436 results about "Database querying" patented technology

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.

A system and method for automatic generation of database queries using zero-shot, context-based machine learning may output and / or execute database queries and / or analytics insights or plots based on text prompts, and may include or involve: wrapping a text prompt to include database structure information; generating, by a large language model (LLM), a query based on the wrapped prompt, where the query may include one or more database operations; and extracting data or information items from a database based on the query. Some embodiments may include additional prompt or query processing operations such as, e.g., wrapping queries to include corresponding database operations, validating that queries do not include malicious or undesirable commands, and automatically performing appropriate computer actions based on generated queries. Some embodiments of the invention may relate to databases and text prompts describing user actions input to a computer and collected by a desktop data collection software.

Provided herein are systems, methods, and computer-readable media for generating one or more database queries based on a natural language input data. An example method comprises configuring, using a conversational artificial intelligence (AI) editing module, information for a task. Moreover, the method may further comprise parsing the configured information based on a database schema to obtain a parsed database schema. Further, the method may further comprise generating, using a natural language question-answering system, one or more database queries based on the parsed database schema and natural language input data.

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 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 belongs to the technical field of equipment knowledge engineering and natural language processing, and discloses an LLM-based constraint and self-repairing uniformization equipment knowledge graph automatic construction method. The method comprises the following steps: firstly, acquiring equipment related document data through network collection, document arrangement and database query; then, utilizing an equipment domain ontology and constraints as preposed soft and hard constraints, driving LLM to generate semantic intermediate representation, and obtaining candidate triples through structured compiling; then, a self-repairing closed loop is formed through semantic unit testing, logic consistency detection and evidence binding verification, and triples which violate constraints and have conflicts or illusions are automatically recognized and repaired; and finally, entity standard identification, cross-document duplicate removal combination and conflict resolution are realized through cross-segment unification and incremental alignment. According to the method, the fragile path that the LLM directly generates the triple and blindly stores the triple is avoided, the illusion and inconsistency problems are effectively inhibited, and the correctness, interpretability and maintainability of the equipment knowledge graph are remarkably improved.

A computer system uses a machine-learned language model to generate an SQL query for a user query. The system receives a user query comprising a task for performing a database query. The system identifies an embedding for the user query to represent the user query. The system generates a prompt for input to a machine-learned language model, and the prompt specifies the user query, metadata associated with the identified data table and a request to generate one or more SQL statements for performing the database query on the data table. The system provides the prompt to a model serving system and receives an output generated that includes the requested SQL statements for performing the database query. The system presents a response to the user query using the received SQL statements.

The invention relates to the technical field of natural language processing and database query, and particularly discloses a large model Text-to-SQL conditional clause improvement method and device based on fuzzy recall field value enhancement, which are used for solving the problem of SQL query errors caused by inaccurate field values input by a user. According to the main scheme, the method comprises the following steps: extracting and recognizing date, field name and field value keywords in user input through a large model entity; 2) dynamically acquiring related field values from a database by combining a multi-stage recall mechanism of ElasticSearch (ES) fuzzy matching and vector semantic retrieval, and improving the recall accuracy through threshold screening; (3) organizing recall field values into structured contexts, injecting the structured contexts into a large model cue word template, and guiding the structured contexts to generate SQL condition clauses (such as WHERE clauses) containing accurate field values; and (4) changing a real-time synchronous database into an ES and vector database to ensure data consistency.

The invention discloses a structured query language generation method and device, equipment and a storage medium, and relates to the technical field of database query, and the method comprises the following steps: obtaining a natural language input by a target user, and analyzing the natural language by using a preset large model to obtain structured data; determining a corresponding mapping relationship based on the structured data and a preset database knowledge graph, determining an initial structured query language based on a preset large model and the mapping relationship, and performing query by using the initial structured query language to obtain a query result; determining a first reward value based on the structured data, determining a second reward value based on the query result, and adjusting a preset large model by using the first reward value, the second reward value and a strategy gradient algorithm to obtain an adjusted large model; and evaluating the adjusted large model by utilizing a preset verification set, and if the evaluation is passed, generating a target structured query language by utilizing the adjusted large model. Therefore, the SQL generation accuracy can be improved.

