1073 results about "Query language" 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 discloses a large model enhanced code security detection method. The method comprises the following steps: dynamically associating CVE vulnerability features with code semantic representation through a knowledge graph construction module; extracting vulnerability features from a CVE vulnerability library by utilizing a knowledge graph construction module, and dynamically associating the vulnerability features with code semantics; based on the CodeQL standard, a query language is generated by adopting a large model and input into CodeQL for AST analysis, and code structure features are extracted; the large model generates a query language meeting the CodeQL standard, codes are analyzed through CodeQL, and key structure features are extracted; integrating a static analysis tool chain to carry out multi-dimensional credible verification on model output; integrating an SAST tool and a symbolic execution tool, and verifying the model output from different dimensions; and summarizing and fusing detection results of the enhancement layers, and determining a final detection result by adopting a decision optimization method. According to the method, through technical fusion and dynamic optimization, the problems of rule stiffness, one-sided detection and low result credibility are systematically solved.

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 discloses a charging station operation maintenance method based on a multi-dimensional data system, and relates to the field of intelligent optimization, and the method comprises the steps: collecting the operation, environment, user and power grid data of a charging pile, preprocessing the data into JSON, carrying out the real-time analysis and classification through Kafka-Flume, storing the data into a distributed database, building a composite index, and carrying out the retrieval through Presto; after cleaning, combining GraphQL and a graph query language to construct a space-time atlas, and extracting a multi-dimensional feature vector; a digital twinborn model is generated, multi-source data are fused through virtual-real mapping, and residual correction and reinforcement learning optimization are combined; fusing multi-modal data by using a hypergraph neural network, and optimizing topology in combination with an entropy regularization multi-target strategy and meta-learning; power is dynamically distributed, LSTM-GAN predicts and positions faults, and PID regulates and controls output; and the block chain contract automatically triggers equipment freezing and power limiting, and on-chain data feedback optimizes scheduling and load balancing. According to the invention, through multi-dimensional data fusion and a dynamic optimization mechanism, the operation and maintenance accuracy and response efficiency of the charging station are significantly improved.

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 present disclosure includes systems and methods for a security policy framework. Various embodiments include responsive to receiving a trigger, fetching one or more policies from a policy catalog service; compiling the one or more policies into a query, wherein the one or more policies can be compiled into a plurality of different query languages; executing the query over customer data, the customer data being located in one or more data sources; and persisting results of the query.

A graphical, hierarchical document stream browser and environment for semantic (e.g. framing) and performance data analysis and interactive visualization integrates three scales: entities (competitive), entity (diachronic), and document (linguistic). The document level includes annotation and computational linguistics facilities; the entity level has calendrical and time-series focus. All levels emphasize deep linkage and network (i.e. connective / relational space) view of objects, with user-configurable connectivity. Large language model (LLM) integrations provide synthetic advisories, public opinions, reports, plot insights, comparisons; traditional natural language processing techniques and neural models are also employed. A smart plot system includes a “plot cart” and interpreter with an analysis snippet library. Graph structure may arise via adjustable blending or perceptual optimization of canned attribute-related distance functions or via link-induction query language with deep “semantic stored procedure” subexpressions, or feed into graph neural network-style inference for predictions. Most non-LLM ongoing computational load is client-side, using precomputed hierarchical summary files.

In one embodiment, a method herein comprises: inputting, by a device, an input prompt to a first large language model to generate an output; computing, by the device, a reward metric in part by using a solver to process the output; tuning, by the device and based on the reward metric, a second large language model configured to correct errors of the first large language model using reinforcement learning; and using, by the device, the second large language model to correct an error of the first large language model.

Systems, methods, devices, and computer readable storage media described herein provide techniques for generating and / or repairing query language (QL) queries. In an aspect, an embedding is determined based on a request to generate a QL query. The embedding is compared to a layer embedding(s) of a deep data map to determine a similarity between a layer embedding and the embedding satisfies similarity criteria. A prompt is provided to a large language (LLM) to cause the LLM to generate the QL query, the prompt comprising a description of an item associated with the layer embedding. In another aspect, an alert indicating an undefined variable of the QL query is received. A query embedding associated with the QL query is compared to the layer embedding(s) to determine a candidate variable. The candidate variable is substituted in for the undefined variable, and a response comprising the repaired QL query is generated.

