806 results about "Semantic search" patented technology

The invention discloses a knowledge question-answering system based on a large language model. The method comprises the steps that a power operation and maintenance knowledge base module obtains data from a data source for preprocessing, constructs a power operation and maintenance field knowledge graph, converts text content into semantic vectors and integrates the semantic vectors to form a power operation and maintenance knowledge base; the problem understanding module is used for identifying and analyzing problems and converting the user problems into semantic vectors; the retrieval and reordering module is used for performing semantic retrieval in the electric power operation and maintenance knowledge base, recalling knowledge information related to question semantics, and ordering retrieval results based on semantic relevance, knowledge confidence and information importance; and the answer generation module is used for integrating the knowledge information and the user question into input of a large language model, generating prompt information, and generating an answer in a natural language form according to the prompt information by adopting the large language model based on data fine adjustment in the field of power operation and maintenance. According to the scheme, the deep learning technology is used for understanding semantics of user questions, and accurate retrieval is carried out in the knowledge base.

The invention discloses a building specification intelligent consultation system and method based on knowledge graph enhancement, and relates to the technical field of artificial intelligence. The system comprises a multi-modal data processing module which is used for carrying out intelligent analysis on a building specification document in a PDF format and realizing extraction of text, chart and formula contents; the knowledge graph construction module is used for converting the structured data into a complete knowledge network and establishing an association relationship between specifications; the graph enhancement retrieval module is used for realizing deep semantic retrieval based on a knowledge graph and supporting multi-hop path discovery; the multi-Agent collaboration module is used for completing a complex consultation task through collaboration of a plurality of professional Agents; the user interaction module provides a query input and result display interface and supports multiple interaction modes; in addition, according to the system, the invention further provides a building specification intelligent consultation method based on knowledge graph enhancement. According to the method, standard documents are processed through a multi-modal AI technology, a complete knowledge graph is constructed, deep semantic understanding is achieved through a graph enhancement retrieval technology, and professional consultation services are provided through a multi-agent cooperation mechanism.

Embodiments of the present disclosure provide systems and methods for enhancing search using semantic search results. A semantic search engine performs a semantic search to identify documents semantically related to a search query. A search query to perform a second type of search is received. The second type of search is scoped to content identified in the previous semantic search.

The invention discloses a multi-modal knowledge graph construction method and device based on a large model and a program product, belongs to the technical field of artificial intelligence and knowledge engineering crossing, and particularly relates to a knowledge graph dynamic construction and evolution method based on a large language model technology and multi-modal data processing. The problems of symbol grounding and semantic understanding obstacle in the prior art are solved. According to the method, innovation and breakthrough are realized in three dimensions of knowledge acquisition, representation and reasoning by fusing deep learning and knowledge engineering technologies. The multi-modal knowledge graph construction method and device based on the large model and the program product are applied to the field of multi-modal knowledge graph construction and are suitable for specific task scenes such as intelligent question and answer, decision support and semantic search.

The invention provides a data reordering retrieval method and system based on RAG, and the method comprises the steps: carrying out the dynamic semantic partitioning processing of an original document, generating corresponding document blocks, storing the document blocks in a vector database, constructing a hierarchical index, analyzing a received query request, extracting keywords in the query request, and carrying out the retrieval of the query request. Boolean keyword matching is carried out through an inverted index in the hierarchical index, semantic retrieval of keywords is carried out in a vector database, an initial candidate set is generated after multi-source retrieval results are fused, a query request and the initial candidate set are input into a generative reordering model, and a query result is obtained; according to the method, the initial candidate set is selected, the corresponding score is generated through the cross-modal attention mechanism in the generative reordering model, the initial candidate set is reordered according to the score, the final retrieval result is obtained, and the reordered document blocks are displayed, so that the overall retrieval speed is increased, the limitation of a single retrieval mode is avoided, and the accuracy of the retrieval result is improved.

