Abstract: Currently, technical expertise is required to construct data objects within integration processes. Disclosed embodiments enable automated generation of data objects using semantic comparisons and artificial intelligence. In particular, data may be converted into reference vector embeddings that define a semantic location of the data within a multi-dimensional vector space. User requests may be converted into input vector embeddings. The input vector embeddings may be semantically compared to the reference vector embeddings to identify semantically similar data, and related data may be identified using a graph database. This semantically similar and related data may then be used to generate a prompt, which may be input to a generative language model to automatically produce a data object for use in an integration process.

Abstract: Currently, technical expertise is required to construct data maps between data sources and targets within integration processes. Disclosed embodiments enable automated generation of data maps using semantic comparisons and artificial intelligence. In particular, data may be converted into reference vector embeddings that define a semantic location of the data within a multi-dimensional vector space. User requests may be converted into input vector embeddings, based on source and target information. The input vector embeddings may be semantically compared to the reference vector embeddings, potentially using a graph database, to identify semantically similar data. This semantically similar data may then be used to generate a prompt, which may be input to a generative language model to automatically produce a data map for use in an integration process.