Self-healing generative ai/ml pipeline for generating complex data queries leveraging semantic data model

Patent metadata

Field	Value
Publication number	US-2026003879-A1
Application number	US-202519067566-A
Country	US
Kind code	A1
Filing date	Feb 28, 2025
Priority date	Jun 28, 2024
Publication date	Jan 1, 2026
Grant date	—

Abstract

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.

First claim

What is claimed is:

1 . A method comprising:
providing 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; generating an initial data access query for retrieving the data from the data topology using the AI/ML pipeline and the semantic data model; determining that the initial data access query includes a hallucination or error; performing an automatic loop one or more times, wherein the automatic loop includes:
generating an updated data access query for retrieving the data from the data topology using the AI/ML pipeline and the semantic data model;
determining whether the updated data access query includes a hallucination or error; and
if the updated data access query includes a hallucination or error, repeating the automatic loop; and
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.

2 . The method of claim 1 , wherein:
the semantic data model represents the data topology and identifies dataspaces, classes, and properties associated with the data topology; agents of the AI/ML pipeline use the semantic data model to identify a specific dataspace, one or more specific classes, and one or more specific properties associated with the user query; and each data access query is generated based on the specific dataspace, the one or more specific classes, and the one or more specific properties.

3 . The method of claim 2 , wherein:
the semantic data model provides context to the agents of the AI/ML pipeline; the agents comprise one or more AI/ML models that generate responses when prompted by the agents; and the responses from the one or more AI/ML models identify the specific dataspace, the one or more specific classes, the one or more specific properties, and the data access queries.

4 . The method of claim 2 , wherein:
the data topology includes tabular data; and the semantic data model allows the AI/ML pipeline to understand columns of data in the tabular data.

5 . The method of claim 1 , wherein the semantic data model models the data topology using multiple classes and associated properties that are semantically aligned with natural language on which the AI/ML pipeline is trained.

6 . The method of claim 5 , wherein at least some of the classes are associated with multiple associations in the semantic data model.

7 . The method of claim 1 , wherein:
the AI/ML pipeline comprises a dataspace agent, a class agent, a property agent, a query agent, and a self-healing agent; the dataspace agent identifies one of multiple dataspaces associated with the user query; the class agent identifies 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 identifies at least one of multiple properties within the at least one selected class; and the query agent generates each data access query based on at least one of: the at least one selected class and the at least one selected property.

8 . The method of claim 7 , wherein the self-healing agent determines, for each data access query, whether:
a syntax of the data access query has one or more errors; at least one property in the data access query exists; one or more values in the data access query are proper; and a data type of a value in the data access query matches an expected data type.

9 . The method of claim 1 , wherein at least one of the data access queries is based on one or more of:
filtering of at least one of classes and properties defined in the semantic data model based on the user query; and joining of at least one of classes and properties defined at multiple levels in the semantic data model based on the user query.

10 . 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 to retrieve the data from the data topology in order to generate the response.

11 . The apparatus of claim 10 , wherein:
the semantic data model represents the data topology and identifies dataspaces, classes, and properties associated with the data topology; agents of the AI/ML pipeline are configured to use the semantic data model to identify a specific dataspace, one or more specific classes, and one or more specific properties associated with the user query; and the AI/ML pipeline is configured to generate each data access query based on the specific dataspace, the one or more specific classes, and the one or more specific properties.

12 . The apparatus of claim 11 , wherein:
the semantic data model provides context to the agents of the AI/ML pipeline; the agents comprise one or more AI/ML models configured to generate responses when prompted by the agents; and the responses from the one or more AI/ML models identify the specific dataspace, the one or more specific classes, the one or more specific properties, and the data access queries.

13 . The apparatus of claim 10 , wherein the semantic data model models the data topology using multiple classes and associated properties that are semantically aligned with natural language on which the AI/ML pipeline is trained.

14 . The apparatus of claim 13 , wherein at least some of the classes are associated with multiple associations in the semantic data model.

15 . The apparatus of claim 10 , wherein:
the 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 identify one of multiple dataspaces associated with the user query; the class agent is configured to identify 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 identify 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.

16 . The apparatus of claim 15 , wherein the self-healing agent is configured to determine, for each data access query, whether:
a syntax of the data access query has one or more errors; at least one property in the data access query exists; one or more values in the data access query are proper; and a data type of a value in the data access query matches an expected data type.

Assignees

• Goldman Sachs & Co Llc

Inventors

• Bansal Jaimita
• De Belen Pierre

Classifications

• G06F16/2471 Primary

  Distributed queries · CPC title