641 results about "Search engine" patented technology

The invention discloses an intelligent recommendation method for optimizing advertisement keyword combination through cross validation, and relates to the technical field of advertisement technology and search engine marketing, which comprises the following steps: constructing a heterogeneous data set through multi-modal data fusion, and layering according to data sparseness: training a Transform-XL time sequence model by adopting time cross validation of a dynamic K value in a high resource layer; a graph neural network association graph is introduced into a low resource layer, semantic expression of a long tail word is enhanced, a stratified sampling-transfer learning two-channel mechanism is designed, and the generalization ability is improved in combination with exposure frequency weighting and a parameter freezing strategy; developing a Bayesian fusion engine, and dynamically weighting a high / low resource layer prediction result by using an improved Materon kernel function Gaussian process; and generating a confidence interval based on neural quantile regression, and outputting an optimal keyword combination sequence under ROI-risk-diversity constraint in combination with multi-target Pareto optimization. According to the method, the cold start efficiency and the long-tail resource utilization rate are improved, and high-robustness decision support is provided for advertisement putting.

The invention relates to the technical field of natural language processing, in particular to an intelligent search engine system and method based on NLP and vector hybrid retrieval, and the system comprises a query analysis module which is used for receiving a query statement input by a user and obtaining structured query information based on the query statement; the symbiotic index module comprises a sparse extension index unit which is used for constructing a sparse inverted index table based on keywords in the structured query information; the vectorization hypergraph index unit is used for forming a hypergraph index based on the business data; the recall arrangement module is used for acquiring a candidate object set according to the structured query information; the constraint rearrangement module is used for calculating a joint priority score between query and candidate objects based on the candidate object set, and obtaining a sorting result under constraint conditions of meeting a supplier proportion, a category proportion and a price interval; and the evidence generation module is used for generating evidence information based on the sorting result and outputting the evidence information to the user interface.

An augmented search engine is provided. The augmented search engine receives an initial query for a search from a user. The augmented search engine determines a next search phase using a search state database. When the next search phase includes requesting additional user input, the augmented search engine generates a user prompt using the initial query, displays this prompt to the user, and stores the prompt in the search state database. Upon receiving user input in response to the prompt, the augmented search engine generates a search query using the initial query, the user prompt, and the user input and determines search results by querying search indexes with the search query and stores these results in the search state database. The augmented search engine generates a search summary using the initial query, the user prompt, the user input, and the search results, and displays this summary to the user.

In various examples, systems and techniques are provided that encapsulate indexing and query operations into an application programming interface (API) that automates and coordinates calls to various local and cloud-based services. When a user has a document(s) to add to a retrieval augmented generation (RAG) database, the API may offer to the user multiple document processing pipelines (DPPs) having pre-set indexing configurations. Similarly, when a user query is received, the API may generate calls to implement query processing that does not require the user to manually configure retrieval and processing of the embeddings. The API may further implement calls that locate a relevant embedding store and provide the stored embeddings, together with the query embeddings, to a search engine that identifies the most relevant matches. The API may then access the embedding-to-text indexing and identify relevant text segments and documents to a prompt generator.

The invention relates to an information retrieval method and device for hierarchical planning reinforcement learning based on retrieval enhancement. The method comprises the following steps: acquiring a natural language query request of a user; calling a large language model of hierarchical planning reinforcement learning training based on retrieval enhancement to generate semantic keywords; the high-level strategy splits the natural language query request into a series of sub-query requests; the low-level strategy generates a retrieval query according to the context of the current sub-query request, and obtains a result verification condition from an external knowledge base through an RAG model; calling a search engine to execute multiple rounds of fine-grained information retrieval to obtain a plurality of candidate retrieval results; and performing deep semantic matching analysis on the candidate retrieval results and the result verification conditions by utilizing the post-trained large language model to calculate a matching degree score, and screening the candidate results according to the matching degree to obtain a final information retrieval result. Compared with the prior art, the method has the advantages that high-quality content retrieval and result display of complex query problems of users in various scenes can be realized.

