention alignment degree, and an optimal SQL statement is selected through a deviation correction factor; and performing formatting processing and statistical abstract on an execution result, and providing data query capability for scenes such as factory quality management and equipment maintenance.

The invention discloses a self-adaptive NL2SQL method and system based on a large language model and retrieval enhancement. The method comprises four key steps of mode linking, example enhancement and SQL generation, multi-path thinking chain fusion reasoning and multi-round self-correction. The system comprises the following modules: a knowledge base management module, an input analysis and preprocessing module, a mode link module, an example enhancement module, an SQL (Structured Query Language) generation module, an SQL fusion and optimization module, an SQL execution and feedback module, a multi-round self-correction module, a Prompt construction and context management module, a system interface module and a content generation module. The method has the advantages that multiple large models are supported, a data source is quickly accessed, a user-defined prompt structure is configured, good engineering maintainability and scene adaptability are achieved, and SQL generation accuracy, performability and universality are improved.

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for generating structured query language using machine learning. In some implementations, a system provides an interface configured to receive input comprising data processing criteria expressed in natural language text. The system receives natural language input through the interface, where the natural language input specifies one or more criteria for retrieving or generating data from a data set. The system obtains a structured query language (SQL) statement generated by an artificial intelligence or machine learning (AI / ML) model based on the natural language input, and the system provides the SQL statement through the interface.

The present application discloses a database query method and apparatus, an electronic device, and a non-volatile storage medium. The method comprises: determining a knowledge graph corresponding to a database to be queried, wherein the knowledge graph is used for representing a logical structure and an association relationship of data in said database; determining similarity scores between user question text and graph nodes in the knowledge graph, and determining a target node from among the graph nodes of the knowledge graph on the basis of the similarity scores, wherein the similarity scores are used for representing the degree of association between the graph nodes and the user question text; and on the basis of the target node, generating database schema information corresponding to said database, and using a large language model to generate, on the basis of the database schema information, a structured query language statement corresponding to the user question text.

A structured query language statement generation method, executed by an electronic device, and comprising: determining a natural language text representing a query task, and determining a preset database, the preset database comprising at least one data table, and each data table comprising at least one field (101); on the basis of the natural language text, determining, from the preset database, at least one task-associated field and at least one task-associated data table which are associated with the natural language text (102); rewriting the natural language text on the basis of the task-associated field to obtain a rewritten natural language text (103); acquiring prompt information for the rewritten natural language text, the prompt information being used for guiding a query statement generation model in generating a structured query language statement (104); performing question classification on the rewritten natural language text to obtain question type information of the rewritten natural language text (105); and generating the structured query language statement of the rewritten natural language text by means of the query statement generation model on the basis of the task-associated field, the task-associated data table, the prompt information, and the question type information (106).

An illustrative method for querying multiple datasets may include generating, based on models each associated with and defining attributes of a different datasets stored in a plurality of data stores, a semantic layer defining relationships between the models and that provides a centralized application programming interface (API) for exposing the datasets by way of a common query language, receiving, by way of the centralized API, a query request for information that depends on data included in multiple datasets included in the plurality of datasets, querying, based on the query request and the relationships between the models defined by the semantic layer, the multiple datasets, and presenting, based on the querying, a query result representative of the information that depends on the data included in the multiple datasets.

The invention discloses a method and a system for querying from a natural language to an SQL (Structured Query Language) based on retrieval enhancement generation and autonomous planning. The method comprises the following steps: acquiring a multi-source knowledge base; acquiring natural language query information of a user; generating preliminary intention information according to the natural language query information of the user; generating a retrieval strategy according to the preliminary intention information; according to the retrieval strategy, performing retrieval in parallel or in series so as to obtain multi-dimensional context information; generating a prompt word with enhanced context according to the multi-dimensional context information and the natural language query information of the user; and generating an SQL statement according to the context-enhanced cue word. According to the intelligent agent method and system, the user query context can be deeply understood, the multi-dimensional knowledge base is autonomously utilized, and the high-accuracy SQL is generated.

The invention discloses a retrieval enhancement generation method and system based on hybrid retrieval and adaptive sorting, and relates to the technical field of artificial intelligence and natural language processing. Comprising the following steps: 1, analyzing a query language and providing multi-path retrieval: receiving a natural language query input by a user, and performing semantic analysis and structured processing; starting dense vector retrieval and sparse semantic retrieval in parallel, and respectively obtaining candidate document sets from the knowledge base; 2, candidate mixed result fusion is carried out, wherein duplicate removal and preliminary fusion are carried out on candidate documents obtained through dense retrieval and sparse retrieval, and a unified candidate document pool is formed; all the candidate documents are evaluated according to the query semantic matching degree, document authority and quality, context coherence and generation task type factors, sorting weights are dynamically generated based on all the factors, and a candidate document pool is resorted; and constructing a structured context prompt prompt, inputting the structured context prompt prompt into a pre-trained large model, and generating final response content.

