50 results about "Knowledge base question answering" patented technology

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 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.

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 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 discloses a knowledge base question and answer method based on dynamic intention analysis and mixed retrieval, and relates to the technical field of artificial intelligence and natural language processing. Comprising the following steps: preprocessing user question data, including word segmentation, entity recognition and completion error correction; performing intention recognition based on the preprocessed user question data, outputting an intention label and extracting a plurality of scene parameters; performing weighted summation on each scene parameter and the corresponding weight coefficient to obtain a scene threshold value; comparing the scene threshold value with the initial threshold value, and if the scene threshold value is greater than the initial threshold value, triggering label slot complementation; obtaining a tag retrieval result and a vector retrieval result through a tag knowledge base based on the tag after slot completion; adjusting the weight ratio of the tag retrieval result to the vector retrieval result based on the scene threshold value, and obtaining a fusion retrieval result through weighted fusion; and generating a natural language answer based on the fused retrieval result and the contextual parameters. According to the scheme, the matching accuracy and retrieval efficiency of questions and answers and prompt scenes can be optimized.

This application relates to an energy knowledge base question answering method and an energy knowledge base question answering device. The method includes: receiving an original question containing a role identifier, and determining a job vocabulary corresponding to the role identifier; splicing the lexical units in the original question that match the job vocabulary to obtain a job-limited text, mapping the job-limited text to a predetermined slot structure to obtain an intent lexical unit group; determining the latest approval node in the energy knowledge base that matches the role identifier and the intent lexical unit group; filtering in the energy knowledge base with the latest approval node to obtain a filtered business segment, matching the intent lexical unit group with the filtered business segment weighted by an approval weight, and obtaining a candidate business segment, where the approval weight is determined according to the level of the approval node corresponding to the filtered business segment; screening out a target business segment from the candidate business segments based on the intent lexical unit group; and generating a reply text based on the target business segment and the intent lexical unit group. Using this method can improve the accuracy of the reply.

The application discloses a knowledge base question answering method and system under full-process large language model small sample learning, the method comprises the following steps: question analysis, identifying candidate triples and question types of original user questions through a large language model; query template generation, generating a knowledge base query statement template with placeholders for user questions; knowledge base linking, obtaining matched expressions for the placeholders in the query statement template in combination with a large language model and an entity information index that has been constructed; answer generation, generating a knowledge base query language and returning a final result. Through the small sample learning capability of the large model, the retrieval process is redesigned for the knowledge base question answering task, a model does not need to be trained separately, and the question answering process is simplified; through the powerful knowledge reserve of the large model, the question answering effect and generalization capability are improved.

The application discloses a data retrieval method, a server, a terminal and a storage medium, which are used for providing a knowledge base question and answer retrieval function for VDI users through a server without occupying the storage space of the server, and providing a safer, more reliable and more intelligent question and answer retrieval scheme. The method comprises the following steps: acquiring first text information from a first remote terminal in one or more remote terminals, wherein the first text information corresponds to a first embedding vector; if the similarity of the first embedding vector and a second embedding vector in a vector database is greater than or equal to a threshold value, determining a retrieval result of the first text information according to the second text information corresponding to the second embedding vector and the first embedding vector, wherein the second text information is obtained by text segmentation according to source documents of the one or more remote terminals.

The application discloses a knowledge base question answering method and device, computer equipment and a storage medium. The method comprises the following steps: in response to a user query, extracting a structured query element corresponding to a preset query dimension; the preset query dimension at least comprises a domain keyword; based on the structured query element, searching in a preset knowledge base to obtain a candidate knowledge record set; the preset knowledge base comprises a plurality of knowledge records, and each knowledge record at least comprises an identifier field and a domain keyword field, and each identifier is associated with a semantic vector generated based on the content of the corresponding knowledge record; encoding the user query into a query vector, and performing similarity matching between the query vector and the semantic vector associated with the identifier of each knowledge record in the candidate knowledge record set; and filtering a target knowledge record set from the candidate knowledge record set according to the matching result, and generating an answer corresponding to the user query based on the target knowledge record set. Thus, the accuracy of the answer is improved.

