Self-healing generative AI/ML pipeline for generating complex data queries leveraging semantic data model

An apparatus comprising:

• at least one processing device configured to:
• provide a user query to a self-healing multi-agent artificial intelligence/machine learning (AI/ML) pipeline, the user query requesting a response based on data stored in a data topology, the data topology modeled using a semantic data model;
• generate an initial data access query for retrieving the data from the data topology using the AI/ML pipeline and the semantic data model;
• determine that the initial data access query includes a hallucination or error;
• perform an automatic loop one or more times, wherein, to perform the automatic loop, the at least one processing device is configured to:
• generate an updated data access query for retrieving the data from the data topology using the AI/ML pipeline and the semantic data model;
• determine whether the updated data access query includes a hallucination or error;
• and
• if the updated data access query includes a hallucination or error, repeat the automatic loop;
• and
• use a final data access query with no hallucination or error, as generated by the automatic loop, to retrieve the data from the data topology in order to generate the response;
• wherein:
• the self-healing multi-agent AI/ML pipeline comprises a dataspace agent, a class agent, a property agent, a query agent, and a self-healing agent;
• the dataspace agent is configured to select one of multiple dataspaces associated with the user query;
• the class agent is configured to select at least one of multiple classes associated with the selected dataspace, the at least one selected class mapped to the data topology;
• the property agent is configured to select at least one of multiple properties within the at least one selected class;
• and
• the query agent is configured to generate each data access query based on at least one of:
• the at least one selected class and the at least one selected property.

A non-transitory computer readable medium containing instructions that when executed cause at least one processor to:

• provide a user query to a self-healing multi-agent artificial intelligence/machine learning (AI/ML) pipeline, the user query requesting a response based on data stored in a data topology, the data topology modeled using a semantic data model;
• generate an initial data access query for retrieving the data from the data topology using the AI/ML pipeline and the semantic data model;
• determine that the initial data access query includes a hallucination or error;
• perform an automatic loop one or more times, wherein the instructions when executed cause the at least one processor, during the automatic loop, to:
• generate an updated data access query for retrieving the data from the data topology using the AI/ML pipeline and the semantic data model;
• determine whether the updated data access query includes a hallucination or error;
• and
• if the updated data access query includes a hallucination or error, repeat the automatic loop;
• and
• use a final data access query with no hallucination or error, as generated by the automatic loop, to retrieve the data from the data topology in order to generate the response;
• wherein:
• the self-healing multi-agent AI/ML pipeline comprises a dataspace agent, a class agent, a property agent, a query agent, and a self-healing agent;
• the dataspace agent is configured to select one of multiple dataspaces associated with the user query;
• the class agent is configured to select at least one of multiple classes associated with the selected dataspace, the at least one selected class mapped to the data topology;
• the property agent is configured to select at least one of multiple properties within the at least one selected class;
• and
• the query agent is configured to generate each data access query based on at least one of:
• the at least one selected class and the at least one selected property.