Abstract: Methods, systems, apparatuses, devices, and computer program products are described. A processing device may receive a natural language query asking a question about a data metric. The processing device may use a large language model (LLM) to generate a summary of the natural language query for vector embedding. The processing device may determine one or more query response portions indicating possible answers to the query based on the summary and a vector database including vector representations of data summaries. To expand the scope of the answers, the processing device may recursively expand a set of data metrics for analysis. For example, the processing device may determine additional data metrics adjacent to the data metric of the query and may search the vector database for additional query response portions based on the additional data metrics. The processing device may use the query response portions to answer the natural language query.

Abstract: Methods, systems, apparatuses, devices, and computer program products are described. An application server or a data processing system may convert a set of metadata associated with a data object (e.g., document, record, asset) from a first structured format into a second serialized format. The set of metadata in the second serialized (e.g., unstructured) format may be input in a large language model (LLM). The LLM may generate a first natural language summary associated with the data object based on the set of metadata. After receiving a natural language query from a user, the LLM may generate a second natural language summary associated with the data object based on the natural language query. The natural language summaries may be vectorized, and the vectorized versions may be compared. Based on the comparison, an indication of the data object corresponding to the natural language query may be displayed.

Abstract: Methods, systems, apparatuses, devices, and computer program products are described. An application server or a data processing system may generate a set of candidate natural language queries that correspond to a data object (e.g., document, report, assert) based on inputting a set of metadata associated with the data object into a large language model (LLM). The system may embed the candidate natural language queries into a first set of vectors, where a query space may include a collection of the first set of vectors related to the data object. In addition, the system may embed a natural language query received from a user into a second vector. The system may perform a vector-space comparison of the second vector to the first set of vectors or the query space, and retrieve a data object associated with the natural language query based on the comparison.

Abstract: Technologies are provided for synchronizing and joining multiple calls from a first thread and one or more second threads to execute a particular code segment. In response to a call from the first thread to execute the particular code segment, a task synchronizer module calls the particular code segment on behalf of the first thread. When execution of the particular code segment starts, and a call is received from a second thread to execute the particular code segment after receiving the call from the first thread, the task synchronizer module suspends execution of the particular code segment so that the second thread awaits for the execution of the particular code segment on behalf of the first thread to complete. When execution completes, a result value or exception is stored in a task object, and the second thread can read the result value or the exception.

Abstract: Technologies are provided for synchronizing and joining multiple calls from a first thread and one or more second threads to execute a particular code segment. In response to a call from the first thread to execute the particular code segment, a task synchronizer module calls the particular code segment on behalf of the first thread. When execution of the particular code segment starts, and a call is received from a second thread to execute the particular code segment after receiving the call from the first thread, the task synchronizer module suspends execution of the particular code segment in response to the call from the second thread so that the second thread awaits for the execution of the particular code segment on behalf of the first thread to complete. When execution of the particular code segment completes, a result value or exception is stored in a task object, and the second thread can read the result value or the exception that is stored in the task object.