2189 results about "Table (database)" patented technology

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

The invention relates to the technical field of data processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a database migration method, device, equipment and medium. And deploying a double-write control module of the application program layer to write the source database and the target database in parallel, and completing consistency verification through a data verification comparison task. And controlling a target database write-in channel according to a difference analysis result, closing a source database write-in channel in combination with a service takeover state, and completing a migration process from the source database to the target database. According to the method, full migration, application layer double writing, differential data verification and dynamic write-in channel control are combined, data consistency of the source database and the target database in the migration process is guaranteed, the source database and the target database are safely switched to after the service takeover capacity is verified, and the migration safety and continuity are improved.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for linking a database schema to a natural language query. In particular, in some embodiments, the disclosed systems determine, from tables in a database schema, a subset of tables relevant to a natural language query by comparing embeddings for the tables in the database schema and embeddings for the natural language query. Additionally, in some implementations, the disclosed systems select, from a schema graph comprising nodes that represent the tables in the database schema, an additional table along a path between a pair of nodes representing a pair of tables from the subset of tables. Moreover, in some embodiments, the disclosed systems determine a set of relevant tables by appending the additional table to the subset of tables. Furthermore, in some implementations, the disclosed systems generate, from the set of relevant tables, a response for the natural language query.

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

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

The invention 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 a method for generating steel field TEXT2SQL based on LLM enhanced by RAG, which comprises the following steps: firstly, establishing connection with a steel database, and defining user semantic information and a database mode; then, obtaining user input and carrying out natural language processing; thirdly, guiding a large language model to generate a more accurate SQL query by utilizing the generated group of guiding words prompt; then, natural language query is converted into SQL query in combination with a large language model and an RAG technology; when a new user query is received, searching a historical query most similar to the new user query and a corresponding SQL query from the vector database; and finally, performing grammar correctness and logic rationality verification on the generated SQL statement, and outputting the SQL statement. According to the method, the problem that table names and field names are difficult to obtain when a large language model generates accurate SQL statements according to user description is solved. According to the method, the high-quality SQL query statement can be effectively generated, and the accuracy and the use efficiency of steel database query are improved.

The invention discloses a method and a device for adaptively generating a structural query statement by using a natural language. The method comprises the following steps: acquiring a natural language query statement input by a user; vectorizing the natural language query statement to obtain a first query statement and a data table and an SQL statement corresponding to the first query statement; rewriting the natural language query statement to obtain a second query statement; recalling a candidate data table and a candidate table field based on the second query statement and a preset to-be-queried data table; the first input data and the first sample data are input into the preset large language model, the query statement and the reasoning chain are structured, the natural language query statement input by the user is corrected, the difference between the natural language query statement and the database table structure is reduced, and the accuracy of data table recall is improved; by recalling the candidate data table, the reasoning difficulty is reduced, so that the accuracy of generating the structured query statement according to the natural language query statement is improved.

The invention provides an intelligent AI question answering method based on domain knowledge graph and table paraphrasing enhancement generation, which comprises the following steps: constructing a domain knowledge graph, and performing paraphrasing enhancement on a domain database table to obtain a table paraphrasing enhancement knowledge base; receiving a user question, and analyzing a keyword of the user question through a natural language processing technology; performing intention understanding analysis on the keyword based on a domain knowledge graph to obtain a knowledge graph retrieval result, the knowledge graph retrieval result at least comprising N pieces of similar path information matched with a user question in the knowledge graph; searching M reference tables in a table paraphrasing enhancement knowledge base based on the N pieces of similar path information; and generating an SQL query statement according to the N pieces of similar path information and the M reference tables by utilizing a language large model, and querying in a database based on the SQL query statement to obtain an answer to the user question.

The invention discloses a heterogeneous data conversion method and system based on a multi-modal large model, and aims to solve the problems of single-modal limitation, high migration cost and the like in traditional heterogeneous data processing. The method comprises the following steps: firstly, carrying out format adaptation and classification preprocessing on multi-source heterogeneous data such as images, texts, audios and videos; then, through a ViT, BERT and Transform cross attention mechanism, deep analysis and cross-modal semantic association of visual, text and audio features are realized, and visual-text-audio semantic mapping is established; and based on a dynamic prompt project, guiding the multi-modal large model to generate structured data according to a preset Schema zero sample. And then error correction and format normalization are carried out through a Drools rule engine to ensure that the data meet the business standard. And finally, supporting multi-format output of JSON / XML / database table and the like, and meanwhile, associating original data storage to realize data traceability. According to the method, the limitation of single-modal processing is broken through, and cross-modal data deep association is realized.

