229 results about "Knowledge question" patented technology

The invention discloses an electric power field knowledge question-answering system construction method based on a large language model, and relates to the field of electric power field knowledge question-answering, and the method comprises the steps: judging the data type of electric power field knowledge, and carrying out the processing of the electric power field knowledge according to the judgment result through matching with a processing technology, and generating an entity relation triple; constructing a power field knowledge graph; optimizing the power field knowledge graph based on the attention network, outputting an answer causal path of the fault problem by using the optimized power field knowledge graph, and marking a confidence score of the answer causal path; and inputting the solution causal path and the confidence score into a language model to obtain a fault question answering result, and optimizing the question answering result according to the consistency of the fault question answering result and the power field knowledge graph. According to the method, on the premise that the fault diagnosis logic is rigorous and the result is traceable, knowledge in large-scale unstructured literatures in the power industry is activated, so that accurate question and answer services can be provided for operation and maintenance personnel in real time.

The invention discloses a knowledge question and answer processing method fusing a large model and a knowledge graph, and relates to the technical field of artificial intelligence and natural language processing. Aiming at the defects of a traditional retrieval enhancement generation technology in the aspects of complex semantic association, context consistency and dynamic knowledge updating, the scheme adopted by the invention comprises the following two stages: knowledge graph construction and mixed index generation: collecting and processing internal and external multi-source data of an enterprise, and performing cleaning preprocessing such as coding normalization and de-duplication to obtain a mixed index; entities and relations are extracted through a pre-training model to generate a triple, a knowledge graph is constructed and stored in Neo4j, then a mixed index is generated through text and graph embedding fusion, and two types of retrieval are supported; retrieval and answer generation: obtaining user query, preprocessing, vectorizing, obtaining a candidate list through low-level semantic retrieval and high-level reasoning retrieval, fusing multi-dimensional indexes, rearranging and screening top-M candidates through Cross-encoder, constructing a JSON evidence list, and generating traceable answers through small model draft, large model fine calibration and consistency verification.

The invention relates to the technical field of artificial intelligence, and discloses a professional knowledge question and answer intelligent customer service system based on a self-optimization mechanism. The system comprises a user intention analysis module, a knowledge processing module, a self-optimization learning module and an interactive presentation module. A semantic understanding unit of the user intention analysis module generates a user intention signal; after the knowledge processing module receives the signal, a knowledge retrieval unit outputs related knowledge fragments and confidence, an answer generation unit forms candidate answers, and a quality evaluation unit determines an optimal answer according to the confidence; in the self-optimization learning module, a feedback analysis unit adjusts answer generation parameters according to user interaction data, a strategy adjustment unit optimizes a retrieval strategy in combination with an optimal answer and a historical dialogue, and a knowledge updating unit updates a knowledge base depending on an external knowledge source; and a multi-round dialogue management unit of the interactive presentation module adjusts a dialogue process, and a visual presentation unit outputs a natural language text and collects user feedback to a feedback analysis unit.

The invention provides a household appliance knowledge question-answering method and system based on retrieval enhancement generation. The method comprises the following steps: acquiring household appliance field multi-modal data from a multi-format document library; extracting text information, table information and chart information in the multi-modal data; performing domain term injection processing on the extracted information, and constructing a packet domain enhancement index; receiving a natural language question input by a user; the natural language problem is analyzed through a query optimizer, and semantic retrieval and keyword retrieval are executed in parallel; carrying out fusion processing on the semantic retrieval result and the keyword retrieval result; selecting matched document fragments by adopting a relevancy sorting algorithm; inputting the matched document fragments into a large language model to generate candidate answers; verifying the compliance and traceability of the candidate answers through a credibility evaluation module; outputting a final answer with a reference source; and storing the high-frequency questions and the final answers into a cache library to solve the problems that the answer accuracy of a knowledge question-answering system is reduced and the response efficiency is limited.

