490 results about "Database retrieval" patented technology

Computer-implemented systems and methods for tokenization. The system includes a network, at least one token database coupled to the network, and a batch tokenization server coupled to the at least one token database via the network, the server having a token table. The server is configured to receive at least one batch request comprising a delta table. In response to the batch request, the server retrieves a plurality of recent tokens from the at least one token database and updates the token table with the plurality of recent tokens. The server determines whether at least one payload to be tokenized from the delta table has a respective at least one corresponding entry in the token table. For each of the at least one corresponding entry that is not found in the token table, the server generates a new token and updates the at least one token database.

A stateful chatbot system leverages generative AI to provide an interface by which users can retrieve information from backend IoT databases of a security provider via natural language queries. Upon receiving a natural language query that corresponds to a request for information from the database, the chatbot generates a corresponding database query having a format compatible with the database. The chatbot comprises a generative model adapted to generate database queries based on natural language queries via prompt engineering using natural language and database query pairs. The chatbot queries the database with the generated database query, retrieves results comprising data / metadata that satisfy the query, and generates a summary of the results, both of which it presents as a response to the user's query. The chatbot also has access to a vulnerability database from which it can obtain information about known vulnerabilities documented therein to respond to user queries.

The invention provides a multi-round dialogue interaction method and system based on context reconstruction and multi-library retrieval, and the method comprises the steps: firstly obtaining a multi-round dialogue data set of a target user, which comprises a plurality of dialogue round sequences composed of user input statements and system response statements; performing context reconstruction processing on the multi-round dialogue data set to generate context reconstruction characteristics of each dialogue round sequence, covering current round semantic focus offset and historical round semantic dependence intensity, and retrieving a matched multi-database retrieval result from a preset heterogeneous database based on a dynamic context window, a candidate response statement set and confidence are included. Performing multi-strategy matching on context reconstruction features and retrieval results, generating a response generation strategy to adjust candidate response semantic priorities and confidence thresholds, and finally feeding back the response generation strategy to a dialogue service system to trigger response optimization operation, and updating heterogeneous database retrieval weights and context window dynamic adjustment parameters.

The present invention proposes a vector database retrieval method and system, which relates to the technical field of big data processing and intelligent information retrieval. The method includes preprocessing high-dimensional multimodal data to obtain a dimension reduction feature vector; constructing a cluster center based on the dimension reduction feature vector; establishing an inverted file and an intra-cluster plane index based on the cluster center, compressing the inverted file and the intra-cluster plane index to obtain a compressed inverted file and an intra-cluster plane index; obtaining a user's query vector, using the inverted file to screen candidate clusters for the query vector, introducing a hierarchical navigable small-world graph in the intra-cluster plane index, performing an intra-cluster search on the cluster cluster, and obtaining a vector database retrieval result. The present invention effectively takes into account the efficiency and accuracy of high-dimensional data retrieval, and improves the retrieval efficiency and accuracy.

The invention discloses a vertical large language model training method and system in the carbon neutralization field, and the method comprises the following steps: collecting data of the carbon neutralization field, carrying out the data preprocessing, constructing a carbon neutralization field knowledge base, and updating the carbon neutralization field knowledge base through a dynamic updating mechanism; performing dynamic semantic partitioning and vectorization coding on the text of the carbon neutralization domain knowledge base, and storing the text into a vector database; performing staged fine tuning on the pre-trained large language model based on a low-rank adaptation technology, wherein the fine tuning comprises general instruction fine tuning and carbon neutralization field professional fine tuning; a retrieval enhancement generation mechanism is adopted, knowledge fragments related to user query are retrieved through a vector database, and a large language model is input to generate answers. Compared with the prior art, the method has the advantages that the answer reliability is improved through conflict detection and source tracing, so that the large language model can more accurately adapt to knowledge requirements in the carbon neutralization field.

A text-to-structured query language conversion method based on a large language model comprises the steps that database outline information is cut, and tables and columns needed by query of a non-target structured query language SQL are eliminated through correlation scoring and Steiner tree problem solving; screening context samples, predicting a similarity score from a text to a structured query language Text-to-SQL by using a cross encoder language model, and selecting a sample most related to a query problem; and injecting the cut database table and column information and the screened context sample into a cue word template, and generating an SQL query statement by using a large language model. The method can be used for optimizing the automatic conversion process from the natural language to the SQL and improving the conversion accuracy, and is used for providing a quick and convenient database retrieval mechanism for a non-SQL user and providing applications such as a data access mode in a wider form for mechanisms such as big language model retrieval enhancement generation RAG and the like.