The present invention relates to the field of intelligent railway technology and provides a method for constructing a railway multimodal knowledge base question-answering system with a hybrid RAG architecture. The method comprises: 1. server local model deployment and fine-tuning; 2. data processing and database construction; establishing a railway specification knowledge base database management system based on MongoDB content storage and PostgreSQL vector search library; optimizing and merging search results for multiple sets of data based on hybrid search and RRF algorithms, and finally performing re-ranking and search filtering; 3. constructing a multimodal knowledge base; using multimodal embedding to embed images and text into a vector database, while simultaneously storing the corresponding original images and text in a document store; obtaining images from the document store during hybrid similarity search, and passing the original images and text blocks to a large model to generate answers; and 4. multi-platform expansion. The present invention can preferably construct a railway multimodal knowledge base question-answering system.

The application discloses an intelligent evaluation system and method for a knowledge base question answering application, and relates to the technical fields of artificial intelligence, natural language processing and multi-agent collaborative system. The system comprises a data preprocessing module, a data synthesis module, a data screening module, a data scoring module and an adaptive weight adjustment module. The data preprocessing module performs data preprocessing on enterprise original documents and generates a data knowledge base. The data synthesis module repeatedly asks and answers each paragraph of the data knowledge base by using a large language model to generate a question and answer pair. The data screening module screens out high-quality samples through screening agent collaboration and a MACA framework mechanism. The data scoring module scores the question and answer pair through scoring agent collaboration and a MACA framework mechanism. The adaptive weight adjustment module dynamically updates the weight of each screening and scoring dimension by using an exponentially weighted moving average algorithm.

The application relates to the technical field of intelligent query of low-altitude flight regulations, and discloses a low-altitude regulation knowledge base question-answering system based on adaptive RAG, which comprises the following modules: a specification document preprocessing module, which is used for data cleaning of low-altitude multi-source heterogeneous regulation documents, extraction of multi-dimensional information, and establishment of a multi-dimensional label system; an adaptive RAG knowledge base construction module, which is used for structured storage of a clause unit and a label system structure and construction of a double-mode knowledge base; an adaptive routing and differentiated retrieval module, which is used for complexity discrimination of user queries and adaptive selection of different retrieval paths; a context integration and generation module, which is used for splicing of retrieval results, source information and user queries after sorting of the retrieval results, and generation of an interpretable answer based on reference materials; and a feedback optimization module, which is used for closed-loop optimization through periodical incremental fine-tuning of the model. The application can deeply understand the space-time constraint characteristics of the low-altitude field, dynamically resolve multi-source regulation conflicts, and realize efficient and accurate intelligent question-answering.

The application relates to the technical field of information retrieval processing, and discloses an aviation knowledge base question answering method and system based on vector retrieval and a large model, which comprises the following steps: analyzing a business document into rule units to generate rule vectors; receiving a question to generate a question vector; judging whether a query condition satisfies necessary conditions; if the necessary conditions are not satisfied, outputting supplementary question information; if the necessary conditions are satisfied, screening the rule units, performing retrieval, and obtaining recalled rule units; judging whether there is a rule conflict among the recalled rule units; if there is a rule conflict, judging whether there is a substitution relationship or an exception relationship; if there is a substitution relationship or an exception relationship, determining an effective rule unit; if there is no substitution relationship or exception relationship, outputting verification information; if there is no rule conflict, determining the recalled rule units as the effective rule units; generating a question and answer result according to the effective rule units; and outputting a final question and answer result. The application improves the accuracy, pertinence and stability of an aviation knowledge base question and answer result.

The invention relates to the field of knowledge base questions and answers, in particular to a data mining system in a digital archive based on network and digital resources, which comprises a question understanding module, an information matching module, a dynamic adjustment module and an answer generation module. By supporting a multi-hop reasoning mode, information fault and insufficient reasoning depth in complex question query are avoided, the real-time similarity between an intention recognition result and an entity is calculated, the connection number in an archive knowledge base is combined, a focus entity is selected, the pertinence is improved, and the real-time similarity between a candidate answer entity and the focus entity is obtained based on the incidence relation between the candidate answer entity and the focus entity. Factors such as policy reference frequency and business association strength are considered, a to-be-selected path is formulated, the next hop entity type is predicted through a probability model, the accuracy of path selection is ensured, the reasoning progress is monitored in real time, the reasoning strategy is dynamically adjusted through a hop strategy adjusting unit, and the adaptive capacity and reasoning efficiency of the question and answer reasoning process are improved. Effective feedback is provided for user consultation, and user experience is improved.