The invention provides a knowledge question and answer library agent construction method and system. Efficient knowledge management and question and answer are achieved through cooperation of multiple agents. According to the system, firstly, a multi-modal information extraction agent is constructed, and heterogeneous data such as texts, images and tables are converted into structured vectors and stored; meanwhile, the knowledge graph is dynamically constructed and continuously optimized by the self-adaptive knowledge graph construction agent, and a new relationship is derived through combination of symbolic logic and a graph neural network, so that an evolvable knowledge network is formed. In the question and answer stage, a query analysis agent deeply analyzes the intention of a user and generates sub-queries; retrieving the vector library and the knowledge graph in parallel by the retrieval enhancement generation agent; and the reasoning and synthesizing agent integrates multi-source information and generates an accurate answer with a complete source label through a large language model. Dynamic knowledge management, precise semantic analysis and system self-evolution are achieved, and the method is particularly suitable for professional field scenes needing high-reliability questions and answers.

The invention discloses a knowledge question-answering method and system based on topic knowledge graph retrieval enhancement, and the method comprises the steps: firstly extracting a local topic represented in a triple form based on an original document through employing a large language model, carrying out the clustering, and generating a global topic triple set representing the global perspective of the whole document; secondly, on the basis of the global topic triple set, topic-guided entity and relation extraction is adopted, and a mixed knowledge graph is constructed; secondly, providing a semantic perception personalized PageRank algorithm, matching query semantics with semantics of edges in the mixed knowledge graph, and dynamically adjusting the weight of score propagation between nodes; and finally, designing a three-level progressive retrieval mechanism, retrieving multi-level information related to user query from the mixed knowledge graph, and inputting the multi-level information into the large language model to generate a final answer. According to the method, the semantic integrity and retrieval precision of the knowledge graph are remarkably improved, and the accuracy, comprehensiveness and enabling performance of generated answers are ensured.

The invention discloses a multi-modal multi-scale retrieval enhancement generation method, system and device applied to external knowledge questions and answers and a medium. The method comprises question perception, multi-modal multi-scale query fusion coding, dense recall and answer generation. Analyzing a key query phrase from the question through a fine-tuned instruction language model, and accurately positioning a region of interest corresponding to the phrase in an image by using an open set visual positioning model; multi-source information is compressed and distilled into an optimal query vector through a deep fusion network integrating multi-head self-attention and an information bottleneck theory; executing a maximum inner product search to recall related knowledge; guiding the large language model to synthesize all information to generate a final answer; the system, the equipment and the medium directly perform feature fusion in the vector space based on the method, so that challenges such as information loss and cascading errors caused by a traditional normal form can be effectively dealt with, high correlation and high accuracy of retrieval knowledge are ensured, and accurate and reliable image-text questions and answers are realized.

The invention discloses a large model industry knowledge question-answering method and system supporting multi-modal input, and the method comprises the steps: obtaining a multi-modal query, and converting the multi-modal query into a corresponding feature set; importing the feature set into a preset semantic alignment and enhancement network to obtain enhanced semantic representation; the preset semantic alignment and enhancement network performs semantic alignment and fusion on features of different modals in a shared semantic space through a cross-modal attention mechanism, a multi-modal consistency constraint and a modal complementarity constraint; matching corresponding multi-modal knowledge fragments from the multi-modal industry knowledge base on the basis of enhanced semantic representation; and importing the multi-modal query, the enhanced semantic representation and the multi-modal knowledge fragment into a pre-training large model to generate a question and answer result. According to the method, semantic alignment and deep fusion are performed on the multi-modal query by establishing the semantic alignment and enhancement network, and the professional knowledge range is expanded by introducing the multi-modal industry knowledge base, so that the retrieval efficiency can be improved, and the question and answer generation quality can be improved.

The invention discloses a system and a method for enhancing generation of hydrofracture questions and answers based on cross-language retrieval. The knowledge question-answering system aiming at the hydraulic fracturing field is developed by utilizing a cross-language retrieval enhancement generation technology, so that a user can obtain an optimal answer and a corresponding source from a multi-language knowledge base by matching only by using Chinese retrieval; the complex process that a user needs to find useful information from numerous and jumbled papers and reports is changed. According to the system, a dynamic text vector library framework is adopted, multi-language and complex document formats are supported, the analysis function of tables and formulas is integrated, documents in the hydraulic fracturing field are stored in the same knowledge base, the knowledge base is continuously updated along with document expansion in the professional field, a user can inquire related problems in the field in the system, and the user experience is improved. By integrating and updating a large amount of document information, required professional knowledge in the field can be efficiently obtained.

