1907 results about "Application programming interface" patented technology

The present disclosure includes computer-implemented methods of guardrails for securely using large language models (LLMs). The method comprises monitoring user data flow using an application programming interface (API) and receiving an administrative policy from an administration communication interface. The method involves dynamically applying a plurality of LLM input inspectors to LLM input data. The application of the plurality of LLM input inspectors is based on the administration policy. The dynamic application of the plurality of LLM input inspectors is in sequence for latency optimization. The plurality of LLM input inspectors serve as LLM input guardrails for a plurality of secure deployed large language models (LLMs). The plurality of LLM input inspectors are configured by the administrative policy and validate the LLM input data to validated LLM input data based on the administration policy. Additionally, the method comprises dynamically applying a plurality of LLM output inspectors to LLM output data.

Systems and methods for large language model (LLM)-agnostic data anonymization. Data anonymization includes data obfuscation (and data deobfuscation) to protect confidential information a user is going to send to an LLM service or application programming interface (API). Encryption can be used for data obfuscation and particularly, for securing data from unauthorized access. Likewise, decryption can be used for data de-obfuscation.

A computer-implemented method can receive a natural language query input from a user interface, extract a target entity from the natural language query input, identify a target application programming interface (API) corresponding to the target entity, formulate an API query using the target API, and execute the API query to generate a query output on the user interface. Identifying the target API includes generating a vector representation of the target entity, searching an entity vector database containing vector representations of a plurality of APIs to return one or more candidate APIs whose vector representations match the vector representation of the target entity, and prompting a generative artificial intelligence model to select the target API from the one or more candidate APIs.

Apparatuses, systems, and techniques to perform a neural network. In at least one embodiment, an application programming interface schedules two or more graph nodes to be performed by two or more parallel processing pipelines based, at least in part, on an order of layers in the neural network.

Apparatuses, systems, and techniques to perform an application programming interface (API) to cause dependency type information of one or more user-indicated graph nodes of a software graph to be indicated. In at least one embodiment, one or more dependency types from a graph are indicated.

Embodiments of the present invention provide computer-implemented methods, computer program product, and computer systems. One or more processors analyze user prompts using one or more natural language understanding techniques. One or more processors then enrich the user prompts by integrating contextual data from user interaction history and adapt the enriched user prompts to align with characteristics of Large Language Models (LLMs) and Application Programming Interfaces (API) requirements of the LLMs.

Action and / or process determination and recommendations for Robotic Process Automation (RPA) using semantic action graphs is disclosed. Semantic action graphs are graphs that store individual actions, and potentially graphical elements and / or text associated with the actions, as nodes, as well as the relationships between nodes as edges. Metadata to develop the semantic action graphs may be derived from task mining applications that can monitor the interactions of users with computing systems, workforce intelligence, etc. The semantic action graphs may be for a user, an organization, an industry, product-wide, etc. At their lowest level of granularity, the recommendations may be for mouse clicks, key presses, Application Programming Interface (API) calls, system events, etc. At higher levels of granularity, the recommendations may be for opening an order, creating a lead, approving a work item, etc.

An illustrative method for querying multiple datasets may include generating, based on models each associated with and defining attributes of a different datasets stored in a plurality of data stores, a semantic layer defining relationships between the models and that provides a centralized application programming interface (API) for exposing the datasets by way of a common query language, receiving, by way of the centralized API, a query request for information that depends on data included in multiple datasets included in the plurality of datasets, querying, based on the query request and the relationships between the models defined by the semantic layer, the multiple datasets, and presenting, based on the querying, a query result representative of the information that depends on the data included in the multiple datasets.

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.

