Abstract: A computing device receives user interaction with a portion of a data visualization that is displayed on the computing device. In response to receiving the user interaction, the computing device generates a first prompt that includes parameters specifying a visualization type of the data visualization, a data array corresponding to data marks of the data visualization, and a title of the data visualization. The computing device inputs the first prompt into a large language model (LLM), and obtains from the LLM a text narrative for the portion of the data visualization. The device generates a second prompt in accordance with the text narrative, inputs the second prompt into the LLM, and obtains from the LLM a title for the text narrative. The device generates a self-contained story comprising the text narrative and the title.

Abstract: Techniques are disclosed relating to the protection of secrets within a software development lifecycle. Developers can use an encryption service to encrypt a secret to be used by an application within a package. The secret can be associated with the application, and then encrypted and included in a package that is signed and passed through a software automation pipeline to a data center that hosts the production server for the application. The application executing on the production server can request that the secret be decrypted by a decryption service after package verification. A developer can also specify, in a manifest file, a set of secrets needed for applications executing in the same data center. The manifest file may be passed from the software development environment to the data center, where the specified secrets are created and used by the applications without ever residing or being accessible outside the data center.

Abstract: A machine learning model hosted on a cloud platform may be used to proactively predict if a maintenance procedure should be performed for a vehicle. In some examples, to support the prediction, the machine learning model may be connected to a different cloud platform that includes a customer relationship management (CRM) system and receives data from sensors of the vehicle. As such, the cloud platform with the CRM data may transmit the CRM data and the sensor data of the vehicle to the cloud platform hosting the machine learning model to aid in generating the maintenance procedure predictions. Further, the maintenance procedure predictions may also include the generation of a prediction score associated with a maintenance procedure. In some examples, the prediction score may satisfy a prediction score threshold, thus a notification may be transmitted to a computing device that indicates the maintenance procedure to be performed for the vehicle.

Abstract: A database with virtual partitioning. A computer system used to implement the database receives a request for a database operation to be performed on data stored in a portion of the database, where the data has a group of logical partitions (e.g., tenants of a multi-tenant database), and where the portion of the database is not further physically partitioned. The computer system identifies a virtual partitioning scheme for the database operation, the virtual partitioning scheme defining sub-groups of the group of logical partitions. The computer system then performs the database operation for the sub-groups defined by the virtual partitioning scheme. Multiple virtual partitioning schemes can be active at the same time, and virtual partitioning schemes can be created dynamically in some instances.

Abstract: A semantic query processing system enables data analytics through a metadata-driven architecture. The system processes analytical queries through a layered execution path that maintains consistent logic across system components. For incoming semantic queries, the system coordinates processing between semantic and storage layers instead of, or in addition to, requiring pre-materialized calculations. A gateway service receives semantic queries while a query preparer generates execution plans based on semantic model metadata defining relationships, measures, and dimensions. A query generator transforms these plans into optimized SQL operations, with complex calculations handled through post-processing. The system reduces analytical complexity by eliminating pre-calculation requirements, enables real-time metric computation through coordinated query processing, and maintains semantic consistency through metadata-driven execution.

Abstract: A system generates consistent copies of data stored in a source database in a plurality of target database systems. The system sends a change data stream from the source database system to the target database systems for performing data updates. The system receives timestamps of a latest transactions processed by consumer processes updating the target database system. The system determines a pause timestamp that occurs after the received transactions. The updates to the target database systems are paused once a transaction corresponding to the pause timestamp is processed. The pause timestamp is used to generate a consistent copy of a source database system in a target database system using multiple partitioned change data streams, each partitioned change data stream processing transactions associated with a partition of the source database system.

Abstract: Techniques are described for expediting communications between a first person of an organization associated with a communication platform and a second person not associated with the organization. The first person requests for the communication platform to generate an invitation to communicate with the second person. The first person provides the invitation to the second person directly or via the communication platform. Responsive to receiving an indication that the second person accepts the invitation, the communication platform identifies whether the second person is associated with the communication platform. If the second user is associated with the communication platform, the communication platform modifies an existing user interface associated therewith to enable communications between the first person and the second person.

Abstract: Techniques for masking data based on annotations are discussed herein. A system may receive a request to perform an action and leverage an LLM to assist in performing the requested action. When generating the input data to input to the LLM, the system can use a template to organize the input data. The template may include static data and/or slot(s) which can include a reference to data to input into such slots. The system may retrieve data to input to the slot based on the reference, retrieve annotations that define a classification of the data, and receive a policy that defines which types of data classifications are to be masked. Based on the data classification and the policy, the system can determine whether to mask the data. The system can generate the input data using the template, the data, and/or the mask(s) and input such data into the LLM.