The invention relates to a low-altitude intelligent question and answer construction method and system based on dynamic parameters. The method comprises the following steps: collecting low-altitude domain data, cleaning the low-altitude domain data, generating a semantic vector index, and constructing a low-altitude domain knowledge base based on the semantic vector index; receiving a natural language query of a user, analyzing a query intention, extracting keywords in the natural language query, and matching a corresponding candidate word quantity based on query types of the natural language query of the user, the query types at least comprising high-frequency phrase query and low-frequency long-tail query; and respectively carrying out fusion semantic retrieval and keyword retrieval, carrying out secondary sorting on the candidate results based on a preset resorter, preferentially sorting the candidate results related to the query intention, and outputting the corresponding candidate results. By adopting the method, a combined domain retrieval enhancement generation mechanism is provided, so that the professionality and accuracy of answers are improved; and the retrieved knowledge base content is re-screened to increase the hit probability of the knowledge base.

The invention discloses a multi-path recall mixed retrieval method and system based on intelligent dynamic weight distribution, and aims to solve the problems that semantic comprehension and keyword matching are difficult to balance and the adaptability is poor due to the adoption of a fixed weight in the existing retrieval technology. The invention provides a multi-path recall mechanism fusing vector semantic retrieval, BM25 keyword retrieval and entity retrieval. A query feature vector containing 13-dimensional features such as semantic complexity, keyword density and entity coverage rate is constructed, a query type is recognized in combination with an SVM and a random forest integration model, a dynamic weight distribution algorithm is designed, and the final weight of each retrieval path is calculated in real time. And an adaptive multi-source enhanced reciprocal ranking fusion (AMSE-RRF) algorithm is further adopted to carry out optimization fusion on multiple paths of results, and a depth reordering model can be selected to improve the precision. According to the method, the accuracy and robustness of retrieval can be remarkably improved in multiple scenes of medical treatment, finance, government affairs and the like according to a millisecond-level self-adaptive adjustment strategy of query features.

The invention provides a multi-knowledge-granularity text retrieval method and device for RAG, and the method comprises the steps: sorting an original text into a JSON file, carrying out the recursive character and semantic dicing processing, generating a target JSON file, and storing the target JSON file in a BM25 database and a vector database; when a user queries, preprocessing to generate a keyword list and a query embedded vector; searching for a target text in the BM25 database, and calculating a score of correlation with the keyword to determine a first to-be-selected text; determining a second to-be-selected text according to the query embedding vector; and performing duplicate removal processing on all the first to-be-selected texts and the second to-be-selected texts, and determining and outputting a final recommended text by utilizing a sorting model. By considering the problem of different search granularities, the keyword granularity is solved by adopting a BM25 algorithm. According to the semantic retrieval strategy combining sentence granularity and segment granularity, challenges of different text information lengths and different retrieval purposes are effectively handled, and therefore the advantages of traditional retrieval and semantic retrieval are fully utilized.

Systems and methods for generating augmented search results are disclosed. An example method is performed by one or more processors of a search results ranking system and includes receiving a transmission over a communications network from a computing device associated with a user of the search results ranking system, the transmission including a search query, submitting, to a vector database, a token query matching a tokenized version of the search query against a plurality of data assets, submitting, to the vector database, one or more vector queries matching a vectorized version of the search query against the plurality of data assets, identifying, based on results of the token query and the one or more vector queries, contextually relevant results among the plurality of data assets, and generating augmented search results for the search query based on the contextually relevant results.

The invention discloses a domain-adaptive retrieval enhancement generation method and system, and the method comprises the steps: carrying out the document analysis and semantic partitioning of a document through a semantic understanding model and a document rule, and storing document fragments; on the basis of the large language model and a preset question library, generating a question and disassembling the question; performing correlation evaluation and sorting on the initial retrieval results in combination with a retrieval technology; candidate content most matched with the cue words is screened out through a large language model, and question-retrieval fragments are scored and marked; performing fusion on the initial retrieval result and the scoring and labeling result to construct positive and negative samples, and constructing a supervised data set; and based on a preset loss function, utilizing the supervised data set to carry out supervised training on the knowledge semantic search model, replacing the RAG mixed search module, and carrying out continuous iteration. By means of the technical scheme, new domain knowledge and operation feedback data can be flexibly applied, and domain self-adaptive retrieval enhancement generation is achieved.