Embodiments of the present disclosure provide a method and system for generating a document, a computing device, and a medium. The method comprises: receiving a prompt input by a user; obtaining a first keyword from the prompt; using a vector representation of the prompt, retrieving, from a vector index database, enhancement information associated with the prompt and a second keyword associated with the enhancement information; on the basis of comparison of the first keyword and the second keyword, using the enhancement information to generate an enhanced prompt; and providing the enhanced prompt to a language model to generate a document.

The invention provides a medical large model intelligent inquiry reasoning method and system, and the method comprises the steps: obtaining the input information of a user, and carrying out the processing of the input information, including intention recognition, entity recognition and historical information summarization; sequentially performing pre-retrieval, multi-modal database retrieval and knowledge reordering according to the information obtained after processing; generating an inquiry strategy according to the information obtained after knowledge reordering, and performing information retrieval enhancement generation on the medical large model according to the information obtained after knowledge reordering and the inquiry strategy; and generating content according to the generated medical large model. According to the method, reasoning and replying of symptom description and health problems input by the user are achieved through the generative medical large model and multi-modal data retrieval, the method is suitable for intelligent health management and medical consultation in the medical field, and the intelligent level of medical services and the user experience are effectively improved.

The invention discloses an automatic control code generation and verification method and device, equipment and a storage medium, and relates to the technical field of automatic control, the method is applied to a large language model, and a natural language command is received; performing matching retrieval on the vector database according to the natural language command to obtain an example code snippet; obtaining API structured information corresponding to the example code snippets from a knowledge graph database; generating an initial control code based on the example code snippet and the API structured information; and performing multi-stage virtual operation verification on the initial control code in the software motion control system, and generating a target control code according to a multi-stage verification result confirmed by a user for multiple times and the initial control code. According to the method, the initial control code is automatically generated through double-database retrieval based on the large language model, then multi-stage code verification is performed through the software motion control system, the target control code is generated in combination with verification results confirmed by a user for multiple times, and the code generation speed and reliability are improved.

Disclosed herein is a system and method for generating computer code for a plurality of components of a software development project. An artificial intelligence code generator can generate computer code in response to a natural language text input describing a component of the software development project. A first database can store natural language text describing components of the software development project. A second database can store computer code generated at least partially by the artificial intelligence code generator and defining components of the software development project and corresponding to the natural language text stored in the first database. Using a pre-trained language model, the system can generate a natural language summary text based on the code intent of the first component, the identified characteristics of the objects, and the natural language text retrieved from the first database.

The invention discloses an academic paper retrieval method and device based on large language model driving, and a medium, and the method comprises the steps: obtaining a preliminary academic paper retrieval result through retrieval of a plurality of academic paper databases according to an input retrieval request; combining the preliminary academic paper retrieval results, and performing weighted sorting processing according to correlation, popularity and time; inputting the sorted preliminary academic paper retrieval results into a pre-trained first large language model to obtain article abstracts of academic papers and comparative analysis results of similar academic papers; wherein the training process of the first large language model comprises the steps of constructing diversified task samples including abstract generation, question and answer, keyword extraction, logical reasoning, information extraction and literature review generation, and constructing a total loss function so as to train the first large language model; the total loss function is the weighted sum of abstract generation, question and answer, keyword extraction, logical reasoning, information extraction and literature review loss functions.

A system and method for accessing drilling records. A semantic web for drilling records is built, wherein building includes converting drilling records retrieved from a database into a Resource Description Framework (RDF) format and storing the RDF formatted drilling records in the semantic web. Transfer learning is applied to drilling domain knowledge to obtain a large language model (LLM) with drilling domain knowledge. A schema is applied to unstructured records to extract information from unstructured records and the extracted information is then stored in RDF format to the semantic web. The LLM is configured to query the semantic web and to manipulate RDF formatted data within the semantic web.

The invention discloses a document auditing method, device and equipment based on a large model and a storage medium, and relates to the technical field of deep learning, and the method comprises the following steps: obtaining document auditing data from a plurality of preset data sources, and analyzing the document auditing data; the document auditing data comprises an auditing material for representing a document auditing rule, a historical document auditing case and a standard document; constructing a document auditing database based on the analyzed document auditing data, and constructing a large model fine tuning data set based on the document auditing database; performing fine tuning on a preset document auditing large model by utilizing the large model fine tuning data set to obtain a target document auditing large model; and determining a current document auditing task, and auditing a to-be-audited document of the document auditing task by using the target document auditing large model to generate an auditing report. Automation, high efficiency and accuracy of document auditing are achieved through intelligent document analysis, auditing database retrieval and compliance judgment of a fine adjustment large model.