Abstract: Techniques are disclosed relating to index metadata that is usable for accessing multi-level data structures. A computer system may operate a database, including maintaining a set of records having a set of corresponding keys. The computer system may create multi-level data structures that facilitate key range lookups against those records. A given multi-level data structure may store key information indicative of a subset of the corresponding keys. The computer system may create separate index metadata that is usable for accessing the multi-level data structures. The index metadata may specify indications of key information that is stored in the multi-level data structures and locations of the multi-level data structures. The computer system may perform a key range lookup that includes using the index metadata to determine a particular set of the multi-level data structures whose key information corresponds to a key range of the key range lookup.

Abstract: Methods, systems, apparatuses, devices, and computer program products are described. A system may obtain a set of documents associated with a knowledge base for retrieval-augmented generation (RAG). The system may generate multiple representations of the information included in the documents using multiple knowledge extraction pipelines. For example, the system may generate a set of metadata-based vector embeddings based on the documents, a set of knowledge graphs based on the documents, and a set of hierarchical tree representations based on the documents. The system may receive a user query and may retrieve contextual information from the set of vector embeddings, the set of knowledge graphs, and the set of hierarchical tree representations to augment the user query for a large language model (LLM) prompt. The system may input the prompt to the LLM, and the LLM may output a response based on the user query and the contextual information.

Abstract: An application server may receive user input indicating a plurality of provisioning parameters for provisioning resources on a cloud substrate. The application server may transmit, to a first artificial intelligence (AI) model, the plurality of provisioning parameters and a request to generate, based on the plurality of provisioning parameters, provisioning code associated with the cloud substrate. The application server may transmit, to one or more second AI models, the provisioning code generated by the first AI model, the plurality of provisioning parameters, and a request to analyze the provisioning code based on the plurality of provisioning parameters. The application server may update respective reputation values associated with the first AI model and the one or more second AI models based on one or more analysis results associated with output of the one or more second AI models.

Abstract: Methods, systems, and devices for data processing are described. A server host may receive a request to access a web domain associated with a tenant of a multi-tenant cloud platform. The server host may retrieve a digital certificate and an encrypted private key associated with the tenant, where the digital certificate includes a public key associated with the tenant. The server host may obtain a symmetric key that is wrapped using an asymmetric public key associated with the server host. The wrapped symmetric key may be provisioned by a symmetric key distribution service. The server host may transmit a request that includes the encrypted private key and the wrapped symmetric key. The server host may receive a response that includes a cryptographic signature associated with the private key of the tenant. The server host may provide the digital certificate and the cryptographic signature to a client device associated with the request.

Abstract: Techniques are disclosed in which a computer system implements schema changes for existing database objects by maintaining a logical name and a current physical name corresponding to an existing database object, where the logical name points to the current physical name. The system receives a schema change request specifying the logical name and format changes for the existing object. The system performs a schema change operation in response to the request, including: creating a new database object having a new physical name, copying and transforming data from the existing object to the new object according to the format changes, mirroring new writes directed to the logical name to both the existing and new objects, and causing the logical name to point to the new physical name instead of the current physical name after completing the copying. The system responds to subsequent queries, specifying the logical name, from the new object.

Abstract: Embodiments described herein provide a multimodal vision-language model. The multimodal vision-language model contains a Generalist Multimodal Transformer capable of complete multiple tasks using the same set of parameters learning from pre-training. The Generalist Multimodal Transformer allows alignment between frozen, unimodal encoders, such as image encoders and large language models. The Generalist Multimodal Transformer eliminates the need for fine-tuning the image encoders and large language models.

Abstract: System and methods are described for generating recommendations from dynamically-mapped data. In one implementation, a database system receives a first request to generate a recommendation objection and a second request to retrieve additional data to include in the recommendation object. The database system retrieves the recommendation data from a first database table. The database system identifies the additional data in a second database table that is stored separately from the first database table. The database system generates the recommendation object to include the recommendation data from the first database, and maps the additional data to one or more fields of the recommendation object.