Disclosed in the present disclosure is a rich-media document auxiliary generation apparatus. The apparatus comprises a material extraction module, a theme sorting module, a semantic retrieval module, a structured data text generation module, an illustration recommendation module and a video composition module. The present disclosure uses intelligent means to assist a user to efficiently generate a high-quality rich-media composite document, thereby quickly and accurately describing a theme event in an all-round way.

A non-transitory, processor-readable medium storing instructions that when executed by a processor, cause the processor to receive data artifacts, encode the artifacts to a standard data type, and compute, for each artifact, a hash function. The hash functions and encoded documents are stored in a first database. The processor is caused to tokenize the encoded artifacts, to produce tokens associated with natural-language identifiers extracted from the encoded artifacts. The processor is caused to transform, using an embedding model, the tokens to produce vectors that are stored in a second database and classified based on categories. The second database is configured to be queried to perform a semantic search in response to receiving a request from a user operating a user compute device. The processor is caused to retrieve, from the semantic search, a subset of vectors from the second database to be displayed on the user compute device.

The invention discloses a power transformer fault auxiliary decision-making method and system based on a knowledge graph and a large language model, and the method comprises the steps: obtaining power transformer fault text data, carrying out the preprocessing, obtaining fault-related text and table data, preliminarily defining an ontology, selecting a part of text data for marking, and carrying out the recognition of the ontology; a plurality of named entity recognition and relation extraction models are trained, an optimal model is determined, then triple extraction is carried out on unlabeled text data, and a knowledge graph is constructed; predicting a potential entity relationship in the knowledge graph based on a link prediction model, and complementing the knowledge graph under the large language model and human assistance; constructing a semantic search model training data set based on the large language model and training a semantic search model; based on the complemented knowledge graph, the trained semantic search model is utilized to retrieve knowledge graph sub-graphs according to the problem, and auxiliary decision making is completed. According to the method, the problem of low potential knowledge utilization level in the existing power transformer fault auxiliary decision-making based on the knowledge graph is solved.

The invention relates to a government affair file information extraction and question answering method and device and a medium, and the method comprises the steps: carrying out the entity extraction of a government affair file through employing a BERT-CRF joint model, and obtaining a structured entity set; performing relation extraction on the structured entity set to generate a semantic relation set between the entities; constructing a knowledge graph according to the structured entity set and the semantic relationship set, storing entity nodes into a graph database, and storing an embedded vector of an entity text into a vector database; when a query request of a user is received, relation query of the graph database and semantic retrieval of the vector database are carried out, sub-graph structures and semantic matching vectors related to query are extracted, and a mixed retrieval result is obtained; and inputting the mixed retrieval result into a large language model, and generating a question and answer response text conforming to a preset format by applying a dynamic prompt template. According to the method, the document processing efficiency and accuracy are effectively improved, and a solid technical support is provided for intelligent management of government affair documents.

The invention relates to a user question information response method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring question information input by a user; based on semantic retrieval and vector retrieval, retrieving corresponding private domain knowledge information from a preset private domain knowledge base; obtaining a first cue word according to the question information, the private domain knowledge information and a pre-configured agent database; inputting the first prompt word into the large model, obtaining an agent calling link through the large model, and calling a corresponding agent according to the agent calling link; obtaining a second prompt word, inputting the second prompt word and an execution result obtained by calling the corresponding agent according to the agent calling link into the large model, and determining whether the execution result meets a condition or not through the large model; if yes, response information for the problem information is output based on the execution result. By adopting the method, resource loss caused by fixed code development can be reduced.

The invention provides a minority language professional question and answer method based on mixed retrieval and retrieval enhancement generation, which comprises the following steps: S1, constructing a multi-language knowledge constructing a vector index database and a term knowledge graph by using a multi-language model according to a related minority language document; s2, multi-layer mixed retrieval: cross-language document recall is realized through a multi-layer mixed retrieval module, and the multi-layer mixed retrieval module is composed of keyword retrieval, semantic retrieval and vector retrieval; s3, answer generation: performing answer generation through an adaptive multi-language model by using a retrieval enhancement generation module, and introducing rule constraint decoding and a dynamic attention mechanism in the generation stage to improve the professionality and accuracy of the answer; and S4, self-adaptive optimization and knowledge updating: through a user feedback reinforcement learning module, optimizing the model based on user error correction data and supervising updating of the knowledge base. According to the method, professional questions and answers of the minority language can be realized based on a small amount of professional data of the minority language, the recall rate of the document of the minority language is improved, and the generation quality is improved.

