235 results about "Text searching" patented technology

The invention discloses an e-commerce platform commodity recommendation method and system based on user preference analysis, and relates to the technical field of e-commerce platforms, and the method comprises the steps: constructing a user multi-modal interest feature vector; the multi-modal interest feature vector fuses the text search history, the image browsing preference, the audio consumption habit and the video interaction behavior of the user; utilizing a cross-modal attention mechanism to match the multi-modal interest feature vector of the user with multi-modal attribute information of commodities to generate a candidate commodity set; and applying a dynamic context-aware reinforcement learning model to reorder the candidate commodity set according to real-time interaction behaviors of the user and environmental factors, and outputting a final recommendation result. According to the method, multi-modal interest feature extraction, dynamic context perception and reinforcement learning technologies are combined, so that comprehensive capture and accurate modeling of user interests are realized.

The invention provides a cross-modal image-text retrieval processing method and a cross-modal image-text retrieval processing system, which are applied to the field of information retrieval, and the method comprises the following steps: obtaining a query text input by a user in an image-text retrieval process; encoding the query text through a text encoder to generate a query text feature vector; through a cross-modal image-text retrieval model, similarity matching is carried out based on query text feature vectors and multi-modal embedding representations stored in an external knowledge base, related results corresponding to the multi-modal embedding representations larger than a matching threshold value are returned, and the multi-modal embedding representations are used for representing joint features of images and texts; when the related result comprises the image and the text at the same time, the related result and the query text are input into a preset multi-modal large model, image question answering with text assistance is carried out, and a retrieval result output by the multi-modal large model is obtained; according to the method, the semantic association between the image and the text can be better captured, so that the accuracy of image-text retrieval is improved.

The invention provides a text search method and system based on vector retrieval and large model optimization, and relates to the technical field of text search, and the method comprises the following steps: obtaining a to-be-retrieved text and generating a semantic vector; retrieving similar texts in a vector database based on the semantic vectors; calculating the correlation of the text pairs by using a cross encoder model and sorting the text pairs; and extracting key entities by adopting a knowledge graph optimization model, analyzing an entity relationship, and reordering and filtering search results. According to the method, through semantic vector retrieval, cross coding calculation of correlation and knowledge graph optimization, the accuracy and correlation of text search are improved, user intentions can be better understood, and more accurate search results can be returned.

The invention discloses an intelligent interactive Agent system based on Langchain and an implementation method, and relates to the technical field of natural language processing and artificial intelligence. Comprising the steps of 1, establishing an intelligent interactive Agent system by using an interface and an integrated tool provided by a Langchain framework, 2, managing calling of various tool modules and services through a central scheduling module, and dynamically registering and unbinding the tool modules by using the interface provided by the Langchain framework, and 3, processing user input by using a large language model through a process management module, and establishing an intelligent interactive Agent system. 4, a series of tool modules are integrated through the integrated tool management module, the tool modules comprise a text search tool, a weather query tool and a video information acquisition tool, each tool module works independently or is in linkage with other tool modules, and the tool modules are connected with the integrated tool management module. And 5, managing a user interaction interface through a front-end management module, supporting a user to query and control in a natural language, displaying a processing progress and a processing result in real time, and providing feedback and help information.

Generating responses to queries with text extracts from unstructured data using AI models includes (i) extracting text from unstructured data sources to create machine-searchable documents, (ii) replacing PII and PHI with entity types and attributes, (iii) determining text extracts that indicate criteria, (iv) using a small-scale ML model to perform text searches and find conceptually associated text strings, (v) generating a custom context for a large language model (LLM), (vi) prompting the LLM to generate a response, (vii) combining the response with an extractive QA model to obtain relevant text extracts as response basis, and (viii) providing system-generated recommendations for next best actions based on responses that produce the most optimal outcomes in historical input documents for manually selected or automatically recommended resolution paths.