Methods and systems for interacting with users via a chatbot. A natural language query is received and processed by submitting a search query to a search engine. The search engine identifies relevant information including textual information and images for formulating a response. The identified information and query are submitted to a Large Language Model which generates a response displayed via the chatbot. The response may include textual information and relevant images. The system can extract text from images of documents and convert textual information into numerical vector representations for processing. Selectable options based on clustered relevant information can be provided to users for query refinement when appropriate. The chatbot interface enables natural language interactions while leveraging search capabilities and Artificial Intelligence to provide informative and helpful responses with both text and visual elements.

This disclosure describes utilizing a generative document system to dynamically build and provide generative text documents using one or more generative artificial intelligence (AI) models. For example, the generative document system efficiently utilizes various systems and one or more generative AI models to determine intents and topics, curate topic sections, and generate a generative text document that includes a directed answer along with select curated topic sections for search queries. In various implementations, the generative document system performs additional actions that enhance the efficiency and accuracy of operations used to produce generative text documents. Additionally, in many cases, these generative text documents provide a foundation for providing an interactive, intuitive, wide-ranging, and flexible curation of answers to users that address the corresponding search queries.

The invention relates to the technical field of text generation, provides an NL2SQL method and system based on thinking reasoning, and aims to enable a model to interact with a database environment by introducing a reinforcement learning mechanism to obtain reward signal feedback. The system comprises a table field recall component and a structured query language generation component. The table field recall component fills table field information into a preset template to construct a structured text, then vectorization processing is carried out, the structured text is stored into an elastic search engine, a relevant table is searched according to user query, and finally specific fields are screened through a field selector model. And the structured query language generation component receives the screening result, generates a structured query language in combination with user query, and performs retrieval replacement on a filtering value. In the aspect of training, the method combines supervised fine-tuning cold start and reinforcement learning reasoning training, and the model performs reasoning and thinking according to an input context through trial and error learning. In the reinforcement learning training process, a reward function iteration optimization model based on task execution result correctness is adopted.

Systems, methods, devices, and computer readable storage media described herein provide techniques for intelligently interpreting and / or responding to search queries. In an aspect, a search query comprising a search term is received. A generative artificial intelligence (AI) model is utilized to generate a question based on the search term. Data semantically similar to the question is identified. The generative AI model is used to determine an answer to the question based on the question and the identified data. In a further aspect, a question-answer pair comprising the question and answer is generated. In an alternative further aspect, the answer is caused to be presented in a graphic user interface corresponding to a search engine. In a further aspect, the answer is determined during an active session with the search engine. In a further aspect, the search term and the question-answer pair are stored in a key-value store.

Disclosed is a computer-implemented method for constructing a domain-specific language learning model (LLM). The computer-implemented method includes a step of ingesting, from a server, a domain-focused dataset. The computer-implemented method includes a step of assimilating, from a search engine database, a real-time digital data stream. The computer-implemented method includes a step of integrating the domain-focused dataset, and the real-time digital data stream within an application logic layer to obtain an integrated dataset. The computer-implemented method includes a step of employing, by a processor, a transformer algorithm on the integrated dataset to extract a plurality of domain-specific textual insights. The computer-implemented method includes a step of utilizing, by the processor, said domain-specific textual insights to execute one or more business tasks. The computer-implemented method includes a step of presenting both the domain-specific textual insights and the executed business tasks on a user interface.

The disclosure is directed to various ways of improving the functioning of computer systems, information networks, data stores, search engine systems and methods, and other advantages. Among other things, provided herein are methods, systems, components, processes, modules, blocks, circuits, sub-systems, articles, and other elements (collectively referred to in some cases as the “platform” or the “system”) that collectively enable, in one or more datastores (e.g., where each datastore may include one or more databases) and systems, the creation, development, maintenance, and use of a set of custom objects (and core objects) for use in a wide range of activities, including sales activities, marketing activities, service activities, content development activities, and others, as well as improved methods and systems for sales, marketing and services that make use of such entity resolution systems and methods as well as custom objects.

Techniques for determining whether retrieved content is sufficient for a language model to generate a response to an input are described. In some embodiments, a system may determine a retrieval complexity (RC) metric based on a question, a reference answer and document results retrieved from a retrieval component (e.g., a search engine, a knowledge search, etc.). The RC metric may be based on whether one of the document results corresponds to the reference answer and whether the reference answer can be determined from the set of the document results (e.g., using two or more document results). The RC metric can be used to determine when retrieved content is to be used by a language model for responding to an input corresponding to the question.