The invention belongs to the technical field of medical treatment and public health, and particularly relates to a hypertension group knowledge recommendation method and system based on a large language model multi-agent. Hypertension knowledge question-answer pair information, medical short video information and user personal health portraits are finely extracted by constructing a hypertension knowledge question-answer pair library; according to the method, video content quality evaluation and matching degree evaluation are performed, and user platform interaction data adjustment is combined, so that personalized and accurate knowledge recommendation services can be provided for hypertension users, the requirements of hypertension patients for health knowledge are met, the accuracy and effectiveness of knowledge recommendation are improved, and self-health management of the patients is facilitated.

The invention discloses a knowledge question-answering method based on multiple agents and heterogeneous data sources, which comprises the following steps of: collecting an original document, identifying data, processing the format of the data, and respectively realizing the construction of a vector database and a graph database; obtaining a standard question and answer data set, and generating expansion questions by utilizing partial sub-graphs of the knowledge graph to complete construction of a basic question pool; obtaining user questions, and performing preliminary retrieval in the basic question pool; for problems needing deep retrieval, according to the types and characteristics of the problems, the routing agent flexibly calls required tools or distributes tasks to different retrieval agents; and the answer agent integrates the obtained retrieval results to generate a final answer. According to the method, a multi-agent cooperation mechanism is utilized, the intention of the user can be accurately recognized, dynamic information retrieval is carried out in different types of data sources, and meanwhile efficient and rapid intelligent question and answer services are provided by constructing the basic question pool.

The invention provides a guarantee knowledge intelligent question answering method and system based on LLM and RAG technologies. The method comprises the following steps: S1, receiving a guarantee knowledge question input by a user; s2, carrying out word segmentation and vectorization on the guarantee knowledge question; s3, adopting a multi-dimensional query recall strategy to query and recall knowledge blocks conforming to semantics from a pre-constructed guarantee knowledge think tank; s4, according to an optimized rearrangement strategy, rearranging the knowledge blocks recalled by query and the guarantee knowledge problem; and S5, submitting the rearranged guarantee knowledge questions and knowledge blocks to a large language model LLM by using a prompt word template of the large language model LLM, generating answers by the LLM, and returning the answers to the user. Intelligent questioning and answering can be performed on guarantee knowledge.

The invention relates to a multi-class archive knowledge question and answer agent based on a large language model, and belongs to the technical field of artificial intelligence and archive information management. The intelligent agent comprises a data preparation module, a semantic knowledge base construction module, a perception and analysis module, a decision and matching module, an execution and retrieval module, a cognition and reasoning module and an interaction and generation module. According to the method, the complex query intention of the user can be accurately understood, automatic association and semantic reasoning across archive categories are achieved, and the semantic splitting problem of a traditional retrieval mode in cross-category and multi-level query is effectively solved.

The invention discloses a knowledge question-answering method and system based on a large language model and a semantic abstract. The system comprises a tree structure abstract generation subsystem which comprises a knowledge base configuration module, a file management module, a document analysis module and a knowledge block management module and is used for automatically generating tree structure hierarchical knowledge blocks for large manual documents or multi-chapter manual documents; the dual-channel retrieval engine subsystem comprises a question rewriting module, a semantic abstract retrieval module, a detail knowledge block retrieval module, a father-son retrieval module and a prompt word construction module, and all the modules work cooperatively to ensure that semantic abstract knowledge blocks and detail knowledge blocks related to the question of the user are retrieved. According to the method, by introducing the semantic abstract based on the tree structure and the two-channel retrieval, perception of global knowledge and accurate extraction of local fine-grained knowledge can be achieved at the same time, and then the ability of a knowledge base question-answering system in processing the general and inductive problems is improved.

The invention discloses an electric power knowledge question-answering method and system based on multi-agent and retrieval enhancement generation. The method comprises the following steps: according to an electric power knowledge query problem, generating an answer to the electric power knowledge query problem based on a predictor agent, and generating a document-query-answer triple; evaluating and quantifying the document-question-answer triple by using a judger agent to obtain a correlation score; noise documents are filtered and sorted, and a document sequence is obtained; and finally, the predictor agent completes power knowledge questions and answers according to the document sequence. According to the method, through specific technical means of Doc-Q-A triple generation, logit difference correlation scoring, self-adaptive threshold filtering, traceability output and the like, the multi-agent question and answer method which is light in weight, explainable and free of training is constructed. According to the technical route, the defects that an existing method depends on large-scale training and lacks traceability are overcome, and the method has obvious engineering application value in the aspects of question and answer accuracy and credibility.

