106 results about "Knowledge base question answering" patented technology

The invention relates to the technical field of database construction, in particular to a knowledge base question-answering system construction method and device, equipment and a storage medium. The method comprises the following steps: acquiring a user question instruction, and performing part-of-speech tagging and keyword vectorization on a target text to generate a retrieval text keyword vector; performing semantic linear embedding and spatial projection processing on the keyword vectors to obtain keyword semantic vectors; logic mining is conducted on the keywords, a logic relation set among the keywords is obtained, logic recognition is conducted on the target text based on the relations, and retrieval text logic is generated; performing matching retrieval with semantic vectors in a knowledge base to obtain a candidate semantic query data set; and performing coherent logic retrieval on the candidate data set according to the retrieval text logic and the keyword vector to generate a final retrieval target answer. According to the method and the system, more accurate and efficient knowledge base question-answering system construction is realized.

The invention discloses a large language model knowledge base question answering system based on a multi-path fusion recall retrieval algorithm, and relates to the technical field of artificial intelligence application, and the system comprises the steps of S1, data collection, S2, data preprocessing, S3, knowledge base construction, S4, analysis and clustering, S5, knowledge recall, S6, weight adjustment, and S7, weight fusion. According to the method, a dynamic weight distribution module is arranged, so that various paths of recall weights such as keyword matching, semantic similarity and a knowledge graph can be flexibly adjusted according to different semantic scenes such as a technical scene and a product query scene, a recall result can be effectively optimized, knowledge related to problems can be accurately screened, and the method is high in practicability and high in practicability. And irrelevant information interference is reduced, the answer quality and the system efficiency are improved, and the user satisfaction is improved.

The invention relates to a knowledge base question answering method and device and a computer readable storage medium, and belongs to the technical field of artificial intelligence. A query question input by a user is received; the query problem is analyzed, and keywords in the query problem are extracted; retrieving slice data matched with the query problem from a retrieval enhancement generation system on the basis of the text correlation between the keyword and the slice data; wherein a plurality of slice data are pre-stored in the retrieval enhancement generation system, the slice data comprise text slices and image-text slices, and the image-text slices comprise storage paths of images and image descriptions with text context information of the images; and inputting the retrieved slice data into a multi-modal large model, generating an answer related to the query question and an image matched with the answer, and outputting the question answer with the image. According to the method, the capability of an original retrieval enhancement generation system is remarkably improved, and the information requirement of a user in an image-text environment is more comprehensively met.

The invention provides a knowledge base question-answering system optimization method and device based on hybrid fine tuning and multi-dimensional evaluation and a readable storage medium thereof, and provides the following scheme: constructing a hybrid progressive fine tuning framework, fusing low rank adaptation (LoRA) and direct preference optimization (DPO), and realizing domain knowledge migration through hierarchical dynamic parameter configuration; establishing a logic-semantic-knowledge three-dimensional quantitative evaluation system, and forming closed-loop optimization by using dynamic weight fusion and a visual decision system; and designing a multi-domain prompt template library with layered parameter freezing, sparse constraint and attention driving, and realizing model lightweight and cross-domain logic constraint. According to the method, the adaptive bottleneck of a general model and domain characteristics is broken through, the small sample training efficiency and the generated content compliance are improved, the computing resource consumption is reduced, an efficient and reliable knowledge service base is provided for professional scenes such as laws, medical treatment and finance, and the technical advantages of specialization, light weight and interpretability are achieved.

The invention discloses a privatized AI question and answer method, system and device and a medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting a file archive data set, and constructing a local digital knowledge base; selecting an AI semantic large model architecture; performing annotation enhancement on a local digital knowledge base by using an AI semantic large model architecture, and constructing a knowledge base AI question and answer model; performing permission marking on the local digital knowledge base based on a user access control rule, and establishing a knowledge base retrieval mechanism; and integrating and fusing the knowledge base retrieval mechanism and the knowledge base AI question-answering model to generate a retrieval enhanced AI question-answering model, performing semantic question-answering retrieval on the request question information, and outputting a user request question-answering result. The technical problem that in the prior art, data security, intelligent question answering and information retrieval efficiency are insufficient is solved, and the technical effect of improving intelligence and data access control of the question answering system is achieved.

