486 results about "Document retrieval" patented technology

The invention belongs to the technical field of multi-modal data processing, and particularly relates to a multi-modal document retrieval enhancement generation method based on a large model, which comprises the following steps: receiving query content input by a user for a multi-modal document; processing the query content by adopting an embedded model, generating a query vector representing user query semantic information, and storing the query vector in a vector database; analyzing the multi-modal document to obtain long text information, segmenting the long text information into data blocks by adopting a recursive partitioning strategy, and numbering and marking the data blocks; carrying out vectorization processing on the data blocks by adopting an embedded model to generate document vectors, storing the document vectors into a vector database, and constructing a hierarchical index structure; retrieving in a vector database based on the query vector, and returning a retrieval result; and processing a retrieval result by utilizing a large language model to generate response content conforming to the query intention of the user. According to the method, the multi-modal document can be effectively analyzed and processed, and the accuracy and comprehensiveness of analysis are improved.

The invention provides a scientific and technological literature intelligent retrieval method and related equipment based on generative artificial intelligence, and the method comprises the steps: carrying out the multi-layer semantic annotation of medical scientific and technological literatures, constructing a symptom-disease dynamic association map, and building a medical scientific and technological literature knowledge base; performing medical context analysis and multi-modal feature extraction on user query, and generating a unified retrieval vector in combination with Boolean operation nested analysis and semantic alignment processing; performing evidence grading retrieval and clinical scene matching based on the unified retrieval vector to obtain a preliminary candidate literature set, and optimizing the preliminary candidate literature set into a target candidate literature set through fine-grained semantic recalculation; and calculating a retrieval prior probability according to the evaluation dimension, performing knowledge weighted fusion on the candidate literature, and generating a medical science and technology literature recommendation report. According to the method, the medical term association relationship is deeply understood through the association map, the result is ensured to be matched with the patient characteristics through evidence grading retrieval and clinical scene matching and screening, and the accuracy of document retrieval is improved.

A knowledge management system may generate a plurality of prompts based on divisions of documents of unstructured text, each prompt relevant to a division of unstructured text. At least one prompt is generated such that a corresponding division of unstructured text is a response to said at least one prompt. The system may generate prompt embeddings for the plurality of prompts corresponding to the plurality of documents of unstructured text. The system may generate prompt-embedding clusters to group similar prompts from one or more documents of unstructured text. The system may receive a query. The system may convert the query to one or more query embeddings. The system may identify one or more prompts that are relevant to the query based on comparing the one or more query embeddings to the prompt embeddings. The system may identify one or more documents in one or more prompt-embedding clusters.

Systems and methods for resource-efficient retrieval of information using a generative AI model are disclosed. An input query requesting information from a set of documents is used in a prompt for a generative AI model to generate a search query to identify the documents relevant to the input query and their respective relevancy scores. The input query is used as an input another model to determine a depth score indicating a predicted number of documents needed to retrieve the information. Based on the depth score and the relevancy scores of the relevant documents, the system extracts grounding data from the identified relevant documents to generate an answer synthesis prompt for the generative AI model. The generative AI model processes the second to produce a response to the input query including the requested information.

The invention relates to the technical field of document retrieval and information processing in the data processing technology, in particular to a document retrieval method based on multistage indexing and feature clustering, which comprises the following steps: performing high-dimensional space mapping on multi-modal features such as texts and images through a quantum embedding layer to generate cross-modal joint feature representation; a first-level index of a multi-level index architecture is dynamically initialized based on a meta-clustering algorithm, and semantic blocks of a second-level index are divided in combination with a multi-head self-attention mechanism. And an optimal transmission matrix is generated by using a Sinkhorn algorithm to align cross-node feature distribution. The multi-target mixed retrieval strategy is fused with vector retrieval, keyword retrieval and graph retrieval results, and weight distribution is dynamically adjusted. Through collaborative optimization of quantum calculation, federated learning and causal reasoning, a closed-loop technical architecture from feature analysis to dynamic index construction is formed, the problems of insufficient cross-modal fusion, static clustering deviation and semantic association deficiency are solved, and the precision, efficiency and dynamic adaptability of heterogeneous document retrieval are improved.

