776 results about "Database query" patented technology

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 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.

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 '.

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.

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 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 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.

The invention discloses a natural language database query method and system based on large-model two-channel context enhancement, and the method comprises the steps: carrying out the parallel execution of a business link and a mode link on a query statement, and respectively outputting business context information and mode link information; carrying out vectorization fusion on the two types of information to generate combined context information; generating a query intention vector based on the combined context information, and outputting a complexity label of the query statement through a complexity calculation model; and generating a prompt template according to complexity label differentiation, inputting a large model to generate an SQL statement, and executing query. Through a double-channel cooperation mechanism, the problems of semantic ambiguity, database structure complexity, poor field adaptability and the like in natural language query are solved, query accuracy and response efficiency are improved, and the method is suitable for multi-industry complex database scenes.

The invention provides a multi-modal database query engine and method based on unified semantic representation, the engine associates and stores a vector library of unified semantic vectors of entities, and the core comprises five modules: a query analysis and semantic coding module for processing single-modal and multi-modal queries; analyzing the semantic structure and generating a corresponding semantic vector or a fused semantic vector; the query execution plan generation module generates an optimal plan including a retrieval strategy, weight allocation and resource scheduling according to semantic features, vector library features and the like; a multi-modal query execution module queries a vector library according to a plan to obtain candidate results; the query result fusion module performs weighted fusion on the candidate results to generate weighted scores of the candidate results; and the output module outputs a result according to weighted score sorting. The engine realizes efficient processing of multi-modal query and accurate result return.

The invention belongs to the technical field of bioengineering and artificial intelligence crossing, and particularly discloses a microbial fermentation experiment intelligent design method and system based on a large language model (LLM). According to the invention, a historical database can be automatically retrieved and an experiment resource state can be inquired according to an experiment purpose input by a user intention, an experiment scheme is generated through large language model reasoning, and a task structure which can be identified by an experiment system is synchronized to a digital system so as to support confirmation, optimization and execution of tasks.

The invention discloses a database query method and device, electronic equipment and a storage medium, and relates to the technical field of big data or other related fields, the method comprises the following steps: obtaining a to-be-executed database query statement, and analyzing a target data table related to the database query statement and index configuration information of the target data table; performing validity detection on query conditions of the database query statement based on the index configuration information to obtain a detection result; adjusting the execution mode of the database query statement according to the detection result to obtain an optimized database query statement; and executing the optimized database query statement to obtain a database query result. Through the method and the device, the technical problem of low database query efficiency in related technologies is solved.

The embodiment of the invention discloses a method and device for constructing a field consanguinity tree, a storage medium and a terminal. Firstly, a target field, a database query statement of the target field and upstream blood relationship information are obtained, the target field serves as a root node, a first executable calculation function packaging calculation logic of the target field is generated, and the calculation process is dominant and modularized. By analyzing input parameters of the function, an upstream field which directly depends on is automatically identified and determined to serve as a child node. And then recursively processing each sub-node, generating a corresponding calculation function, analyzing the dependency of the calculation function, tracing layer by layer until all leaf nodes (namely original bottom table fields), outputting a complete blood relationship tree containing all the nodes and the corresponding calculation function, and displaying the complete blood relationship tree through a visual interface. According to the method, automatic and accurate tracing from a target field to an original data source is realized, a computable and reusable blood relationship knowledge framework is constructed, and a solid foundation is provided for data understanding, problem investigation and intelligent data service.

The invention discloses a database query method, system and equipment based on large model data distillation and a medium, belongs to the technical field of databases, and aims to solve the technical problem of how to extract high-quality training data, optimize complex query and improve execution efficiency by utilizing a distillation technology. Knowledge related to database query is extracted from multiple data sources, and a high-quality training data set is generated; performing knowledge transfer on the student model through the teacher model by utilizing a distillation technology, and training a lightweight model by optimizing a loss function; analyzing a natural language query input by a user through the student model, and generating a corresponding structured query language statement, namely an SQL statement; on the basis of a prediction result of the student model, a database execution plan is optimized, and query efficiency is improved; and the content returned by the query is verified and adjusted through the model prediction result, so that the accuracy of the query result and the matching degree of the user demand are ensured.

The invention discloses an intelligent NL2SQL generation and optimization system, an application method and a storage medium, and belongs to the crossing field of natural language processing and database query technology.According to the system, four core components including a dynamic SQL RAG cache module, an intention-Schema double-channel alignment module, an incremental TableSchema sensing module and a multi-table index guide JOIN optimization module are used for optimizing the NL2SQL generation and optimization system; the defects of the prior art in the aspects of high-frequency query efficiency, fuzzy intention analysis, dynamic Schema adaptation, multi-table JOIN performance and SQL credibility are overcome. The method has the beneficial effects that the query efficiency and the resource utilization rate are improved, the accuracy and the interaction efficiency of fuzzy intention analysis are enhanced, the dynamic adaptation of industrial level bins is realized, and the method is particularly suitable for large-model asset query in an enterprise-level operation scene.