The invention discloses a knowledge base question answering method and system based on intention recognition and a medium, and the method comprises the steps: receiving a user question, inputting the user question into a large language model subjected to intention recognition training, and carrying out the preliminary classification; determining a corresponding search strategy according to a classification result obtained by the preliminary classification; if the search strategy is knowledge base search questions and answers, selecting a knowledge base matched with the search strategy for knowledge retrieval, inputting the retrieved reference knowledge and cue word templates into a large language model, and answering the user questions by the large language model; and if the search strategy is large-model direct question answering, selecting a cue word template, inputting the cue word template into the large-language model, and answering the user question by the large-language model. According to the method, user questions can be preliminarily classified through user intention recognition, different search strategies are adopted for different categories, and knowledge base question answering efficiency and accuracy are effectively improved.

The invention discloses a big language model-driven RAG knowledge base quality evaluation method and system. The method comprises the following steps of: constructing QAR triples of different problem types by utilizing a big language model; performing review optimization on the triple by utilizing a large language model; inputting the constructed questions into an RAG knowledge base question-answering system, and recording replies of the question-answering system, retrieved reference text segments and reordering model scoring for each question; according to a recording result, automatically calculating scores of various preset indexes; and according to the question type, weighting and calculating a comprehensive score. According to the method, the external information source can be flexibly adjusted according to user requirements, and the dynamic QAR set containing a plurality of problem types is automatically created, so that the flexibility and individuation of an evaluation scene are realized. Besides, a set of complete evaluation indexes and weighting rules is established to comprehensively consider the retrieval accuracy, the generation quality, the user preference and the requirements in practical application, so that the evaluation result can comprehensively and objectively reflect the performance of the RAG knowledge base question-answering system.

The invention relates to the technical field of natural language processing, and discloses a knowledge base question and answer platform construction method based on a large language model, which comprises a knowledge acquisition module, a data preprocessing module, a text processing module, a vectorization module, a question understanding module, a mixed retrieval module, a prompt generation module, an answer generation module and an answer quality analysis module. A secondary inquiry processing module and a feedback learning module; according to the method, a semantic segmentation algorithm is combined with semantic retrieval and keyword retrieval, so that the flexibility is high; normalized prompts are constructed, input is performed according to correlation sorting, and the accuracy of answers is improved; multi-dimensional confidence evaluation is introduced, strict multi-layer security and compliance filtering is set, and the reliability of the system is ensured; the relevance of multiple rounds of dialogues is judged and complemented, so that interaction is more natural and efficient; knowledge is collected and updated in real time, a knowledge base and a retrieval strategy are continuously optimized, and a closed loop of data-application-feedback-tracing-optimization is formed.

The invention discloses a policy question reply method, device and equipment and a computer program product, and relates to the technical field of natural language processing, and the policy question reply method comprises the steps: receiving a policy consultation question; based on a pre-generated business knowledge base, replying the policy consultation question to generate a policy question reply result, the business knowledge base being obtained based on a pre-generated business knowledge decision tree, and the business knowledge decision tree being generated through a preset agent according to the initial business knowledge question-answer pair. In the application, the business knowledge base is obtained based on the pre-generated business knowledge decision-making tree, the business knowledge decision-making tree is generated through the preset agent according to the initial business knowledge question-answer pair, and when the business knowledge base is constructed, the knowledge text does not need to be sliced, so that the problem that an existing knowledge base question-answer system is relatively low in accuracy is solved; the accuracy of information retrieval of the business knowledge base is improved, and then the accuracy of policy question reply is improved.

