90 results about "Relevance (information retrieval)" patented technology

The invention provides a cross-modal retrieval method for semantic and vector fusion in a data space, which belongs to the field of cross-modal information retrieval, and comprises the following steps: firstly, collecting and preprocessing multi-modal data; generating modal embedding and storing by utilizing the pre-training model; a shared semantic space is constructed, cross-modal vector alignment is optimized through comparative learning, and a modal mapping network is designed to enhance the embedding projection effect; storing the aligned embedding by using a Milvus database, and constructing an HNSW index; user text or image query is processed, text query analyzes limiting conditions to generate enhanced embedding, and image query extracts characters through OCR and fuses the characters with image features to generate embedding; in a database, through condition screening and semantic similarity calculation, a Top-K candidate item is retrieved; performing multi-modal correlation sorting on the candidate results and returning the results; according to the method, the shared semantic space is constructed, the alignment effect of different modal embedding is optimized, efficient storage and index management of multi-modal embedding are carried out, and real-time retrieval of large-scale cross-modal data is achieved.

The invention belongs to the technical field of natural language processing and information retrieval. The invention provides a bidding document multi-modal duplicate checking method and system based on a large model, and the method comprises the steps: carrying out the structural analysis and multi-modal feature extraction of an input bidding document, and generating the feature representation of semantic blocks and non-text elements; performing deep semantic coding on text blocks by using a dynamic context-aware large language model, and retaining logic relevance of a long text in combination with hierarchical position coding; efficient matching of massive semantic vectors is achieved through a mixed retrieval framework, and calculation efficiency is optimized in combination with a distributed calculation framework and a hardware acceleration instruction; and finally, performing structured feature reconstruction on non-text contents such as tables, charts and the like to realize cross-modal semantic association analysis.

The invention provides a full-text retrieval method and system fusing various types of documents, and relates to the technical field of information retrieval, and the method comprises the following steps: obtaining document representation through document content extraction and structure recognition, generating a cross-modal semantic vector by using word embedding and nonlinear transformation, constructing a hierarchical index and a cross-document association graph, and obtaining a full-text retrieval result; the basic correlation score is calculated after the query request is received, and the comprehensive score of the candidate content segments is calculated based on the association graph to determine the optimal retrieval result, so that unified representation and retrieval of heterogeneous documents are realized, the cross-document retrieval precision and relevance are improved, and the processing capability of a retrieval system on complex queries is enhanced.

Methods and systems for retrieving relevant information in response to an input question. The method includes obtaining text content related to the input question and partitioning the content into one or more paragraphs based on predefined rules. The method further involves extracting one or more evidence spans that are relevant to the input question by inputting the text content and the question into a trained language model. A semantic search is then performed on both the paragraphs and the extracted evidence spans, ranking the candidate passages based on their relevance to the input question. Each candidate passage may comprise either a paragraph or an evidence span that addresses the question. The disclosed methods and systems improve the quality and relevance of retrieved information by combining heuristic-based content partitioning with machine learning-based evidence extraction.

Techniques for multi-index retrieval in knowledge databases to enhance retrieval augmented generation (RAG) systems. The techniques involve a query processor that receives a query from a RAG agent, compares it to index summaries, and selects target indexes. The processor then searches these indexes, fuses the retrieved content items, and reranks the results before sending them back to the RAG agent. This approach combines query routing, multi-index fusion, and reranking to improve information retrieval for RAG applications. The technique offers several advantages, including enhanced retrieval efficiency through specialized indices, increased relevance and precision of retrieved information, scalability for large datasets and high query volumes, and optimized querying across diverse data sources. The techniques address challenges in managing extensive, distributed datasets and are compatible with existing RAG frameworks, providing a solution for complex information retrieval tasks.