Abstract: Methods, systems, apparatuses, devices, and computer program products are described. A flow generation service may receive a natural language input that indicates instructions for automating a task according to a first process flow. Using a large language model (LLM), the flow generation service may decompose the natural language input into a set of elements (e.g., logical actions) and connectors, where the LLM may be trained on first metadata corresponding to a second process flow that is created manually by a user. In addition, using the LLM, the flow generation service may generate second metadata corresponding to each of the set of elements based on decomposing the natural language input. The flow generation service may sequence and merge the set of elements to generate the first process flow. In some examples, the flow generation service may send, for display to a user interface of a user device, the first process flow.

Abstract: Techniques are disclosed relating to deploying application containers. A computer system may receive a request to deploy an application container onto resources of a target environment such that program code of the application container is executed. The deployment of the application container may be carried out in a mode in which the computer system does not allocate resources for the application container. The computer system may receive a request to deploy a placeholder container in the target environment. The deployment of the placeholder container may be carried out in a different mode in which the computer system allocates resources for the placeholder container. The computer system may cause an allocation of additional resources to the target environment on which to execute program code of the placeholder container. In response to the additional resources being allocated, the computer system may deploy the application container onto the additional resources for execution.

Abstract: Embodiments described herein provide a framework designed to enable personalized image generation capabilities in a pretrained text-to-image generation model. The architecture comprises two replicas of the pretrained text-to-image model—a reference UNet dedicated to extracting visual features from reference images and a base UNet for the actual image generation process. The reference UNet processes reference images to collect the features before each Self-Attention (SA) layer of the reference UNet. The base UNet's SA layers are modified to “Reference Self-Attention” (RSA) layers that allow conditioning on extra features. Using the collected reference features as input, the base UNet equipped with the RSA layers estimates the noise in the input to guide the image generation towards the reference objects.

Abstract: Methods, storage systems and computer program products create a transactionally consistent backup of a database by generating, during a time period beginning with a first time and ending with a second time, a copy of the database including multiple tables. During the time period, queries that request updates to the tables are periodically generated and conveyed to a software system managing the database, responses including updates to the tables are received responsively to the periodic queries, and the received updates are stored to a journal. A set of the updates not stored in the copy are identified, in the journal, and copy of the database is updated with the updates so as to ensure that the copy includes the transactionally consistent backup of the database.

Abstract: A multi-cloud system permits installation of software by an independent software vendor (ISV) in different manners, such as within a user space of an organization tenant of the multi-cloud system, a space of the ISV within the multi-cloud system, or a space of the multi-cloud system itself. Packaging and installation functionality of the multi-cloud system performs necessary configurations so that an ISV can simply specify how its code is to be made available to users via the multi-cloud system, without itself needing to create different configurations depending on the desired manner of usage.

Abstract: Systems, devices, and techniques are disclosed for a horizontally scalable system for managing container clusters. A monolithic management system may perform a first task on a first level of a cloud computing server system. The monolithic management system may perform a second task on a second level of a cloud computing server system. The monolithic management system may invoke instances of a container manager on a level of the cloud computing server system below the second level. The instances of the container manager may update container clusters of the cloud computing server system. The instances of the container manager may be associated with container clusters and the instances of the container manager update their associated container clusters.

Abstract: Disclosed herein are system, method, and computer program product embodiments for implementing grounding of prompt templates using record snapshot. An embodiment operates by parsing a data record to generate a hierarchical tree graph of context data corresponding to a prompt template selected based on a type of a prompt request, and the data record is identified based on a data-record reference in the prompt request. The embodiment then generates a prompt from the prompt template by grounding the prompt template with a data object comprising the hierarchical tree graph of context data. The embodiment then queries a large language model with the prompt to generate an output specific to the data record.

Abstract: Gradient-free optimization of deep learning models it performed by iteratively improving the text prompt used to instruct the deep learning model to perform a given task. An initial prompt is obtained and used to generate an initial output by the model to be optimized. This output is evaluated by an evaluation model along with reference data, evaluation criteria, etc. The evaluation model's reasoning can be used to generate a new prompt that can be passed back to the deep learning model and used to generate a new model output. This process can continue iteratively until an end condition is met.

Abstract: An agent service can perform effect detection using a convex hull representation of an input. The agent service includes at least one agent large language model (LLM) trained to perform a plurality of tasks. A representation of the input is extracted from an attention layer of the at least one agent LLM. A value space representation is determined based on the extracted representation. A task is identified from the plurality of tasks to be performed by the agent based on the value space and an embedding corresponding to the task.