The invention relates to the technical field of intelligent work order scheduling platform design, and discloses an intelligent work order scheduling system, method and device and a medium, and the method comprises the steps: the system achieves the efficient and intelligent scheduling of a device fault work order through integrating a plurality of function modules. Firstly, the work order access module receives equipment fault information of different channels, and it is ensured that the information is comprehensive and timely. Then, the portrait construction module constructs a multi-dimensional ability portrait by using the historical service data of the maintenance personnel, and provides a data basis for intelligent matching; and then, the intelligent distribution module accurately matches and associates the most suitable maintenance personnel with the work order according to the portrait data and the work order parameters. And the order grabbing interaction module generates a to-be-grabbed work order list and an order grabbing interface, so that the scheduling flexibility is enhanced, and the scheduling efficiency is improved. The execution tracking module monitors the work order processing progress in real time, and transparent and controllable scheduling is ensured. And finally, the knowledge base question and answer module provides professional knowledge support for the maintenance personnel and helps the maintenance personnel to quickly solve the work order problem.

The invention relates to the technical field of artificial intelligence, and discloses a knowledge base question answering method and system fusing a knowledge graph and RAG, and the method comprises the steps: adjusting a user question based on a large model; constructing a local knowledge graph by utilizing the internal information data of the enterprise; constructing a global knowledge graph; searching the question by using a global knowledge graph to obtain a graph search result, and if the graph search result and the user question type meet a search success condition, generating an answer and sending the answer to the user; and if the atlas retrieval result and / or the user question type do not meet the retrieval success condition, enhancing and refining the question by using the atlas retrieval result, and then performing retrieval in other modes on the enhanced and refined question. According to the method, the capabilities and advantages of the RAG, the knowledge graph and the large model are fused, and the knowledge base retrieval effect based on the large model is improved through the constructed global knowledge graph and the step-by-step retrieval enhancement problem.

The invention discloses a knowledge base question and answer method based on a large language model agent, and the method comprises the following specific steps: S1, receiving the input of a user, returning an answer to the user, and supporting multiple rounds of dialogues and context preservation; s2, understanding the natural language input of the user, and generating an answer based on a pre-trained language model; s3, storing and managing domain knowledge, and supporting query of structured and unstructured data; s4, receiving the output of the large language model, and generating a final answer according to the knowledge base information; and S5, monitoring and optimizing the reasoning efficiency of the large language model so as to improve the response speed of the system. According to the method, complex question and answer tasks can be better understood and processed, the answer accuracy and response efficiency are improved, in addition, an intelligent agent is formed by integrating the knowledge base and the large language model, more flexible and diversified knowledge base question and answer can be carried out, and many problems in the prior art are solved.

The invention discloses a coal enterprise knowledge management system construction system and method in the technical field of computer information. The method comprises the following steps: collecting coal enterprise knowledge data; preprocessing the coal enterprise knowledge data to obtain knowledge graph data and large model module data; after performing data entity identification on the knowledge graph data by using an entity identification technology, creating a knowledge graph module; after large model module data are classified and labeled, a large model module is created; and creating a coal enterprise knowledge management system based on the knowledge graph module and the large model module. Through a mode of fusing the knowledge graph and the large model, the knowledge base answering field is enriched, the knowledge base question answering mode is optimized, the large model is enhanced by using the professional knowledge graph knowledge, the professional question answering result is generated, the question answering ability of the knowledge base in the professional field is improved, and contribution is made to digital, intelligent and green development of the coal industry.

The embodiment of the invention provides a retrieval enhancement generation method and system based on multivariate fusion, and the method comprises the steps: firstly structuring a knowledge base document, generating a knowledge graph, and importing an ES to establish an index; inputting question sentences, carrying out word segmentation and other processing, querying instance nodes from the map, and outputting meeting conditions according to answer sentence patterns; if not, fragmenting and blocking the document according to a title level, obtaining candidate results through vector, ES and atlas retrieval, normalizing scores, merging and optimizing the scores of the candidate retrieval results according to a retrieval source, calculating comprehensive scores, and outputting the comprehensive scores in a descending order; and finally, carrying out semantic integrity aggregation on the combined candidate results, and inputting into a large language model to obtain a final answer. According to the method, the relevance of the retrieved content is greatly improved, the semantic integrity of the retrieved content is improved, the model magic view problem is reduced, and the question and answer accuracy and the answer quality of a knowledge base are improved.

