101 results about "Query Rewriting" patented technology

Artificial intelligence techniques for query management are described. A method comprises generating, by a context detection module, context information for a first query comprising natural language information to request a result from one of a plurality of machine learning models, modifying, by a query modification module, the first query based the context information to form a first modified query, determining, by an intent module, an intent type for the first modified query, selecting, by a routing module, a machine learning model from the plurality of machine learning models based on the intent type, and routing, by the routing module, the first modified query to the selected machine learning model. Other embodiments are described and claimed.

The invention discloses a construction method of a traditional Chinese medicine alert question-answering system based on a large language model. The method comprises the following steps: establishing a traditional Chinese medicine knowledge base; constructing a retrieval enhancement generation model PV-RAG oriented to drug alert, and training and optimizing the PV-RAG model by taking the traditional Chinese medicine knowledge base as a training set; the PV-RAG model comprises a retrieval module, a text extraction module, a query rewriting module and a generation module; and constructing an intelligent question and answer application end, and generating a traditional Chinese medicine alert answer according to a natural language question input by a user. According to the traditional Chinese medicine alert question-answering system based on the large language model and the retrieval enhancement generation technology, deep semantic understanding of traditional Chinese medicine literatures is achieved through the text embedding model, multi-source data such as traditional Chinese medicine specifications, traditional Chinese medicine classics and clinical research literatures are effectively integrated, and the traditional Chinese medicine alert question-answering system is established. And rich knowledge support is provided for traditional Chinese medicine alert questions and answers.

The invention relates to the technical field of computer data processing, and discloses a data segmentation and query processing system under a distributed architecture. The system comprises a data segmentation module which divides data blocks based on a self-adaptive partitioning algorithm; the distributed storage module is used for distributing data blocks by using a consistent Hash algorithm and dynamically adjusting the distribution of virtual nodes; the index construction module is used for generating a global index table through a multi-layer inverted index algorithm; the query optimization module is used for converting query statements by utilizing a query rewriting algorithm; and the result aggregation module is used for processing the sub-query results by adopting a parallel aggregation algorithm. The system can dynamically process data according to data characteristics, optimizes the data storage and query process, effectively improves the data processing efficiency and query performance of a distributed system, and is suitable for scenes such as big data storage and analysis.

The invention relates to the technical field of retrieval enhancement generation, and particularly provides an RAG query rewriting method based on multi-stage retrieval feedback. The method comprises the following steps: constructing an improved RAG framework by introducing a query rewriter; training a query rewriter based on two stages of static feedback and dynamic feedback of a retrieval task; according to the method, the query is rewritten, the information alignment strategy is designed, the rewritten query is calibrated to the information range and semantics of the retrieval requirements, the query is optimized, the matching degree of the retrieval requirements is improved, and the coverage rate and accuracy rate of retrieval are improved.

The invention relates to the technical field of intelligent retrieval, in particular to an engineering knowledge base construction and deep retrieval method, which comprises the following steps of: in a knowledge base construction stage, performing knowledge level identification on an engineering knowledge document to form a multi-level tree structure taking chapter titles and corresponding contents as nodes; performing analysis processing on the heterogeneous content of each node to generate knowledge fragments; and generating embedded vectors of knowledge fragments by adopting a way of combining path embedding and content embedding, and storing association relationships among knowledge, vector data and original information to complete knowledge base construction. In the deep retrieval stage, after a user question is received, a knowledge blank question list is generated through query and rewriting, a knowledge base is retrieved through traversal of the list, the retrieval process is optimized through knowledge grouping, correlation sorting and loop termination judgment, and finally reply content subjected to traceable verification is generated and output based on existing knowledge accumulated in a circulation mode. According to the method, the reuse efficiency and the retrieval accuracy of the engineering knowledge can be improved.

The invention relates to a query optimization method and device based on agent planning, electronic equipment and a medium. The method comprises the following steps: pre-retrieving a target tree structure index based on a user query request to obtain a pre-retrieval result set; generating a retrieval-reasoning double-stage directed acyclic graph task chain comprising a task type, a retrieval / reasoning target and a dependency relationship; executing each task according to a topological sequence, and obtaining a target text fragment through query rewriting and expansion; and performing multi-hop logical reasoning in combination with the task chain and the target text fragment, and outputting a final answer. Therefore, dynamic task planning of environment perception is carried out by constructing a retrieval-reasoning dual-stage directed acyclic graph task chain and combining a knowledge base tree structure index; the problems that when an existing retrieval enhancement generation system processes a multi-hop question and answer task, logic association understanding ability among retrieval fragments is insufficient, semantic matching precision is low, and a reasoning path is prone to deviation are solved, and the retrieval recall rate and the answer accuracy under a multi-hop question and answer scene are improved.