The invention discloses a query statement conversion method and device based on a large model, equipment and a medium, and relates to the field of computer science, and the method comprises the steps: obtaining a natural language query statement sent by a user side, comparing the natural language query statement with a high-frequency problem in a target knowledge base, and obtaining a query statement conversion result; whether a high-frequency problem with the similarity with the natural language query statement exceeding a preset similarity threshold value exists in the target knowledge base or not is judged; if the high-frequency problem that the similarity with the natural language query statement exceeds a preset similarity threshold does not exist, determining a target database table corresponding to the natural language query statement based on the target knowledge graph, and extracting a creation statement corresponding to the target database table, so as to determine target information corresponding to the target database table according to the creation statement; and analyzing the target information by using a preset large model to generate an SQL statement corresponding to the natural language query statement. According to the method and the device, the natural language query statement can be efficiently and accurately converted into the structured SQL statement.

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 discloses an SQL (Structured Query Language) statement generation method and equipment based on a natural language and a medium, and the method comprises the following steps: constructing an RAG knowledge base and a data table knowledge graph of a project, then obtaining a to-be-converted natural language of the project, and performing keyword retrieval on the natural language through the RAG knowledge base to obtain an association table set; performing graph traversal on the natural language through the data table knowledge graph to obtain an associated node set; packaging the screening condition of the natural language to be converted, the association table set and the association node set to generate a context knowledge base of the natural language; and finally, according to the context knowledge base and the semantic conversion model, obtaining an SQL statement of a natural language. The problem of low efficiency of multi-table association path discovery is solved by constructing a dynamic extensible database relationship graph; a unified retrieval enhancement mechanism for the RAG knowledge base, the extensible knowledge graph and the historical query features is established, and the problem that target context retrieval is low in efficiency is solved.

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 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 natural language query method and system for a relational database, and the method comprises the steps: extracting a list structure from a local connection database, and generating a sub-database based on a natural language problem; inputting the sub-database mode and the natural language question into a screening cue word template to obtain a complete cue word, inputting the complete cue word into a large language model, and generating a simplified database mode after obtaining a table name and a column name; putting a natural language question into a prediction cue word template, obtaining a predicted SQL keyword skeleton, then converting the predicted SQL keyword skeleton into a prediction vector, screening out an example database, extracting question and answer pairs in the example database, extracting a keyword skeleton from an SQL statement of the question and answer pairs, and after extracting the keyword skeleton, converting the extracted keyword skeleton into a question and answer pair vector; calculating the similarity to obtain a similarity result and a corresponding prediction vector; the content is input into a cue word template, cue words are input into a large language model, the large language model generates SQL statements through a thinking chain, and natural language query is completed.

The invention discloses a fault diagnosis method and system for a coil cutting all-in-one machine based on a large language model, and relates to the technical field of equipment fault diagnosis, and the system comprises a table data intelligent processing module, a knowledge base construction module, a diagnosis reasoning module and a user interaction module. The table data intelligent processing module is used for carrying out automatic fault mode labeling and description standardization on a cutting and rolling all-in-one machine fault diagnosis case library provided by a user; the domain knowledge graph construction module is used for integrating the standardized table database and automatically constructing a domain knowledge graph; the diagnosis reasoning module is used for performing fault diagnosis reasoning based on a large language model and a knowledge graph; and the user interaction module provides a multi-modal user interaction interface and supports visual display of fault diagnosis. Therefore, by adopting the fault diagnosis method and system for the cutting and winding all-in-one machine based on the large language model, the intelligent level of fault diagnosis can be improved, the knowledge base construction and expansion cost can be reduced, and the usability and maintainability of the system can be enhanced.

The invention provides a Text2SQL generation method and system driven by a large language model, and the method comprises the steps: recognizing key entities and relationships in a natural language question through the large language model, and carrying out the construction to obtain a query graph; constructing a hierarchical graph representation model based on the database mode of the target database; searching candidate sub-image sets which have the same structure as the query graph and are in semantic association with the query graph from the constructed graph representation model; and constructing a structured Prompt template based on the candidate sub-image set and the user question, generating an initial SQL statement, and outputting a final query result after grammar and semantic detection correction. According to the method, a traditional database is converted into the graph knowledge base, so that the SQL generation accuracy and performance are improved; guiding the large model to generate an SQL (Structured Query Language) meeting business requirements through knowledge graph storage and retrieval table and column information; and the accuracy of the SQL is continuously optimized by utilizing a self-adaptive feedback mechanism, so that the method is particularly suitable for a large-scale complex query scene, and self-improvement and customization of the model are realized.

The invention provides an SQL statement analysis method and device, equipment and a storage medium, and relates to the technical field of databases. According to the specific scheme, the method comprises the steps that SQL statements and metadata thereof are obtained, wherein the metadata comprises table names, column names and predicates of the SQL statements; target statistical information corresponding to the metadata is obtained from a preset statistical information base, and the target statistical information comprises table statistical information, column statistical information and index statistical information. And loading the statistical information to a test environment database to obtain a simulation database corresponding to the SQL statement. And based on the statistical information, determining an execution plan of the SQL statement, and executing the SQL statement in the simulation database according to the execution plan to obtain a statement analysis result. The method can effectively solve the problem of analysis distortion caused by the distribution difference of test and production environment data, and can obtain a more accurate SQL analysis result without complex data preparation.