The invention relates to a knowledge question answering method, system and device, electronic equipment and a computer program product, relates to the technical field of natural language processing, and is applied to scenes for providing matched answers for user questions in different fields. The method comprises the steps that a pre-constructed knowledge question-answering system is adopted to conduct knowledge retrieval processing on a target question, a target answer corresponding to the target question is obtained, and the knowledge question-answering system is obtained by training recommended question-answering pairs of a specified field, the recommended question and answer pair in the specified field is a feedback result of a to-be-processed document in the specified field after knowledge retrieval enhancement processing, and the to-be-processed document in the specified field comprises table data. Through the knowledge retrieval enhancement scheme, when the problem of insufficient training corpora is faced, knowledge query in a specified field can still be effectively solved; and knowledge graph storage is carried out on table data, so that the model has the capability of processing non-standard natural language data.

The invention relates to the technical field of knowledge questions and answers, in particular to a large language model knowledge question and answer method and system fused with a multi-modal knowledge graph. The method comprises the following steps: acquiring structured and semi-structured data and unstructured multi-modal data in an enterprise business scene; capturing data change through a real-time synchronization technology and executing targeted preprocessing to ensure the consistency of data quality and modal characteristics; constructing a dynamically updated multi-modal knowledge graph based on the preprocessed data, and integrating a multi-modal embedded model, a reordering model and a fine-tuned generative large model to obtain a knowledge storage-feature matching-semantic generation integrated architecture; according to the method, the defect of knowledge lag of a traditional system is overcome, and seamless fusion of the AI capability and the enterprise core business process is realized.

The invention discloses a question answering method and device for credit review knowledge based on a large model and a medium, and the method comprises the steps: obtaining a to-be-processed credit review question, extracting a core keyword from the question through a natural language processing algorithm, and converting the question into a deep semantic vector through a semantic vector model; inputting the questions into an intention classification model, generating filtering labels, inputting the questions into a distributed full-text search engine, screening a target knowledge base according to the filtering labels, and performing retrieval in the target knowledge base based on the core keywords and the deep semantic vectors to obtain a preliminary retrieval result set; calculating the score of each preliminary retrieval result in the result set according to the core keyword, the deep semantic vector and the weight factor, and screening out final reference content in a descending order; and on the basis of credit and loan business compliance requirements, risk control rules and question and answer output specifications, the credit and loan prompt words are constructed, and the final reference content is input into the large model to obtain structured credit and loan review answers, so that the question and answer efficiency and accuracy are improved.

The invention discloses an RAG question and answer optimization method and system based on fusion retrieval, and relates to the technical field of large model knowledge question and answer. The method comprises the steps of obtaining user query, executing keyword retrieval and semantic vector retrieval in parallel, and obtaining a keyword retrieval result list and a semantic vector retrieval result list respectively; and calculating the quality score of each text unit in each retrieval result list based on multi-dimensional quality evaluation, and filtering out the text units with the quality scores lower than a preset quality threshold value. And sorting the text units in all the filtered retrieval result lists by adopting a quality weighted reciprocal sorting fusion algorithm. And based on the length of each text unit and the document format to which the text unit belongs, performing context content enhancement and combination on each text unit after sorting, and generating an enhanced context block list. And constructing structured prompt information based on the enhanced context block list and user query, inputting structured prompt words into the large language model, and generating and outputting a final answer.

The invention discloses a big language model knowledge question-answering method based on semantic analysis correction, and belongs to the field of natural language reasoning, the method comprises the following steps: extracting a key entity from a natural language question, matching a target entity in a knowledge graph, and mining a plurality of knowledge paths in the knowledge graph for model training; adopting a prompt instruction and a knowledge path to supervise and train the large language model, so that the large language model has the capability of generating a multi-hop reasoning path conforming to a knowledge graph structure; performing knowledge path decoding based on the trained model, converting a natural language problem into a plurality of candidate reasoning paths, and converting the candidate reasoning paths into logic query statements to be executed in a knowledge graph, thereby completing fact verification and completion of the paths; and inputting the high-quality path subjected to graph verification and the natural language question into a large language model to obtain an accurate and explainable answer. According to the method, deep fusion of structured knowledge and a language model is realized, and the reasoning ability, the answer accuracy and the interpretability in a complex question and answer task are improved.