The invention discloses an intelligent finance and tax question answering method based on an artificial intelligence question answering system, and the method comprises the steps: S1, generating a structured semantic representation through a natural language processing technology, and generating an intention tag through a machine learning algorithm; s2, constructing and maintaining a knowledge graph containing finance and tax field related information, storing the information in the knowledge graph in a structured manner, and reasoning by using a logical reasoning algorithm to generate a preliminary answer; s3, designing a document retrieval and indexing mechanism, and verifying and supplementing the preliminary answer; s4, training the system based on the finance and tax related data and question and answer pairs, and continuously updating the model; s5, identifying common finance and taxation question modes by using a mode identification technology, and providing standardized answers; and S6, supporting multiple rounds of dialogues with the user, and generating more detailed and accurate answers by gradually clarifying the questions of the user. The method has the advantages of high accuracy, high response speed and capability of continuously learning and adapting to new problems.

The invention provides a large language model fact verification method, system and device based on retrieval enhancement and a medium, and belongs to the technical field of natural language process.The method comprises the following steps that document retrieval is conducted from a credible knowledge base based on an input task, screening is conducted according to a document evaluation result, and a file is obtained; performing deletion and compression according to the relevance between the retrieved document and the user input task to obtain a document set; extracting a fact unit from the original output of the large language model and performing semantic enhancement; labels are set for the fact units based on the document set, and scoring is carried out according to the similarity between the fact units and the document set; correcting, processing and optimizing the fact unit according to the label; and revising the output of the large language model by using a fact unit on the basis of a user input task, and outputting after consistency verification. According to the method, by introducing a document retrieval and fact verification mechanism of the credible knowledge base, the accuracy and reliability of the generated content of the large language model are improved.

The invention relates to the technical field of database retrieval, in particular to a knowledge database retrieval method based on feature extraction. The method comprises the following steps of obtaining a document to be retrieved, performing local hash de-duplication processing and extracting multi-modal features, performing feature spectrum clustering processing and associated entity analysis on the extracted features, generating a feature associated entity structure, performing document topic modeling and semantic direction positioning, and extracting a semantic topic of the document. The method comprises the following steps: performing word vector retrieval on a to-be-retrieved document and a knowledge database by utilizing a document semantic topic, generating word vector retrieval data, performing sentence vector retrieval, generating a final document retrieval result, integrating word vectors and the document retrieval result, and executing feature retrieval of the knowledge database through an intelligent retrieval engine, thereby realizing efficient knowledge query. The knowledge database retrieval method is more intelligent and more accurate.

The invention discloses a multi-document question and answer method and equipment based on multi-head self-attention and hierarchical enhancement, belongs to the technical field of natural language understanding, and aims to solve the problems that most of existing RAG solutions focus on document retrieval of single query and neglect the requirements of multi-aspect query, and the query efficiency is low. And the application of the RAG method in the multi-document question and answer task is limited. The method comprises the following steps: extracting hierarchical structure metadata from a document in a preset format, constructing a hierarchical document tree based on a hierarchical relationship, and obtaining a context relationship in the document in the preset format; determining one or more target retrieval paths based on the retrieval information; performing multi-document retrieval on the document under each target retrieval path through a multi-head self-attention embedding module to obtain a plurality of related document paragraphs; screening a target document paragraph from the related document paragraphs; and based on the paragraphs of the target document and the corresponding context relationships, sorting out question and answer results of the retrieval information in a large language model.

The invention provides a multi-agent query enhancement generation method, device and equipment and a storage medium, and the method comprises the steps: carrying out the sub-problem decomposition processing of an original query problem through a problem rewriting agent, and obtaining a group of sub-query problem sets; performing retrieval method selection processing on each sub-query problem through the retrieval agent according to the entity type contained in each sub-query problem in the sub-query problem set to obtain a corresponding retrieval method; performing document retrieval processing on the knowledge base by using a retrieval method to obtain a group of candidate documents, and performing correlation screening processing on the candidate documents through a document selection agent to obtain related target documents; and inputting the target document and the original query question into an answer generation agent for answer generation processing to obtain an answer result. According to the method, a multi-agent cooperation framework is adopted, joint optimization of all agents is achieved through end-to-end reinforcement learning training, and the retrieval and answer quality of complex questions is effectively improved.

The invention discloses an intelligent legal instrument retrieval system based on a vector technology, which relates to the technical field of information retrieval and comprises an instrument vector generation module, an index construction module, a query vector generation module, a similarity calculation module and a result optimization module, comprising but not limited to word segmentation and stop word removal operations, and converting the legal document into vector representation by using a pre-trained word vector model; through the intelligent legal instrument retrieval system based on the vector technology, semantic meanings of the legal instruments can be effectively understood, and even in the face of fuzzy or inaccurate query input of a user, related legal instruments can be quickly and accurately positioned by means of similarity calculation in a vector space; the efficiency, correlation and accuracy of legal document retrieval are greatly improved, and the urgent demands of the legal industry and related demanders for efficient legal information retrieval are met.