Aspects of the disclosure include methods and systems for an intelligent chat powered by a large language model that leverages both public and private data to answer user questions. An exemplary method includes receiving a user query including natural language input from a user and executing the user query against at least one public data source and at least one private data source. Queries executed against a public source are retrieved using public search indices and queries executed against a private data source are retrieved using user credentials. A query rewrite and a query context including the user query and retrieved information from the public and private data sources are input to a large language model. A response is received from the large language model that includes a natural language answer to the user query and a link to the retrieved information.

The invention provides a big language model user query rewriting method and system based on historical context, and belongs to the technical field of artificial intelligence. The method comprises the following steps: firstly, collecting a data set containing user query and historical dialogues, redesigning a new instruction, taking a DeepSeek model as a query revisor, and further optimizing the preliminarily rewritten query to obtain a final query version; the method comprises the following steps: calling a DeepSeek model through an API (Application Program Interface), rewriting a query by utilizing the DeepSeek model under the guidance assistance of a designed instruction, optimizing an original query, generating a plurality of improved candidate queries, storing the improved candidate queries in a query pool, and then scoring all queries in the query pool by adopting a candidate query-context alignment scoring mechanism, and selecting the candidate query with the highest score as a preliminary optimization result. According to the method, the query rewriting frame based on the open source large model is constructed, so that the rewriting quality of user query is effectively improved, and the performance of information retrieval is improved.

The invention relates to the technical field of natural language processing, can be applied to business system platforms of financial science and technology, medical health and the like, and discloses a document retrieval method, system, equipment and medium. Extracting semantic features of the text data and visual features of the image data, extracting time sequence features of the interaction log, carrying out cross-modal fusion on the semantic features, the visual features and the time sequence features to obtain fusion features, carrying out query rewriting on the text data according to the fusion features to obtain a query text, and sending the query text to the server. The method comprises the steps of obtaining a query text, performing document retrieval according to the query text to obtain a retrieval result set, determining the correlation between each retrieval result in the retrieval result set and the query text to obtain a correlation score set, and sorting the retrieval result set according to the correlation score set to obtain a final retrieval result. And the document retrieval accuracy is improved.

The embodiment of the invention discloses a database query optimization method and device based on a large model and a medium, and relates to the technical field of databases. The method comprises the steps that under triggering of a current query request, a query SQL text of the current query request is collected in real time, and real-time environment data of a current execution engine is obtained; vectorizing the query SQL text and the real-time environment data to determine a query reference vector; the query reference vector is input into a pre-trained space-time attention model, performance bottleneck positioning data for the current query is determined, a query optimization strategy is determined based on the performance bottleneck positioning data, and the query optimization strategy comprises an index optimization strategy and a query rewriting strategy; and optimizing the current query request according to the query optimization strategy, determining optimized query information, performing rewriting security verification on the optimized query information in a preset database copy environment, and executing database query based on the optimized query information after the rewriting security verification is passed.

In some embodiments, a system transforms an initial user query into a first rewritten query using a first query rewriting algorithm, executes a search of a data repository using the first rewritten query to generate a set of results, executes a chunking process on the set of results to generate chunks of data, transforms the initial user query into a second rewritten query using a second query rewriting algorithm, generates corresponding embeddings for the second rewritten query and the chunks of data using a reranking model, selects a subset of the chunks of data based on a comparison of the embeddings for the chunks of data and the embedding for the initial user query, generates a prompt based on the initial user query and the subset of the chunks of data, submits the prompt to a Large Language Model (LLM) to generate a response to the initial user query.

The invention discloses an autonomous controllable heterogeneous data driven agent collaborative optimization system and method, and the method comprises the steps: constructing a private domain knowledge database containing knowledge information of an interested field through deploying an inference engine and a functional component; the intelligent agent development and management collaborative platform deployment is used for developing a multi-task intelligent agent based on cooperation of a functional component and an inference engine, and the multi-task intelligent agent is sequentially subjected to iterative questions and answers of intention recognition, query rewriting, dynamic planning, retrieval recall coarse screening and secondary fine arrangement of coarse screening results on the basis of a private domain knowledge database. Therefore, collaborative optimization of the multi-task agent based on autonomous controllable heterogeneous data driving is realized. According to the method, an agent development and management collaboration platform is constructed based on a private domain knowledge database, full-link autonomous controllability of a multi-task agent is achieved, domain knowledge and user feedback pairs are introduced to conduct multi-round iterative problem retrieval, retrieval results are collaboratively optimized, and the problem retrieval efficiency and retrieval precision are remarkably improved.