The invention provides an SQL (structured query language) statement generation method based on large-model multi-stage iteration, which is used for database structure understanding and natural language SQL query generation, constructing multi-level semantic embedding of a database structure through advanced semantic modeling, and deeply analyzing a natural language query request of a user by utilizing an intention recognition model; a query intention frame is extracted in combination with a pre-trained large model, the query intention frame and database semantics are embedded and fused by adopting a calibration correction model and in combination with a multi-stage feedback mechanism, and a query intention and model parameters are optimized step by step; an optimal practice SQL statement is generated through deep optimization and a retrieval strategy model, model parameters and retrieval strategies are continuously optimized through self-adaptive evolution and user preference feedback, and a knowledge base is updated. According to the method, the problems of much manual intervention, inaccurate intention understanding, insufficient structure understanding and lack of a feedback mechanism are effectively solved, and efficient and accurate SQL query generation is realized.

The invention provides an intelligent data query method based on a natural language, and relates to the technical field of intelligent data processing and natural language interaction.The intelligent data query method comprises the steps that firstly, enterprise original data is subjected to standard treatment, and a standardized theme database and a data directory and index definition document are constructed; key semantics are extracted based on unstructured knowledge, and a domain knowledge vector library is fused and constructed by combining document text fragments and vector representation of a mapping relation between historical questions of a user and an SQL (Structured Query Language). And after receiving a natural language question of a user, calling a large language model to identify a task type, and distinguishing knowledge questions and answers, data query and complex analysis. Executing corresponding operations according to different types: directly retrieving a vector library by knowledge questions and answers to generate answers; extracting keywords in data query and generating a query request in combination with context; and in the complex analysis, predefined workflow is judged and executed or intelligent agent processing is called, and query or analysis requirements are output.

A natural language to database query converter (converter) receives a natural language query from a user (i.e., a user utterance) and identifies a cybersecurity domain related to intent of the natural language query. The converter then generates a database query for a query language of the cybersecurity domain corresponding to the natural language query with a large language model (LLM). An initial prompt to the LLM generated by the converter specifies a grammar of the query language and instructs the LLM to generate an initial database query that functions like the natural language query and satisfies the grammar. If a lint program determines that the initial database query is not valid for the query language, the converter generates a follow-up prompt to the LLM that indicates valid database queries from which to generate a follow-up database query. A query parser retrieves data that satisfy the initial or follow-up database query and a visualization / summarization module generates graph visualizations and summaries of the retrieved data.

The invention discloses a multi-table combined natural language query SQL (structured query language) generation method, which comprises the following steps of: 1, constructing a database meta-knowledge graph, and establishing a triple storage comprising a table structure, a primary and foreign key relationship and business description for each data table; 2, receiving a natural language query request, and calculating the topic relevancy between query semantics and each database table through a pre-trained topic matching model; 3, dynamically constructing a view, and logically associating the database tables of which the theme relevancy exceeds a threshold value to form a temporary view; 4, generating a context enhancement prompt, and combining the temporary view structure, the field semantic description of the view and the view content sample to form a structured prompt; 5, inputting the natural language query and the structured prompt into the large language model to generate candidate SQL statements; and step 6, executing verification and iterative optimization on the candidate SQL statements, verifying logic correctness through SQL execution plan analysis and result sampling, and triggering and prompting a reconstruction mechanism when detection is abnormal.

The invention relates to the technical field of database optimization, in particular to a slow SQL (Structured Query Language) statement optimization method and system, electronic equipment and a storage medium, the slow SQL statement optimization method comprises the following steps: preprocessing a collected target slow SQL statement to obtain multi-modal data of the target slow SQL statement; inputting the multi-modal data of the target slow SQL statement into the multi-modal large model, and performing feature extraction and fusion to generate comprehensive semantic representation; generating an optimization suggestion of the target slow SQL statement according to the comprehensive semantic representation; the optimization suggestions are verified, and then the optimization suggestions which are verified to be qualified are executed in a database. Through multi-dimensional data acquisition, large-model deep analysis and automatic strategy execution, full-life-cycle quality detection and optimization of the slow SQL statement are realized.