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.

System, methods, apparatuses, and computer program products are disclosed for using a large language model (LLM) to generate an index recommendation for a database table. A prompt is provided to an LLM to request an index recommendation for a table of a database. The prompt includes index recommendation guidelines and information associated with the table that is determined from one or more sources. An index recommendation is received from the LLM, and an action is performed based on the received index recommendation.

Methods, systems, and devices for data management are described. The method may include generating, for a database table and using a language model, candidate structured query language (SQL) queries and candidate query intents using a set of SQL query templates and table metadata for the database table, where each candidate SQL query is associated with a respective candidate query intent. The method may include validating the respective candidate query intents by translating the candidate query intent into a test SQL query and determining whether the test SQL query corresponds to a candidate SQL query associated with the candidate query intent. The method may include augmenting each query intent with contextual cues resulting in anchor query candidates for each query intent and generating anchor queries for the database table based on whether the anchor query candidates for each query intent results in identification of the database table by the language model.

Latency, response times, and efficiency improvements for data querying are provided herein, particularly in the context of querying large database systems and data tables from disparate data sources. There are provided systems and methods for automatic query and data retrieval optimization through procedural generation of data tables from query patterns. A service provider may utilize different computing services for query processing and data retrieval for different applications and services used by internal and / or external users. Instead of querying large database systems and numerous data tables, pre-aggregated data tables may instead be used and searched by procedurally generating such tables based on precomputation rules and query patterns. Once patterns have been identified in queries, corresponding data may be aggregated from data sources in a pre-aggregated data table. Query optimization rules may then be used to have these data tables queried in place of their original sources.

A method for extracting data from a database for use in a well construction process includes receiving a question from a user. The question is in a well construction language. The method also includes determining context based upon the question. Determining the context includes retrieving key performance indicators (KPIs) based upon the question, and retrieving a plurality of tables from the database. The tables are retrieved based upon the question. The method also includes generating a prompt based upon the question and the context. The method also includes generating a structured query language (SQL) query based upon the prompt using a large language model (LLM). The method also includes running the SQL query against the tables in the database in an attempt to produce a new table. The method also includes performing a wellsite action in response to the new table.

The invention relates to the technical field of data processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a database data verification method which comprises the following steps: configuring source database connection parameters and target database connection parameters, setting comparison task parameters, and associating the comparison task parameters with corresponding database connection parameters; determining a source table and a target table corresponding to the source table identifier and the target table identifier, executing table structure comparison to generate a structure difference record, determining a data range according to a comparison mode parameter, executing data volume comparison to generate a quantity difference value, determining a data sampling strategy, and performing content comparison to generate a feature identifier comparison result; and finally, integrating the structure difference record, the quantity difference value and the feature identification comparison result to generate a verification report. According to the method, the data range and the sampling strategy are driven by comparing the mode parameters, so that the integrated processing of structure comparison, quantity comparison and content comparison is realized, and the accuracy and efficiency of data synchronization verification are improved in a heterogeneous database environment.

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 a Text-to-SQL (Structured Query Language) complex query statement generation method and system based on a pre-trained large model. The method comprises the following steps: acquiring an input sequence comprising a user question and dictionary interpretation of table names and column names in a database; respectively inputting the user question and the dictionary explanation into a Sension-BERT model, obtaining Top-K explanations most related to the user question, and obtaining an enhanced input sequence according to the Top-K explanations most related to the user question; encoding the enhanced input sequence to generate an enhanced semantic vector, and constructing a hierarchical database mode pattern according to the enhanced semantic vector; inputting the hierarchical database mode graph into a hypergraph neural network model to update node features, and obtaining optimized node features; and obtaining a structured query statement according to the optimized node features. According to the method, complex association and multi-level query logic among multiple tables in a complex query scene can be better processed, and the performance in the complex query scene is improved.

The invention relates to the technical field of data quality management, and particularly provides a data quality monitoring method and device based on metadata and a large language model.The method comprises the steps that word segmentation is conducted on a quality rule described by a text, and a word sequence corresponding to the quality rule described by the text is obtained; matching n question and answer pairs with the highest similarity with the word sequence in a historical high-frequency question and answer pair knowledge base; table names and field names in the word sequence are extracted, and x pieces of metadata information with the highest similarity with the table names and the field names are matched in a metadata information vector library; taking the quality rule described by the text, the n question and answer pairs and the x pieces of metadata information as input of a large language model to obtain a quality rule sql script output by the large language model; executing the quality rule sql script in a power industry database and checking the data table quality level; wherein n and x are preset positive integers. According to the technical scheme provided by the invention, efficient and intelligent data quality management can be realized.