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.

The invention provides an intelligent number asking method for large model index query, and belongs to the field of natural language processing and database query. An auxiliary information base is constructed to store a database structure and historical question and answer examples, and key entities and fields are extracted by conducting structured format processing on natural language questions; a large language model guided by a small number of examples is utilized to generate an intermediate expression close to an SQL grammar and a plurality of SQL query candidates, and a consistency alignment mechanism is designed to verify candidate SQL in the aspects of field matching, aggregation functions, data tables and the like so as to screen out an optimal SQL query. Finally, the generated correct SQL and the corresponding question and answer pairs are fed back to the auxiliary information base, and self-learning enhancement is achieved. According to the method, under the condition that only a small number of training examples are needed, the accuracy and expandability of large language model text generation SQL query are improved.

A request to update a database system based on a natural language input document may be received. An input metadata extraction prompt may be determined based on the natural language input document and a prompt template including a fillable portion. The input metadata extraction prompt may be determined at least in part by filling the fillable portion with natural language input text selected from the natural language input document. The prompt template and the input metadata extraction prompt may each include natural language instructions to generate novel text identifying data values characterizing the natural language input document and corresponding to one or more database fields in the database system. A completed metadata extraction prompt may be determined by executing the natural language instructions via a generative language model to generate the novel text. A database query updating the database system may be determined based on the novel text.

A method for querying a graph database using natural language queries comprises: receiving a natural language user query; identifying one or more node types from a type graph in the natural language user query, wherein the one or more node types correspond to one or more words or phrases in the natural language user query; generating, using a large language model, a graph database query based on the one or more node types identified in the natural language user query; querying a graph database using the graph database query generated by the large language model; receiving results of the graph database query; and generating, using the large language model, a natural language response to the natural language user query based on the results of the graph database query.

A method and system for AI based insight extraction from format-bound financial transaction data includes transforming a structured dataset in ISO format into a transformed dataset having metadata interpretable by a LLM Metadata includes descriptions of field names, expected values, entity relationships, and business rules. The transformed dataset is analyzed using machine learning models such as regression analysis, principal component analysis, predictive modelling, or anomaly detection. An intent and content of a natural language query are determined using an NLP model. Based on the intent, context, and metadata, the LLM generates a database query, which is executed on the structured dataset to retrieve relevant data. Insights are generated by combining the retrieved data with machine learning results.

The invention discloses an intelligent SQL (Structured Query Language) generation method and system based on multistage intention recognition. The method comprises the following steps: receiving a natural language query of a user; vector matching: carrying out vector matching based on a knowledge base to obtain Top-K candidate query objects, the knowledge base containing multilayer structure description information; a large language model intention understanding step: determining a final query object in combination with the candidate query object description; loading a branch workflow, analyzing a field and a table structure, and dynamically configuring a database table and field information; sQL generation: generating SQL statements conforming to grammatical rules, including automatic field addition, time condition conversion and field priority matching operation; and executing the SQL statement and returning a result, and if the result fails, recording a log to prompt correction. By combining a natural language processing technology with a database knowledge base, an efficient, accurate and user-friendly database query solution is provided.

The invention relates to the technical field of pharmaceutical knowledge maps, in particular to a pharmaceutical knowledge map construction method and system based on artificial intelligence, and the method comprises the following steps: querying and collecting a molecular structure of a drug and a corresponding target protein sequence through a database, and carrying out the numerical coding of the molecular structure data of the drug, molecular fingerprints and protein structural domain features are extracted, and a drug and protein feature set is formed by combining drug chemical attributes and protein sequence features. According to the invention, through accurate analysis of the molecular structure of the drug and the target protein sequence thereof, the innovative scheme significantly enhances the understanding of the interaction of the drug and the protein, so that researchers can directly extract key features from data and monitor the dynamic change of the drug effect, thereby not only accelerating the development process of the drug, but also improving the development efficiency of the drug. By dynamically tracking the interaction between the side effect of the medicine and the pathological characteristics, the scheme provides powerful data support for personalized medical treatment.