A method and system provide the ability to process a construction domain query. A natural language user query is obtained within a construction software system. The user query is pre-processed to validate the query. Text from the query is embed into search vectors for a semantic search. A data source having multiple different sections is obtained. The semantic search is performed within each of the sections and identifies semantically relevant sections. The relevant sections are consolidated into a contextual data prompt that is input into an LLM. The LLM, which is trained based on construction data, generates a response that identifies the relevant sections. The response and an identification of the relevant sections is output.

The invention relates to the technical field of intelligent questioning and answering, in particular to a domain intelligent questioning and answering method and system based on a multi-modal knowledge graph and RAG, and the method comprises the steps: constructing a concept layer knowledge graph based on a directory structure of a domain multi-modal document, and constructing an instance layer knowledge graph based on document content; obtaining a user question, pruning and positioning the user question in combination with the concept layer knowledge graph and the thinking chain, and determining a target chapter; splitting the question into sub-questions through intention analysis, and performing semantic retrieval in the instance layer knowledge graph corresponding to the target chapter to obtain a graph retrieval result; optimizing the original problem based on the atlas retrieval result, and executing semantic retrieval in a vector database to obtain a vector retrieval result; and fusing the atlas retrieval result and the vector retrieval result to generate a preliminary answer, and performing iterative optimization until a final answer is generated. According to the method, the semantic coverage, the expression accuracy and the response efficiency of the vertical domain question-answering system are remarkably improved by constructing the multi-modal knowledge graph and optimizing the retrieval process.

The invention discloses a knowledge construction method and system based on a large model and an RAG technology, and belongs to the technical field of large models.According to the knowledge construction method and system based on the large model and the RAG technology, through cross-text-block mutual information calculation and RAG enhanced reasoning, the system can quantify statistical correlation between entities, and in combination with context information of an external knowledge base, potential incidence relations of cross-paragraphs or documents are mined, so that the knowledge construction efficiency is improved. A dynamic semantic segmentation strategy is adopted to ensure that semantics in text blocks are consistent, context continuity is maintained by retaining overlapped parts, multiple expressions of the same entity are comprehensively judged through a multi-dimensional anaphora resolution mechanism, the error resolution rate is reduced, dynamic segmentation and mixed retrieval are combined, and the problem of large model input length limitation is solved. And through semantic retrieval and keyword matching complementation, the recall rate is improved, and the key problems of semantic fracture, cross-text association missing, low entity alignment precision and the like in traditional knowledge construction are remarkably solved.

The invention discloses a file retrieval and management method fusing an AI large model and graph data, and belongs to the technical field of retrieval management, the retrieval and management method comprises the following specific steps: I, a user imports a multi-format file into a file retrieval and management system, and the system extracts characters and structured content through a file analysis module; iI, the file retrieval and management system gradually constructs a multi-level database based on the analyzed file content and metadata; iII, performing full-process management on the file from creation to archiving, and providing an intelligent tool to optimize a document processing process; according to the method, the automation and efficiency of file management can be greatly improved, a high-precision semantic search and sorting result is provided, complex association analysis and multi-dimensional query are supported, professional document translation requirements in an internationalized scene are met, document content management is optimized, and the knowledge precipitation capacity is improved.

The invention relates to the technical field of artificial intelligence, and discloses a man-machine dialogue history and semantic retrieval-based prompt automatic construction method, which comprises the following steps of S1, performing text preprocessing on historical dialogue data and user input, including dynamically generating query keywords, verifying and analyzing a file uploaded by a user, and extracting core contents; s2, utilizing a pre-training language model to map the historical dialogue and the user request into semantic vectors, matching in combination with the keyword weight, and constructing a historical vector set and a user request vector; and S3, extracting a context fragment most relevant to the user request from the historical dialogue vector based on a convex optimization method. The context information highly related to the user request is accurately extracted through the sparse optimization technology, and the background information, the user request and the generation instruction are organically integrated in combination with the dynamic Prompt template design, so that the semantic correlation and context coherence of the generated reply are improved, and the interference of redundancy and irrelevant information is avoided.