The invention discloses a root cause analysis method and device based on a large language model, equipment and a medium. The method comprises the steps that a target entity and a topological relation are extracted in response to a root cause analysis request; searching matched historical root cause cases in a vector database, inputting the topological relation, the historical root cause cases and cue words into a large language model to generate a doubtful point list, and extracting downstream entities in the doubtful point list; calling a detection tool to collect entity diagnosis data and identify abnormal downstream entities; returning to execute the operation of retrieving the historical root cause case until a preset iteration ending condition is met; and inputting the final doubtful point list, the abnormal diagnosis data, the topological relation and the historical root cause case into the large language model again to obtain a root cause reasoning result and generate a root cause report. According to the embodiment of the invention, through the full-chain design of natural language understanding, topological constraint, historical cases, dynamic detection and iterative reasoning, the fault positioning efficiency and the root cause accuracy are improved, and the operation and maintenance labor cost and the service fault time consumption are remarkably reduced.

The invention relates to the technical field of artificial intelligence, and particularly discloses a policy and regulation intelligent question answering method and system based on a large language model, and the method comprises the steps: segmenting a policy and regulation text into text blocks through a recursive character text segmentation method, and constructing a knowledge base in combination with expert labeling and automatic information extraction; performing semantic embedding on the text block by utilizing a pre-trained Transform model, generating a high-dimensional vector representation, and storing the high-dimensional vector representation in a Chroma database; the user question is converted into an embedded vector, relevant laws and regulations are retrieved through a Chroma database, a large language model and a knowledge fusion attention generation module are combined, and text embedding and knowledge graph information are fused to generate an answer; and the user obtains answers through the intelligent question-answer interaction interface and can view the original texts and explanations of laws and regulations. The system comprises a data acquisition and preprocessing module, a semantic embedding and storage module, a retrieval and generation module and a user interaction module. According to the method, large-size regulation texts are effectively processed, and the retrieval efficiency and the answer quality are improved.

The invention discloses an agent optimization method, device and system based on reinforcement learning, and a storage medium. The method comprises the steps of obtaining an initial task instruction input by a user; performing instruction deepening and expansion on the initial task instruction; optimizing and adjusting a task instruction and a data collection strategy by using a reinforcement learning algorithm; the task instruction is converted into a specific execution plan, and data collection, data cleaning and analysis, API calling, database retrieval and exception handling tasks are executed; evaluating whether the data collected by the intelligent agent is enough to support the analysis requirement of the current task; and generating an industry analysis result based on optimization data obtained by instruction deepening and expansion, and by adopting the technical scheme of the invention, the intelligent agent can adaptively generate, adjust and optimize the task instruction in a complex task environment, so that the intelligence, flexibility and efficiency of task processing are improved.

Disclosed herein is a system and method for generating computer code for a plurality of components of a software development project. An artificial intelligence code generator can generate computer code in response to a natural language text input describing a component of the software development project. A first database can store natural language text describing components of the software development project. A second database can store computer code generated at least partially by the artificial intelligence code generator and defining components of the software development project and corresponding to the natural language text stored in the first database. Using a pre-trained language model, the system can generate a natural language summary text based on the code intent of the first component, the identified characteristics of the objects, and the natural language text retrieved from the first database.

The invention provides a mass spectrum structure analysis method and device, a storage medium and a program product, and relates to the technical field of bioinformatics, and the method comprises the steps: calculating the similarity between each reference mass spectrum in a reference database and a to-be-analyzed target mass spectrum, screening at least one similar reference mass spectrum from the reference mass spectrums according to the calculated similarity; the reference database comprises a plurality of reference mass spectra and corresponding reference molecular structures; according to a preset mass spectrum structure analysis task instruction, the target mass spectrum, the similar reference mass spectrums and the corresponding reference molecular structures, generating prompt information; and inputting the prompt information into the large language model to determine a target molecular structure corresponding to the target mass spectrum. Therefore, a relatively good mass spectrum structure analysis effect is realized by combining the advantages of database retrieval and deep learning by utilizing the few-sample learning capability of the large language model.