A system and method for generating a database query based on a natural language query is presented. The method includes receiving an unstructured natural language query directed to a security database, wherein the security database includes a representation of a computing environment; selecting a group of database queries from a plurality of preexisting database queries based on a similarity to the unstructured natural language query; generating a context for processing by a language model, the context including the selected group of database queries, an identified technology, and a schema of the computing environment; processing a prompt and the generated context utilizing the language model to generate a second database query; and executing the second database query on the security database.

Aspects of the subject disclosure may include, for example, obtaining a user query to access information from a group of knowledge graph databases, the user query corresponding to user-generated input, and identifying a group of terms within the user query that reduces a likelihood of identifying a match within the group of knowledge graph databases. Further embodiments can include adjusting the user query resulting in a first adjusted user query that enables identifying a knowledge graph database from the group of knowledge graph databases, identifying the knowledge graph database from the group of knowledge graph databases based on the first adjusted user query, and generating a first database query based on the first adjusted user query and the knowledge graph database, in which the first database query would have a computer-readable syntax compatible with the identified knowledge graph database. Other embodiments are disclosed.

A system and method for querying, processing, and ranking governmental data across heterogeneous databases may include integrating large language models (LLMs) and multi-agent orchestration to enhance accuracy and mitigate bias. The system receives a natural language query, processes the query using an agent orchestration LLM to generate search query instructions, and executes specialized data record processing agents to retrieve data from diverse sources, including relational, vector, and graph databases. A plurality of candidate natural language responses is generated and validated by data verification LLMs based on relevance and accuracy metrics to reduce hallucinations. The system further employs cross-source validation, statistical and linguistic consistency analyses, and weighted scoring to refine responses, which may include textual summaries, tables, or downloadable files presented via a graphical user interface. Accordingly, the approach ensures precise and contextually relevant results, particularly for applications involving sensitive or regulated data such as legislative and governmental records.

A database query processing method includes receiving a natural language request for information contained within a database from a user in an application session, prompting a large language model to generate a SQL request, and receiving a particular SQL request from the large language model that is parsed to identify a command to access one or more database structures. A security predicate is appended to the command, creating a modified SQL request, to enforce one or more database access constraints constraining a user-authenticated client device that submitted the request that is not enforced in a database session between the application and a database. The modified SQL request is used to access data in the database session, and a visualization of the accessed data is caused to be displayed in the application session.

The invention discloses a Text-to-SQL (Structured Query Language) generation method and device, a medium and a product, and relates to the field of geographic information technology application, the method comprises the following steps: analyzing a natural language query text input by a user to obtain a source field set and a target field set; determining corresponding nodes of each field in the source field set and the target field set in the river-lake long-system database mode knowledge graph to obtain a source node set and a target node set; the nodes in the source node set serve as starting points, the nodes in the target node set serve as terminal points, the knowledge graph is processed through the shortest path algorithm, and a path set is obtained; decomposing the natural language query text into a plurality of sub-problems by adopting a large language model and a thinking chain technology, and obtaining a dependency relationship among the sub-problems; according to the dependency relationship and the target field set, a public table expression is obtained, and then the SQL statement is obtained, and the accuracy of database query language generation under the river and lake long-term system scene can be improved.

The invention provides a natural language intelligent analysis and data query instruction generation method based on a large model, which relates to the technical field of natural language processing and comprises the steps of performing semantic analysis by pre-training a large-scale language model, constructing a dynamically enhanced query semantic graph, performing semantic enhancement in combination with an external knowledge graph, and generating a data query instruction. And extracting a structured feature vector and mapping the structured feature vector into a database query syntax tree, and finally optimizing an execution plan based on deep reinforcement learning to generate a standardized query instruction. According to the method, intelligent conversion from natural language query to efficient database instructions is realized, and query precision and execution efficiency are improved.

