Semantic Query Pipeline With Self-Healing Hallucination Correction
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Solution Overview
Problem
Generative AI/ML models face challenges such as hallucinations and context length limitations, failing to understand and process relational data like big tabular data with columns having relationships and patterns, and requiring specialized knowledge for SQL queries.
Innovation Solution
A self-healing generative AI/ML pipeline leveraging a semantic data model that iteratively generates and refines data access queries using a multi-agent system to correct errors and hallucinations, enabling natural language interaction for complex data queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generative AI/ML models use statistical similarity vector search with RAG or fine-tuning, then they can respond to user queries from large text corpus, but they produce hallucinations and cannot directly understand relational data
Solution Approach 1:
The patent introduces a semantic data model as an intermediary layer between the generative AI/ML model and the relational data. This semantic model translates natural language queries into structured data access queries while maintaining accuracy through iterative refinement and self-healing mechanisms, thereby reducing hallucinations while preserving query response capability
Solution Approach 2:
The system implements feedback loops where the generative AI/ML model's output is continuously evaluated and refined. The model generates initial data access queries, receives feedback on their accuracy and completeness, and iteratively improves them through self-healing processes, thereby reducing hallucinations while maintaining versatility
2Measurement precision
If SQL queries are used to fetch relational data, then data retrieval is accurate, but users must know the physical schema of the data
Solution Approach 1:
The semantic data model acts as an intermediary that translates natural language queries into accurate SQL queries. Users interact with the system using natural language without needing to understand the physical data schema, while the semantic model ensures accurate data retrieval by generating precise SQL queries through iterative refinement
Solution Approach 2:
The system performs self-service by automatically generating and refining data access queries without requiring user expertise in SQL or physical data schemas. The multi-agent system with self-healing capabilities autonomously corrects errors and improves query accuracy, making data retrieval easy for end users
3Ease of operation
If generative AI/ML models are used to generate data access queries, then natural language processing is enabled, but context length limitations prevent processing of complex relational data
Solution Approach 1:
The system segments the complex task of processing relational data into manageable components handled by multiple specialized agents. Each agent focuses on specific aspects of query generation and refinement, allowing the system to process complex relational data structures without exceeding context length limitations while maintaining natural language processing capabilities
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.