Aspects of the disclosure include methods and systems for an intelligent chat powered by a large language model that leverages both public and private data to answer user questions. An exemplary method includes receiving a user query including natural language input from a user and executing the user query against at least one public data source and at least one private data source. Queries executed against a public source are retrieved using public search indices and queries executed against a private data source are retrieved using user credentials. A query rewrite and a query context including the user query and retrieved information from the public and private data sources are input to a large language model. A response is received from the large language model that includes a natural language answer to the user query and a link to the retrieved information.

The embodiment of the invention discloses a search query information complementing method and device, equipment, a medium and a program product. A specific embodiment of the method comprises the following steps: generating a candidate complementation information set corresponding to search and query information according to the input search and query information; determining whether candidate completion information meeting a preset query rewriting condition exists in the candidate completion information set or not; in response to determining that the candidate completion information meeting the preset query rewriting condition exists in the candidate completion information set, performing rewriting processing on the candidate completion information meeting the preset query rewriting condition in the candidate completion information set so as to update the candidate completion information set; and generating a complementation information set corresponding to the search query information based on the updated candidate complementation information set. According to the embodiment, the accuracy and the conversion rate of the complemented search query information can be improved, so that the difficulty of detecting the required article can be reduced, and the conversion rate of the searched article can be improved.

Artificial intelligence techniques for query management are described. A method comprises generating, by a context detection module, context information for a first query comprising natural language information to request a result from one of a plurality of machine learning models, modifying, by a query modification module, the first query based the context information to form a first modified query, determining, by an intent module, an intent type for the first modified query, selecting, by a routing module, a machine learning model from the plurality of machine learning models based on the intent type, and routing, by the routing module, the first modified query to the selected machine learning model. Other embodiments are described and claimed.

The invention relates to the technical field of natural language processing and artificial intelligence, and particularly discloses a generation and retrieval collaborative optimization method and system for a ReGenSearch task. The system comprises a reflection module, a query rewriting module, a retrieval and generation collaborative optimization module, a reinforcement learning and reward mechanism module and an answer optimization and generation module. The method comprises the following steps: analyzing problems and expanding backgrounds through a reflection module, generating an optimized retrieval query through a query rewriting module, dynamically selecting a generation or retrieval strategy by a retrieval and generation collaborative optimization module, and judging whether iteration is carried out by combining a self-commenting mechanism; a joint strategy is optimized through a DAPO algorithm, and feedback is evaluated through a layered reward mechanism; and finally, comparing the candidate answers by using a DPO algorithm, and selecting an optimal reasoning path for outputting. The method does not need cold start or fine tuning in the specific field, can directly run based on the pre-trained large language model, adapts to multi-step reasoning and long thinking chain tasks, realizes deep fusion of generation and retrieval, and remarkably improves the accuracy, stability and generalization ability of complex questions and answers.

This disclosure describes a workload reduction system that reduces the complexity of workloads sent to a database system. For instance, the workload reduction system pre-processes workloads sent to a database system by generating reduced workloads that include less complex queries that reference fewer tables or columns than the original workloads. In various implementations, the workload reduction system uses table reduction functions and query re-writing functions to generate the reduced workloads. As a result, the workload reduction system improves computational efficiency by rewriting complex queries from workloads into simpler ones that speed up index tuning and decrease individual what-if call times.

The present disclosure provides a training method and device of a query rewriting large model, equipment and medium. The method comprises: performing preliminary optimization on the query rewriting large model based on a fine-tuning dataset to obtain a reference model; and performing re-optimization on the reference model based on a reinforcement learning method to obtain a target model, wherein a reward signal in the reinforcement learning process is generated based on a deviation between a question and answer result of the reference model and a standard answer. Thus, the model can be quickly learned to the basic query rewriting mode by using the fine-tuning dataset for preliminary optimization. Then, the reinforcement learning is introduced, and the deviation between the question and answer result of the reference model and the standard answer is used as the reward signal for fine adjustment, which can effectively correct the generation deviation that may exist in the preliminary model, so that the final target model can generate a rewriting result closer to the real intention of the user in a complex query scenario, thereby improving the overall performance of the downstream question and answer system.