The invention relates to the technical field of intelligent information retrieval, in particular to an image-text retrieval generation method and device based on reference semantics and electronic equipment. The method comprises the steps of performing semantic segmentation on a document in a document knowledge base to obtain a plurality of paragraphs, performing semantic similarity judgment, and merging similar paragraphs; extracting semantic features of the merged paragraphs to form semantic features of lower-layer paragraphs; abstract description is carried out on the merged paragraphs, semantic features are extracted from abstract description, upper layer description semantic features are formed, and text level semantic features are constructed; image semantic features in the image knowledge base are extracted, normalization processing is carried out on the text level semantic features and the image semantic features, an inverted product quantitative index is constructed, and construction of an image-text reference semantic feature index is achieved; and processing the input text based on the image-text reference semantic feature index to obtain a retrieval result. According to the method, the problems of lack of previous knowledge, insufficient detail information and poor image-text relevance in image-text retrieval are solved.

The invention discloses a user participation degree prediction method based on distillation multi-modal retrieval enhancement, and belongs to the technical field of social media data mining and user behavior analysis. According to the method, the correlation of the UGC is evaluated by introducing the self-enhanced distillation module, and the top-K related UGC is selected in combination with the selected retriever, so that the interference of irrelevant information on the related UGC is effectively avoided, and the noise caused by irrelevant artifacts can be filtered out when the related UGC is retrieved, thereby keeping the original feature representation of the related UGC. The heterogeneous graph construction module enhances the interactive representation capability between UGCs through multi-relation modeling, and can more accurately optimize a prediction result in user participation prediction. According to the method, information retrieval between the related UGC and the unrelated UGC can be better balanced, excessive diffusion of unrelated information is avoided, and the performance of the model in user participation degree prediction is improved. Particularly, when complex multi-modal data containing a large number of irrelevant UGCs is processed, the mechanism can effectively enhance the distinction degree of the relevant UGCs, and the prediction accuracy is improved.

The invention relates to the technical field of data processing, can be applied to the fields of financial information search, multi-dimensional information retrieval in intelligent investment advisers, medical information search and the like, and provides a multi-modal data search method and device, a storage medium and computer equipment. Performing multi-modal unified coding on different modal data in the knowledge base to obtain a multi-modal knowledge base; acquiring input information of a user; retrieving multi-modal information in a multi-modal knowledge base, wherein the similarity between the multi-modal information and the input information meets the requirement; the multi-modal information comprises feature vectors corresponding to the information of different modals, wherein the similarity of the information and the input information meets the requirement; and sorting the feature vectors corresponding to the information of different modes based on the historical behavior information of the user, and determining target search information corresponding to the input information based on a sorting result. According to the embodiment of the invention, a fusion retrieval mechanism of multi-modal data is introduced, so that the precision and correlation of information retrieval are effectively improved, and the user experience is improved.

Systems and method for generating a response to a query. The method includes using a first large language model (LLM) to generate synthetic information related to a query; generating an amended query based on the synthetic information related to the query; using an information retrieval system to retrieve, from a plurality of chunks, a set of chunks that are relevant to the amended query, wherein each chunk of the plurality of chunks is all or a portion of a document in a corpus of documents; using a second LLM to rank the set of chunks based on a relevance to the query; selecting a subset of chunks from the set of chunks based on the ranking; and using a third LLM to generate a response to the query based on the subset of chunks.

There are provided systems and methods for identifying relevance of documents for automated retrieval models using large language models. An online transaction processor or other service provider may provide computing services and platforms to entities, which may include chatbots, information retrieval systems, question-and-answer systems, and the like. To provide better retrieval model training and refinement, the service provider may generate training data from user interaction logs, which may include user feedback that may be used to determine if documents are relevant to queries, and therefore should be retrieved for answering those queries by automated retrieval models. An LLM may be used as a judge to determine whether chatbot responses reference certain document. If not references, the query may be analyzed to determine whether certain retrieved documents are relevant. Data pairs may be generated for the training data from these processes and used for model refinement.

The invention discloses a self-adaptive multi-feature fusion hybrid retrieval sorting method and system, and belongs to the technical field of information retrieval. The method comprises the steps that after user query is received, a mixed retrieval process and an intention recognition process are executed in parallel; performing multi-dimensional feature extraction on the candidate documents obtained by the mixed retrieval, wherein the multi-dimensional feature extraction comprises semantic correlation features, keyword matching features, document authority features and timeliness features; and according to the identified query type, adaptively selecting a fusion weight, and carrying out weighted fusion on the multi-dimensional feature vector to calculate a final score and sort the final score. According to the method, the problem that weight distribution is rigid in traditional mixed retrieval is solved through an intention self-adaptive dynamic weight mechanism, meanwhile, by introducing multi-dimensional service features, the ranking result not only ensures the correlation, but also meets the quality requirement under a service scene, and the accuracy and practicability of a retrieval system are remarkably improved.