The invention relates to an image-text search database construction method and device, a database and a storage medium. The method comprises the following steps: analyzing an obtained document, and extracting to obtain image information and text information of the document; based on the image information and the text information, constructing at least one image-text pair of the literature, the image-text pair comprising an image and a corresponding image annotation text; generating an image feature vector of the image based on the image and the image annotation text; key text information of the literature is extracted from the text information, and a text feature vector of the literature is generated based on the key text information; and based on the image feature vector and the text feature vector of each document, constructing an image-text search database. By adopting the method, literature feature extraction of multi-modal data such as fusion texts and images is realized, deep professional association among literatures is mined, and the accuracy of carrying out image-text search on related literatures in the astronomical field can be improved.

The invention relates to the technical field of information retrieval, and discloses a retrieval method for aggregating multi-dimensional retrieval strategies. When retrieval is carried out, a retrieval word input by a user is obtained; based on the retrieval word, performing retrieval by using each retrieval strategy to obtain a retrieval result of each retrieval strategy; inputting the retrieval word into a semantic judgment model to obtain a retrieval strategy weight adjustment parameter; calculating according to the retrieval strategy weight adjustment parameters to obtain the final weight of each retrieval strategy; and according to the final weight of each retrieval strategy, sorting the retrieval results of each retrieval strategy to obtain a final retrieval result. According to the method, the weights of full-text retrieval, vectorization retrieval and graph database retrieval are dynamically adjusted according to semantic features of user retrieval words through retrieval strategy weight adjustment parameters accurately obtained by using a semantic judgment model, and it is ensured that optimal retrieval results can be obtained in different query scenes.

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.

The embodiment of the invention provides a cross-modal data search method and device based on Surreal DB. The method comprises the steps that multi-modal data input by a user at a data query interface is received, and the multi-modal data comprises text, picture, audio, video, webpage and document data; the method comprises the following steps: converting audio, video, webpage and document data into texts and pictures, performing keyword extraction on the texts to obtain text cues, and performing vectorization processing on the texts and the pictures to obtain query vectors; performing full-text retrieval on the text cue word based on a Surreal DB database to obtain a text search result, and performing vector retrieval on the query vector to obtain a vector search result; reordering the text search result and the vector search result by using a ranking-based search result fusion algorithm, and outputting a preliminary search result; and performing associated query on the preliminary retrieval result to obtain associated data.

The invention provides a novel large model traditional Chinese medicine course resource intelligent question answering method and system, and the method comprises the steps: enabling a large model to convert a user question into a question vector, and carrying out the similarity retrieval matching in a Fast vector library, and constructing a first reference answer; performing entity relationship and different noun mining on the question by the large model, performing knowledge graph filtering, analyzing the real intention of the question asked by the user, generating a corresponding Cypher statement according to the filtered entity and relationship, executing the Cypher statement in Neo4j, and constructing a second reference answer; performing fine word segmentation on the questions by the large model by using a predefined word segmentation device, executing full-text search in a pre-constructed Elasticsearch engine index kernel, sorting search results according to a characteristic sorting strategy, and constructing a third reference answer; and the large model carries out knowledge summarization and generation according to the first, second and third reference answers, strictly screens data reference sources through a verification mechanism, and generates the most accurate comprehensive answer. Intelligent question answering can be performed on traditional Chinese medicine course resources.

A computer-implemented method for computational textual search to find and display search identified information in documents. Search queries are processed over one or more documents either at a server or user device using match schemes that produce both binary (match or no match) and non-binary (i.e. multiple matching values) results and have relative fuzziness relationships. A fuzzier match scheme implies more results and fewer false negatives and a less fuzzy match scheme implies fewer results and fewer false positives. This fuzziness relationship allows, without changing a search string, for users to quickly change from a match scheme to a fuzzier or less fuzzy match scheme depending on user evaluation of results as containing too many false negatives or too many false positives—without becoming a programmer of complex search string metadata or an expert user of advanced search capabilities.

A text processing method includes obtaining a query text, invoking a search engine interface based on the query text to obtain a plurality of text search results corresponding to the query text, obtaining, from the plurality of text search results, a plurality of answer text segments matching the query text, determining a relevance between the query text and each of the plurality of answer text segments, determining one of the plurality of answer text segments that corresponds to a maximum relevance as a reference text of the query text, and invoking a language model based on the query text and the reference text to obtain a reply text of the query text.