The invention belongs to the technical field of electric power artificial intelligence, and discloses an electric power knowledge question-answering method based on a knowledge reasoning thinking chain embedded large model and a related device. The method comprises the steps of obtaining a power problem text; and inputting the power question text into a pre-trained knowledge reasoning thinking chain embedding large model, and outputting a text answer of the power question. According to the method, a supervised learning and preference optimization mechanism is fused, and collaborative optimization of large-model professional knowledge learning and thinking path modeling is realized by constructing an electric power knowledge question and answer and analyzing a thinking chain data set; the technical problems that an existing large model is weak in knowledge, inconsistent in thinking process and insufficient in generalization ability in power professional text questions and answers are solved.

The invention provides a knowledge question-answering method and device based on a large model and a medium, and relates to the technical field of knowledge questions.The method comprises the steps that a data query period corresponding to a question text is obtained through a first large language model; if the duration of the data query time period is smaller than the preset duration, determining a target operation and maintenance tool from a plurality of preset operation and maintenance tools through a second large language model; controlling the target operation and maintenance tool to perform data acquisition, and taking the acquired data as intermediate data corresponding to the target operation and maintenance tool; performing data compression on the intermediate data based on the character number of the intermediate data to obtain target data; merging the target data into the context information, and obtaining an answer text according to the context information and a specified large language model after merging is completed; according to the method, input overflow, model reasoning failure or response delay caused by too large data volume or exceeding of the context length limit of the large language model can be avoided, the stability of answer generation is ensured, and the user experience is improved.

The invention relates to an enterprise knowledge question-answering system, method and device and a medium, and the enterprise knowledge question-answering system comprises a knowledge base module, a knowledge retrieval module, an answer generation module and a user feedback module. Determining the text similarity, the vector similarity and the feedback confidence of the query request and each knowledge fragment, and outputting a knowledge fragment retrieval result based on the text similarity, the vector similarity and the feedback confidence of each knowledge fragment; and the user feedback module is used for collecting the knowledge fragment retrieval result output by the user for the query request and the feedback information of the natural language answer, and updating the feedback confidence of the corresponding knowledge fragment and the query request based on the feedback information, so that the retrieval precision and the answer accuracy are improved.

The invention provides a bridge operation and maintenance knowledge management and question answering method based on a knowledge graph and a large language model. The method comprises the following steps: performing semantic dicing and dynamic cue word generation on texts such as a design report and an overhaul report through a large language model, automatically extracting an entity-relationship-attribute triple, completing entity co-reference resolution and relationship combination, and constructing an initial knowledge graph; further utilizing a curiosity algorithm to continuously merge low-similarity new knowledge in a subsequent detection report to realize iterative growth of the atlas along with an operation and maintenance life cycle; forming a multi-level community by adopting Leiden clustering, generating an abstract pyramid, and enhancing macroscopic semantic expression; and finally, through a semantic embedding model and a GraphRAG technology, precise matching and traceable answering from natural language questions to graph knowledge points are realized. According to the method, the problems of knowledge fragmentation, update lag and illusion in the bridge operation and maintenance field are solved, and the accuracy and robustness of operation and maintenance knowledge questions and answers are improved.

The invention belongs to the cross technical field of artificial intelligence and engineering construction, and relates to a knowledge question-answering method based on improved RAG and agent workflow. Comprising the steps of document preprocessing, hierarchical title enhancement, complex content processing, text cleaning, document information enhancement, metadata binding, segmentation and vectorization, retrieval and recall, recursive retrieval triggering, result fusion and duplicate removal, and final generation and sorting. According to the method, through structured preprocessing and semantic enhancement, the problem of retrieval failure caused by semantic fracture and title missing of technical specification text slices in the field of engineering structures is solved; the authority and timeliness of an output result are ensured through metadata binding and priority ranking; by means of the recursive retrieval workflow driven by the intelligent agent, automatic tracking and complete recall of the reference relation between the clauses are achieved, and the retrieval accuracy and the generation integrity of the RAG system in the professional field are remarkably improved.