The invention discloses an RAG performance improvement method based on process optimization. The method comprises the following steps: S1, preprocessing text data; s2, performing hierarchical text segmentation, obtaining a document, storing the text by utilizing a database after the document is segmented, converting the text into vectors, and storing the vectors into a vector knowledge base; s3, querying and rewriting to obtain a rewritten question; s4, combining parent document search with mixed retrieval, and retrieving related contexts for the rewritten questions; s5, performing rearrangement and denoising to obtain a final context; and S6, generating an answer, and sending the final context into a large language model for understanding questions and answers. According to the method, the links of text preprocessing, text partitioning, text embedding, query optimization, retrieval, retrieval post-processing, generation and the like are sequentially optimized according to the data flowing direction, the semantic retrieval precision is improved, and the performance of RAG in the aspects of text retrieval precision, answer generation quality, user interaction and the like can be remarkably improved; and the flexibility and the practicability of the system are enhanced, so that the system is more excellent in practical application.

The invention discloses a literature semantic search method and system based on elastic search, and relates to data retrieval. The literature semantic search method comprises the steps that vectorization processing is conducted on a noun phrase list by means of a text2vec-based-multilingual model trained based on a CoSENT method; according to the semantic vector, performing approximate nearest neighbor search in a second retrieval module to obtain first candidate data; inputting the query text data into a first retrieval module, and performing keyword matching through a BM25 algorithm to obtain second candidate data; fusing the first candidate data and the second candidate data to obtain third candidate data; a Sequence Matcher algorithm is adopted to calculate character string similarity between expansion words in the third candidate data, a similarity threshold value is set based on the length of the longest common subsequence, duplicate removal is carried out, and fourth candidate data is obtained; and performing weight distribution based on positions and similarity scores on the fourth candidate data, and enhancing the distinction degree of the extension words by expanding a score interval to obtain extension word recommendation list data. According to the method, the accuracy of document retrieval is remarkably improved.

The invention provides a knowledge question and answer rapid processing method and system based on artificial intelligence, and relates to the field of artificial intelligence. A multi-modal knowledge graph is constructed, collected multi-source teaching data is fused through a mixed retrieval strategy, and the mixed retrieval strategy comprises semantic retrieval, vector retrieval and metadata retrieval; multi-level question and answer processing is executed based on an RAG enhancement framework, a multi-modal input intention is analyzed, cross-library joint retrieval is performed, and an optimization answer is generated in combination with a teaching scene; distilling the global model to a lightweight TinyBERT architecture, dynamically optimizing question and answer quality through a cognitive reinforcement learning framework, positioning a key document from a comprehensive retrieval list, evaluating an optimized answer, and reconstructing an answer with a key document verification score; according to the invention, the professional skill level of teachers and students in the fields of artificial intelligence and large model application can be improved, and the personalized requirements of teachers and students in teaching, scientific research and innovation courses can be met.

A computer-implemented method for searching electronic documents is provided. The method executing a non-semantic search, such as lexical search, to identify documents from a document corpus that meet the search criteria of the non-semantic search. A subsequent semantic search can be scoped based on the results of the non-semantic search. The method can thus include executing a semantic search scoped to the documents identified in the non-semantic search result to generate a semantic search result that identifies content that is semantically relevant to a natural language query. Thus, the semantically relevant content can have both non-semantic (e.g., lexical) and semantic relevance.

The invention relates to the technical field of multi-modal data processing and semantic retrieval, in particular to a multi-modal heterogeneous knowledge fusion construction and semantic enhancement retrieval system based on a large model, which comprises a data acquisition module, a semantic analysis module, a knowledge fusion module and a retrieval optimization module. Multi-modal data such as texts, images and audios are uniformly expressed and deeply analyzed by introducing a large model technology, a knowledge graph is dynamically constructed, a structure is optimized in combination with a user query intention, and meanwhile accurate sorting and screening are achieved through a semantic enhancement algorithm. According to the method, the semantic comprehension capability and the intelligent level of the system can be improved, the real-time and diversified scene requirements are met, and the accuracy and the adaptability of a retrieval result are remarkably enhanced.