A method and system for enhancing information retrieval and understanding in language models when processing heterogeneous documents containing text, tables, figures, formulas, and images is described. The method involves traversing a document to identify specific types of content and using a language model to generate text descriptions of the identified content, including detailed interpretations. These descriptions are stored in a storage means, indexed by content type. During retrieval, the language model references these descriptions to enhance its understanding and analysis of the document, improving accuracy and contextual relevance. The system comprises a document traversing module, a language model, a storage means, and an information retrieval module, with a feedback loop for continuous improvement based on user feedback. This invention addresses limitations in current retrieval-augmented generation applications by improving the interpretation of complex technical content, thereby enhancing the performance of language models.

The present invention belongs to the field of natural language processing and information retrieval technology. It provides a large-scale model-based multimodal bid duplication checking method and system, which performs structured parsing and multimodal feature extraction on input bids to generate feature representations of semantic blocks and non-text elements. It uses a dynamic context-aware large language model to perform deep semantic encoding on text blocks, combining hierarchical position encoding to preserve the logical relevance of long texts. It achieves efficient matching of massive semantic vectors through a hybrid retrieval architecture, and optimizes computing efficiency by combining a distributed computing framework with hardware acceleration instructions. Finally, it reconstructs structured features of non-text content such as tables and charts, achieving cross-modal semantic association analysis.

The invention discloses an AI medical information accurate retrieval method based on a knowledge graph, and belongs to the field of medical information, and the method comprises the steps: collecting and integrating multi-source medical data, employing an entity recognition and relation extraction technology, accurately extracting medical entities and semantic relations, and constructing a complete and accurate medical knowledge graph; according to the method, the medical knowledge graph is constructed, query is converted into a query sub-graph structure in combination with a natural language processing technology, and the FFGR algorithm is applied to fuse attributes, structures and semantic features of nodes to calculate a comprehensive score, so that the correlation between the nodes and the query can be accurately evaluated; the potential relationship can be deeply mined when the complex disease query is processed; the DKU algorithm utilizes a deep learning model to carry out feature extraction and relation prediction on new data, and the structure and content of the knowledge graph are updated in real time. And new diseases, medicines and relationships can be timely reflected in the knowledge graph, so that the retrieval result is always based on the latest medical knowledge.

The invention belongs to the technical field of information retrieval, and discloses a multi-modal patent semantic retrieval method and system.The reliable cross-modal relation is recognized by establishing relation anchor points, extracting local cross-modal relation representation around the anchor points and verifying the cross-modal consistency of the relations, and further, the multi-modal patent semantic retrieval method and system have the advantages that the reliability of the multi-modal patent semantic retrieval is improved; technical features / components and successfully aligned cross-modal relationships are constructed into graph structure representation, and similarity calculation is performed by using a graph matching algorithm, so that the overall logic and details of technical schemes can be more effectively understood, schemes only containing the same technical features but having different relationship structures can be distinguished, and the accuracy of the technical schemes can be improved. The accuracy and correlation of multi-modal patent retrieval can be improved.

The invention provides an RAG mixed retrieval method and device, and relates to the technical field of information retrieval, and the method comprises the following steps: obtaining a query text and a term library, and calculating to obtain a vector retrieval weight and a keyword retrieval weight according to the query text and the term library; obtaining a document library, and performing vector retrieval on the document library according to the query text and the vector retrieval weight to obtain a first candidate document set; performing keyword retrieval on the document library according to the query text and the keyword retrieval weight to obtain a second candidate document set; merging the first candidate document set and the second candidate document set to obtain a fused document set; and calculating, screening and sorting comprehensive feature scores of the documents in the fused document set to obtain a final document sequence, and inputting the final document sequence into the large language model to obtain an answer text. According to the method, the limitation of a single scoring dimension in professional field retrieval is overcome, and the correlation between the answer text and the query text is improved through multi-dimensional feature joint evaluation.