The invention discloses an energy field intelligent knowledge base question answering system based on multi-model cooperation, and aims to solve the problems of data type identification, image structured extraction and cross-modal indexing in energy field multi-modal document question answering. The system comprises 13 core modules, dynamic cooperation of a multi-mode large model and a large language model is achieved through a collaborative scheduling module, data types involved in user problems can be recognized, and corresponding modules can be called; structured information of images such as charts and flow charts can be extracted from documents and coded; and cross-modal retrieval is realized through a unified semantic vector. When answers are integrated, an attached source is quoted, and traceability is ensured. The system improves the accuracy and efficiency of complex document question answering in the energy field, and is suitable for professional document question answering scenes containing multiple types of images.

Aspects of the subject disclosure may include systems and methods, for example, including receiving a user input in natural language, retrieving a first answer including a list of a first number of documents relevant to the user input by searching indexed documents in a knowledge base, applying the first answer to a large language model reader, resulting in a second answer, re-ranking the first answer by using the second answer, resulting in a third answer including a re-ranked list of the first number of documents, and generating a final response including the second answer and one or more documents among the third answer.

The invention provides a question answering method based on a large language model, and the method comprises the steps: carrying out the text similarity retrieval of a question proposed by a user in an internal knowledge base, and obtaining the answer content of an internal large language model based on a retrieval result. Meanwhile, the output content of the external large language model is obtained through the problem subjected to data desensitization, and the problem that internal sensitive information is leaked due to the fact that the problem is directly input into the external large language model is avoided. And calculating the text similarity coefficient of the output content of the external large language model and the text blocks in the internal knowledge base, and after multiple answer contents are obtained through circulation for multiple times, summarizing and answering by synthesizing the answer contents through the internal large language model. The quality and accuracy of the answer content output by the knowledge base question-answering system can be improved, meanwhile, leakage of internal sensitive information caused by directly using an external large model to assist an internal knowledge base in outputting the answer content is effectively avoided, and the information safety risk is reduced.

The invention relates to the technical field of large model data processing, in particular to an agent automatic configuration method and system based on a large model, a knowledge base and a tool, and provides the following scheme: obtaining training data, inputting a natural language into an inference model to generate a query task set, and after optimization, inputting the query task set into an Embedding model to generate a preliminary retrieval result set; the optimization result is input into a reordering model to generate a final retrieval result; and when the training conditions are met, packaging the inference model, the Embedding model and the reordering model into an intelligent agent. The system consists of an inference model configuration module, a retrieval model configuration module and a reordering model configuration module, and cooperatively realizes task analysis, semantic retrieval and result optimization. The method improves the retrieval accuracy and efficiency, and is suitable for knowledge base question answering and complex data processing scenes.

The invention provides an intelligent product recommendation method, system and device based on a Dify platform and a medium, and belongs to the technical field of artificial intelligence information recommendation, and the method comprises the steps: uploading structured product data and a question and answer text based on Dify, generating a product knowledge base according to the structured product data, and generating a question and answer knowledge base according to the question and answer text; receiving a question input by a user, and calling a Dify semantic analysis service to extract a structured query object; according to the extracted structured query object, performing retrieval matching on the question and answer knowledge base to obtain an optimal answer, and calling the AI large model to generate the optimal answer when retrieval matching fails; extracting a keyword from the optimal answer, and screening an optimal product from a product knowledge base by using the keyword; and returning the screened optimal product and the matched answer text to the user. Through combination of the product knowledge base, the question and answer knowledge base, the Dify semantic analysis service and the AI model, the product recommendation efficiency and accuracy are improved.