Embodiments of the present invention provide systems, methods, and computer storage media for a visual and text search interface used to navigate a video transcript. In an example embodiment, a freeform text query triggers a visual search for frames of a loaded video that match the freeform text query (e.g., frame embeddings that match a corresponding embedding of the freeform query), and triggers a text search for matching words from a corresponding transcript or from tags of detected features from the loaded video. Visual search results are displayed (e.g., in a row of tiles that can be scrolled to the left and right), and textual search results are displayed (e.g., in a row of tiles that can be scrolled up and down). Selecting (e.g., clicking or tapping on) a search result tile navigates a transcript interface to a corresponding portion of the transcript.

The invention discloses a multi-hop agricultural question-answering system based on knowledge graph reasoning and a large model and a text fragment retrieval and answer generation method thereof, and belongs to the field of agricultural intelligent question-answering. The method is characterized by comprising the steps that S10, key entities and relations of input questions are extracted to construct structured representation of the input questions; s20, according to the structured representation of the input question, retrieving a reasoning path related to the question in an agricultural field knowledge graph; s30, embedding the reasoning path into the input problem to generate a knowledge enhancement problem representation containing multi-hop reasoning information; s40, performing text retrieval according to the knowledge enhancement question representation to obtain related text fragments; and S50, inputting the knowledge enhancement question representation and the text fragment into a retrieval enhancement generation model (RAG), and outputting an answer of the input question by the retrieval enhancement generation model, so that the effect is that the capability of processing a complex multi-hop reasoning question by the question and answer system is remarkably improved.

The invention relates to the technical field of data management and query, and discloses a multi-format data quick query method based on RAG, which comprises the following steps: step a, a user uploads a target file through a front-end interface, and the file is any one of PDF, Word or Excel formats; b, carrying out format verification on the uploaded file; c, selecting a corresponding analysis tool according to a file format, and performing text and structured data extraction on the file by using a PyMuPDF library, a python-docx library or an openpyxl library; and step d, respectively storing the analyzed data into a PostgreSQL (Structured Query Language) database and a Faiiss database. By combining structured data storage and non-structured data vectorization storage, efficient vector similarity retrieval is carried out by utilizing Fiss, and the response time of data query is greatly shortened. Compared with a traditional query method based on full-text search, the method has the advantages that the result most related to user query can be quickly and accurately returned from a large amount of data, so that the data query efficiency is improved.

A system for automated processing and analysis of audio files for large data sets in a cloud environment. A unified analytic environment can integrate audio machine learning models for processing and analysis with a knowledge management system, including graph presentations of tracked entities, linked to audio files and / or associated translations and transcripts. Entities within such data can be searched or filtered and proposed for tracking, or identified as tracked objects. These features can allow triage and prioritization of audio files for analysis. User interfaces can facilitate feedback on transcription and translation outputs, thereby improving present outputs and future inputs and outputs. Entities speaking or referred to can be found, tagged, and distinguished in audio files (e.g., using speaker identification in audio files, text searching in transcripts, etc.) Users can provide feedback and input on various aspects of a system, to enhance or adjust initial automated or other machine learning outputs.

This application proposes an enhanced question-answering method that integrates context awareness and proposes an innovative solution. In the process of building a knowledge base, a context-preserving strategy is adopted to retain key semantic information, ensure semantic coherence, and prevent context loss. In the question-answering process, the user's query intent is first identified and refined to generate more precise query statements, and keywords are extracted from them to improve the accuracy and relevance of semantic and full-text retrieval; and multiple embedding models are used to search the knowledge base to identify text fragments that are highly consistent with the user's query intent, and the relevance and continuity processing of the initially identified text fragments are performed to provide continuous and relevant information retrieval and answers.