Abstract: One method of identifying resource intensive queries involves receiving queries from at least one database server into an ingestion pipeline within a computing device, wherein keywords are extracted from the queries at multiple intervals; applying tokenization in the ingestion pipeline to remove uninformative terms; detecting at least one query from the received set of queries that are resource intensive at a fleet level, individual server level, or user level based on the keywords and using an enrichment analysis module, wherein the at least one query detected to be resource intensive is given a drill-down analysis to capture insights from execution profiles; and providing alerts based on the at least one query detected to be resource intensive.

Abstract: Logical implication steering in a machine learning model. In some implementations, a machine learning model with a logical implication circuit installed receives a text-based version of a prompt. The text-based version of the prompt is tokenized in an input embedding layer of the machine learning model to generate a token-based version of the prompt. From the token-based version of the prompt, a token-based version of a response is generated. This generation includes iteratively predictively selecting tokens for the token-based version of the response. The iteratively predictively selecting tokens comprises applying the logical implication circuit that, when a first vector is similar to a representation on an input to a linear mapping operation in the machine learning model, steers the generating toward a second vector. The token-based version of the response is converted to a text-based version of the response and output by the machine learning model.

Abstract: One method of receiving a web page request by a user, identifying a plurality of components to be included on the web page responsive to the web page request based on an initial assessment of the user's preferences, transmitting the web page comprising the plurality of components to the user, inserting a plug-in mechanism in response to the web page to incorporate a plurality of selectable graphical user interface (GUI) elements associated with respective components of the plurality of components of the web page, dynamically determining an updated assessment of the user's preference based at least in part on the user selection and the initial assessment of the user's preferences, and iteratively updating the respective components of the plurality of components of the web page associated with the respective ones of the plurality of selectable GUI elements.

Abstract: Database systems and methods are provided for interacting with external systems using versioning schema of the external systems. One method involves identifying, within code of a component of a graphical user interface (GUI) display, a reference to an external system and an associated version designation, which may utilize a custom versioning schema of the external system that is different from a standard versioning schema at the database system. The method constructs a versioned path for the reference to the external system using the version designation and an integration file associated with the external system, retrieves data from the external system in accordance with the version designation for the reference to the external system using the versioned path, and generates, using the retrieved data according the version designation, a graphical representation of the component within the GUI display of a virtual application provided to a client application at a client device.

Abstract: Database systems and methods are provided for managing relationships between records in a database. An exemplary method involves identifying a virtual container for a first record at the database system based on one or more fields of the first record and updating the virtual container to associate the first record with one or more additional records. In response to an operation with respect to at least one of the records, the method detects a violation of a constraint associated with the virtual container based on a relationship between respective values associated with the respective records, updates a second field of the first record to include an indication of the constraint, and generates a graphical representation of the first record that includes a graphical indication of the first constraint based at least in part on the second field of the first record.

Abstract: A voice-based agent service provides one or more voice agents to respond to voice-based requests. For example, when a question is received by the voice-based agent service it can be matched to a voice-ready knowledge base. The voice-ready knowledge base has been generated offline such that it includes concise answers without language that results in artifacts when converted to voice. These answers are organized in nodes in a conversation graph. The question can be matched to a node and then subsequent nodes can be predicted by a conversation model based on the previously traversed nodes and further user input during a real-time conversation.

Abstract: Techniques are described herein for decreasing the likelihood of out-of-domain LLM responses. In operation, it is determined whether a representation of an input token, generated during an intermediate step of the LLM operations, is in-domain or out-of-domain based on a previously detected sub-space. In addition, the response being generated by the LLM is constrained when it is determined the response being generated is not within the previously detected sub-space.

Abstract: Disclosed herein are apparatus, system and method embodiments for sending an interactive customized push notifications with customized actions. An embodiment operates by determining occurrence of an event associated with an object, forming a notification and a list of actions defined by a customer using fields from the object associated with the event, and presenting the notification and the actions to a user device that's associated to the object. Further, the action is executed in response to the user's selection.

Abstract: A method of data processing is described. The method includes receiving, at a server host, a login request that includes a clear text password for an account. The method further includes retrieving, from a database, a payload stored in association with the account. The payload may indicate a hash value of a user configured password for the account, a hashing algorithm used for generation of the hash value, an initialization vector, and a key agreement public key associated with the account. The payload may be encrypted using a secret key derived from the key agreement public key and a private key provisioned to a key protection component of the server host. The method further includes transmitting a request that includes the payload and the clear text password. The method further includes receiving a response that indicates whether the clear text password corresponds to the user configured password.