The invention relates to the cross technical field of artificial intelligence and data retrieval, and particularly provides an RAG question and answer optimization method and device.Firstly, original knowledge documents of various formats and natural language query of a user are received on an input layer, then a knowledge base construction module and a knowledge retrieval module are arranged on a processing layer, and a knowledge retrieval module is arranged on the processing layer; the middleware layer is provided with an MCP Server module and a vector database, the MCP Server module is a standard interface agent, and the vector database is a distributed storage engine for efficient approximate nearest neighbor search and mixed retrieval; and finally, deploying an LLM generation module at an output layer for generating a final answer based on the retrieved enhanced context. Compared with the prior art, the method has the advantages that irrelevant or low-signal-to-noise-ratio knowledge contacted by a large language model (LLM) can be effectively reduced, and the accuracy, the reliability and the practicability of knowledge base questions and answers are comprehensively improved.

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 a dialogue generation method and device integrating knowledge base questions and answers and intelligent agents. According to the scheme, description information of N tools and a target problem proposed by a user are obtained; screening out M target tools from the N tools through a large language model according to the target problem and the description information; if the knowledge base question-answering tool exists in the M target tools, the knowledge base question-answering tool is utilized to retrieve related information of the target question from a preset knowledge base, and first reply information is generated according to the related information; and if the knowledge base question and answer tool does not exist in the M target tools, analyzing and reasoning the description information and the target question of the M target tools through the intelligent agent, and generating second reply information. According to the technical scheme, the accuracy of the generated reply information is improved, and the technical problem that in the dialogue generation process in the prior art, the accuracy of the generated reply information is low is solved.

The invention provides a pension knowledge base question and answer method and system based on deep learning, and relates to the field of natural language processing and graph neural networks. The method comprises the following steps: collecting pension policy text data from a plurality of channels; carrying out feature division on the pension policy text data, carrying out structured classified storage according to a feature division result, and constructing a pension knowledge base; performing semantic probability screening on the currently input oral user question, and converting the oral user question into a standardized user question; performing semantic matching on the standardized user question and the old-age care knowledge base to obtain a preliminary matching result; constructing a knowledge graph by taking the pension policy text data as nodes and the incidence relation between the pension policy text data as edges; and inputting the preliminary matching result into a graph neural network GNN, learning the knowledge graph through the graph neural network GNN, performing multi-hop reasoning on the preliminary matching result, and outputting a final question and answer result.

The invention provides a graph knowledge base question and answer optimization method and device, equipment and a storage medium, and relates to the technical field of computer systems.The optimization method comprises the steps that when query information is detected, intention recognition is conducted on the query information, a recognition result is obtained, and a corresponding preset graph knowledge base is determined according to the recognition result; when the preset graph knowledge base is a document class graph knowledge base, determining target entity data according to the query information, and inputting the target entity data into the document class graph knowledge base for query to obtain related entity data and a corresponding related entity relationship; and based on a preset large language model, obtaining a final question and answer result according to the related entity data and the corresponding related entity relationship. Through intention recognition and selection of the preset graph knowledge base, the core intention of user query can be accurately positioned, so that the corresponding graph knowledge base is quickly determined, invalid search in irrelevant knowledge bases is avoided, and the query efficiency is remarkably improved.

The invention discloses an intelligent evaluation system and method for knowledge base question and answer application, and relates to the technical field of artificial intelligence, natural language processing and multi-agent collaborative systems. The system comprises a data preprocessing module, a data synthesis module, a data screening module, a data scoring module and a self-adaptive weight adjustment module. The data preprocessing module carries out data preprocessing on the enterprise original document and generates a data knowledge base; the data synthesis module adopts a large language model to perform repeated question and answer on each paragraph of the data knowledge base to generate question and answer pairs; the data screening module screens out high-quality samples through a screening agent collaborative screening MACA framework mechanism; the data scoring module performs question and answer scoring through a scoring agent collaborative scoring MACA framework mechanism; and the adaptive weight adjustment module dynamically updates the weight of each screening scoring dimension by adopting an exponential weighted moving average algorithm.

