Multi-Agent Query Routing for Heterogeneous Government Data Ranking
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Solution Overview
Problem
Traditional database systems struggle to efficiently integrate and analyze structured, unstructured, and graph-based governmental data, leading to inefficiencies in data retrieval and analysis, and user queries often require nuanced processing to ensure relevance, accuracy, and bias mitigation.
Innovation Solution
A system that iteratively populates heterogeneous databases with governmental data elements using vector, relational, and graph databases, employing a multi-agent orchestration framework to process user queries and rank responses based on relevance, incorporating dynamic ontology updates and advanced machine learning models for accurate and contextually relevant outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional database systems are used to store governmental data, then data storage is simple, but integration of structured, unstructured, and graph-based data is inefficient
Solution Approach 1:
The patent merges multiple database types (relational, unstructured, graph, and vector databases) into a unified heterogeneous database system. This allows the system to store and integrate different data types together, resolving the contradiction by achieving better data integration capability while accepting increased system complexity as a necessary trade-off for handling diverse governmental data formats.
Solution Approach 2:
The system implements a universal database architecture that can handle multiple data types through a single integrated framework. The multi-functional database system can process structured data, unstructured data, graph data, and vector data simultaneously, enabling versatile data integration while managing complexity through unified design patterns.
2Measurement precision
If multi-agent system is implemented for query processing, then query relevance and accuracy improve, but system complexity increases
Solution Approach 1:
The query processing system is segmented into multiple specialized agents (routing agent, structured data agent, unstructured data agent, graph data agent, semantic search agent, validation agent, bias mitigation agent, and fallback agent). Each agent handles specific aspects of query processing, which improves overall query relevance and accuracy while managing complexity through functional decomposition and clear agent responsibilities.
3Reliability
If iterative population of heterogeneous databases is performed, then data completeness improves, but data processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and transforming data from multiple governmental sources before final database population. The iterative population process continuously refines data completeness by re-processing data in subsequent iterations, achieving high data reliability while managing time through efficient transformation pipelines and incremental updates rather than complete re-processing.
Data Source
AI summary
A system and method for iteratively populating, querying, and ranking governmental data across heterogeneous databases may include iteratively populating at least one database with governmental data elements by obtaining source data from governmental data sources at predefined times, transforming the source data using at least one database schema, and populating the database, which may be a vector, relational, or graph database. A user query is received, comprising a query string, and processed using a routing agent to determine the data type or semantic scope and select at least one agent from a plurality of agents, including structured data agents, unstructured data agents, graph data agents, semantic search agents, validation agents, bias mitigation agents, or fallback agents. The query is modified using metadata, executed to retrieve responses, and input into a relevance machine learning model to determine relevance scores and rank responses. Ranked query responses are outputted to the user.