SELF-HEALING GENERATIVE AI/ML PIPELINE FOR GENERATING COMPLEX DATA QUERIES LEVERAGING SEMANTIC DATA MODEL
개요
발명자
Jaimita Bansal; Pierre De Belen
IPC 분류
CPC 분류
A method includes providing a user query to an AI/ML pipeline. The user query requests a response based on data stored in a data topology, and the data topology is modeled using a semantic data model. The method also includes generating an initial data access query for retrieving the data from the data topology using the AI/ML pipeline and the semantic data model. The method further includes determining that the initial data access query includes a hallucination or error and performing an automatic loop one or more times. The automatic loop includes generating an updated data access query for retrieving the data; determining whether the updated data access query includes a hallucination or error; and, if so, repeating the automatic loop. In addition, the method includes using a final data access query with no hallucination or error to retrieve the data from the data topology in order to generate the response.
원문 (중국어)
A method includes providing a user query to an AI/ML pipeline. The user query requests a response based on data stored in a data topology, and the data topology is modeled using a semantic data model. The method also includes generating an initial data access query for retrieving the data from the data topology using the AI/ML pipeline and the semantic data model. The method further includes determining that the initial data access query includes a hallucination or error and performing an automatic loop one or more times. The automatic loop includes generating an updated data access query for retrieving the data; determining whether the updated data access query includes a hallucination or error; and, if so, repeating the automatic loop. In addition, the method includes using a final data access query with no hallucination or error to retrieve the data from the data topology in order to generate the response.