Abstract: Methods and apparatus for implementing a framework for managing IT telemetry data through Foreign Data Wrappers (FDWs) such as PostgreSQL FDWs are provided. The framework allows for the integration of diverse data sources from multiple IT platforms, such as AWS, Microsoft Azure, Google Cloud, and VMware, into a unified FDW database. Data is dynamically schematized into virtual tables, allowing Large Language Models (LLMs) to efficiently analyze the telemetry data, generate actionable insights for IT operations, and improve the overall efficiency of IT monitoring and troubleshooting processes.

Abstract: Methods and apparatus for enhancing Large Language Models for IT Operations (Ops) are provided. Logs, alerts, infrastructure configuration, traces, and other telemetry data from a variety of data sources including cloud based IT platforms are selected and dynamically schematized into a database in response to a query by IT personnel. The database is leveraged by a structured LLM to generate insights and responses to queries, enhancing diagnostic and troubleshooting capabilities. The approach aims to streamline data management, improve accuracy, and reduce operational inefficiencies by utilizing dynamic virtual tables and curated data sources while addressing potential challenges such as LLM hallucinations.

Abstract: Methods and apparatus for enhancing Large Language Models for IT Operations (Ops) are provided. Logs, alerts, infrastructure configuration, traces, and other telemetry data from a variety of data sources including cloud based IT platforms are selected and dynamically schematized into a database in response to a query by IT personnel. The database is leveraged by a structured LLM to generate insights and responses to queries, enhancing diagnostic and troubleshooting capabilities. An IT administrator query may be deconstructed into subqueries by the LLM and LLM generated responses to the subqueries lead to generation of a response to the IT administrator query.

Abstract: Methods and apparatus for dynamically updating subqueries for IT Operations (Ops) are provided. When an IT administrator query is received, a system generates multiple subqueries based on the IT administrator query. As responses to initial subqueries are generated, subsequent subqueries are dynamically modified and updated based on the results of previously executed subqueries. This dynamic subquery updating process allows the system to refine its analysis approach in real-time. In various embodiments, an LLM leverages telemetry data from disparate IT platforms including logs, alerts, infrastructure configurations, and traces that are dynamically schematized into a structured database. The iterative subquery modification process enhances diagnostic accuracy and provides for more targeted troubleshooting by continuously adjusting the query strategy based on emerging insights.

Abstract: Techniques and mechanisms are provided for maintaining data privacy in the context of Information Technology Generative Operations (IT GenOps). Enterprise curated data and selected telemetry data from various IT platforms is secured in a dynamically schematized database in response to an IT administrator query. In some examples, Foreign Data Wrappers (FDWs) are used to dynamically schematize select data into structured virtual tables accessible by Large Language Models (LLMs). This allows for advanced analysis and querying without exposing confidential data. Mechanisms to enhance data privacy, reduce operational inefficiencies, and improve the accuracy of insights generated by LLMs are provided, to ensure that enterprise data remains secure while enhancing IT Operations.

Abstract: Methods and apparatus for implementing a framework for managing IT telemetry data through Foreign Data Wrappers (FDWs) such as PostgreSQL FDWs are provided. The framework allows for the integration of diverse data sources from multiple IT platforms, such as AWS, Microsoft Azure, Google Cloud, and VMware, into a unified FDW database. Data is dynamically schematized into virtual tables, allowing Large Language Models (LLMs) to efficiently analyze the telemetry data, generate actionable insights for IT operations, and improve the overall efficiency of IT monitoring and troubleshooting processes.

Abstract: Methods and apparatus for enhancing Large Language Models for IT Operations (Ops) are provided. Logs, alerts, infrastructure configuration, traces, and other telemetry data from a variety of data sources including cloud based IT platforms are selected and dynamically schematized into a database in response to a query by IT personnel. The database is leveraged by a structured LLM to generate insights and responses to queries, enhancing diagnostic and troubleshooting capabilities. The approach aims to streamline data management, improve accuracy, and reduce operational inefficiencies by utilizing dynamic virtual tables and curated data sources while addressing potential challenges such as LLM hallucinations.