6337results about "Text database querying" patented technology

Embodiments of the disclosed technologies include generating a search prompt based on an input portion of an online dialog involving a user of a computing device. The search prompt includes a dialog summarization instruction configured to instruct a generative artificial intelligence model to generate and output a dialog summary. The search prompt is sent to a first generative model. In response to the search prompt, a search query is generated and output by the first generative model based on the dialog summary. The search query is sent to a search system. Search result data is determined based on an execution of the search query by the search system. At least some of the search result data is included in an output portion of the online dialog. The output portion is configured to be displayed at the computing device in response to the input portion of the online dialog.

The invention discloses an equipment fault intelligent question-answering method and system based on knowledge graph enhancement. The method comprises the following steps: constructing a knowledge graph of a professional field; developing a natural language processing module; designing an enhanced generation model; designing an interactive question and answer interface; the system comprises a user interaction layer, a language understanding processing layer, a knowledge graph query reasoning layer, a question and answer generation layer and a data storage management layer. Through the natural language processing technology, the system can quickly understand the query intention of a user, and the manual information input and interpretation time in traditional fault diagnosis is shortened, so that the overall fault response speed is increased; the large language model has deep learning and generalization capabilities, various complex and unknown fault conditions can be processed, accurate diagnosis and operation suggestions are provided, and the potential capability of the system for processing new fault types is enhanced.

Disclosed herein are methods and systems for providing individualized responses and recommendations based on a shared knowledge language. A method of receiving a user query for a personalized response associated with a profile; interpreting the query by executing a machine-learning model; generating a data query corresponding to the user query by executing the machine-learning model, the data query configured for execution in a computer model comprising one or more nodes having an identifier corresponding to a series of nouns and verbs generated in accordance with a schema associated with a shared knowledge language; receiving a first node of the computer model, wherein the first node is associated with the profile and generated based at least in part on an application accessed by the profile; presenting the personalized response, wherein the personalized response comprises an indication of the first node of the computer model.

The invention relates to the field of data processing, in particular to a memory retrieval method based on a large language model and a related device. The method comprises the following steps: in response to an interaction instruction sent by a target user, performing context analysis processing on the interaction instruction to obtain a real-time interaction intention of the target user; performing memory retrieval on a short-term memory module and a long-term memory module based on the real-time interaction intention to obtain target retrieval information; and in combination with a personalized knowledge base of the target user, carrying out personalized processing on the target retrieval information to obtain response information matched with the use habit of the target user, and using the response information to respond to the interaction instruction. According to the method, the memory capacity of the large language model in the interaction process is improved, the information forgetting rate is reduced, the information retrieval efficiency is improved, effective classification and organization of personalized information are achieved, and the user experience is improved.

This disclosure describes a system and method, using one or more processors, for grounding large language models using real-time content feeds and reference data. The system is able to receive / generate queries, prompt an AI model and evaluate the answer given by the AI model. A hallucination score is generated according to the evaluation of the answer. According to the hallucination score, the AI model may be iteratively accessed to improve the truthfulness of the answer.

A composite AI system and method for advanced reasoning and automation that integrates symbolic knowledge graphs and algorithms with non-symbolic, or connectionist, models such as neural embeddings. A hierarchical architecture enables dynamically distributed, cooperative reasoning through layperson and expert-led challenge-based verification, model blending, model fitness and retraining and selection, comprehensive feedback loops at individual model or model blend or process flow with or without supervision, and specialized routing of processing to account for various operational risk, regulatory, legal, privacy, or economic considerations. Models, datasets, knowledge bases, simulations and simulation components, and embeddings are iteratively refined using knowledge graph elements and model, process, simulation or flow / process optimal hyperparameters which are recorded and tracked. Extraction of symbolic representations from connectionist models links them to curated ontologies of facts and principles.

The invention provides a commodity information processing and querying method and system. Belongs to the technical field of information processing. The method comprises the steps of collecting text information of commodities, preprocessing the text information of the commodities through natural language processing information, and constructing a structured commodity text database; based on the high-definition image of the commodity, key features of the commodity are extracted through machine vision and are associated with information in the text database to form multi-modal commodity information representation; the method comprises the following steps: encoding a commodity text by adopting a BERT model based on Transform, and obtaining a deep semantic representation of the text; performing feature extraction on the commodity image by using a pre-trained convolutional neural network to obtain an advanced visual feature of the image; through natural language processing and machine vision technologies, the text information and the image information of the commodity are subjected to structured and multi-modal fusion, so that the processing efficiency of the commodity information is greatly improved.