An adaptive random access system and method with learned query optimization for compacted data files that enhances random access performance through machine learning and pattern recognition. The system incorporates a query pattern learning module that analyzes historical access patterns and user behavior to build statistical models of data usage. An adaptive estimator module improves location estimation accuracy by incorporating learned patterns rather than relying solely on mathematical calculations. A predictive boundary detector uses learned codeword patterns to more accurately identify boundaries in compacted data, reducing misalignment errors. An intelligent search engine coordinates optimization strategies including context-aware search string parsing and encoding strategy selection based on learned performance data. A dynamic codebook optimizer reorganizes sourceblock layout based on access frequencies and co-occurrence patterns to improve retrieval speed. An enhanced search cache implements predictive caching algorithms that anticipate user queries and proactively load relevant data.

The invention provides an automatic SEO setting system and method in a station building CMS, and belongs to the technical field of station building CMS. According to the method, the AI technology is utilized, the keywords are deeply mined and screened, the keywords with high conversion potential and the long tail keywords are accurately recognized, the real demands of users are insighted, the competition situation of the keywords is intelligently evaluated, and the popularity change is predicted. In the aspect of content creation, a content outline, a first draft and a specific paragraph can be generated according to a target keyword and a user portrait, optimization suggestions can be provided in the aspects of readability, keyword layout and the like, and content personalized recommendation is achieved. Compared with a traditional SEO technology, the method has the advantages that the keyword screening efficiency and the long tail keyword coverage rate are improved, the matching degree of content and user requirements is optimized, ranking and flow of websites in a search engine are improved, and the method is efficient and accurate.

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.

The invention discloses a knowledge graph-based search engine relation chain recommendation method, which comprises the following steps of: receiving a relation chain recommendation request submitted by a user through a search engine to obtain initial query information; performing semantic analysis and intention recognition on the initial query information, and determining a corresponding entity type and a relationship type; calling a dynamic knowledge selection and evaluation mechanism according to the entity type and the relationship type, and dynamically screening the original knowledge graph to obtain effective knowledge sub-graphs; performing diffusion type reasoning based on a knowledge graph diffusion model to generate a candidate relation chain path set; evaluating the quality score of each candidate path in real time; and performing dynamic screening according to the path quality score, and outputting a relation chain path recommendation result. According to the method, accurate mining and dynamic optimization of the knowledge graph relation chain path are realized, and the accuracy of a recommendation result and the flexibility of user interaction are improved.

An RAG auxiliary generation type search engine method based on multiple rounds of expert and self-adaptive mixed retrieval comprises the steps that a crawler is used for crawling data, the crawled data are processed, a graph knowledge base is constructed through text partitioning and entity relation extraction, the graph knowledge base is coded into semantic vectors, and a vector database is established; by calculating similarity between subject vectors and literature vectors in a vector database, retrieving related literatures, utilizing a large language model to simulate a multi-expert role to generate differentiated viewpoints, and combining multiple rounds of dialogues and user feedback, generating a multi-view fused article to complete multiple rounds of expert answers; designing a double-layer retrieval mechanism according to different nodes and relationships in the retrieval atlas based on the atlas knowledge base; meanwhile, dense retrieval of a vector knowledge base of text coding is combined and utilized, a map-vector collaborative retrieval mechanism is designed, a self-adaptive mechanism and a session memory function are introduced, and multi-round question and answer response is carried out; according to the method and the system, higher logical contents can be generated.

The invention discloses a biochemical experiment automation script training generation method based on RAG and DPO. The method comprises the steps that biochemical experiment technical documents are collected to serve as an external knowledge base; the BM25 and the Fiss are fused to construct a hybrid retriever; generating experimental process description by using a large language model, determining equipment, protocols and materials, and retrieving related documents from a knowledge base on the basis of the experimental process description, the protocols and the materials; generating an experiment script based on a retrieval result, and performing platform simulation verification, and performing iterative optimization on the script which fails in verification; marking successful and failed scripts as preference data pairs, and constructing a training set; carrying out LoRA fine tuning on a local large language model by adopting direct preference optimization; and according to a target and appliance prompt input by a user, generating a verified experiment script by using the large language model after direct preference optimization training. According to the method, the script generation automation level and the passing rate are remarkably improved, the manual intervention cost is reduced, and an efficient solution is provided for automation of biochemical experiments.