This invention relates to the field of intelligent work order scheduling platform design technology, and discloses an intelligent work order scheduling system, method, device, and medium. The method includes: the system integrates multiple functional modules to achieve efficient and intelligent scheduling of equipment failure work orders. First, the work order access module receives equipment failure information from different channels to ensure comprehensive and timely information. Next, the profile building module uses historical service data of maintenance personnel to build a multi-dimensional capability profile, providing a data foundation for intelligent matching. Then, the intelligent allocation module accurately matches the profile data and work order parameters, associating the most suitable maintenance personnel with the work order. The order grabbing interaction module generates a list of work orders to be grabbed and an order grabbing interface, enhancing scheduling flexibility and improving scheduling efficiency. The execution tracking module monitors the work order processing progress in real time, ensuring transparent and controllable scheduling. Finally, the knowledge base question-and-answer module provides professional knowledge support to maintenance personnel, helping them quickly resolve work order issues.

The invention discloses an optimization method, device and equipment for generating a vertical domain through retrieval enhancement based on a BGE large language model and a storage medium. The method comprises the steps that a vertical domain professional corpus is constructed and optimized, a retriever model is finely adjusted, an FAISS retrieval strategy is upgraded, a rearrangement device after fine adjustment is called, and rearrangement optimization is completed. And dynamic caching and updating are implemented, and performance verification is completed. According to the method, through multi-scene corpus training and double-stage fine tuning, the core problem of low distinction degree of synonymous terms, abbreviations and full names is solved; an FAISS mixed index and dynamic threshold adjustment strategy effectively adapts to a scarce terminology retrieval scene, and the retrieval response time is controlled while the recall ratio is ensured; and the weighted sorting design of the resorter model meets the accurate sorting requirements of the user on multiple related documents. The whole scheme of the invention is subjected to low-cost transformation based on the existing open source model and framework, and is widely applied to general scenes such as knowledge base question answering, professional document retrieval and multi-field consultation response.

The invention relates to a power multi-mode knowledge base question and answer pair construction method and device. The method comprises the following steps: screening at least two target electric power knowledge graph types, and constructing a plurality of business knowledge units based on target scene information of electric power production environment inspection business of each target electric power knowledge graph type; performing knowledge association analysis on the plurality of business knowledge units to obtain a logic mapping relationship among the plurality of business knowledge units; based on the logic mapping relationship, performing structured recombination on the plurality of business knowledge units to obtain a structured recombination result, and matching a corresponding question and answer sentence pattern template for the structured recombination result; and based on the question and answer sentence pattern templates adapted to the business knowledge units, converting the structured recombination result into candidate power knowledge question and answer pairs, verifying the candidate power knowledge question and answer pairs by using a large language model, and outputting a target power knowledge question and answer pair. By adopting the method, efficient answers can be generated for cross-type power inspection data.

This application relates to the field of retrieval enhancement and generation technology, and in particular to a method and system for collaborative construction of multi-source knowledge bases and text question answering in a professional domain. The construction method includes acquiring professional domain text data and performing block processing on the professional domain text data; constructing an initial knowledge graph, a text knowledge base, and a question answering knowledge base; binning the entity set in the initial knowledge graph by type, extracting connected components to obtain candidate entity clusters, employing a two-layer scheduling strategy for semantic determination using a large language model, and progressively solidifying the determination results and performing targeted supplementary judgments on unresolved nodes; updating the initial knowledge graph to form a knowledge graph library; and obtaining a multi-source heterogeneous knowledge base. The method and system for collaborative construction of multi-source knowledge bases and text question answering in this professional domain can solve the problems of insufficient quality in vertical domain knowledge graph construction, low entity disambiguation accuracy, and high computational cost, and also features high stability and adaptive adjustment.

The application discloses an enterprise knowledge question answering method and device based on a large language model, equipment and a medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: performing deduplication verification and multi-engine degradation analysis on industrial technical documents uploaded by an enterprise, obtaining text blocks with context overlap marks through semantic perception segmentation, and establishing a hybrid index knowledge base after vectorization, wherein the hybrid index knowledge base comprises semantic vector indexes and keyword indexes; during question answering, two-way parallel retrieval is performed according to a user question, the two-way results are ranked, fused and deeply reordered, high-relevance knowledge segments are obtained, a large language model is driven by a prompt word to generate an answer, the success rate of analyzing complex-format industrial documents is improved, context breakage of the knowledge segments is avoided, semantic recall and term accurate matching are considered, and the generated answer is more accurate and reliable.

