Systems and methods for determining historically similar incidents using multivariate embeddings

A system for finding historically similar incidents in a system, the system comprising:

• a memory having processor-readable instructions stored therein;
• and
• at least one processor configured to access the memory and execute the processor-readable instructions to perform operations including:
• receiving a plurality of historical data objects corresponding to a plurality of previous incidents, each of the plurality of historical data objects indicating an occurrence of a previous incident and including previous incident multivariate information;
• processing the plurality of historical data objects;
• converting the plurality of processed historical data objects to a plurality of historical embeddings by utilizing a large language model;
• storing the plurality of historical embeddings in an index, the index being a vector embedding database;
• receiving a data object indicating an occurrence of a current incident associated with a configurable item, the data object including multivariate information, the multivariate information including multiple data types, the data types including integers, free field text, and categories/subcategories;
• processing the data object;
• converting the data object into a first embedding by utilizing the large language model;
• identifying a set of historically similar incidents by applying a Euclidean distance formula to the first embedding and the plurality of historical embeddings;
• and
• determining a similarity score for each of the set of historically similar incidents based on the application of the Euclidean distance formula.

A non-transitory computer readable medium storing processor-readable instructions which, when executed by at least one processor, cause the at least one processor to perform operations including:

• receiving a plurality of historical data objects corresponding to a plurality of previous incidents, each of the plurality of historical data objects indicating an occurrence of a previous incident and including previous incident multivariate information;
• processing the plurality of historical data objects;
• converting the plurality of processed historical data objects to a plurality of historical embeddings by utilizing a large language model;
• storing the plurality of historical embeddings in an index, the index being a vector embedding database;
• receiving a data object indicating an occurrence of a current incident associated with a configurable item, the data object including multivariate information, the multivariate information including multiple data types, the data types including integers, free field text, and categories/subcategories;
• processing the data object;
• converting the data object into a first embedding by utilizing the large language model;
• identifying a set of historically similar incidents by applying a Euclidean distance formula to the first embedding and the plurality of historical embeddings;
• and
• determining a similarity score for each of the set of historically similar incidents based on the application of the Euclidean distance formula.