The invention discloses an information search method and device, computer equipment and a storage medium, and belongs to the technical field of artificial intelligence. According to the method, through intelligent keyword combination and weight evaluation, the accuracy and relevance of search and query are improved, and compared with traditional keyword matching search, the method is based on the pre-trained keyword weight model, the importance of different keyword combinations can be automatically evaluated, invalid or low-relevance query is reduced, and the search efficiency is improved. Meanwhile, a deep learning model is adopted to optimize search query statements, so that the search query statements are more in line with user intentions, the quality of search results is enhanced, the accuracy of information retrieval is improved, and the cost of manually screening and adjusting search keywords is reduced. In a mass data environment, the information method provided by the invention can be applied to business management program systems of financial science and technology, medical treatment, health, old-age care and the like, and more intelligent and efficient information search experience is provided for users.

The invention provides a multi-path information retrieval method and system based on a language model and uncertainty evaluation. The multi-path information retrieval method comprises the following steps: receiving a user query; performing semantic analysis on user query by using a pre-trained language model so as to output a quantitative uncertainty index in an unsupervised manner; dynamically determining a quantity ratio for controlling query generation and a balance parameter for controlling result sorting based on the index; based on the user query and the strategy parameters, generating and executing two groups of differential queries at least comprising exploratory queries and utilizable queries to obtain multiple paths of retrieval results; and combining multiple paths of results, and carrying out dynamic balance reordering between the correlation and diversity of the results according to balance parameters by adopting an ordering mechanism. According to the method, the retrieval strategy can be adaptively adjusted according to the uncertainty of query, and the coverage degree and diversity of results are remarkably improved.

The invention provides an information retrieval method, device and equipment based on large model questions and answers and a medium, and the method comprises the following steps: firstly, receiving a natural language question input by a user through a terminal, and forwarding the natural language question to a semantic analysis engine after authentication of an API (Application Program Interface) gateway; then oral vocabularies are removed through a normalization module, abbreviation is completed, a retrieval category is determined in combination with an intention recognition module, key information is extracted through a slot extraction module, and meanwhile permission-semantic coupling retrieval is completed in combination with a company organization structure; then adopting a multi-path recall mode of vector semantic retrieval and keyword supplementary retrieval to obtain a candidate result, and performing correlation rearrangement and permission filtering to obtain a target retrieval result; and finally, performing fragment positioning on different types of contents, and displaying a result with specific position information at a front end in a preset form. According to the method, the retrieval accuracy and efficiency can be improved, cross-modal unified retrieval is realized, authority security is guaranteed, and efficient and accurate information retrieval requirements in large enterprises are met.

The present invention discloses a policy traceability dynamic retrieval method based on multi-level intent recognition and comparative learning, which involves the fields of natural language processing, information retrieval and generation technology, and introduces multi-level intent recognition, dynamic field classification, embedded multi-round retrieval, comparative learning mechanism and generation optimization strategy. The present invention can accurately capture query intent, dynamically select the most relevant knowledge base, and generate high-precision traceability results through multi-level verification and optimization, significantly improving the accuracy, relevance and added value of retrieval, and meeting the high-precision scenario requirements such as complex policy tracing and compliance inspection.

The invention provides a mixed retrieval method, system and device based on multi-algorithm fusion and a storage medium, and the method comprises the steps: obtaining various types of document data, preprocessing the document data, and obtaining plain text data; segmenting the plain text data into a plurality of pieces of block node data, and generating a plurality of derivative problems for each piece of block node data by using a large language model; carrying out vectorization coding on the block node data and the derivation problem, and storing the block node data and the derivation problem in a vector database; receiving a user query question, and generating a plurality of related preset questions for the query question by using the large language model; converting the query question and the preset question into a vector form, executing keyword retrieval and vector retrieval, and obtaining corresponding candidate results in a vector database; and carrying out weighted fusion on the candidate results through a weighted reciprocal rearrangement algorithm to obtain most relevant retrieval data. According to the method, the efficiency of information retrieval and the correlation of results can be improved, and the retrieval requirement of a user in a complex semantic scene is met.