The invention provides an electricity-carbon knowledge question-answering method and related equipment for the power industry, and the method comprises the steps: responding to a received question text inputted by a user, carrying out the retrieval in a pre-constructed electricity-carbon knowledge database based on the question text, and obtaining a plurality of pieces of associated data; acquiring identity information of the user; based on the question text, the multiple pieces of associated data and the identity information, a target answer text is generated through a pre-constructed electricity-carbon question and answer model, the technical problem that in the prior art, the efficiency of querying the electricity-carbon field knowledge by a user is low is solved, and the purpose of accurately and efficiently querying the electricity-carbon field knowledge is achieved.

The invention relates to an electricity marketing knowledge question-answering system and method based on multi-modal retrieval enhancement generation in the technical field of artificial intelligence and electricity marketing crossing. A three-layer framework of retrieval enhancement, generation enhancement and feedback optimization is adopted. A multi-modal retrieval engine is adopted, a large amount of related data is extracted from multi-source heterogeneous data, deep association, unstructured text fragments and structured data are fused and converted into a plurality of associated knowledge chains through a feature alignment technology, and therefore fragmented data are converted into data with high association. Therefore, comprehensive retrieval and deep association of the multi-modal data are realized; generating a unified knowledge representation through the plurality of associated knowledge chains, and analyzing the unified knowledge representation through a generative large model to generate a preliminary answer; the technical problems that a traditional power marketing knowledge question-answering system cannot effectively fuse and directly output answers by associating various data, so that the retrieval efficiency is low, and the accuracy of obtained answer data is poor are solved.

The invention provides a knowledge question-answering method and device for a long thinking chain, electronic equipment and a storage medium. The method comprises the following steps: receiving user input; based on a pre-constructed long thinking chain knowledge base, determining a target knowledge block corresponding to user input through a keyword-vector similarity mixed matching mode; wherein the long thinking chain knowledge base comprises a plurality of knowledge blocks with internal logic association; and determining a target reply input by the user according to the target knowledge block. According to the method, the target knowledge blocks are matched from the pre-constructed long thinking chain knowledge base in a keyword-vector similarity mixed matching mode to integrate and determine the target reply input by the user, so that the problem of insufficient understanding of a deep context is effectively improved, retrieval of irrelevant information is also effectively avoided, and the user experience is improved. Accuracy and relevance of answer content are ensured, and remarkable improvement of question and answer accuracy and intelligent degree in a complex context is realized.

The invention provides a knowledge question-answering method and device, a processor and a storage medium, and belongs to the field of information retrieval, and the method comprises the following steps: constructing a professional knowledge base for a specific field; wherein the professional knowledge base at least comprises a full-text database and a vector database; receiving a user query, and preprocessing the user query to obtain a standardized query; executing multi-path knowledge recall from the plurality of heterogeneous data sources in parallel based on the standardized query to generate a fusion recall result; wherein the plurality of heterogeneous data sources comprise but are not limited to a full-text database, a vector database, an internal retrieval API (Application Program Interface) plug-in and an internet retrieval interface; the historical interaction information, the fusion recall result and the standardized query are assembled into cue words through the instruction template; and inputting the cue word into the large language model to generate a reply text, and outputting the reply text in multiple modes. Through the method provided by the invention, the accuracy and coverage rate of knowledge recall can be improved, the answer is closer to real consultation, and the user experience is improved.

The embodiment of the invention discloses a question and answer method and device, a computer readable storage medium and electronic equipment, and belongs to the technical field of knowledge question and answer. The method comprises the steps of firstly obtaining dialogue text information of a user; then performing structured retrieval on the dialogue text information to obtain first answer information, and performing unstructured retrieval on the dialogue text information to obtain second answer information; and finally, based on the first answer information and the second answer information, determining target answer information. Thus, more accurate target answer information can be provided for the user by simultaneously performing structured retrieval and non-structured retrieval on the dialogue text information, so that the accuracy of knowledge base retrieval is improved while the method is not limited to the data structure type of the dialogue text information.