1259 results about "Database query" patented technology

A method for directing a domain-specific request to a machine learning (ML) agent of a plurality of ML agents configured to generate domain specific language (DSL) scripts includes, at a computing system: processing a request received from a user to determine a particular domain associated with the request; identifying an ML agent of the plurality of ML agents that is associated with the particular domain; generating, using the ML agent, a DSL script for the particular domain based on the request; executing, using the ML agent, at least one of an API call and a database query based on the DSL script; processing, using the ML agent, a response to the at least one of the API call and the database query; generating, using the ML agent, an output based on the response; and processing the output using at least one other ML agent of the plurality of ML agents.

Described herein are a system, method, and device for configuring a generative machine learning model using a syntactic interface. A system may include a user interface, a memory, and a processor configured to, using a syntactic interface displayed using the user interface, receive a syntactic interface input from a user; identify an electronic medical record (EMR) by generating an EMR database query as a function of the syntactic interface input, querying an EMR database using the EMR database query, and receiving, from the EMR database, an EMR database response; generate a prompt as a function of the syntactic interface input; generate a first generative model output as a function of the prompt and the EMR using a trained generative machine learning model; and using a conversational interface displayed using the user interface, display the first generative model output to the user.

A stateful chatbot system leverages generative AI to provide an interface by which users can retrieve information from backend IoT databases of a security provider via natural language queries. Upon receiving a natural language query that corresponds to a request for information from the database, the chatbot generates a corresponding database query having a format compatible with the database. The chatbot comprises a generative model adapted to generate database queries based on natural language queries via prompt engineering using natural language and database query pairs. The chatbot queries the database with the generated database query, retrieves results comprising data / metadata that satisfy the query, and generates a summary of the results, both of which it presents as a response to the user's query. The chatbot also has access to a vulnerability database from which it can obtain information about known vulnerabilities documented therein to respond to user queries.

A system allows generation of information used by an integrated development environment using a machine learning-based language model, for example, a large language model. The integrated development environment is for developing applications, e.g., database applications. The system receives a natural language request for information related to development of the database application. The system determines contextual information describing a development task associated with the database application and generates a prompt for a machine learning based language model based on the natural language request and the contextual information and receives a response. The system extracts the information related to development of the database application from the response. In response to a natural request, the system may generate a database query, provide a resultset by executing the database query, automatically determine a type of chart and generate one or more charts for visually displaying the result.

A method for using a compiler to modify prompts for MLMs used to generate database queries includes receiving, at a query compiler, a first query of a database. The first query is at least in part generated by an MLM. The method includes determining, by the query compiler, whether the first query comprises an uncorrectable error. The method includes, responsive to determining that the first query comprises an uncorrectable error, generating a prompt element that describes the uncorrectable error and that is structured for inclusion in a prompt requesting the MLM to generate a modified first query that corrects the uncorrectable error. The method may include generating the prompt for the MLM, providing the prompt to the MLM, and responsive to providing the prompt, receiving, at the database query compiler, the modified first query that corrects the uncorrectable error.

A system and method for generating a database query based on a natural language query improves database utilization is presented. The method includes receiving a natural language query directed to a security database, wherein the security database includes a representation of a computing environment; selecting a first database query from a plurality of database queries; generating a second database query based on the first database query adapted by the received natural language query; and executing the second database query on the security database.

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.

An LLM is adapted to generate database queries that are compatible with a proprietary database of a security provider. Adapting the LLM includes evaluating performance of the LLM after initial prompt engineering / fine-tuning to ensure that generated database queries are valid (i.e., comport to the database schema and can be executed to return results). When the LLM performance is satisfactory, a dialogue system uses the LLM to generate database queries from user queries. The dialogue system determines intent of each user query, which informs whether the query is supported. Supported user queries are converted to database queries using the LLM and submitted to the database. The dialogue system leverages another language model to generate a summarized, natural language representation of the database query results and constructs a response from the summary. The dialogue system also checks for XSS and prompt injection before database queries are ultimately submitted to the database.

A database query processing service is provided for efficiently processing query workloads with natural language statements and native database commands. The database query processing service may receive some database queries that do not contain a natural language marker and process these database queries without using large language models to generate replacement query content. The database query processing service may also receive other database queries that do contain the natural language marker and process the other database queries using large language model(s) to generate replacement query content or leverage replacement query content already generated by the large language model(s). The replacement query content is checked to ensure the content is natively valid for the content to retrieve data from database structures referenced in the content. The database query processing service may use the natively valid replacement query content to cause execution of operations responsive to the other database queries.