The invention relates to the technical field of document processing, and discloses a document knowledge retrieval method and system, electronic equipment and a storage medium. Based on a language model, synchronously generating abstract contents and a semantic association query set for each text fragment, and aggregating the abstract contents of all text fragments corresponding to the same document to form a fragment abstract set; constructing a multi-path retrieval pool based on the text fragment, the abstract content and the semantic association query set, and respectively constructing a plurality of independent retrieval data sources; the method comprises the following steps: constructing a dual-granularity abstract index system, receiving a query request of a user, executing query routing processing based on dual-granularity abstract index and a hierarchical retrieval process based on a metadata structure, returning retrieval result data, performing fusion calculation and duplicate removal processing on the retrieval result data recalled by multiple paths, and obtaining a retrieval result. And meanwhile, the document abstract short sentence group is injected into a search suggestion function of a search engine. According to the method, the retrieval accuracy can be improved, and the user experience is improved.

The invention discloses a unified search and page configuration rendering method and system based on a data model. The method comprises the following steps: S1, data acquisition and model mapping; s2, indexing and synchronizing data; s3, unified search request processing; s4, processing and returning a result set; and S5, dynamically rendering the page. The system comprises a data source layer, a data unification layer, an index layer, an application service layer and a display layer. The invention provides a set of end-to-end integrated solution for solving the problems that multi-source heterogeneous data is difficult to integrate, the cross-system search efficiency is low, page rendering lacks flexibility and the like. According to the scheme, by constructing a standardized data model, a unified search engine index system and a dynamic page rendering mechanism driven by configuration, a complete technical link from data source access to front-end display is achieved, and the efficiency and expansibility of the system in the aspects of data management, information retrieval and page display are remarkably improved.

Disclosed is a method and system for advanced retrieval-augmented generation through metadata and search engine mixing. A retrieval-augmented generation method may include acquiring data of a user; constructing a vector database by embedding the data of the user; receiving and embedding a query of the user; retrieving data corresponding to the embedded query of the user from the vector database using a plurality of retrievers, each retriever of the plurality of retrievers differing from each other; and generating a response to the query of the user by inputting a prompt to an artificial intelligence model, the prompt including the query and the data retrieved using each retriever of the plurality of retrievers.

Systems and methods for machine learning-informed response and augmentation of incomplete queries and query artifacts that include executing parallel search engines in a machine learning pipeline based on an input of a query to retrieve one or more corpora of candidate digital artifacts, constructing a merged corpus of candidate digital artifacts based on the retrieved corpora of digital artifacts, ranking each candidate digital artifact of the merged corpus of candidate digital artifacts based on a computed relevance to the input query, and returning, via a user interface, a response to the input query based on a subset of prioritized candidate digital artifacts most relevant to the input query.

Various embodiments improve search technologies and computer information retrieval by executing a query via ranking a set of search result candidates higher than another set search result candidates based at least in part on the query and determining that a first set of search result candidates are indicative of a sub-accessory to an accessory or an accessory itself.

The invention relates to the technical field of knowledge management, in particular to a construction technology knowledge management method and system based on a knowledge base and a large language model.The method comprises the steps that original construction technology documents are collected and preprocessed to obtain construction technology documents, then the construction technology documents are stored, and a knowledge base of the construction technology field is constructed; based on a natural language processing technology, identifying entities and semantic relationships among the entities from the construction technology document, and constructing a knowledge graph of the construction technology field; generating a construction technology question and answer model based on a pre-trained language model, a knowledge base and a knowledge graph; on the basis of a search engine, querying a construction technology document most related to query content input by a user from a knowledge base; and receiving query content input by a user through the interactive operation page, and displaying a query result or displaying a construction technology question and answer result. According to the invention, intelligent integration, dynamic updating and accurate service of knowledge can be realized.