Certain aspects of the present disclosure describe a method of information retrieval. In certain aspects, the method includes identifying a set of relevant nodes of a document graph embedding semantic units associated with the document based on a document search query. The method further includes reconstructing a structural context for each relevant node in the set of relevant nodes. The method further includes processing the set of relevant nodes and the structural context of each relevant node with a large language model to generate a contextual response to the document search query.

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.

Embodiments of the present description provide a document retrieval method and an automatic question answering method. The document retrieval method comprises: obtaining data for retrieval; retrieving at least one candidate document from among a plurality of documents in a knowledge base on the basis of the data for retrieval; selecting at least one reference document from among the at least one candidate document on the basis of the association relationship between the data for retrieval and the at least one candidate document; and on the basis of the at least one reference document, updating the data for retrieval, to obtain updated data for retrieval, and retrieving a target document from among the plurality of documents by means of the updated data for retrieval. A reference document is obtained by means of coarse ranking retrieval and fine ranking retrieval, and thus, the accuracy of the reference document is guaranteed; the reference document is used for updating data for retrieval, so that positive and negative feedback interactions are achieved in the retrieval pipeline, making the data for retrieval more accurate, effectively solving retrieval errors caused by expression diversity and indirectness, and improving the accuracy of document retrieval.

The present disclosure provides an answer information generation method and apparatus based on a large language model, and a device, and relates to the technical field of artificial intelligence, and in particular, to the fields of document retrieval, natural language processing, and large language models. An implementation solution includes: obtaining, in response to receiving a question text from a user, a semantic vector of the question text and event information related to a specific field; obtaining a plurality of candidate documents from a document library of the specific field based on at least two of the semantic vector of the question text, the at least one piece of argument information and the event category; determining quality evaluation information for a candidate document in the plurality of candidate documents based on the event category; and determining at least one target document from the plurality of candidate documents based on the quality evaluation information of the candidate document and a correlation between the candidate document and the question text, to obtain, based on the at least one target document, answer information used to answer the question text.

Methods and systems for artificial intelligence (AI)-based document retrieval during a virtual meeting are provided herein. Discussion data based on multi-media stream(s) provided by client devices of participants of a virtual meeting is obtained. A determination is made of whether the discussion data corresponds to an information access query. In response to a determination that the discussion data corresponds to the information access query, the discussion data is provided as input to an AI model trained to identify one or more electronic documents including content that is relevant to given discussion data. An electronic document associated with at least one of the participants including content that is relevant to the given discussion data is identified based on output(s) of the AI model. At least a portion of the content of the electronic document is provided for presentation via a user interface (UI) at each client device associated with the participants.

The invention discloses an intelligent construction and retrieval method for a medical knowledge graph based on GraphRAG and LLM. The intelligent construction method of the medical knowledge graph comprises the steps of data collection and processing, text segmentation, entity and relation extraction, entity analysis, weak connected component identification, word distance filtering, LLM evaluation, element abstract generation, graph community generation and community abstract generation. The intelligent retrieval method comprises the steps of knowledge graph vector library construction, text vector library construction, user question entity extraction and vectorization processing, multi-layer entity and relation retrieval, text retrieval, LLM summarization, result output and user feedback collection. According to the method, the GraphRAG and the LLM large model technology are combined, and efficient retrieval and accurate presentation of the medical information are achieved. According to the system, the accuracy and efficiency of medical literature retrieval are remarkably improved through deep integration of graph structured data and natural language processing capacity.

The invention provides a retrieval method and device based on document segmentation and a document retrieval system. The method comprises the following steps: acquiring a to-be-segmented document; based on an NLP algorithm, calculating the semantic similarity between the partial texts of the to-be-segmented document to obtain a first semantic relevancy; according to the first semantic relevancy of all the partial texts, the document to be segmented is segmented, a plurality of semantic text blocks are obtained, and each semantic text block comprises at least one partial text; under the condition that a query request is received, calculating semantic similarity between a query text corresponding to the query request and each semantic text block based on an NLP algorithm to obtain a plurality of second semantic relevancy, and determining the semantic text block with the highest second semantic relevancy of the query text corresponding to the query request as a target semantic text block, and displaying the target semantic text block in a display interface. According to the scheme, the problem that in the prior art, the accuracy rate is low during text retrieval is solved.