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 method and a device for converting a natural language statement into an SQL (Structured Query Language) and a storage medium, relates to the technical field of data processing, and is used for improving the accuracy of converting the natural language statement into the SQL statement. The method comprises the steps that semantic analysis is conducted on a data query text, a target entity and at least one query field are obtained through extraction, and the target entity indicates a query intention corresponding to the data query text; determining a comprehensive similarity between each query field and each first candidate node according to a semantic similarity and a structural similarity between each query field in the at least one query field and each first candidate node in a pre-stored knowledge graph; determining a target node corresponding to each query field according to the comprehensive similarity between each query field and each first candidate node; according to the information, the data query text can be converted into the structured query statement.

The invention discloses a database test case extension method and device based on a large language model and a readable medium. The method comprises the following steps: constructing a feature knowledge base corresponding to each database management system; identifying an original dialect feature in an original query SQL statement of the initial database management system through an error recovery mechanism, and performing dialect mapping on the original dialect feature in a feature knowledge base corresponding to the target database management system to obtain a target dialect feature and a description and an example corresponding to the target dialect feature; inputting an original SQL (Structured Query Language) statement of the initial database management system into the conversion model, constructing a first cue word, and guiding the conversion model to generate an original SQL statement of the target database management system through the first cue word; and inputting the original query SQL statement of the target database management system into the fine-tuned variation model corresponding to the selected variation strategy, and generating the variation query SQL statement of the corresponding target database management system, thereby effectively improving the universality and reliability.

The invention relates to the technical field of artificial intelligence large language models, and discloses a data attribution analysis task processing method, system and device based on a large language model and a storage medium. The data attribution analysis task processing method is applied to data attribution equipment and specifically comprises the following steps that S101, user input is received through a multi-mode input interface, and the user input comprises natural language problems, structured data files or API data streams; by means of the natural language understanding ability of the large language model, the system can directly analyze service problems put forward by a user in a daily term, professional data query languages are not needed, non-technical personnel can conveniently use the system, the data retrieval time is shortened to be within 3 minutes from 30 minutes on average through the automatic SQL query generation technology, the efficiency is improved by 10 times, and the method is suitable for large-scale popularization and application. The system can intelligently identify the database fields corresponding to the business indexes and generate optimized query statements.

The invention relates to the technical field of semantic processing, in particular to a context-aware SQL (structured query language) generation method based on table field semantic enhancement, which comprises the following steps of: acquiring financial management and investment combination data of a user and mode information of a background database, constructing a mixed semantic index database, receiving natural language query input by the user, and generating a query result. Performing intention judgment on the query by utilizing the lightweight intention classification model, and if the intention is judged to be matched, calling a parameterized SQL template bound with the query intention and filling parameters to generate a final SQL query; if it is judged that the query intention is an exploration type intention, executing a two-stage SQL generation process, including generating an SQL skeleton according to the query intention, performing target type field recall based on the SQL skeleton, and filling the recalled field into the SQL skeleton to generate a final SQL query; and monitoring the execution performance of the final SQL query, and updating the query popularity metadata and the calculation cost metadata. According to the method, the SQL generation efficiency is improved through intelligent shunting and self-optimization.

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 relates to the technical field of resource matching, and discloses a cross-platform e-commerce resource dynamic matching search method, which comprises the following steps: carrying out real-time analysis on commodity description information from different e-commerce platforms, extracting a core attribute of a commodity and generating a unified semantic representation; when cross-platform data conflicts occur, a conflict resolution strategy is dynamically generated, and the conflict resolution strategy is executed by a local preprocessing module; acquiring data content after conflict resolution, selecting a target edge node based on the data content in combination with user geographical location information, and dynamically configuring a cache data set of the target edge node; and generating corresponding multi-dimensional matching features based on user intention information in a natural query language in combination with the commodity data in the configured cache, determining a candidate commodity set and a corresponding search result based on the multi-dimensional matching features, obtaining feedback information of the user on the search result, and updating the intention analysis model and the matching strategy based on the feedback information. According to the invention, efficient cross-platform resource matching can be realized.