The invention relates to the technical field of enterprise data management, in particular to a lightweight-based enterprise data dynamic integrated management system, which is characterized in that an acquisition module is used for deploying edge computing nodes, receiving multi-source heterogeneous information streams from manufacturing execution systems and equipment logs, dynamically analyzing and standardizing the information streams, and adding metadata tags; uploading is carried out in a batch processing mode; the map construction module is used for constructing a semiconductor blood relationship map by taking the standardized key information as a blood relationship clue; a graph database is used for efficient storage, and a RESTful API interface is configured to support batch import, so that data storage and relevance expression are more flexible and efficient; the prediction module performs reasoning on the atlas by adopting a graph neural network to generate predictive risk distribution, and a correlation analysis set generated by the prediction module is stored back to the atlas in a structured manner; by introducing a multi-thread concurrent write-in and lock mechanism, the atlas supports complex combination query based on a Cypher query language, and supports multi-level and traceability query.

The invention discloses a data analysis method and system based on a large model. The method comprises the steps that natural language query of a user is received; performing semantic coding and intention analysis on the natural language query based on a pre-trained large language model; based on metadata retrieval of vector similarity, obtaining metadata related to natural language query semantics from a vector database; generating corresponding structured query parameters through the large language model in combination with the obtained metadata and the natural language query; according to the structured query parameter, generating an executable structured query language statement; and executing the structured query language statement to obtain a data query result. According to the method, the problems of insufficient semantic comprehension ability, low metadata retrieval precision and poor query generation controllability in the prior art are solved.

The invention discloses a version updating method and device, equipment and a storage medium, and relates to the technical field of version updating, and the version updating method comprises the following steps: obtaining to-be-updated version information of to-be-updated software, intercepting an original structured query language (SQL), and generating a syntax tree corresponding to the original SQL, the to-be-updated version information comprises to-be-updated SQL and to-be-updated code blocks; according to the to-be-updated SQL, performing field updating on the syntax tree and then converting to generate a target SQL; according to the code task identifier of the to-be-updated code block, determining a code position of an original code block, and generating a target code; based on the target SQL and the target code, version updating and testing are conducted on the software to be updated, and a version updating task is completed. According to the method, a low-code platform can be helped to accurately identify data and code change ranges, codes and scripts are automatically combined, and the purpose of reducing version updating time is achieved.

The invention discloses a Text-to-SQL (Structured Query Language) agent collaboration system and method, and relates to the field of database technology, natural language processing and large language models.The system comprises a generator used for generating a plurality of candidate SQL based on a parallel strategy by adopting a predefined mixed different thinking chain prompt template, scoring each candidate SQL and screening out an optimal SQL; the refiner is used for correcting the optimal SQL when the optimal SQL is abnormal in the database execution stage; the reflex device is used for generating a reflex text when a user is not satisfied with a result or execution of the modified SQL database is abnormal, splicing the reflex text and the mixed thinking chain prompt template to obtain a new mixed thinking chain prompt template, sending the new mixed thinking chain prompt template to the generator and carrying out next SQL optimization, and the generated SQL statement has the characteristics of complete structure and high efficiency. And the consistency and robustness are high.

Retrieval of structured tabular data in a retrieval-augmented generation (RAG) platform is described. An example method includes receiving a tabular file including structured data, extracting short and long metadata from the file, and generating a dataset agent object using the extracted metadata. A human-readable description is associated with the structured data, and the dataset agent object and metadata are stored in a metadata index. A natural language query is received through a query agent object rendered within a canvas including interconnected agent objects. A dataset agent object is identified based at least in part on metadata filtering using the natural language query and short metadata. A structured query conforming to structured query language syntax is generated using a large language model (LLM) and executed on the structured data. A response including the result set and dataset agent identifier is generated and transmitted to a downstream agent object for further processing.

Systems, methods, and computer-readable media for presenting one or more bets to a user based on a natural language query received from the user. In some embodiments, a natural language query from a user may be received. The natural language query may be translated into computer-readable data by a language processing engine. The language processing engine may use a large language model to translate the natural language query into computer-readable data. The computer-readable data may be in the form of an embedding. A bet engine may match the computer-readable data to a bet when the bet and the computer-readable data exceed a predetermined threshold with regard to similarity. The bet engine may generate a new bet corresponding to the computer-readable data. The system may then present the bet to the user.