Systems and methods are provided for a natural language question answering service to provide answers to natural language questions regarding network-based services or computing domains. The natural language question answering service may receive the natural language question from a customer computing device. An aggregator of the natural language question answering service can retrieve passages from search systems based on the question and generate a prompt. A large language model (LLM) of the natural language question answering service may receive the prompt and provide an answer. The answer may be verified by a verifier of the natural language question answering service. Attribution may be applied to the answers and retrieved passages to produce references, inline citations, and similar questions. A watermarking module of the natural language question answering service may watermark the answer if it is verified.

The invention provides a plasticizing product recommendation method and system based on user demands, and relates to the technical field of plasticizing product recommendation, and the method comprises the steps: S1, constructing a plasticizing industry knowledge graph; s2, acquiring user historical data and current user requirements, including historical search data, historical browsing data, historical interaction data and historical transaction data; s3, preprocessing the historical data of the user, analyzing the preprocessed historical data of the user, and extracting historical demand features; s4, performing keyword extraction on the current user demand by utilizing the semantic understanding model, and generating an optimized query condition in combination with the chemical and plastic industry knowledge graph; s5, searching in a plasticizing product database by utilizing the optimized query condition to obtain a first list; and S6, carrying out personalized sorting on the first list by utilizing the chemical and plastic industry knowledge graph, generating a recommendation list from the first k lists, and presenting the recommendation list to the user. According to the invention, the relevance and accuracy of recommendation can be improved, so that the user experience is improved.

A system, such as an online system, allows users to ask natural language questions requesting information stored in a database. The system receives a natural language question. The system determines database tables and database queries associated with the natural language question. The system generates a prompt for input to a machine learned language model. The prompt specifies the natural language question, information describing database tables, and the example database queries. The system sends the prompt to the machine learned language model for execution and receives a response generated by the machine learned language model. The response includes a database query corresponding to the natural language question. The system sends the database query for execution on a database system and provides the result of execution of the database query to the client device.

The invention relates to the field of knowledge maps and intelligent questioning and answering, in particular to a domain intelligent questioning and answering system and method based on a knowledge map library and a text vector library. Firstly, a user stores unstructured texts and structured knowledge which are collected and arranged in a certain domain into the text vector library and a knowledge map respectively; the system adopts a customized knowledge retrieval algorithm to perform parallel retrieval on the text vector library and the knowledge graph so as to identify and extract related knowledge points. And based on the retrieved knowledge points, the system generates a question-answer reply of a natural language by using a large language model, so that a knowledge-driven question-answer process is realized. The domain questions and answers based on the knowledge graph library and the text vector library improve the accuracy and integrity of system answers, the illusion of a large language model is avoided, the answers of the system are traceable, and the credibility of the system is realized.

An example operation may include one or more of training a generative artificial intelligence (GenAI) model based on architecture diagrams of a software architecture and descriptions of the architecture diagrams, displaying one or more prompts on a user interface, receiving one or more natural language responses associated with the software architecture in response to the one or more prompts, generating a text-based response to the natural language query submitted via the user interface based on execution of the GenAI model on the one or more prompts and the one or more natural language responses associated with the software architecture, and displaying the text-based response via the user interface.

Embodiments described herein relate to systems and methods for automatically generating content for a generative answer interface of a collaboration platform. The system receives a natural language user input identifies corresponding blocks of text using an content extraction service. A prompt is generated using the blocks of text and used to obtain a generative response. The generative response and links to corresponding content are displayed in the generative answer interface and can be inserted into content of the collaboration platform. The systems and methods described use a network architecture that includes a prompt generation service and a set of one or more purpose-configured large language model instances (LLMs) and / or other trained classifiers or natural language processors used to provide generative responses for content collaboration platforms.

Systems and methods are provided for a natural language question answering service to provide answers to natural language questions regarding network-based services or computing domains. The natural language question answering service may receive the natural language question from a customer computing device. An aggregator of the natural language question answering service can retrieve passages from search systems based on the question and generate a prompt. A large language model (LLM) of the natural language question answering service may receive the prompt and provide an answer. The answer may be verified by a verifier of the natural language question answering service. Attribution may be applied to the answers and retrieved passages to produce references, inline citations, and similar questions. A watermarking module of the natural language question answering service may watermark the answer if it is verified.