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.

Queries may be made against a non-relational database without impacting the capacities of the non-relational database by enabling performance of queries to a non-relational database via an external query management, backup management, and a non-relational database backup hosted in an external data storage. The query management and backup management of the non-relational database service may split transactional load to the non-relational database and analytical load, and furthermore allow selection of a desired data freshness for queries including various time ranges, such as seconds, minutes, or days.

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 database information extraction and data analysis method based on a large language agent, and belongs to the crossing field of database management and artificial intelligence. In order to solve the problems that traditional database query depends on manual SQL writing, and complex analysis efficiency is low, the method constructs an intelligent agent through a large language model (LLM) and a prompt engineering technology, and end-to-end conversion from a natural language instruction to structured data analysis is achieved. The method specifically comprises the following steps: 1) disassembling an instruction and associating database metadata; 2) generating a precise SQL query; 3) automatically adapting a visual chart according to a query result, and generating an analysis report; and 4) integrating data conclusions in different scenes. According to the method, in a judicial operation situation judgment scene test, the complex query generation accuracy is improved to 80%, meanwhile, the method can be applied to data analysis platforms in the fields of science, finance, medical treatment and the like, and non-technical users are supported to rapidly complete the whole process of'instruction-query-visualization-decision '.

Systems and methods described herein relate to automatic index recommendations for improved database query performance. Candidate indexes are identified. The candidate indexes are associated with a database query that is classified as a slow query. A feature vector is generated for each candidate index to represent statement features and statistical features associated with the candidate index. The feature vectors are provided to one or more machine learning models to obtain an index recommendation value for each candidate index. An index recommendation is presented at a user device. The index recommendation identifies a first index of the candidate indexes based at least partially on the index recommendation value obtained for the first index. User input indicative of a user selection of the first index is received. A database schema is updated to include the first index in response to the user input.

The embodiment of the invention provides a processing method and device based on database query, equipment and a medium. The method comprises the steps that a user query problem is obtained; according to the user query question, screening out candidate database modes from a plurality of database modes in a preset database, and extracting key content information from the user query question; converting the key content information into a content identifier which can be identified by a database, and screening out a target database mode from the candidate database modes; function structure information of the user query problem is determined, and a query statement skeleton of the user query problem is determined according to the function structure information; and filling the content identifier and the target database mode in a query statement skeleton to obtain a database query statement, and executing a query operation in a database through the database query statement. The database query statement is automatically generated according to the user query problem, the query operation is executed, and the query efficiency and accuracy are remarkably improved.

Systems, methods, devices and non-transitory, computer-readable storage mediums are disclosed for a wearable multimedia device and cloud computing platform with an application ecosystem for processing multimedia data captured by the wearable multimedia device. In an embodiment, a method for using a machine learning model to provide data corresponding to a query comprises receiving a query for data stored in one or more databases. A prompt file is determined for the query. The prompt file and the query are provided as input for a machine learning model configured to generate a prediction for the query. Training the machine learning model can include updating network parameters in the machine learning model based on the prompt file. The prediction generated for the query is received. The prediction comprises a predicted database query corresponding to the query, and a level of accuracy is determined for the predicted database query.

Provided herein is a process that converts unprotected data into protected data based on permissions defined using logical operators. As such, user permissions can be managed with a simple string of data. In one example, the method may include receiving a request for data from a user device, identifying a permission object corresponding to the user device, retrieving a string comprising a logical expression with a logical operator and one or more values from the permission object, wherein the logical expression defines access permissions of the user device with respect to a database, executing a database query on the database based on the request to generate results, and filtering the results from the database query based on the logical expression to generate filtered results and outputting the filtered results to the user device.

The invention provides a dynamic rule combination-based data query method and apparatus, and an electronic device.The method comprises the steps of firstly obtaining query parameter information input by a user, and then analyzing fields, operators and values in each query parameter group; based on the analyzed fields, operators and values, a corresponding target processing rule is matched for each query parameter group from a preset processing rule set, and then the query parameter group is processed according to the corresponding target processing rule for each query parameter group; and generating an SQL query statement based on a preset logic operator set and a processing result of each query parameter group, and finally executing the SQL query statement to query the database to obtain a database query result. By adopting the method and the device, the query parameter information can be automatically converted into the SQL query statement, the operation complexity of multi-condition complex query can be reduced, and meanwhile, the universality of the multi-condition complex query in diversified query scenes is improved.