The invention discloses a medical information retrieval method and system based on multi-modal vectorization and a storage medium, and relates to the field of intelligent retrieval. The method comprises the following steps: constructing a corpus and performing field self-adaptive fine tuning to obtain a field optimization recall model; generating a mixed modal vector through a cross-modal attention mechanism and a gating fusion mechanism according to the domain optimization recall model; carrying out slicing processing on the retrieval original text; and after to-be-retrieved information is rewritten and vectorized, retrieval is performed according to the mixed modal vector and the father-child node slice structure. According to the retrieval method, professional corpus semantic optimization, multi-modal information fusion, intelligent document slicing and query rewriting can be combined, and the problem of semantic fracture caused by insufficient multi-modal information fusion and document slicing in a medical RAG system is solved.

The invention discloses a retrieval enhancement generation method fusing dynamic adaptive prompt engineering and semantic enhancement, and relates to the technical field of intelligent question answering. The method comprises the steps of performing semantic enhancement preprocessing on original data in a target domain knowledge base; vectorizing the enhanced data and constructing a corresponding vector knowledge base; after receiving a natural language query of a user, converting the natural language query into a structured sub-query through a preset prompt project; judging whether the structured sub-query contains a preset high-frequency word or not, and selecting a corresponding processing path to construct a context; a target answer is generated based on the context and the original query. According to the method, the problem of semantic sparsity of data is solved through semantic enhancement, accurate intention understanding is realized through query rewriting, and high-frequency word retrieval prejudice is avoided through dynamic path selection, so that the intelligent question and answer quality in specific fields such as college enrollment and consultation is remarkably improved, and the robustness and the adaptive capacity of an intelligent question and answer system are enhanced.

The invention relates to a query information rewriting model training method, a music searching method, equipment and a medium. The training method comprises the following steps: acquiring sample music query information of a sample user and user preference of the sample user for a sample music search result; the sample music query information carries emoticon information, and the sample music search result is a search result obtained by searching through the sample music query information; inputting the sample music query information and the user preference into a query information rewriting model to be trained, and rewriting emoticon information carried in the sample music query information through the query information rewriting model to obtain prediction query rewriting information corresponding to the sample music query information; and training the query information rewriting model by using the difference between the predicted query rewriting information and the actual query rewriting information to obtain a trained query information rewriting model. By adopting the method, the application range of the query information rewriting model can be expanded.

The application provides an EXISTS related subquery optimization method based on a multi-association condition of an MPP database, relates to the technical field of database query optimization, and comprises the following steps: after obtaining a SQL query statement to be optimized, firstly, conditions in an EXISTS related subquery statement of the SQL query statement are extracted; then, the row repetition rate of each condition is calculated to filter out duplicate keys; and finally, the original SQL query statement is rewritten based on the duplicate keys to obtain an optimized SQL query statement. The method directly embeds the duplicate removal operation in the query rewriting process, so that the system can effectively eliminate duplicate data and avoid repeated calculation. Compared with the traditional global duplicate removal method, the method can significantly reduce the calculation complexity and resource consumption of the database system while ensuring the accuracy of the query result, thereby greatly improving the query efficiency.

The invention belongs to the technical field of artificial intelligence, and particularly discloses a multi-modal intelligent question and answer method and system in the automobile field and a program product, multi-type knowledge in the automobile field can be analyzed by introducing a multi-modal technology, and effective information such as pictures or texts can be accurately retrieved; multiple scenes in the automobile field can be covered through intention recognition, local knowledge management is facilitated, meanwhile, efficient intention classification can be conducted on query of a user, and the retrieval matching speed is increased; through query rewriting, the intention of the user can be more accurately understood; by optimizing the reply, the picture and text information in the knowledge base can be effectively fused, so that the replied answer is more accurate. According to the method, the defects of an existing question-answering system in multi-round question-answering and complex query are effectively overcome, the interactive experience of the user can be enhanced, the practicability and user satisfaction of the automobile field question-answering system are improved, and the development of intelligent services in the automobile field is promoted.