The invention relates to the technical field of artificial intelligence and information retrieval, in particular to a dynamic retrieval strategy self-adaptive adjustment method and system based on reinforcement learning. According to the method, a retrieval process is modeled into a Lukov decision process, and a strategy model is trained to realize autonomous optimization and dynamic adjustment of a retrieval strategy. The method comprises the following steps: defining a state space comprising a query type, context correlation, a historical retrieval effect and knowledge base characteristics; defining an action space including vector retrieval, keyword retrieval and mixed retrieval; constructing a dual-target reward function fusing the retrieval income and the retrieval cost; training a strategy model by utilizing a historical retrieval track; calling the strategy model in real time to dynamically adjust the retrieval mode when the retrieval enhancement generation system runs; and the model is iteratively optimized through continuous feedback. According to the method, the problems of low recall rate and low calculation efficiency of a traditional fixed retrieval strategy under complex query are solved, dynamic balance between retrieval precision and resource consumption is realized, and the method is suitable for intelligent retrieval scenes of multi-field and multi-modal knowledge bases.

The invention discloses a bidirectional information retrieval enhancement generation method for a large language model, and belongs to the technical field of artificial intelligence. In order to overcome the defects that noise is introduced and key evidences are omitted due to the fact that traditional RAG only executes'query-document 'one-way retrieval, a two-way semantic perception retrieval enhancement generation model and a two-stage training framework are constructed, wherein in the first stage, the positive / negative example distance is increased in an embedded space in a contrast learning self-supervision mode; in the second stage, fine-grained correlation discrimination is carried out on query-document bidirectional sentences through supervised dichotomy, and probabilistic correlation scores are output; in the reasoning stage, the bidirectional probabilities are fused according to Bayesian to obtain final relevancy, document reordering is carried out, and plug and play can be achieved without fine adjustment of LLM in the whole process. According to the method, the accuracy and consistency of single-hop and multi-hop questions and answers and fact checking tasks are remarkably improved, and the method has the advantages of light weight and low deployment cost.

The invention discloses a retrieval enhancement generation method and system based on long-term memory and multi-source knowledge iterative fusion. The method comprises the following steps: firstly, after user inquiry is received, triggering long-term memory database retrieval, external knowledge base retrieval and large language model internal knowledge generation in parallel to construct a multi-source candidate knowledge base, and improving result correlation by adopting a self-adaptive retrieval method based on a dynamic top-k value; secondly, inputting user query and multi-source knowledge into the large language model for iterative fusion, and generating a final answer through conflict detection, fusion processing and confidence evaluation; and finally updating the long-term memory database. The method can improve the accuracy and stability of the generated answers in a multi-round interaction and complex task scene, reduces the risk of answer one-sidedness, inference incompleteness or fact deviation, enhances the multi-source knowledge utilization ability and long-term learning ability of the system, and improves the user experience. Therefore, the comprehensive performance and reliability of the system in applications such as intelligent question answering, information retrieval assistance and text generation are obviously expanded.

The invention discloses a retrieval report generation method and device based on a local language model, equipment and a medium, and relates to the technical field of information retrieval, and the method comprises the following steps: receiving query configuration information and a query statement; using a local language model to determine an enhanced query based on chain thinking; splitting the enhanced query, and calculating a correlation score between the sub-query and the enhanced query based on text embedding so as to perform sub-query screening; on the basis of query configuration information, a preset recursion strategy and a dynamically adjusted correlation threshold value, correlation-guided recursion webpage retrieval is conducted on the screened or selected sub-queries; constructing a target knowledge base based on the recursive retrieval result and a local knowledge base supporting multiple modes; and generating a target retrieval report based on the local language model, the target knowledge base, the enhanced query and the target recursive retrieval result. According to the method, the quality, the accuracy, the comprehensiveness and the relevance of the retrieval report are improved by combining relevance-guided adaptive recursive exploration and multi-modal knowledge fusion.