Systems and methods are provided for enabling search and query functions. A query system having a framework, executed on one or more processors, includes a plurality of Artificial Intelligence (AI) agents working to support one of text-to-Structured Query Language (SQL) or text-to-Python, wherein the plurality of AI agents include a first AI agent prompted to act as a research analyst for performing a task of interpreting a natural language query from a user, wherein the natural language query relates to natural language to the one of text-to-SQL or text-to-Python; a second AI agent prompted to act as a search data engineer for performing a task of executing a search based on the natural language query; and a third AI agent prompted to act as a code developer for performing a task of writing code based on the search, wherein the plurality of AI agents act autonomously yet collaboratively.

A disclosed method facilitates translation of natural language queries into query language statements usable to retrieve data from or write data to a particular database. The method includes obtaining a pool of shots. Each shot in the pool includes a natural language query component and a corresponding database translation component. The method further provides for vectorizing the natural language query component for each of the shots into a common vector space; receiving a natural language query from a user interface; vectorizing the natural language query within the common vector space; identifying a subset of vectorized natural language query components that satisfy a similarity metric when compared to the vectorized natural language query; and generating an LLM prompt that includes shots from the pool corresponding to the subset of the vectorized natural language query.

The invention relates to the field of artificial intelligence and data analysis, in particular to a report data automatic analysis report generation system based on an AI agent. Comprising a multi-modal intention understanding and dialogue analysis module; a dynamic domain knowledge graph construction and reasoning module; the self-adaptive narrative generation and visualization module is used for dynamically generating a report structure according to an analysis result and providing an interpretable insight and a personalized report style; and the agent arrangement and self-learning closed-loop module is used for decomposing the user intention into sub-tasks, scheduling specialized micro-agents to cooperatively complete the sub-tasks, and optimizing an analysis strategy and updating a knowledge graph based on user feedback. Through multi-modal interaction and conversational analysis, a user does not need to learn a complex query language, and can naturally explore data like a conversation with human experts, so that the analysis process is more intuitive and more efficient.

A communications session with a user may be automated using a language model. The language model may be instructed to select a next action to be performed where the next action may include transmitting a responsive communication to the user or performing an API call. The prompt used to query the language model may include one or more of the following: a representation of text of the communications session, a list of available API calls, instructions to select a next action, a representation of API calls performed, or a representation of API call responses received. The language model may be sequentially queried to continue the communications session by transmitting responsive communications or performing API calls. In some implementations, a prompt template may be used to generate the prompt and a prompt template may be selected using text of the communications session.

The invention provides an execution feedback and iterative optimization-based natural language to SQL (Structured Query Language) conversion method. The method comprises the following steps of: generating a plurality of candidate SQL statements; performing parallel execution on the candidate SQL statements, and capturing grammar error information in execution results and line number classification numbers of different candidate SQL return results; generating an error pattern abstract based on the grammar error information, converting the error pattern abstract into a natural language constraint instruction, and injecting the natural language constraint instruction into a prompt word generated in the next round; when the line number classification number is smaller than or equal to a preset threshold value, a self-consistency voting mechanism is adopted, a candidate SQL set with the largest number of returned same lines is selected, and the SQL with the shortest execution time is selected from the set to serve as final output. According to the method, the accuracy, robustness and execution efficiency of conversion from the natural language to the SQL can be remarkably improved by executing a feedback-driven multi-round iterative optimization mechanism.

The present application provides a data query method and apparatus, and a related device. The data query method comprises: semantically parsing a natural language inputted by a user; converting the semantically-parsed natural language to generate a structured query language; querying a data report library or a database on the basis of the generated structured query language, wherein the data report library comprises one or more data reports, and the data reports are obtained by performing sorting on the basis of the service logic relationship of data; displaying to the user data reports, in the data report library, matching the natural language; or retrieving data, in the database, matching the natural language inputted by the user to form data reports, and displaying the data reports to the user. By means of the natural language inputted by the user, data reports can be directly and rapidly retrieved. In this way, the obtained data reports are user-customized, thereby improving the efficiency of data query.

Disclosed is a system and method for grounded query generation over heterogeneous data sources. The method includes receiving a user input corresponding to a user requirement from at least one user, extracting a plurality of sub-models corresponding to the received user input, creating a context of the received user input based on the extracted plurality of sub-models, generating an executable query in a specific query language based on the context using a Large Language Model (LLM) by processing the context and the user input within a structured prompt, validating the generated executable query using a multi-stage validation process, generating an LLM response for the received user input based on results of the validation, and outputting the generated LLM response on a user interface of a user device.