The invention discloses a water conservancy design archive retrieval system and method based on a local lightweight large model, and the method comprises the steps: S1, constructing a Python automatic preprocessing assembly line, extracting texts for PDF and Word multi-format archives, correcting metadata, and outputting standardized data; s2, constructing a full-text retrieval and semantic retrieval dual-mode cross-document retrieval service by relying on a Weavi ate local vector database and a lightweight text embedding model; s3, analyzing a user query intention through a local large model, synchronously triggering metadata accurate retrieval and content semantic retrieval, and generating a structured result; and S4, integrating the core module into a local area network Web platform, adopting Docker containerization deployment, and combining an RBAC permission model and JWT authentication to guarantee security. The system comprises a preprocessing module, a cross-document retrieval module, an intelligent agent module and a background management module, and collaboration is achieved through a standardized API. According to the method, the problem of archive fragmentation is solved, multi-mode retrieval breaks through keyword limitation, an intelligent agent reduces manual intervention, a localized architecture prevents secret-related leakage, background management adapts to an existing I T environment, and full-process intelligent archive service is provided for water conservancy design.

The invention relates to the technical field of artificial intelligence, and discloses an enterprise-level large-model agent application system supporting multi-modal collaboration, and the system comprises a user interaction terminal, an agent engine server, a knowledge engine server, a plug-in integration center, and a distributed storage unit. The agent engine server is responsible for intention recognition and task arrangement of a multi-modal input signal, and dynamically loads a differential reasoning strategy based on an environment isolation mechanism. And the knowledge engine server constructs a cross-modal semantic anchor point, analyzes an unstructured document into a tetrad knowledge unit, and realizes accurate recall of images and texts by using a hybrid retrieval algorithm. And the plug-in integration center executes outbound replacement and inbound restoration of the sensitive data through the context-aware dynamic desensitization gateway. According to the method, through a multi-modal semantic association and closed-loop verification mechanism, the problems of low complex document retrieval precision and leakage of external calling data are solved, and the service processing capacity and safety of the system are improved.

Systems and methods for resource-efficient retrieval of information using a generative AI model are disclosed. An input query requesting information from a set of documents is used in a prompt for a generative AI model to generate a search query to identify the documents relevant to the input query and their respective relevancy scores. The input query is used as an input another model to determine a depth score indicating a predicted number of documents needed to retrieve the information. Based on the depth score and the relevancy scores of the relevant documents, the system extracts grounding data from the identified relevant documents to generate an answer synthesis prompt for the generative AI model. The generative AI model processes the second to produce a response to the input query including the requested information.

The invention discloses an intelligent document duplicate checking system and method based on a vector database and a large language model, and the method comprises the following steps: S1, collecting document data in various formats, and carrying out the preprocessing of the document data; s2, semantic coding is carried out through a large language model, and a document semantic vector is generated; s3, storing the document semantic vector into a vector database, constructing a vector index and recording historical query data; s4, carrying out preliminary candidate document retrieval, and carrying out approximate nearest neighbor retrieval based on outlier identification; s5, calculating the similarity between the candidate document and the document to be subjected to duplicate checking, and screening a final high-similarity document; s6, generating a duplicate checking report, and recording user operation behaviors; and S7, receiving user feedback, and dynamically updating the document semantic vector and the vector index. According to the method, efficient and accurate intelligent document duplicate checking is realized by utilizing the large language model and the vector database, the semantic matching capability is improved, the duplicate checking efficiency is optimized, and the intelligence and adaptability of a duplicate checking system are improved.

The invention provides an AI-agent-based multi-document retrieval method and system, and the method comprises the steps: carrying out the preloading according to the format of a document, thereby achieving the intelligent partitioning of a multi-source heterogeneous document, i.e., decomposing a large text into smaller and easier-to-manage blocks; the method comprises the following steps of: performing fine tuning by using vertical domain data on the basis of general BAAI Genral Embedding, and completing the conversion from a text to a vector; carrying out preliminary retrieval on the vectorized data by adopting a mixed retrieval and reordering two-segment method; the intelligent agent technology is used for achieving self-reflection learning retrieval and adjustment and optimization, and retrieval results are adjusted and optimized through a reflection mechanism. According to the method, intelligent identification segmentation and vectorization can be carried out on the electronic archives in different formats, a multi-dimensional mixed retrieval result is optimized by using a reflection mechanism, the correlation between texts and themes is fully considered, and the method has wider applicability, higher robustness and controllability.

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.

Techniques for multimodal document retrieval are disclosed herein. Multimodal documents that include both textual and graphical components are retrieved from a knowledge base by a multimodal retrieval augmented generation (RAG) agent in response to a query. The documents and / or components or chunks thereof are retrievable by the RAG agent from the knowledge base using the semantic summaries and / or vector search of embeddings in the knowledge base that are generated from text extracted from processing non-textual components of the data. The RAG agent classifies the query type to determine whether to use a semantic match for text or image summaries, full text semantic search, vector cosine similarity search, and / or other multimodal vector search. The RAG agent performs types of searches selected based on the modality used to generate the response to the query.