The invention relates to the cross technical field of artificial intelligence and knowledge engineering, in particular to a local knowledge base question-answering system based on a new energy automobile and a construction method thereof, and the method comprises the following steps: integrating a knowledge graph, a ChatGLM2-6B language model and a LangChain framework to construct a local knowledge base question-answering system; obtaining a ternary collaborative architecture for structured management, retrieval and semantic question and answer of heterogeneous data in the new energy field; and based on the ternary collaborative architecture, correspondingly designing a plurality of functional modules for providing an intelligent question-answering solution for the field of new energy vehicles, and forming the local knowledge base question-answering system for the new energy vehicles. According to the'knowledge graph-pre-trained large model-LangChain 'ternary collaborative architecture provided by the invention, structured management, efficient retrieval and high-precision semantic question and answer of heterogeneous data in the field can be realized, and structural management, efficient retrieval and high-precision semantic question and answer of multi-source heterogeneous data in the new energy automobile field can be realized; and local deployment, large model generation and interpretable retrieval can be combined.

The invention relates to the technical field of data processing, and discloses a medical knowledge base question answering method and system, electronic equipment and a storage medium. Performing mixed retrieval on the medical problem based on a medical knowledge base to obtain a set number of text blocks as retrieval results, constructing cue words based on the medical problem and the retrieval results, and generating a preliminary answer containing a preliminary source identifier through a large language model; source verification and source tracing processing is carried out on the preliminary answer to generate a final answer, and the source verification and source tracing processing comprises the steps of analyzing the preliminary answer to obtain a plurality of independent claim points, carrying out sentence-level and block-level correlation judgment on text blocks pointed by the claim points and the preliminary source identifier based on a reordering model, and executing source judgment based on a threshold value; generating a corresponding source mark; according to the medical knowledge retrieval method and system, the accuracy and integrity of medical knowledge retrieval can be improved, and the fact accuracy of answer generation is ensured.

The invention relates to the technical field of intelligent legal question answering, and discloses a knowledge base question answering method and system based on a large language model agent. According to the method, a legal question and answer task is planned and decomposed through a large language model agent; extracting question keywords, and calling a combined query tool to retrieve related legal provisions, cases and other documents from a database; embedding the content related to the question in the document into a large language model agent, and assisting the large language model agent to generate an answer; according to the method, the workflow of the intelligent agent is reasonably planned by utilizing thinking chain prompt so as to excite the capability of a large model, and the problems of low retrieval efficiency and high cost of a vector database are solved by directly retrieving problem-related documents from a structured database; and the content most relevant to the question is embedded into the intelligent agent through the memory module, so that the problem of low legal question and answer result accuracy caused by insufficient knowledge in the large-model legal field is solved.

The invention relates to the technical field of deep learning, in particular to a large model knowledge base question answering method and system with a timeliness map. The method comprises the following specific steps: constructing a graph database capable of updating data in time and regularly performing timeliness data cleaning; inputting contents in a natural language, and performing triple extraction through a natural language large model; obtaining a specific query result from a graph database and a vector library according to the obtained triple result; the natural language large model carries out reference answering according to the graph data and the query result of the vector library; the method has the advantages that the data in the knowledge graph has the capacity of updating along with time change, the expired data in the knowledge graph can be deleted in time, and the timeliness value of the graph data is improved.

The present invention proposes an optimization method, device and readable storage medium for a knowledge base question-answering system based on hybrid fine-tuning and multi-dimensional evaluation, and proposes the following solutions: constructing a hybrid progressive fine-tuning framework that integrates Low-Rank Adaptation (LoRA) and Direct Preference Optimization (DPO), and achieving domain knowledge transfer through hierarchical dynamic parameter configuration; establishing a three-dimensional quantification evaluation system of "logic-semantics-knowledge", and forming a closed-loop optimization by using dynamic weight fusion and a visual decision-making system; designing a multi-domain prompt template library with hierarchical parameter freezing, sparse constraints and attention driving to achieve model lightweight and cross-domain logical constraints. The present invention breaks through the adaptation bottleneck between the general model and domain characteristics, improves the small-sample training efficiency and the compliance of the generated content, reduces the consumption of computing resources, provides an efficient and reliable knowledge service foundation for professional scenarios such as law, medicine, finance, etc., and has technical advantages of specialization, lightweight and interpretability.