An intelligent attack vector analysis and mitigation system incorporates an intelligent process to analyze potential attack vectors from a suspicious attacker. The intelligent attack vector analysis and mitigation system leverages a generative artificial intelligence (AI)-enabled simulation environment to isolate and / or simulate attackers using federated identity and a hypermedia application programming interface (API). The system analyzes actual and / or potential attack vectors by leveraging the generative AI simulation and provides behavioral analysis with a specific focus on federated identity and / or hypermedia API components. As such, the system provides insights into novel attack vectors, vulnerabilities, and effective mitigation strategies that may then be automatically incorporated and / or implemented on the enterprise network by the intelligent attack vector analysis and mitigation system. The process utilizes continuous improvement, adaptation to evolving threats, and a holistic understanding of the system's security posture to improve and enable the enterprise organization's network security system.

The invention discloses a resume analysis method and system based on multiple large language models, and belongs to the technical field of large language models.The resume analysis method and system based on the multiple large language models.The resume analysis method and system based on the multiple large language models comprise the following specific steps that firstly, model parallel analysis is conducted, simultaneously inputting the data into at least two heterogeneous large language models through an application program interface; and each large language model independently performs information extraction and analysis according to the model structure and the training data of the large language model, and outputs a structured data result containing a plurality of preset fields. Through the multi-model parallel analysis and conflict re-judgment mechanism, the misjudgment risk of a single model is effectively reduced, the robustness of the whole system is improved, the resume analysis accuracy is remarkably improved, the model pool is automatically optimized and updated through the dynamic scoring mechanism, and the problem that the model is difficult to select and update is solved.

Various aspects of the disclosure relate to automated testing for application programming interfaces (APIs). A dynamic API validation computing system leverages a generative AI model to dynamically generate a multitude of data sets and rules for use during API validation activities. A federated byzantine agreement mechanism performs the validation of each API using the generated test cases and test data. A generator engine incorporates a generative AI model that may be trained on a large corpus of API metadata and / or data characteristics to predict data patterns and / or validation rules for each of the APIs under test. The generator engine may also predict a structure and format of requests based on the training model inputs. Multiple Test cases for an API created through use of the generative AI model may be distributed and executed on different testing nodes that reach a consensus about whether each API has passed or failed.

The present disclosure provides a system for decentralized handling of application programming interface (API) calls. The system comprises a peer-to-peer network including a plurality of nodes, each node being a user device, a relayer, or a resolver. Each node includes at least one hardware processor and at least one non-transitory memory storing instructions. When executed, the instructions cause the node to receive an encrypted API request from a user device, forward the encrypted API request to at least one resolver node, process the API request to generate an API response, and transmit the API response back to the user device. The peer-to-peer network may comprise a mesh network and utilize a Remote Procedure Call (RPC) architectural style. The system enables decentralized management of API calls without relying on centralized servers.

A network device may receive one or more first user inputs in a security gateway creation user interface (UI) provided by a controller. A network device may query, by the controller, the CSP using Application Programming Interfaces (APIs) to retrieve information about applications within the at least one virtualized network environment including any services deployed within the at least one virtualized network environment. A network device may present a security status user interface that identifies the at least one virtualized network environment applications configured in the CSP account and a respective status indicating whether the at least one virtualized network environment is protected by the security gateway. A network device may receive a second user input within the security status user interface, the second user input is effective to enable protection of the at least one virtualized network environment by the security gateway.

A method and system for action preset based application programming interface (API) call is disclosed. In some embodiments, the method includes receiving a natural language instruction from a user, processing the natural language instruction to extract a set of parameters through a large language model, comparing the extracted parameters against a preset database to find a matching action preset, formatting an API request by combining the extracted parameters with default values obtained from the matching action preset, and sending the formatted API request to an external API endpoint to execute the API request.

Various embodiments include a system. The system comprises processing circuitry. The processing circuitry obtains an Application Programming Interface (API) call that is associated with a Large Language Model (LLM). The processing circuitry generates a feature vector that numerically represents data included in the API call associated with the LLM. The processing circuitry provides the feature vector to a security LLM trained to detect security threats to the LLM. The processing circuitry obtains an output from the security LLM that indicates a security threat to the LLM. The processing circuitry determines a security policy based on the security threat. The processing circuitry provides the security policy to a security proxy that screens the API call.