The invention belongs to the technical field of data statistics, and particularly discloses a Python-based dynamic SQL (Structured Query Language) generation and data statistics method and system, a terminal and a medium. The method comprises the following steps: acquiring statistical demand configuration in an Excel file; analyzing the Excel file to generate a structured data object, and carrying out semantic analysis and metadata consistency verification on the natural language description; constructing an SQL abstract syntax tree through a Python analysis module, and executing syntax mapping and function replacement according to a database adaptation rule; connecting a target database through a Python database driver and executing an SQL query statement; and after a query result is obtained, outputting an Excel file, a visual chart or interface data. According to the method, automatic analysis, SQL dynamic generation, safe execution and intelligent result output of data statistics requirements are realized.

In one example, a method performed by a processing system including at least one processor includes automatically inferring metadata about a structured query language database, constructing, based on the metadata, a prompt for generating a new structured query language query to be executed against the structured query language database, and providing the prompt to a language model for use in generating the new structured query language query.

The invention discloses a Text2SQL (Structured Query Language) system and method based on cue words. The system comprises a natural language input module for receiving and preprocessing a natural language query statement; the metadata management module is used for managing database metadata information; the cue word generation module is used for generating cue words containing database modes, user problems and SQL requirements on the basis of the query statements and the metadata; the large language model module is used for generating a preliminary SQL based on the cue word; the SQL grammar verification module is used for verifying grammar and feeding back errors; the manual auditing module is used for auditing and editing the SQL statements passing the verification; and the SQL execution module is used for executing the SQL passing the audit and returning a result. According to the method, the accuracy is improved by combining cue word guidance and a large model, the field adaptability is enhanced by relying on metadata, the data security is ensured by introducing manual auditing, the accuracy and security of SQL generation in a complex query scene are remarkably improved, and the development and maintenance cost of the system is reduced.

The invention relates to the technical field of database development, in particular to an SQL generation method and device based on a large model and an ER graph. The method comprises the following steps: analyzing a user query statement to obtain a database candidate table set; querying table field information of each table related to the user query statement from a database candidate table set; according to the table field information of each table, querying an association relationship among the tables in the database candidate table set from a preset ER map, and determining intermediate table information, the intermediate table being used for associating candidate tables without direct association; and generating an SQL query statement according to the intermediate table information and the database candidate table set. Through combination of candidate set generation and SQL optimization, logic errors of the generative large model are reduced, and execution efficiency is improved. The method does not depend on manual annotation of training data of a query language and an SQL template, the labor cost is greatly reduced, and good mobility is achieved.

The invention discloses an anaphora disambiguation method and system based on big language model enhanced text and structured query language generation. The method comprises the following steps: receiving a natural language question of a user, and analyzing and generating structured table field information through mode information; identifying and rewriting fuzzy time expression in the problem; extracting ambiguous entities, and replacing the ambiguous entities with database standard values through semantic matching and fuzzy matching; synthesizing the information to generate a structured query language statement and executing the structured query language statement; and if the execution result is null, automatically triggering an anaphora disambiguation process, performing error correction and re-matching on field values in the statement through a dual matching mechanism, and generating and executing a corrected query statement. According to the method, the problems of fuzzy anaphora, indefinite time expression, low matching accuracy, resource waste and the like in a traditional SQL system are effectively solved through a strategy of combining pre-processing and post-processing, and the accuracy, robustness and execution efficiency of complex query are remarkably improved.

The embodiment of the invention discloses an intelligent number asking method and system based on dynamic feedback, a storage medium and electronic equipment. The method comprises the steps that a subject domain and a semantic model are constructed, a data view is established, data mapping is configured, and a natural language problem and a standardized index are analyzed; generating an SQL (Structured Query Language) logical expression based on the Few-shot example and the large model, and carrying out semantic correction on the SQL logical expression; sQL query is executed, dynamic feedback storage is carried out based on an execution result, and SQL generation parameters are updated by utilizing a self-adaptive learning mechanism. According to the method, through semantic modeling and data mapping, precise alignment of a natural language and a database structure is achieved; few-shot examples and large model generation are combined with semantic correction rules, so that the accuracy and stability of SQL generation are improved; and a continuously optimized intelligent number-asking closed loop is constructed by executing a feedback and adaptive learning mechanism, so that the self-learning and evolution capabilities of the system are improved.