The invention relates to an e-commerce search service recovery method and device, equipment and a medium, and the method comprises the steps: starting a local search service deployed by a current fault node, and sending a missing data request to a remote search service of any healthy node, the request comprising a latest timestamp of a latest log record in a local transaction log library, the healthy nodes and the fault nodes are disaster recovery nodes; receiving a complete transaction log file which is returned by the remote search service and is generated after the latest timestamp, grouping log records in the complete transaction log file according to the commodity identifier, and determining a target log record with the maximum global version number in each group; constructing the target log record into an incremental transaction log file, writing the incremental transaction log file into a local transaction log library, and replaying the incremental transaction log file into search engine index data; and after the playback is completed, the local search service is registered to the service registration center again. According to the method, the fault node can be quickly recovered, and high consistency of the commodity data among the search service nodes is ensured.

The invention discloses a cross-system data acquisition method based on an AI large model, and relates to the technical field of cross-system integration, and the method comprises the steps: collecting a user behavior track, and generating a multi-modal behavior feature vector; executing the unified instruction set, compiling the SQL query template into a native query statement through a protocol converter, and converting the RAG enhanced retrieval path into an unstructured data component scanning operation to obtain an initial data set; dynamically scheduling the cleaning priority of the initial data set by using an operation time interval sequence in the user behavior track, and generating a time sequence optimization data set; and inputting the time sequence optimization data set into an enhanced retrieval engine, and fusing statistical features, extracted by an AI large model, of the structured data component and semantic fragments, generated by RAG, of the unstructured data component to generate a cross-system association knowledge graph. According to the method, the neural pulse coupled oscillator is used for synchronizing the phases of the behavior characteristics and the natural language instruction, accurate alignment of cross-modal intentions is achieved, and phase difference fluctuation is controlled.

The invention belongs to the technical field of search engines, and particularly relates to an optimized search engine construction method and system based on feature deep learning, and the system comprises a search operation unit, a monitoring and early warning unit and a management and control terminal, the search operation unit comprises a multi-dimensional semantic feature mining module, a user implicit intention analysis module, a deep feature fusion modeling module and a dynamic retrieval strategy generation module; a multi-dimensional semantic feature set and a user intention feature set are formed through a multi-dimensional semantic feature mining module and a user implicit intention analysis module, and a deep feature fusion modeling module performs deep fusion of data features and user intention features through an attention mechanism and cross validation optimization. The dynamic retrieval strategy generation module is used for providing high-quality feature support for retrieval strategy generation, the accuracy of retrieval results is guaranteed, the dynamic retrieval strategy generation module generates dynamic strategies adaptive to different requirements and data states on the basis of reinforcement learning, and the retrieval accuracy and retrieval efficiency of a search engine are remarkably improved.

The invention discloses a self-adaptive K-Means clustering method based on a learning enhancement framework, and belongs to the technical field of search engine clustering. The problem that the query performance obtained based on an existing method is poor due to the fact that the data access frequency is not considered in the existing method is solved. According to the method, initial classification is carried out based on an external predictor, the initialization quality can be improved, the access frequency of data in a search engine serves as a weight, a guiding effect can be provided for candidate centroid selection and dimension sampling, it is ensured that hot spot samples get full attention in initial classification, the clustering effect is ensured, and the clustering efficiency is improved. And searching is performed according to the clustering result, so that the query performance can be improved. In addition, redundancy calculation can be reduced through the design of the jump window, unnecessary data points are skipped, and therefore the clustering speed is increased. According to the method, rapid and accurate K-Means clustering can be realized on each search engine data set, and the method can adapt to various query load modes. The method can be applied to data clustering in a search engine.

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.

The invention provides a scientific research knowledge retrieval method based on RAG and illusion correction, an enhancement system, a storage medium and server equipment, and belongs to the technical field of knowledge retrieval. According to the method, an RAG core module is constructed, and related literatures and knowledge fragments are retrieved from a large-scale scientific research knowledge base through a retrieval engine; generating a natural language answer based on the retrieval information by using a generative model; establishing an illusion correction module, performing authenticity detection on the generated answers by means of a fact checking technology, and correcting illusion contents by means of re-retrieval, generation model input adjustment and the like; and constructing a knowledge updating and optimizing module, regularly updating and optimizing the scientific research knowledge base, introducing the latest scientific research achievements, and optimizing a system model and an algorithm according to user feedback. According to the method, the illusion problem of RAG in scientific research knowledge retrieval is effectively solved, the accuracy and reliability of retrieval results are improved, more credible knowledge retrieval service is provided for scientific research personnel, and development of scientific research work is assisted.