The present technology relates to a computer-implemented method for enhancing enterprise-wide security when using Artificial intelligence (AI) models. Embodiments involve a JavaScript injection process facilitated by a proxy server. This process does not require manual installation as it is automatically injected during operation. The JavaScript injection process operates by intercepting unsecured AI input and output data provided by users interacting with AI websites. The input and output data are sent to a security Application Programming Interface (API) which applies an enterprise security policy to the data in real-time, thereby transforming unsecured input and output into secure data. Embodiments provide a robust framework for safe interaction with Artificial intelligence models while mitigating the risks associated with the transmission of sensitive information over the internet.

The invention discloses a network application vulnerability automatic detection method and system based on a large language model, and solves the problems of low vulnerability detection efficiency and low accuracy in the prior art. The method comprises the following steps of: S1, searching a path of an application programming interface (API) (Application Program Interface); s2, a big language model vulnerability analysis step; s3, a step of generating a script of a vulnerability PoC (Proof Of Concept: Conceptual Verification); s4, an IDOR (Insecure Direct Object Reference) vulnerability crawler identification step is carried out, and the step S4 is carried out according to the vulnerability crawler identification step and the step S4, the step S4 is carried out according to the vulnerability crawler identification step, and the step S4 is carried out according to the vulnerability crawler identification step. S5, a vulnerability verification step; and S6, a report generation step. According to the method and the system, the whole process from path searching to vulnerability hypothesis generation to automatic vulnerability verification is realized through driving the synergistic effect of multiple modules, the targets of zero false alarm and near-zero manual confirmation are achieved, the efficiency and the accuracy of network application security testing are greatly improved, and powerful support is provided for network security protection.

An apparatus and method for data generation for user engagement are disclosed. The apparatus includes an application programming interface, an interactive user interface, at least a processor and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to receive user data from the interactive user interface, identify first user engagement data as a function of the user data, label the first user engagement data to at least an engagement label, generate action data as a function of the first user engagement data containing the at least an engagement label, trigger the action data using the application programming interface, receive second user engagement data as a function of the triggered action data from the interactive user interface and update the action data as a function of the second user engagement data.

Apparatuses, systems, and techniques to perform an application programming interface (API) to identify processor settings to be used when performing one or more software workloads. As an example, one or more processors comprising one or more circuits perform an API to indicate one or more computing resources to be used by one or more instructions based, at least in part, one or more characteristics of that software workload.

The invention provides a service recommendation decision-making method based on large model retrieval enhancement generation and related equipment. The method comprises the following steps: acquiring an unstructured service demand; calling a large language model to perform semantic analysis and extension on the service demand, and generating an unstructured extended semantic text; encoding the extended semantic text to obtain a semantic query vector; querying a service description of each pre-stored service from a service database, respectively calculating a first similarity score between the semantic query vector and the service description, expanding a second similarity score between each keyword in the semantic text and the service description, and screening out a candidate service set having semantic association with the service demand; all corresponding application programming interfaces in the candidate service set are determined, and a final application programming interface set is screened out and recommended according to the use frequency of the various application programming interfaces, the semantic similarity among the labels and the semantic query vectors. According to the method and the device, the API recommendation accuracy is improved.

The invention provides a hydropower station fusion computing power intelligent server system, and relates to the technical field of carbon emission. The method comprises the following steps: an acquisition correction module acquires hydropower station generation power data and server cluster instantaneous energy consumption data in real time, establishes a time sequence corresponding relation between a power generation side and a power utilization side, and obtains clean energy supply quantity data; the prediction scheduling module generates a non-tampering carbon footprint tracking chain number, and predicts a clean energy supply fluctuation trend sequence by adopting a long short-term memory network algorithm; training a random forest model to predict a computing power demand trend sequence; optimizing task scheduling according to a carbon emission minimization target, and generating dynamic balance configuration data of a computing power task and clean energy supply; and the monitoring evaluation module outputs computing power task level carbon emission and a clean energy use ratio in real time through an application programming interface according to the dynamic balance configuration data, and generates quantitative evaluation data of a computing power service carbon neutralization target by adopting a data visualization technology monitoring technology.