The invention discloses a question answering method and device for credit review knowledge based on a large model and a medium, and the method comprises the steps: obtaining a to-be-processed credit review question, extracting a core keyword from the question through a natural language processing algorithm, and converting the question into a deep semantic vector through a semantic vector model; inputting the questions into an intention classification model, generating filtering labels, inputting the questions into a distributed full-text search engine, screening a target knowledge base according to the filtering labels, and performing retrieval in the target knowledge base based on the core keywords and the deep semantic vectors to obtain a preliminary retrieval result set; calculating the score of each preliminary retrieval result in the result set according to the core keyword, the deep semantic vector and the weight factor, and screening out final reference content in a descending order; and on the basis of credit and loan business compliance requirements, risk control rules and question and answer output specifications, the credit and loan prompt words are constructed, and the final reference content is input into the large model to obtain structured credit and loan review answers, so that the question and answer efficiency and accuracy are improved.

Systems or methods of the present disclosure may provide a design tool for adjusting designs implemented on programmable logic devices. The present disclosure includes receiving documentation and receiving a full text search. The documentation may include user guides, technical specifications, and design files such as HDL code, constraints, timing reports, and / or design assistant / rule violation (DRC) reports. The present disclosure also includes determining semantic search for embedding vectors based on the documentation and the full text search. Furthermore, the present disclosure includes providing the semantic search to a large language model (LLM).

The invention discloses a BEV-based scene-level text search point cloud retrieval method and device and electronic equipment, and the method comprises the steps: projecting a three-dimensional point cloud into an aerial view, and generating a BEV image; encoding the image by using a pre-trained vision-language model to obtain BEV feature vectors, storing the BEV feature vectors in a vector database, and constructing a point cloud feature library; meanwhile, encoding a user query text to obtain a text feature vector; searching a matching scene from the feature library by calculating the cross-modal similarity between the text and the BEV feature vector; and finally, mapping a retrieval result to original point cloud data and outputting the original point cloud data. By means of the method, the feature alignment problem of the point cloud and the natural language is solved, and the retrieval method which directly and efficiently utilizes essential features of the point cloud data to be in seamless joint with the natural language is achieved.

The invention belongs to the technical field of ciphertext retrieval, and discloses a homomorphic encrypted ciphertext retrieval method and system based on a hardware encryption card, and the method comprises the steps: constructing a ciphertext database, encrypting large-scale document data based on an encryption algorithm realized by the hardware encryption card of an encryption server, and storing the encrypted data in the ciphertext database; the method comprises the following steps: constructing a ciphertext database containing encrypted data such as a ciphertext document and a homomorphic ciphertext index table, storing the ciphertext database in a cloud server, performing symmetric encryption on the document, and performing homomorphic encryption on keywords extracted from a full text of the document; and in the ciphertext document query step, a querier can input a keyword to query the ciphertext document content of the ciphertext database through the client, the cloud server and the encryption server are matched through the ciphertext keyword, the ciphertext document required by the user is retrieved in a ciphertext state, and an obtained ciphertext document information set is decrypted by the encryption server and then is returned to the client. According to the method, safe and efficient secret-state full-text search can be realized in a full-text search scene of a large-scale secret-state document.

The invention discloses a PDF (Portable Document Format) image screening method driven by intelligent analysis, which belongs to the technical field of document processing and comprises the following steps of: intelligently analyzing a plurality of PDF files to obtain a plurality of pieces of image information for format conversion; traversing the plurality of PDF files based on the plurality of bitmap formats for image capture; performing image content identification on the multiple pieces of image information according to the multiple pieces of image proportion data; identifying the plurality of pieces of image information based on a plurality of text data sets, and traversing a plurality of PDF files according to a plurality of image indexes; simulation preselection is carried out based on the target PDF file, full-text search is carried out based on multiple preselected images, preselected feedback information is sent to the remote control terminal, and an image screening instruction is generated to carry out intelligent screening response on the PDF images. According to the method and the device, the problems of low PDF file image processing efficiency and inaccurate information extraction in the prior art are solved, so that a user faces tedious manual screening and processing flows in document management and data analysis.

