Semantic Query Pipeline With Self-Healing Error Validation
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Solution Overview
Problem
Generative AI/ML models struggle with hallucinations and context length limitations, failing to understand and process relational data effectively, and users unfamiliar with data storage schemas cannot create SQL queries in a simple manner.
Innovation Solution
A self-healing multi-agent AI/ML pipeline leverages a semantic data model to generate and validate data access queries, using a semantic data model to validate data access queries, and validate data access queries, and validate data access queries, and validate data access queries, and validate data access queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If generative AI/ML models are used to respond to user queries from large text corpus, then the models can provide responses based on statistical similarity and retrieval-augmented generation, but the models suffer from hallucinations where they make up incorrect responses
Solution Approach 1:
The patent introduces an intermediary verification layer between the generative AI model and the final response. This layer includes fact-checking mechanisms, confidence score thresholds, and validation processes that mediate the output of the generative model before presenting it to users, thereby reducing hallucinations while maintaining response generation capability
Solution Approach 2:
The system implements feedback loops where model outputs are continuously evaluated against ground truth data, user corrections, and confidence metrics. This feedback mechanism allows the system to learn from errors and improve response accuracy over time while maintaining high productivity
2Productivity
If SQL queries are created to fetch relational data, then the queries can retrieve data from databases, but the queries are limited to users who know the physical schema of the data
Solution Approach 1:
The patent introduces a natural language processing intermediary layer that sits between the user and the SQL query execution. This intermediary automatically translates user-friendly natural language queries into appropriate SQL queries, eliminating the need for users to understand physical database schemas while maintaining full data retrieval capability
Solution Approach 2:
The system creates an abstract logical schema representation that copies the essential structure and relationships of the physical database schema in a user-friendly format. Users interact with this simplified schema copy rather than the complex physical schema, making query creation accessible to non-technical users
3Productivity
If AI/ML models use a limited amount of contextual information due to context length limitations, then the models can process queries within their capacity, but the models fail to satisfactorily generate responses for complex queries requiring extensive context
Solution Approach 1:
The patent segments the context into multiple manageable chunks or blocks that fit within the model's context window. The system processes these segments separately and then synthesizes the results, allowing the model to handle complex queries with extensive context by dividing the information into processable units without losing important details
Solution Approach 2:
The system transitions from a single-dimensional context approach to a multi-dimensional approach by organizing context in hierarchical layers (e.g., immediate context, related context, background context). This dimensional organization allows the model to access relevant information at different levels of detail, effectively overcoming context length limitations while maintaining comprehensive understanding
Data Source
AI summary
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.