A voice-controlled question answering system that is capable of answering questions using both a knowledge base and a search engine. The knowledge base is used to answer questions when answers to those questions are contained in the knowledge base. If an answer using the knowledge base is unavailable, and if the question is suitable for answering using an unstructured search approach, the system may obtain an answer using a search engine. The search engine results may be processed to obtain an answer to the question suitable for output using a voice user interface.

A method for controlling an artificial intelligence (AI) device can include obtaining, via a processor in the AI device, a knowledge base including a plurality of nodes, and flattening, via the processor, the knowledge base by transforming the knowledge base into a first plurality of documents, each of the first plurality of documents identifying a node within the knowledge base. Also, the method can further include receiving, via the processor, a user query, performing, via the processor, matching based on the user query and the first plurality of documents to generate a ranked results list, reducing, via the processor, the ranked results list based on a reducing operation to generate a reduced list, and outputting, via the processor, linked entities based on the reduced list.

The application relates to the technical field of data processing, and discloses a question and answer processing method, device, equipment and medium. Through joint training of a sorting task and an answer type prediction task, a model learns logical type constraints between questions and answers while optimizing semantic correlation, effectively suppresses false correlations caused by training data bias or high-frequency answer interference. Further, in the question and answer reasoning process, with the aid of an answer path sorting model and a cross-sequence interaction attention mechanism, comprehensive sorting results of each candidate answer path fusion semantic score and type consistency score can be obtained, multi-granularity accurate matching from overall semantics to local elements is realized, and the sorting accuracy and model robustness in a complex query scene are significantly improved. Therefore, in the knowledge base question and answer task in the fields of finance and insurance, medical treatment and the like, questions with similar semantics but different logical types can be effectively distinguished, information confusion is avoided, and the final answer has accuracy and reliability.

A computer-implemented method for providing artificial intelligence (AI)-driven interactive knowledge base question-answering based on multiple large-language model (LLM) Iterations with separate software tools for LLM evaluation, the method comprising receiving a query; generating, based on the query, prompts comprising contextual information associated with the query, and a reference to a database comprising associated with the query; initiating a first LLM to generate, based on the prompts and the database, a first response to the query; receiving from the first LLM, the first response; evaluating an accuracy of the first response using at least one software tool separate from the first LLM; initiating, based on the evaluating, a second LLM to generate a final response based on the prompts and the first response from the first LLM; and providing the final response to the query.

The application discloses an education field knowledge base search optimization method based on question generation, which comprises the following steps: firstly, obtaining the education field text by analyzing the education knowledge base direction field corpus; obtaining the semantic model by using the education field text to pre-train the language model migration learning; designing the fixed question and answer pair template based on the existing structured text information in the knowledge base to obtain the knowledge base question and answer pair; training the question generation model by using the knowledge base question and answer pair data and the Chinese open source question and answer pair data, deploying the question generation reasoning service; generating the question and answer pair to expand the knowledge base; simultaneously coding the entity node text in the knowledge base structured information and the question text in the question and answer pair by using the semantic model to construct the vector library, and performing the semantic similarity calculation after the user query input; recalling the best result in the online semantic matching; and the application greatly improves the recall rate of the returned result of the user search behavior, improves the learning efficiency and improves the user experience.

This invention relates to a query graph generation method and system for question answering in complex knowledge bases. The method includes: selecting top-ranking historical questions from historical cases based on the semantic similarity between the target question and historical questions; forming a candidate relation set from the relations in the standard query graphs corresponding to the selected historical questions; generating a query graph based on the candidate relation set using a retrieval-based semantic parsing method; converting the query graph into an executable query statement according to the syntax rules of knowledge base query statements; and executing the query on the knowledge base to obtain the answer to the target question. This invention proposes a method to significantly reduce the retrieval space in the reasoning process by utilizing historical cases and pre-screening the retrieved candidate relations, which can significantly improve the efficiency of question answering while keeping the impact on the performance of natural language question answering within an acceptable range.