The invention provides a heterogeneous network resource virtualization modeling and intelligent arrangement method and system based on a knowledge graph, and relates to unified modeling, dynamic retrieval and intelligent resource arrangement of heterogeneous network equipment. The method specifically comprises: 1, a unified modeling method based on a knowledge graph: integrating protocol attributes, dynamic states and topological relationships of heterogeneous devices such as a 5G base station, an SDN switch, a router, an Internet of Things gateway and the like into a structured knowledge graph, breaking the barrier of a manufacturer private data model, and realizing semantic-level collaborative scheduling of cross-domain resources; 2, designing a semantic retrieval engine: querying dynamic conditional reasoning through a natural language, replacing traditional manual rule definition, and improving retrieval response speed and accuracy; and 3, developing a graph-driven intelligent arrangement framework: combining a graph neural network (GNN) and reinforcement learning (RL), automatically generating a resource allocation strategy according to a real-time network state, reducing manual intervention and improving the resource utilization rate.

The invention discloses a construction method of an aerospace manufacturing knowledge graph based on a heterogeneous data joint extraction mode. The construction method comprises the following steps: designing and constructing a knowledge model of the knowledge graph by using an automatic modeling tool based on a top-down theory; performing knowledge graph bottom layer storage in a manner of combining a table structure and a graph structure; knowledge extraction and knowledge mining are carried out from structured, semi-structured and unstructured data; carrying out data mode layer fusion and data layer fusion on data from different sources; knowledge discovery and knowledge graph completion are carried out based on rapid knowledge reasoning of a graph neural network; and carrying out intelligent question answering and visual decision support based on semantic search in combination with a specific application form of the knowledge graph and aerospace manufacturing field data and business scenes. According to the method, challenges of various knowledge sources and data formats on knowledge extraction can be handled, so that the aerospace manufacturing knowledge graph can be applied to the ground, and the method has mobility, scientificity and seriousness.

The invention discloses an intelligent log retrieval and analysis system based on a model context protocol MCP, and relates to the technical field of log analysis. The system comprises an MCP log semantic conversion and retrieval engine, a log format irrelevant feature extraction and indexer, a dynamic log format recognition and context enhancement system, a real-time retrieval analysis and aggregation controller and a multi-level semantic retrieval and visualization engine. According to the context enhancement system, unknown formats can be identified, semantic tags can be complemented, and semantic consistency and behavior tracking capability can be improved. The retrieval analysis controller supports semantic expression analysis, strategy generation and feedback closed loop, and strategy scheduling and multi-dimensional aggregation analysis are achieved. And finally, outputting a structured semantic result by the system, and visually displaying the structured semantic result through components such as a semantic composition device and a context expander. All the modules are managed in a unified mode through a capability registration mechanism, dynamic arrangement and upstream and downstream closed-loop linkage are supported, and a log intelligent analysis framework with high semantic driving and a clear structure is formed.

The invention discloses an intelligent question answering system and method based on question decomposition and community semantic search. According to the method, firstly, a complex question is decomposed into a plurality of single-hop sub-questions through an original loop question decomposition technology, then information needed for answering each sub-question is obtained based on communities in a vector retrieval knowledge graph, and the sub-questions are answered by using a large language model. And finally, generating a final question and answer for the original complex question by combining the LLM with the answers of all the sub-questions. Compared with other methods in the industry, the method has the advantages that the information integration capability is remarkably improved, information fragmentation is avoided through community-level search and abstract generation, and high-quality and logically consistent answers are provided. When a complex query is processed, the effect of the method is superior to that of a general method in the industry and is improved by 29% at most; meanwhile, on the basis of ensuring a better question and answer effect, the efficiency is remarkably improved, and on the premise that the effect exceeds that of an existing optimal method GraphRAG, the token consumption is only 3.2% of that of the existing optimal method GraphRAG.