The invention discloses an intention recognition intelligent dialogue method and system based on a large model, relates to the technical field of dialogue processing, and is used for solving the problem that multiple conflict intentions in multiple rounds of dialogues cannot be efficiently recognized and processed in the prior art. The method comprises the following steps: performing semantic analysis on input data in multiple rounds of conversations by utilizing a pre-trained large-scale language model, and extracting a plurality of initial intentions of a user; establishing an intention conflict detection mechanism based on a predefined intention conflict rule base, analyzing the extracted preliminary intention, and screening to obtain a logic conflict intention combination; the opposition degree of the logic conflict intention combination in logic mutual exclusion and fuzzy overlapping is evaluated through a semantic graph and a graph neural network, the dynamic relation entropy of the logic conflict intention combination is analyzed, the fluctuation change degree of semantic dependency in multiple rounds of dialogues is evaluated, and finally the processing sequence of the logic conflict intention combination is dynamically adjusted. And the intention conflict processing accuracy and the priority distribution reasonability are improved.

Systems and methods are provided for a natural language question answering service to provide answers to natural language questions regarding network-based services or computing domains. The natural language question answering service may receive the natural language question from a customer computing device. An aggregator of the natural language question answering service can retrieve passages from search systems based on the question and generate a prompt. A large language model (LLM) of the natural language question answering service may receive the prompt and provide an answer. The answer may be verified by a verifier of the natural language question answering service. Attribution may be applied to the answers and retrieved passages to produce references, inline citations, and similar questions. A watermarking module of the natural language question answering service may watermark the answer if it is verified.

Techniques are disclosed herein for contextual query rewriting. The techniques include inputting a first user utterance and a conversation history to a first language model. The first language model identifies an ambiguity in the first user utterance and one or more terms in the conversation history to resolve the ambiguity, modifies the first user utterance to include the one or more terms identified to resolve the ambiguity to generate a modified utterance, and outputs the modified utterance. The computing system provides the modified utterance as input to a second language model. The second language model performs a natural language processing task based on the input modified utterance and outputs a result. The computing system outputs a response to the first user utterance based on the result.

The disclosed embodiments include systems and methods configured to provide a Generative AI framework that uses the power of multiple LLMs by separating the generative aspect into multiple distinct large language models. In some disclosed embodiments, a first large language model evaluates an input prompt and transforms it if needed (e.g., in a first processing stage of the framework); a second large language model performs a generative function based on an input prompt it receives from the first large language model (e.g., in a second processing stage); and a third large language model analyzes and as necessary transforms the output of the second large language model to ensure accuracy, no hallucinations, and no harmful content in the final generated response to the input prompt (e.g., in a third processing stage).

This disclosure relates to methods, systems, and devices for AI / ML assisted management of a radio access network (RAN). In particular, an LLM is employed to generate answer to user queries, for example in the form of code that can be executed upon streaming data from the RAN, including performance management and control management databases. To provide prompts the LLM can understand, an LLM agent employs a knowledge base and experience base to transform the language of the queries from the highly technical telecommunications domain to a general domain. Frequently asked questions and their answers may be stored in the experience base and used instead of using the LLM.

Enumerated source text passages may be determined based on one or more source text documents. The enumerated source text passages may include source text passage identifiers uniquely identifying the passages. A novel text passage including novel text portions may be determined based on a query and the enumerated source text passages. One or more of the novel text portions may be verified by a large language model to produce text verification information. A novel text generation message including novel text generated by the large language model may be determined based on the text verification information and sent to a client machine.

Provided in the embodiments of the present disclosure are a question answering processing method and system, a device and a storage medium. The method comprises: on the basis of a user question, a first prompt template containing first tool set information, and an LLM, a first agent acquires, successively generated by the LLM, a plurality of pieces of solving task information that are required to be executed by invoking a second agent and summary task information that is required to be executed by the first agent, and successively transmits the plurality of pieces of solving task information to the second agent; on the basis of received current solving task information, a second prompt template containing second tool set information, and the LLM, the second agent acquires task execution information that is generated by the LLM and corresponds to the current solving task information, and accordingly acquires from an application server a task execution result of the current solving task information; and the first agent summarizes the task execution results of the plurality of pieces of solving task information, so as to determine reply information corresponding to the user question. By means of the cooperation of different agents, user questions are gradually and accurately solved.

Systems and methods for enriching raw user text with a database to identify relevant context, wherein generated prompts provide responses to user queries is provided. A method includes receiving a query, wherein the query comprises the raw text, creating a first embedding based on the query, retrieving a plurality of other embeddings, wherein the plurality of other embeddings are complementary to the first embedding, creating a contextual prompt including context from at least one of the plurality of other embeddings, processing the contextual prompt using a trained machine learning model, thereby generating a response to the query, and causing the response to be displayed by a display device.