The invention discloses a soil organic pollutant identification method and system based on combination of AI and high-throughput screening, and relates to the technical field of pollutant screening, and the method comprises the following steps: extracting a substance peak from high-resolution mass spectrum data of a soil sample; the method comprises the following steps: establishing an organic pollutant mass spectrum database, extracting spectrogram characteristics and structural characteristics of compounds in the organic pollutant mass spectrum database, establishing a molecular fingerprint prediction model, and establishing a mapping relation from a spectrogram to a structure; constructing a spectrogram vector prediction molecular fingerprint according to the extracted substance peaks, and retrieving by using an organic pollutant mass spectrum database to obtain candidate chemical structures; scoring the candidate chemical structures by predicting the molecular fingerprints, selecting the chemical structures meeting a preset standard, constructing a recognition basis according to the selected chemical structures, and obtaining an organic pollutant recognition result of the soil sample. According to the method, non-targeted intelligent analysis is carried out through cross fusion of artificial intelligence and mass spectrometry, and rapid and accurate identification of soil organic pollutants is realized.

The invention belongs to the technical field of data processing. According to the multi-source heterogeneous data multi-mode mixed retrieval method and system based on large model reasoning, in the reasoning process of each step, a large language model determines sub-query questions of different retrieval modes and corresponding sub-query answers according to an initial query question; according to the sub-query question and the sub-query answer corresponding to the query mode of each inference process, adaptively determining an optimal retrieval path by taking the maximum reward function as a target, and determining a final answer corresponding to the initial query question according to the optimal retrieval path; wherein the optimal retrieval path comprises an optimal answer of each retrieval mode and a retrieval sequence of each retrieval mode, and the retrieval modes comprise database retrieval, semantic retrieval and atlas retrieval; according to the method, multi-hop reasoning of complex query is supported while the retrieval efficiency is ensured, dynamic fusion of multiple types of retrieval modes is realized, and the retrieval efficiency and the retrieval precision are greatly improved.

Computerized systems and methods are disclosed for automating Configure to Order (CTO) and Quote to Order (QTO) processes. Methods include receiving user inputs for desired product configurations, retrieving corresponding data from a bill of materials database, and calculating optimized pricing through intelligent rules based on real-time market data. Automated quotes are generated and transferred to orders in a vendor system, selected based on pre-set criteria like vendor reputation and delivery time. Validation steps reduce errors, and real-time reports are generated. The system integrates a Real-Time Data Mesh for data aggregation, a Single Pane of Glass User Interface for user interactions, and Advanced Analytics and Machine Learning Modules for implementing rule-based and learning algorithms. The system is accessible across various devices and standardizes data for uniform consumption, while also employing machine learning models to continually optimize processes. Notifications are sent to users upon successful execution of orders or completion of quotes.

The invention relates to the field of medical information processing and traditional Chinese medicine curative effect evaluation, in particular to a diabetic nephropathy traditional Chinese medicine curative effect evaluation method based on patient report outcome. The method comprises the following steps: searching related symptoms and clinical characterization data of diabetic nephropathy through a medical database, extracting target entries, performing language standardization processing, constructing an initial candidate entry set, calculating the occurrence frequency of each symptom entry, and screening entries meeting a set threshold value to form an entry set; carrying out dimension division on the entry set according to feature attributes to form a preliminary dimension structure set; forming a screening result set through preset logic screening conditions; and carrying out weighted regression fusion on the comprehensive curative effect score and clinical detection data, and outputting a patient curative effect index. In an item weight determination process, dynamic balance between subjective experience and objective statistical information is realized through a mixed weighting model, and the defects of strong subjectivity of expert scoring and large result fluctuation in a traditional method are overcome.

As disclosed herein, a computer-implemented method for travel planning is provided. The computer-implemented method may include receiving, via a conversational user interface (UI), a user input associated with a product offered by a booking application. The computer-implemented method may include retrieving, from a database associated with the booking application, at least one attribute of the product. The computer-implemented method may include determining, using a first machine learning (ML) model of a plurality of ML models, a user intent associated with the user input. The computer-implemented method may include generating, based on the at least one attribute and the user intent, a first response to the user input. The computer-implemented method may include providing, via the conversational UI, the first response to the user input. A system and a non-transitory computer-readable storage medium are also disclosed.

A method and system for verifying a structured query language (SQL) query are provided. The method includes: receiving a first request to retrieve first data that is accessible via a database; identifying an intention of the first request; generating, based on the first request, a first SQL query to retrieve the first data from the database; predicting, based on the generated first SQL query, an output of the generated first SQL query; determining whether the predicted output matches the identified intention of the first request; generating, based on the determining of whether the predicted output matches the identified intention of the first request, a second SQL query to retrieve the first data that is accessible via the database, when the predicted output of the first SQL query does not match the identified intention of the first request.