Computer-based techniques for performing systematic literature reviews are provided. Some embodiments include an SLR system including various components that interoperate to generate regulation-compliant SLR reports regarding an issue based on user-defined parameters. In several embodiments, the SLR system may integrate various artificial intelligence techniques to facilitate generation of SLR reports. For example, various SLR systems disclosed hereby may utilize generative pre-trained transformers to assist in aspects of systematic literature reviews, such as literature database queries, literature screening, data question generation, level of evidence classification, qualitative assessments, data extraction, and report generation.

A database query optimization method, comprising: acquiring a query statement, the query statement comprising a connection operation and a group aggregation operation; determining that a connection key and a group key in the query statement are the same, wherein an aggregation function in the query statement can be split, and an input parameter of the aggregation function comes from the same side as a connection condition in the query statement; creating a fusion operator, the fusion operator being used for completing the connection operation and the group aggregation operation; when a cost for executing the fusion operator is less than or equal to a target cost, on the basis of the fusion operator, generating an optimal execution plan related to the fusion operator, wherein the target cost is a cost for executing a connection operator and a group aggregation operator; and performing the operations step by step on the basis of an operation sequence in the optimal execution plan, so as to obtain a query result set. Therefore, when the query statement meets corresponding features, the connection operation and the group aggregation operation can be fused into one operator, thereby reducing the processing flow of an intermediate result, accelerating the execution of the query statement, and improving the query execution performance.

The invention discloses an intelligent data query system and method based on natural language processing, and relates to the technical field of natural language processing and database query. According to the method, natural language input and database mode information are received, a historical query log is combined to construct mode knowledge representation, a query skeleton is generated on the basis, a fine-tuned large language model is called to generate candidate SQL statements, error detection and ambiguity recognition are carried out on the candidate statements, and the query result is obtained. And if necessary, triggering interaction clarification and updating a query result according to user feedback. And meanwhile, the wrong clauses are locally repaired through a gating mechanism, and the clauses are returned and regenerated when multiple times of repair fails, so that the correctness of the query statement is ensured. And finally, after the SQL is executed in the database, a result is fed back to the user. Besides, the system records generation and repair tracks in the operation process, and continuously optimizes the model based on reward shaping, comparative learning and self-game training, so as to improve the generalization ability in different business scenes.

The database query optimization method comprises the steps that a query statement is obtained, and the query statement comprises a connection operation and a grouping aggregation operation; determining that a connection key and a grouping key in the query statement are the same, an aggregation function in the query statement is detachable, and input parameters of the aggregation function are from the same side of a connection condition in the query statement; creating a fusion operator, wherein the fusion operator is used for completing a connection operation and a grouping aggregation operation; under the condition that the cost of executing the fusion operator is smaller than or equal to a target cost, an optimal execution plan related to the fusion operator is generated according to the fusion operator, and the target cost is the cost of executing the connection operator and the grouping aggregation operator; and executing operations step by step according to the operation sequence in the optimal execution plan to obtain a query result set. Therefore, when the query statement meets the corresponding characteristics, the connection and grouping aggregation operations can be fused into one operator, so that the processing flow of an intermediate result can be reduced, the execution of the query statement is accelerated, and the execution performance of the query is improved.

The embodiment of the invention discloses a data query method and device, electronic equipment, a medium and a program product. The method comprises the following steps: receiving a target data query request sent by a user side; the target data query request carries a target natural language query statement input by a user of the user side; performing intention recognition on the target natural language query statement to obtain a target intention recognition result; the target natural language query statement and the target intention recognition result are input into a target large model, a target database query statement is output, and the target large model is obtained through training on the basis of multiple known database query statements and natural language query statements of the intention recognition result; verifying whether the target database query statement meets a preset database query condition or not; and if yes, in response to the target data query request, executing the target database query statement to obtain a target data query result.

A computer system receives, from a programmatic interface of a client device via one or more external API calls, a query that specifies a data source and one or more data fields of the data source. The computer system, in accordance with receiving the query, generates a query specification according to the one or more data fields of the data source, wherein the query specification is an extended version of the API calls. The computer system transmits the query specification to a data service and causes the data service to execute one or more database queries to retrieve data against a database to retrieve query results from the data source, according to the query specification. The computer system receives the query results from the data service, configures the query results to obtain configured data, and transmits the configured data to the client device for display in the programmatic interface.