The application discloses a kind of self-controllable heterogeneous data-driven agent collaborative optimization system and method, by deploying reasoning engine and functional component, construct the private domain knowledge database containing the knowledge information of field of interest, utilize agent development and management collaborative platform to develop multi-task agent based on functional component and reasoning engine cooperation, based on private domain knowledge database, the iteration question and answer of the secondary fine arrangement of the coarse screening result of the iterative question and answer of the intention recognition, query rewriting, dynamic programming, retrieval recall coarse screening of multi-task agent in turn is carried out, to realize multi-task agent based on self-controllable heterogeneous data-driven collaborative optimization.The application is built by private domain knowledge database Agent development and management collaborative platform, realize the full-link self-controllable of multi-task agent, introduce the problem retrieval of multiple rounds iteration of field knowledge and user feedback, collaborative optimization is carried out to retrieval result, significantly improve the efficiency and retrieval precision of problem retrieval.

This application relates to the field of human-computer interaction technologies, and provides a dialog interaction method, an electronic device, and a computer-readable storage medium. In this application, a historical dialog record is screened for selecting an associated historical dialog record to perform query rewriting on query information, to reduce impact of a historical dialog record with an unrelated topic on the query information, improve accuracy of the query information, improve accuracy of reply data of the query information, and improve interaction accuracy. In addition, the associated historical dialog record is selected to perform query rewriting on the query information, to support a user in simplifying input of the query information in a multi-round interaction process.

The invention relates to the technical field of artificial intelligence technology application, in particular to an electric power knowledge question-answering system and method based on large language model generation, and the system comprises the steps: constructing an electric power knowledge graph and double indexes through a question-answering knowledge base; the query rewriting module analyzes an original query of a user and outputs a standard rewriting query; the knowledge retrieval module performs double recall rearrangement and outputs a Top-K result; the content generation module generates answers, and closed-loop optimization is carried out after reflection evaluation. The method comprises the steps of constructing an electric power knowledge graph and double indexes, rewriting original queries based on a large language model, obtaining Top-K results through double recall and rearrangement, and generating and optimizing answers in an inverse mode. The problems that in an existing electric power knowledge question-answering technology, retrieval semantic understanding is weak, the professional degree and the reasoning ability of a basic language model are insufficient, and knowledge organization lacks a systematic structure are solved.

The invention discloses a query rewriting model training method, a retrieval method and a related device, and relates to the technical field of information retrieval, the query rewriting model training method comprises the following steps: rewriting a query problem sample based on a query rewriting model to obtain a rewriting result set of the query problem sample, performing knowledge retrieval on rewriting results in the rewriting result set, determining retrieval effect rewards according to retrieval results of the rewriting results in the rewriting result set, determining diversity rewards according to the rewriting result set, and determining semantic consistency rewards according to query problem samples and the rewriting result set; and optimizing the query rewriting model according to the retrieval effect reward, the diversity reward and the semantic consistency reward. Through the query rewriting model training method disclosed by the invention, the query rewriting model with a relatively good rewriting effect can be obtained through training.

The present invention discloses a materialized view design method for database query optimization, comprising the following steps: for a PostgreSQL database, collecting historical query loads and corresponding patterns; constructing a Cosette pattern of the query load; using Cosette to perform equivalent identification, merging, and frequency statistics on subqueries; recommending the most frequently appearing candidate subqueries for actual materialization based on frequency and a greedy strategy; creating a materialized view in PostgreSQL, scoring and replacing the materialized view using a Newton's cooling law calculation formula; and rewriting the user's original query statement in the created materialized view environment for query optimization. The present invention conducts an in-depth analysis and research on view selection and view elimination strategies in materialized view design under a big data environment, proposes a view selection strategy based on a Cosette query statement equivalence prover, and proposes a materialized view scoring and elimination strategy based on Newton's cooling law. The efficiency and effectiveness of view design solutions under a big data environment are improved.

Embodiments of the present application provide a natural language-based query processing method and device and electronic equipment, and relate to the technical field of industrial internet. The method extracts multi-dimensional features from an obtained query sentence to obtain a feature vector representing the complexity of processing the query sentence, the feature vector being constructed based on at least one of the dimensions of semantic entropy, entity size, multi-modal dependency, and governance demand correlation. The complexity value of the query sentence is calculated based on the feature vector, and the corresponding target processing path is matched for the query sentence according to the complexity value, the target processing path including at least one of a direct generation path, a query rewriting optimization path, a query decomposition execution path, and a multi-modal coordination path. The query sentence is processed according to the target processing path to generate an execution result. The method guarantees the query processing efficiency while improving the cross-modal query accuracy of multi-source heterogeneous data.