The invention relates to an AI Agent agent based on a big language model collaborative knowledge graph, which comprises a natural language dialogue module, a big language model module, an RPA SOP process knowledge graph module and an RPA execution engine, and is characterized in that the natural language dialogue module receives a natural language and converts the natural language into a structured query statement; the big language model module interacts with the natural language dialogue module and the RPA SOP process knowledge graph module respectively, and the big language model module understands user requirements, retrieves a knowledge graph according to query statements and generates an RPA process; the RPA SOP process knowledge graph module stores standard SOP knowledge of vertical business of a specific industry, and provides professional knowledge and standard constraints for knowledge query and reasoning; the RPA execution engine executes the generated RPA process; the invention further comprises an agent method and a storage medium. The RPA process knowledge graph is utilized to enhance the knowledge and reasoning ability of the large language model, RPA process steps meeting requirements are generated, and the RPA process generation accuracy and efficiency are improved.

A system and method are disclosed for reducing false responses from a large language model. The method includes: mapping a data field from a first source to a semantic layer, the semantic layer including a plurality of data fields; storing data from the first source in a database based on the semantic layer; tokenizing each data field for a first large language model (LLM); fine-tuning the first LLM based on the tokenized semantic layer; providing a prompt to the first LLM, which configures it to generate an output answer; providing the output answer to a second LLM, which configures it to generate a query for the database; executing the query on the database to generate a database output based on the stored data; and providing the output answer in a user interface (UI) in response to determining that the database output and the output answer are within a predefined threshold.

Methods, computer systems, computer-storage media, and graphical user interfaces are provided for providing comprehensive search results. In embodiments a content item having text is obtained. Thereafter, via a text embedding model, semantic search data in association with the content item is generated. Lexical search data in association with the content item is also generated. The semantic search data and the lexical search data are stored in association with the content item for subsequently performing a semantic search using the sematic search data and a lexical search using the lexical search data to determine that the content item is relevant to a search query.

The invention relates to the technical field of archive information query, and discloses an AI-based archive information resource intelligent sharing method and system, and the method comprises the steps: firstly constructing a multi-dimensional data module according to a business data type, and the multi-dimensional data module comprises an archive system database, a high-dimensional document vector database and an archive business knowledge graph; and performing deep analysis on user query through a natural language processing tool to form a structured keyword list. Then, performing semantic analysis on the keyword list by utilizing a pre-trained archive information resource understanding large model, and generating an enhanced query text; the query text is recalled from the data modules of the multiple dimensions, candidate answers are obtained, and the candidate answers are fused and optimized through the general large language model. And finally, returning the optimal answer to the user. By introducing the AI technology, the accuracy and comprehensiveness of file information resource sharing are improved, the retrieval result is optimized, and the retrieval efficiency is improved.

The present disclosure relates to systems and methods for automatic generation of summaries for record objects using one or more large language models. The system can identify a plurality of electronic activities matched to a record object of a customer relationship management system. The system can generate one or more text strings from the plurality of electronic activities using the large language models. The system can generate a first set of topics by inputting a first set of text strings into the large language models. The system can generate a second set of topics by inputting a first subset of text strings of the first set of text strings into the large language models. The system can transmit one or more topics to a computing device for presentation.

The invention provides a data security analysis method and an intelligent calculation data security workstation, and the method comprises the steps that the intelligent calculation data security workstation obtains data of different data sources, and the data comprises a user operation log and a data access and transmission log; classifying and grading the data based on a preset rule base and a machine learning model, and marking sensitive data; analyzing a user behavior mode based on the user operation log, and identifying an abnormal behavior; and based on the data access and transmission log, evaluating a data leakage risk, and generating a risk report.

Systems and methods are described for a managed multidimensional search based on an application query and management policies. The application can receive a pipeline endpoint. The query can be sent to the pipeline endpoint. The pipeline can vectorize the query for comparison against a vector database of an identified dataset. The closest vectors can be converted back to content chunks. The system can generate prompts related to the content chunks and send those prompts to an AI model. The AI model can then output a response that includes the most relevant content, citations, and hyperlinks. These can be displayed in the application.

Certain aspects of the disclosure provide techniques for information retrieval for large datasets. A method comprises receiving input text files and a query for the files; obtaining an index associated with the input text files to process the query, wherein: the index comprises key-value mappings, each key of a respective mapping identifying a voronoi cell of the index, and each value of a respective mapping identifying vector embeddings associated with text files associated with a voronoi cell of the index; creating a query embedding based on the query; identifying a first key-value mapping having a first key associated with a first voronoi cell in the index and corresponding to the query embedding; obtaining a set of vector embeddings associated with the first value; comparing the query embedding to the set of vector embeddings to determine closest vector embeddings; and generating a textual output based on the closest vector embeddings.