The invention discloses a server hardware testing terminal and method, and relates to the technical field of server hardware testing, and the method comprises the steps: obtaining server hardware configuration parameters and storage equipment specification information, collecting the number of processor cores, memory capacity and storage equipment read-write speed reference data through a system monitoring interface, building a hardware performance baseline file, and storing the baseline file in a server; obtaining a complete hardware resource list and a performance index range; the method comprises the following steps: constructing a multi-dimensional workload model according to information retrieval scene features, creating query request sets of different scales by adopting a random number generator, simulating a mixed load mode of full-text retrieval and database query, and determining resource consumption weights and execution time distribution of various query operations; and analyzing query request distribution characteristics in the workload model through a dynamic load balancing algorithm, if query requests are concentrated in a specific time period, adjusting a load distribution strategy, obtaining a uniformly distributed test load sequence, and judging the impact degree of a load peak value on hardware resources.

The invention provides a data processing method, a server, a storage medium and a program product. The method relates to a computer technology, and comprises the following steps: in a multi-database and mass data table scene, in a global range of databases, according to description information of each database and description information of a table connection sub-graph of each database, roughly recalling related candidate databases; according to the natural language query, the description information of each data table in the candidate database and the description information of each table column, finely sorting the candidate database, determining a target database, a target data table and a target table column associated with the natural language query according to a fine sorting result, and combining rough recall and multi-level fine sorting to obtain a target database; according to the method, the user query is intelligently routed to the associated target database, target data table and target table column, the global data query requirement of the user is met, complex data treatment and scene isolation do not need to be depended on, the multi-database query process is simplified through uniform intelligent routing, and the operation convenience of multi-database query is improved.

A system is adapted to automatically identify patterns of potentially suspicious activity, and includes a processor configured to receive, with a user interface, a natural language user query regarding a potentially suspicious transaction, entity, or event. The processor converts the query to a structured query, and fetches relationship data related to the transaction, entity, or event from a relationship repository. For each relationship in the relationship data, the processor constructs a database query based on the structured query, retrieves a record from a query database, fetches customer data from a customer database, and aggregates all of this data into a preliminary prompt. With a prompt composer, the processor receives attributes from an attributes repository, and aggregates the attributes and the preliminary prompt to compose a response prompt. A large language model generates a natural language response related to the potentially suspicious transaction, entity, or event based on the response prompt.

Computer implemented methods, systems, and computer program products include program code executing on a processor(s) identifies a query with low performance. The program code generates a small data environment for use in optimizing the query. The program code identifies table(s) and field(s) related to the query with low performance. The program code samples a portion of each table of the one or more tables based on, for each table, parameters of the one or more fields in each table, where the portion sampled comprises records from each table with common data traits to a whole of each table. The program code generates a small data environment comprising the portion of each table. The program code performance tests an optimized version of the query by executing it on the small data environment.

The invention discloses an HTAP database query optimization method and device and a storage medium. The query performance is remarkably improved by combining the advantages of row storage and column storage. Comprising the steps of analyzing SQL query to generate a logic query plan; evaluating the execution cost of each logic operator in different storage modes by using a mixed cost model, wherein hyper-parameters of the mixed cost model are optimized through Bayesian search; selectively adopting a dynamic programming algorithm or a greedy algorithm to generate a hybrid physical query plan with the minimum execution cost according to the table number; and executing the plan to obtain a query result. In addition, an MCTS algorithm is further utilized, the optimal column subset is selected in the preset memory budget limit based on the query historical record and loaded into the memory column storage, and the subsequent query performance is further optimized. The problem that row storage and column storage in an existing HTAP system cannot work cooperatively is solved, operator-level mixed execution is achieved, and meanwhile memory resource utilization is optimized.

The invention relates to the technical field of database optimization, and discloses a multi-table connection sequence query optimization method, device and equipment, and the method comprises the steps: carrying out the feature extraction of a received database query statement, obtaining a query feature representation, and enabling the database query statement to comprise the data query of a plurality of tables; inputting the query feature representation into a pre-trained target network model, and predicting execution time of different connection sequences in a database query statement to obtain an execution time degree; and on the basis of a Monte Carlo tree, taking the query feature representation and the execution time degree as reward functions, and optimizing the connection sequence of the database query statements to obtain a target connection sequence. According to the method, feature extraction, model prediction and an optimization process based on the Monte Carlo tree are integrated, the execution time and complexity are comprehensively considered, the optimal connection sequence is selected, the limitation that a traditional method is only based on a cost model is overcome, and more accurate connection sequence selection is achieved.

The present disclosure involves systems, software, and computer implemented methods for managing database query plans with dynamic conditions. One example method includes receiving a request to process a query based on at least one dynamic condition. A serialized abstract query plan is identified, deserialized, and converted into a query optimizer tree. A condition provider procedure is invoked to generate a first filter string for each view in the query optimizer tree. Comparisons are made to determine matches between first filter strings and corresponding second filter string stored in the query optimizer tree. In response to determining that a first filter string does not match the corresponding second filter string, query plan compilation is triggered. In response to determining that each filter string matches each corresponding second filter string, a query plan is generated based on the converted query optimizer tree and the query is executed using the query plan.

The invention relates to a database query method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: analyzing a to-be-processed problem for data query to obtain a problem field; obtaining a vector library related to the target database, wherein the vector library is constructed according to vectors of semantic similar fields of each target field in the target database; querying a target field matched with the question field according to the vector of the question field and a vector library, and obtaining table information related to the queried target field from the knowledge graph; based on the table information and the to-be-processed problem, a generation prompt text used for generating a query statement is constructed, a large language model is called, and a target query statement about the to-be-processed problem is output according to the generation prompt text; and based on the target query statement, performing data query on the target database to obtain a query result. Therefore, the accuracy of data query is improved.

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 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.