The invention discloses a financial data query method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence or other related fields, the method comprises the following steps: receiving a data query demand, and retrieving a knowledge document according to the data query demand to obtain index information of a target data table indicated by the data query demand; based on the data query requirement, N reasoning tasks associated in sequence are generated, the N reasoning tasks are executed in sequence to obtain N reasoning results, and N is a positive integer; generating a prompt text based on the data query demand, the retrieval result and the N reasoning results, inputting the prompt text into the target large language model, and outputting a database query statement; and executing the database query statement to obtain a financial data query result. Through the method and the device, the technical problem of low financial data query accuracy caused by difficulty in accurately understanding user query requirements under the condition of complex financial system database environment in related technologies is solved.

The invention discloses an interpretable database query optimization method and system, and relates to the field of database query optimizing.The method comprises the steps that a random forest model is constructed based on a genetic programming algorithm; obtaining a to-be-queried SQL statement; generating a prompt set based on a scanning method and a connection method, and generating a query plan set for each to-be-queried SQL statement according to the prompt set; encoding each query plan in the query plan set to obtain a feature vector set; inputting the feature vector set into a random forest model, and outputting a judgment rule set, feature importance ranking and a prediction execution time set; determining the query plan corresponding to the minimum prediction execution time in the prediction execution time set as an optimal query plan; based on the optimal query plan, a database query engine executes a query. According to the method, the database is queried through the determined optimal query plan, so that the execution efficiency and credibility of query can be improved.

The invention discloses a database query execution method and device based on dynamic query compiling cache optimization, and belongs to the field of database management systems. Receiving a query statement input by a user and constructing an abstract syntax tree; generating a corresponding identifier according to the abstract syntax tree, searching a matched machine code, and loading and executing the matched machine code which can be reused to obtain an execution result; generating a corresponding executable plan tree for the query statement input by the user without finding the matched machine code or the query statement input by the user with the matched machine code incapable of being reused, generating and optimizing the machine code through dynamic compiling, obtaining the optimized machine code, and loading and executing the optimized machine code to obtain an execution result; and then an execution result is sent to the user, and machine codes in the cache are cleaned regularly. According to the method, whether the machine codes in the cache are multiplexed or not is accurately determined, so that unnecessary compilation overhead is reduced, and query execution efficiency is improved.

The invention relates to the technical field of data query and artificial intelligence, and provides an endogenous security database query method and device based on a large model and an MCP protocol. The method comprises the following steps: step 1, acquiring question information input by a user; 2, the MCP client receives the questioning information, performs synonymous rewriting and intention recognition on the questioning information to obtain a request information set, and sends the request information set to an MCP server in combination with a preset MCP connection voucher set; step 3, the MCP server receives the request information set and analyzes the MCP connection voucher set, a heterogeneous agent set is constructed based on the request information set, and each heterogeneous agent in the heterogeneous agent set performs database query operation to obtain a return information set; and 4, deciding the return information set based on a deciding strategy to obtain a final database query result. According to the method, the multi-source heterogeneous data can be subjected to whole-process controllable, safe and credible query.

Data structures and methods are described to provide a unique extendable group identifier for efficient aggregation query processing using multiple grouping keys. A method comprises retrieving a database query comprising an aggregate function of a selected column from a database table, grouped by a plurality of columns. The method further comprises maintaining a plurality of hash tables that map column values to grouping keys and identify a maximum grouping key. The method further comprises updating a plurality of bitmasks that define per-column grouping key bit positions, capable of storing the maximum grouping key, within a combined index value. The method further comprises allocating a result array sized according to the plurality of bitmasks. The method further comprises using the plurality of bitmasks and the plurality of hash tables to determine the combined index value to apply the aggregate function on the selected column for each row in the database table.

Described herein are a system, method, and device for configuring a generative machine learning model using a syntactic interface. A system may include a user interface, a memory, and a processor configured to, using a syntactic interface displayed using the user interface, receive a syntactic interface input from a user; identify an electronic medical record (EMR) by generating an EMR database query as a function of the syntactic interface input, querying an EMR database using the EMR database query, and receiving, from the EMR database, an EMR database response; generate a prompt as a function of the syntactic interface input, generate a first generative model output as a function of the prompt and the EMR using a trained generative machine learning model and using a conversational interface displayed using the user interface, display the first generative model output to the user.