The embodiment of the invention provides a data processing method and device, equipment and a storage medium. The method comprises the steps of guiding a large language model to extract key information in a natural language text through a retrieval agent; obtaining a retrieval result from a pre-stored database through the retrieval agent, wherein the similarity between the retrieval result and the key information meets a set similarity condition; guiding the large language model to use the retrieval result to dismantle the natural language text through a dismantling agent to obtain a plurality of corresponding sub-questions; wherein the solution of each sub-problem depends on the query result of the previous sub-problem; guiding the large language model to generate sub-SQL (Structured Query Language) corresponding to a plurality of sub-questions by utilizing the retrieval result through the disassembling agent; wherein each sub-SQL is obtained through conversion based on the corresponding sub-question and the previous sub-SQL, and the sub-SQL corresponding to the last sub-question is the target SQL corresponding to the natural language text. Therefore, the accuracy of the generated SQL is improved, and the follow-up database query precision and query efficiency are improved.

The present disclosure provides an artificial intelligence-based alarm management based on structured time series data and unstructured contextual information where the conventional methods fails to do. Initially, the system receives a user query associated with an industry automation system and classifies into one of a) data request query b) an anomaly detection and diagnosis query c) a log request query and d) feedback for previous response using a query classifier. Simultaneously, the user query is converted into an associated timeseries query representation. Furthermore, a plurality of database queries is generated based on the timeseries query representation using query generator. Further, data pertaining to the user query is retrieved from an associated database based on database queries using Deep Learning models, thereby identifying a critical alarm, and to take appropriate actions if there is any anomaly. Finally, the retrieved data is displayed to the user in a user readable format.

A multimodal content management system having a block-based data structure can include a query engine configured to perform automatic data discovery for natural language queries. The system can generate and render, at a computing device, a page comprising a graphical user interface (GUI) with a displayable item from a first block of a block-based data structure. The system can generate and bind, to the page, a schema definition comprising a first reference to the first block and a second reference to a set of blocks, wherein the first block is relationally linked to the set of blocks via the second reference. The system can use at least a portion of a natural language prompt, received at the GUI, to generate an input feature for a large language model, the input feature having a schema-question pair that includes at least a portion of the schema definition. The large language model can generate a query configured to operate on the block-based data structure.

A system for returning synthetic database query results. The system may include a memory unit for storing instructions, and a processor configured to execute the instructions to perform operations comprising: receiving a query input by a user at a user interface; determining, based on natural language processing, a type of the query input; determining, based on the received query input and a database language interpreter, an output data format; returning, based on a generation model and the output data format, a result of the query input; providing, to a plurality of training models and based on the determined query type, the query input and the result; and training the training models, based on the query input and the result.

Generating a natural language context from a ticket management system includes receiving an unstructured natural language query from a client device; generating a first prompt for a first large language model (LLM) based on: a predefined template and the unstructured natural language query, the first prompt when processed by the first LLM outputs a structured database query; executing the structured database query on a database, the database including a representation of a cloud computing environment; generating a second prompt for a second LLM based on a result of executing the structured database query, the second prompt when processed by the second LLM outputs a natural language response; and sending the natural language response to the client device.

A system and method for generating a database query based on a natural language query is presented. The method includes receiving an unstructured natural language query directed to a security database, wherein the security database includes a representation of a computing environment; selecting a group of database queries from a plurality of preexisting database queries based on a similarity to the unstructured natural language query; generating a context for processing by a language model, the context including the selected group of database queries, an identified technology, and a schema of the computing environment; processing a prompt and the generated context utilizing the language model to generate a second database query; and executing the second database query on the security database.

Systems and methods for mapping natural language to queries using a query grammar are described. For example, methods may include generating, based on a string, a set of tokens of a database syntax; generating a query graph for the set of tokens from a finite state machine representing a query grammar, wherein nodes of the finite state machine represent token types, directed edges of the finite state machine represent valid transitions between token types in the query grammar, vertices of the query graph correspond to respective tokens of the set of tokens, and directed edges of the query graph represent a transition between two tokens in a sequencing of the tokens; determining, based on a tour of the query graph, a sequence of the tokens in the set of tokens, forming a database query; and invoking a search of a database using a query based on the database query to obtain search results.