The invention discloses an intelligent literature screening method and system based on zero sample voters. The method comprises the following steps: expanding a literature set based on seed literatures, constructing a classification system comprising a plurality of zero sample voters, screening literatures related to a target topic through a majority voting mechanism, and iteratively expanding the literatures to obtain more literatures related to the target topic. The zero sample voting device integrates subject term matching, language model pre-training, multi-large language model voting and a semantic embedding vector technology, adaptively determines a threshold value in combination with an improved binary search algorithm, and realizes high-precision classification without training data. The system supports parallel processing and intelligent API management, and the processing efficiency of a large-scale literature set is remarkably improved. The method is suitable for the fields of academic research, literature retrieval and the like, can quickly construct a large-scale high-correlation literature information base, and particularly meets the retrieval requirements of emerging technologies or interdisciplinary themes.

The invention discloses a retrieval enhancement generation system and method for customizing multilevel intention recognition. The retrieval enhancement generation system comprises a multilevel intention label and document management module, a multilevel intention recognition module and a retrieval enhancement generation module. The multi-level intention label and document management module can self-define and build a corresponding relationship among a multi-level intention label, an intention label and a document level by level to form a label document corresponding relationship form; the multi-level intention recognition module recognizes intentions of the user level by level according to the multi-level intention labels and the multi-level intention labels established by the document management module, and gives corresponding intention labels; and the retrieval enhancement generation module performs vector similarity search in a document range delineated by the intention tag, and transmits a vector similarity search result to the large language model for result generation. Through step-by-step subdivision of the user intention, the document retrieval range is gradually reduced to the corresponding intention label, so that the answer generated by the large language model in the generation stage is more accurate.

The invention relates to the technical field of literature information retrieval, and particularly discloses a literature information retrieval analysis system and method based on AI intelligence. The system comprises a representation vector output module, a preliminary retrieval result output module, a retrieval result updating module, a dynamic knowledge graph construction module, an enhanced literature representation output module, a recommendation list output module and a knowledge graph feedback updating module. A representation vector output module extracts literature text features; a preliminary retrieval result output module executes semantic enhancement retrieval and query expansion; a retrieval result updating module optimizes a preliminary result; a dynamic knowledge graph construction module constructs a graph based on the retrieval result; the recommendation list output module provides personalized recommendation, and the knowledge graph feedback updating module updates the knowledge graph according to the user interaction flow data. According to the method, the accuracy, intelligence and dynamic adaptability of literature retrieval are comprehensively improved, and user requirements can be deeply met.

The invention relates to a document retrieval method and system based on electric power semantic enhancement and electronic equipment, belongs to the technical field of natural language processing, and solves the problem of low retrieval accuracy caused by low complex knowledge utilization rate and insufficient electric power professional semantic understanding in the prior art. Comprising the following steps: receiving user query content, and obtaining a query embedding vector by utilizing a modal joint embedding model; based on the electric power knowledge graph, utilizing a large language model and a text embedding model to obtain a structured query vector of user query content; according to the query embedded vector and the structured query vector, obtaining a plurality of candidate documents and document-level similarity scores and page-level similarity scores thereof, and further obtaining a comprehensive similarity score of each candidate document by using a double-path prediction model; and obtaining a total score according to the document-level similarity score, the page-level similarity score and the comprehensive similarity score of each candidate document, and selecting a plurality of candidate documents with the highest total score as a retrieval result. And the retrieval precision is improved.

The invention provides an automatic network protocol testing method and system based on a large language model. According to the method, through three core technologies of structured protocol specification extraction, hybrid test case generation and retrieval feedback enhanced code generation, full-process automation from a protocol specification document to an executable test code is realized. The method comprises the following steps: firstly, carrying out preprocessing and structured analysis on an RFC document, and extracting protocol field information and state machine description; a complete test case covering normal, abnormal and boundary conditions is automatically generated based on protocol elements; and finally, an initial test code is generated in combination with a test equipment API document retrieval result, and multi-round iterative optimization is performed through running log analysis and an experience knowledge base. Compared with the prior art, according to the scheme, an end-to-end automatic test work chain is achieved, new protocol testing can be adapted without manual protocol modeling, meanwhile, a large language model and expert experience are fused through a feedback mechanism, and the test efficiency and the code quality are remarkably improved.