Apparatuses, systems, and techniques to perform an application programming interface (API) to identify processor settings to be used when performing one or more software workloads. As an example, one or more processors comprising one or more circuits perform an API to identify processor settings to be used to configure processors assigned to perform a software workload based, at least in part, on one or more characteristics of that software workload.

A computer system for use with a data center includes a monitoring system configured to receive telemetry data from the data center and to generate alert data in response to a data center fault indicated by the telemetry data; a data center orchestrator coupled to the monitoring system that is configured to manage operation of the data center; and a self-healing engine that operates via an application programming interface (API) configured to receive the alert data, topology data corresponding to a topology of the data center, and user intent data and to select and execute one or more skills in conjunction with the data center orchestrator to correct the data center fault.

The system can receive data, including a specification for a cloud solution, indicating a type of cloud solution and at least one requirement. The system can determine a list of cloud service providers with cloud infrastructure capable of hosting the cloud solution and an ability to satisfy the at least one requirement. The system can rank the cloud service providers based on the capability to host the cloud solution and the ability to satisfy the at least one requirement. The system can select the cloud service provider based on the ranking. The system can generate an implementation procedure based on an Application Programming Interface (API) specification, where the API specification determines a requirement for the cloud solution to be deployed on the cloud infrastructure. The system can deploy, using a large language model (LLM), the cloud solution on the cloud infrastructure in accordance with the implementation procedure.

Embodiments described herein related to systems and methods for generating comments in a page of a content collaboration platform. In some examples, the user interface of the platform is configured to display a page within a content region. The content region includes a comment control that, upon user selection, causes a prompt to be generated and submitted to a generative output engine via an Application Programming Interface (API). The prompt may include a portion of the content selected by a user and user-specific data, such as a user role. This data is used as context to the prompt alongside a predefined query. The output from the generative output engine may be used to generate a set of suggested comments, which are displayed in a comment interface within the content region of the content collaboration panel.

Apparatuses, systems, and techniques to facilitate graph code synchronization between application programming interfaces. In at least one embodiment, one or more circuits are to perform an application programming interface (API) to cause graph code to wait on a semaphore used by another API.

The present disclosure provides a system for decentralized handling of application programming interface (API) calls. The system comprises a peer-to-peer network including a plurality of nodes, each node being a user device, a relayer, or a resolver. Each node includes at least one hardware processor and at least one non-transitory memory storing instructions. When executed, the instructions cause the node to receive an encrypted API request from a user device, forward the encrypted API request to at least one resolver node, process the API request to generate an API response, and transmit the API response back to the user device. The peer-to-peer network may comprise a mesh network and utilize a Remote Procedure Call (RPC) architectural style. The system enables decentralized management of API calls without relying on centralized servers.

Apparatuses, systems, and techniques to perform an application programming interface (API) to identify processor settings to be used when performing one or more software workloads. As an example, one or more processors comprising one or more circuits perform an API to identify processor settings to be used to configure processors assigned to perform a software workload based, at least in part, on one or more characteristics of that software workload.

An example operation may include one or more of receiving at least one natural language input via a software application, determining that the at least one natural language input matches an application programming interface (API) call from among a plurality of API calls configured for prescriptive tasks, identifying at least one parameter value of the API call from the at least one natural language input and transmitting the API call to a prescriptive artificial intelligence (AI) model, executing the prescriptive AI model on the at least one parameter value to generate a natural language response, and displaying the natural language response via a graphical user interface (GUI) of the software application.