A server receives and rebroadcasts live streaming video content from a video capture device, such as a mobile phone or unmanned surveillance vehicle. The server includes a media server configured to stream selected video content to a client device, a video analysis system configured to analyze the live video content and generate object detection data, a storage system configured to store the generated object detection data and an identifier of the associated live video content, and a search engine configured to receive a text-based search request, search the object detection data stored in the storage system for relevant search results, and generate a list of live and stored video content associated with the relevant search results.

A computing system may include an image access engine configured to access a panoramic point cloud image of a physical environment. The computing system may also include an environment location-aware text engine configured to transform the panoramic point cloud image into an alternate representation that reduces distortion in the panoramic point cloud image and perform an optical character recognition (OCR) process on the alternate representation to determine text in the panoramic point cloud image. The environment location-aware text engine may further be configured to construct text labels to track the text determined in the panoramic point cloud image and support text searches for the physical environment through the text labels.

The present application provides a kind of urban rail vehicle electronic history data processing method, the method establishes vehicle operation configuration in combination with vehicle structure design, and vehicle configuration data structure is structured using database technology;Vehicle history data granularity refined to the smallest maintainable unit is used, the correctness and timeliness of data are guaranteed through data analysis and integration technology based on business process, the whole life cycle stage document of vehicle is stored using data storage technology, and preliminary vehicle knowledge framework is established in combination with full-text search technology, to provide basic data support for later subway vehicle procurement, technical reform, operation.In history data management, block chain technology is added to ensure the confidentiality and integrity of data flow, to prevent sensitive data from being leaked and tampered with during transmission, and to enhance the security and correctness of vehicle history data, to provide reliable data security guarantee for subsequent history sharing.

Devices, systems, and methods for tokenizing search attributes and terms of a search query for an index-based search. A method may include receiving, by a search service of a provider network, a first search query to search a first searchable document set, the first search query including a first search term in a first language; applying a first tokenization rule to identify the first search term in the first search query; determining that the first search term is in the first language; applying a second tokenization rule to tokenize the first search term based on the first search term being in the first language; causing a launch of a search instance by a managed compute service of the provider network, the search instance to execute a search function for a keyword-based text search using the tokenized first search term.

A text search method is disclosed. The text search method includes, based on a query including a text being input, computing a vector value having a preset size by using a preset encoding algorithm, the vector value corresponding to the text, generating a query ciphertext by homomorphic encryption for the computed vector value, transmitting the generated query ciphertext to a server, receiving a calculation result ciphertext having similarity information with the query for each of a plurality of indexes, determining an index having a preset similarity by restoring the calculation result ciphertext, and receiving information corresponding to the index by transmitting the determined index to the server.

In an approach, a processor indexes system documents of a computer system using a text search engine, resulting in a search index, where the computer system is described by a set of system parameters with values stored in the system documents. A processor provides a set of scheduling rules for allocating computing resources of the computer system, each scheduling rule comprising a search query referencing one or more of the system parameters. A processor executes search queries by the text search engine against the search index, resulting in scored system documents and associated search queries. A processor ranks at least part of the set of scheduling rules using scores of the associated search queries. A processor selects scheduling rules in accordance with the ranking. A processor executes the selected scheduling rules for scheduling a workload on the computer system.

The invention belongs to the field of artificial intelligence, and particularly relates to an intelligent customer service question and answer processing method, a server and a storage medium. The intelligent customer service question and answer processing method comprises the following steps: acquiring consultation questions input by a user, and intelligently classifying the consultation questions to obtain category labels of the consultation questions; searching the consultation question through a vector search channel and a full-text search channel to obtain two search result sets; adjusting the initial weight based on the similarity score of each candidate answer in the two retrieval result sets to obtain a fusion weight of the consultation question in the two retrieval channels; calculating a comprehensive score of each candidate answer based on the fusion weight, and merging and sorting the candidate answers according to the comprehensive score to obtain a candidate answer set; and generating a final answer to the consultation question based on the candidate answer set. According to the method provided by the invention, the retrieval precision and recall ratio are improved, the sorting and correlation of retrieval results are optimized, and the solution rate of user questions is improved.