The invention provides an atmospheric pollution process analysis method, electronic equipment and a storage medium, and relates to the technical field of data processing, and the method comprises the following steps: receiving an atmospheric pollution problem sent by a user, and respectively carrying out vector database retrieval and knowledge graph retrieval based on the atmospheric pollution problem to obtain a first retrieval result and a second retrieval result; wherein the vector database retrieval comprises the following steps: calculating cosine similarity between background information vectors in a vector database and atmospheric pollution problem vectors, and determining multiple pieces of background information of which the cosine similarity accords with a preset threshold value as a first retrieval result; the knowledge graph retrieval comprises the following steps: performing entity recognition and standardization on the atmospheric pollution problem, and generating a target knowledge triple as a second retrieval result; and fusing and inputting the atmospheric pollution problem, the first retrieval result and the second retrieval result into a large language model, and outputting an analysis result of the atmospheric pollution problem. Through the method provided by the invention, the illusion problem of a general large language model in the atmosphere field is relieved.

A disclosed method facilitates translation of natural language queries into query language statements usable to retrieve data from or write data to a particular database. The method includes obtaining a pool of shots. Each shot in the pool includes a natural language query component and a corresponding database translation component. The method further provides for vectorizing the natural language query component for each of the shots into a common vector space; receiving a natural language query from a user interface; vectorizing the natural language query within the common vector space; identifying a subset of vectorized natural language query components that satisfy a similarity metric when compared to the vectorized natural language query; and generating an LLM prompt that includes shots from the pool corresponding to the subset of the vectorized natural language query.

A machine learning based computing method for automatic managing credit risks of first users, is disclosed. The machine learning based computing method includes: receiving inputs from electronic devices associated with second users; retrieving data associated with first users from databases; preprocessing the data to remove noises, outliers, and missing values, from datasets; determining, the credit risks of the entities based on the pre-processed data by machine learning models; generating credit decisions for the entities; generating confidence scores for credit decisions to classify the credit decisions, based on correlation between the data and credit decisions; determining recommended credit values, recommended first credit limits, and recommended second credit limits, based on classification of the credit decisions; and providing an output of the credit decisions, the recommended credit values and the recommended credit limits, to the second users on user interfaces associated with electronic devices.

The embodiment of the invention discloses a medical first draft generation method and device based on a large model and electronic equipment. A specific embodiment of the method comprises the following steps: generating a multi-database retrieval formula in the medical research direction by utilizing a large language model; executing a retrieval formula to obtain a preliminary retrieval literature set and a cross-database causal network; screening and extracting the preliminary retrieval literature set to obtain a first draft data literature set and a clinical heterogeneity factor set; performing dynamic evidence chain fusion to obtain a fusion effect quantity set and a conflict mark set; meanwhile, the heterogeneity judgment script is executed, and a visual chart set is obtained; according to the cross-database causal network and the conflict mark set, executing standardized conflict analysis to obtain a conflict analysis report; and according to the medical first draft template and the conflict analysis report, integrating the literature and the chart by using a large language model and generating a medical first draft. According to the embodiment, the medical first draft is automatically generated by using the large model, the influence of manual intervention is reduced, and the integrity of the medical first draft content is improved.

In embodiments, an apparatus includes a user data interpretation circuit structured to interpret user data corresponding to a first user; a bootstrap circuit structured to: match the first user to a second user via querying one or more databases based at least in part on the user data; retrieve historical time sequence data associated with the second user via querying the one or more databases; extract a time sequence trend from the historical time sequence data; identify a portion of the historical time sequence data corresponding to the extracted time sequence trend; and generate, based at least in part on the identified portion, time sequence data corresponding to the first user; and a time sequence data provisioning circuit structured to transmit the time sequence data.

A stateful chatbot system leverages generative AI to provide an interface by which users can retrieve information from backend IoT databases of a security provider via natural language queries. Upon receiving a natural language query that corresponds to a request for information from the database, the chatbot generates a corresponding database query having a format compatible with the database. The chatbot comprises a generative model adapted to generate database queries based on natural language queries via prompt engineering using natural language and database query pairs. The chatbot queries the database with the generated database query, retrieves results comprising data / metadata that satisfy the query, and generates a summary of the results, both of which it presents as a response to the user's query. The chatbot also has access to a vulnerability database from which it can obtain information about known vulnerabilities documented therein to respond to user queries.