Abstract: Techniques are disclosed that pertain to upgrading a database application. A computer system may determine to upgrade a database application from a current version associated with a first instance of a database catalog that defines the structure of a database that is managed by that database application. The first instance is associated with a first catalog signature that is indicative of the first instance of the database catalog. The computer system generates a second catalog signature that is indicative of a second instance of the database catalog that is associated with the different version. The computer system compares the first catalog signature and the second catalog signature to determine whether the database catalog changes between the current and different versions. Based on the comparing, the computer system then selects one of multiple upgrade processes performable to upgrade the database application to the different version and performs the selected upgrade process.

Abstract: In view of the potential misuse of AI to disseminate dangerous knowledge, embodiments described herein provide an AI safety training framework with a data generation pipeline for generating datasets indicative of multiple different severity levels of potentially harmful LLM-generated text to identify and respond to a potentially harmful query, and/or moderate a potentially harmful LLM-generated text. In this way, an LLM moderator may be integrated as a safety layer within an AI system's architecture, acting as a “firewall” to filter responses generated by another AI chat engine.

Abstract: A method for meeting management that includes receiving an indicator of a user access of a meeting digest of a meeting, filtering communications of the user to identify communications received from a participant of the meeting, identifying at least one outstanding request from the participant or at least one unanswered communication from the participant, and generating a task for the meeting digest for the identified at least one outstanding request or the identified at least one unanswered communication.

Abstract: Processor-implemented methods and systems are disclosed for interacting with multiple artificial intelligence (AI) agents. Logging data is generated that captures actions the AI agents take. The logging data includes data inputs, decision processes, and final decisions of the AI agents. Primary data features that guided the final decisions of the AI agents are determined. Control of the AI agents is in response to human-understandable explanations of the AI agents'final decisions.

Abstract: Disclosed herein are system, method, and computer program product aspects for automatically generating recommended actions for a user. During operation, many different data points are examined, including user actions, calendar data, company guidelines, etc. A machine-learning model is used to analyze these data points and determine any number of recommended actions for the users. Such actions may include any known actions to be carried out in the course of the user’s business, such as following up with a customer, drafting an introductory email, submitting expense reports, etc. Once the recommended actions have been identified, they are provided to the user in a chat interface or list interface, where the user can view, select, edit, or authorize such actions. The model can then execute authorized actions as needed.

Abstract: Techniques are described herein for a method of machine learning model compression. The method includes receiving a machine learning model comprising a plurality of blocks. The method further includes removing one or more blocks of the plurality of blocks to obtain an intermediate machine learning model comprising a subset of the plurality of blocks. The method further includes adding a block to the intermediate machine learning model to obtain a compressed machine learning model. The block generates an output corresponding to an output of the removed one or more blocks of the plurality of blocks. The method further includes executing the compressed machine learning model on a low resource device.

Abstract: A contextual processing engine architecture. The architecture utilizes data objects retrieved from a database to form a new transactional item data structure as input into a contextual processing engine. The transactional data structure includes a prior context pointer to point to historical context. The historical context can be null for new transactions or one or more basis transaction item data structures for contextual transactions. The processing engine processes the input using process functions lists and aggregates the results for output.

Abstract: Embodiments described herein provide a two-stage model-agnostic approach for generating counterfactual explanation via counterfactual feature selection and counterfactual feature optimization. Given a query instance, counterfactual feature selection picks a subset of feature columns and values that can potentially change the prediction and then counterfactual feature optimization determines the best feature value for the selected feature as a counterfactual example.

Abstract: Embodiments described herein provide a method of sanitizing a user input. A system receives the user input, and may retrieve one or more documents from a database based on the user input. The system then generates, via a first neural network based language model, a sanitized version of the user input in response to a determination to sanitize based on at least one of the user input or the one or more documents. The system then generates, via a second neural network based language model, an output text based on a prompt, the one or more documents, and the sanitized version of the user input.

Abstract: A system may include a generative language model platform facilitating generation of novel text via a generative language model. The system may include a communication gateway communicating with client systems located outside of the computing services environment and within a first geographic region. The system may include a feedback service configured to write generative language model logging input data to a storage location within the geographic region. The system may include a data service instance configured to store database records determined based on generative language model logging input data retrieved from the storage location.