The invention relates to the field of information retrieval, and discloses an efficient document search method and device, equipment and a storage medium. The method comprises the steps that a document set is preprocessed, and an index structure is constructed; analyzing and optimizing a query word input by a user to generate a target retrieval request; retrieving an initial document set containing the target retrieval request based on the index structure; and carrying out correlation scoring on the initial document set by adopting a preset multi-dimensional scoring strategy, sorting according to a scoring result, and screening out a document set most related to the target retrieval request. According to the efficient document searching method provided by the invention, the document set most relevant to the user search query is screened out by analyzing and optimizing the user search query and combining the relevance score and the behavior data sorting result, so that the problems of low retrieval efficiency and poor result relevance of a traditional document searching method are effectively solved; and the document searching efficiency and the result quality are improved.

The present invention discloses an artificial intelligence-based data search method and system, which relates to the technical field of information retrieval and includes receiving a user query for query parsing and calculating the similarity with the feature vectors of data points; constructing a distance matrix to construct a simplex and calculating the topological features of the simplex through a boundary operator; converting the topological features into topological feature vectors and fusing them with the feature vectors of data points, constructing a deep neural network DNN, and generating search results. The present invention receives a user query for query parsing and calculates the similarity with the feature vectors of data points; constructs a distance matrix to construct a simplex, calculates the topological features of the simplex through a boundary operator; converts the topological features into topological feature vectors and fuses them with the feature vectors of data points, constructs a deep neural network DNN, and generates search results; significantly improves the discrimination ability for similarity differences, enhances the efficiency of data processing and query response, and improves the accuracy and relevance of search results.

The application discloses a bidirectional information retrieval enhancement generation method for a large language model, and belongs to the technical field of artificial intelligence. In order to overcome the defects of traditional RAG that only performs one-way retrieval from 'query to document' and introduces noise and omits key evidence, a bidirectional semantic perception retrieval enhancement generation model and a two-stage training framework are constructed. In the first stage, the positive / negative example distance is pulled apart in the embedding space in a contrast learning self-supervised manner. In the second stage, a supervised two-classification is used to finely distinguish the relevance of the query-document bidirectional sentence pair, and an output probability correlation is obtained. In the reasoning stage, the bidirectional probability is fused according to Bayes to obtain the final correlation degree, and the document is reordered. The whole process can realize plug and play without fine-tuning the LLM. The application significantly improves the accuracy and consistency of single-hop, multi-hop question answering and fact checking tasks, and has the advantages of light weight and low deployment cost.

A dense encoder is adapted as a hierarchical corpus encoder in an information retrieval system to use negative samples from sibling nodes in a hierarchical tree of vector embeddings for documents in a corpus. Both the encoder and hierarchical tree are co-trained using a loss function that takes the document hierarchy into account. The hierarchical corpus encoder may be used in both supervised training cases where query-document relevance judgments are present and in zero-shot cases where a query dataset is absent. The hierarchical corpus encoder demonstrates significant performance improvements over a variety of dense encoder and generative retrieval baselines, under both supervised and unsupervised scenarios, thereby establishing the effectiveness of jointly learning a document hierarchy.

The application relates to the technical field of artificial intelligence, in particular to a retrieval method and device based on multiple agents, equipment and a medium. Applied to a financial scene, the application realizes the intellectualization and dynamicization of the retrieval process through a multiple-agent cooperative working mechanism. The retrieval core elements are accurately extracted and a dynamic cognitive graph is updated by analyzing the agents, deep semantic mining and correlation expansion are carried out based on the cognitive seeds of semantic agents, the dependence on keywords in traditional retrieval is effectively broken, and the breadth and depth of semantic understanding are improved. The query statement is optimized by combining the extended semantics and the dynamic cognitive graph of the context agent, so that the query is more in line with the real intention and context of the user. The retrieval agent efficiently retrieves the candidate path in the updated dynamic cognitive graph, the recommendation agent filters the optimal result through cognitive consistency scoring, and the accuracy, relevance and logical coherence of the retrieval result are ensured, and the accuracy of the complex information retrieval task is significantly improved.