The invention relates to the technical field of databases, can be applied to the field of science and technology finance / digital medical treatment, and discloses a database migration method and device, electronic equipment and a storage medium. The method comprises the steps that database structured query language identifiers are defined, strategy classes corresponding to all databases are packaged, and a strategy context management container is created; receiving a target database type, dynamically loading a strategy type instance according to the type, and generating an executable query strategy chain according to the strategy type instance; when a database query request is received, matching a corresponding strategy type instance, and processing an original database query statement to generate a target database executable instruction; controlling a query strategy chain to execute a single-path or double-write-path mode through a common switch, and transmitting the instruction to a strategy type instance for execution; and analyzing a return result of the target database, performing conversion adaptation through the strategy type instance, and returning a conversion adaptation result. The method realizes automatic migration, reduces cost and risk, and improves system performance.

The invention relates to the field of data management and knowledge maps, and provides a civil administration data consanguinity knowledge map construction method and device based on a large model, and the method comprises the steps: constructing a basic entity network based on the metadata feature extraction of civil administration data; based on the basic entity network, analyzing the data table structure to generate a data table structure relation model; analyzing the complex structured query language statement, identifying field-level blood relationship, and generating a preliminary blood relationship graph; all physical source tables are traversed, virtual table redundancy and data node repetition are eliminated, and an optimized blood relationship graph is generated; and based on the optimized blood relationship graph, identifying public data nodes and combining field-level data blood relationships to generate a final civil administration data blood relationship graph. According to the method, the problems of difficulty in accurately analyzing complex SQL statements, processing virtual table redundancy and data node repetition in the prior art are solved, and high-precision and automatic civil administration data consanguinity knowledge graph construction is realized.

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.

Various embodiments described herein support or provide operations including receiving a natural language text from a device; using a first machine learning model to generate a first embedding vector that represents the natural language text; matching the first embedding vector with a second embedding vector that represents a data table; generating, based on the natural language text, a prompt as an input to a second machine learning model; using the second machine learning model to generate a response based on the prompt; and causing display of the SQL query in a user interface of the device.

Techniques for grammar powered retrieval augmented generation for domain specific languages are disclosed. In some embodiments, a system, a process, and / or a computer program product for grammar powered retrieval augmented generation for domain specific languages includes automatically generating a seed dataset for a domain specific language (DSL) (e.g., a resource query language (RQL), and wherein the RQL is generated for RQL for multi-domain security applications); expanding the seed dataset for the DSL using a Large Language Model (LLM); and validating the seed dataset for the DSL, wherein the seed dataset for the DSL is input to the LLM for fine tune training of the LLM (e.g., fine-tuned for a cloud security application).

The invention belongs to the field of SQL generation, and particularly discloses an SQL generation method and system based on a large language model, a terminal and a medium, a natural language query statement and a target database mode are transmitted into the large language model, and N candidate tables most relevant to user query are selected and fed back by the large language model according to a first prompt word; selecting and feeding back a plurality of candidate data columns most relevant to the user query from each candidate table by the large language model according to the second cue word; retrieving an example question and answer pair similar to the user query from the vector database, wherein the example question and answer pair comprises an example question and an example SQL statement; generating a plurality of candidate SQL statements by the large language model according to the third cue word; and carrying out confidence coefficient filtering on the plurality of generated candidate SQL statements, and selecting a final SQL statement. According to the method, the natural language query statement is converted into the SQL statement through the large language model, the SQL statement generation efficiency and accuracy are improved, and then the usability and accuracy of database query are improved.

The invention discloses a structured query language generation method and device and a nonvolatile storage medium. The method comprises the steps that a data query instruction of a user is received, and keywords in the data query instruction are extracted; related nodes are determined in a knowledge graph according to the keywords, the nodes in the knowledge graph are used for recording entity information in a database, and edges in the knowledge graph are used for recording the relation between entities; obtaining node information of related nodes and node information of associated nodes of the related nodes; and inputting the node information of the related nodes, the node information of the associated nodes and the data query instruction into a structured query language generation model to obtain a structured query language output by the structured query language generation model. According to the method and the device, the technical problem that the generated structured query language is inaccurate due to low retrieval accuracy and incomplete information during retrieval in the prior art is solved.