LLM Query Orchestration for Accurate Cross-Database Retrieval

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Conventional information retrieval systems face challenges in integrating heterogeneous data sources, achieving high precision, and adhering to regulatory standards, particularly in domains like legislative data analysis, due to the lack of coordination among task-specific modules and reliance on heuristic search algorithms.

Innovation Solution

A multi-agent orchestration system leveraging large language models (LLMs), retrieval augmentation generation (RAG) techniques, and dynamic graph database architectures, which employs specialized agents for intelligent query routing, cross-source verification, and validation mechanisms to ensure accuracy and relevance.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If multiple task-specific modules are used for data retrieval and processing, then the system can handle diverse data sources and query types, but coordination and resource management become complex and resource-intensive

Engineering Contradiction:

Improvecapability to handle diverse data sources and query typesVSAvoidcoordination and resource management complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system segments the complex information retrieval task into specialized agent modules (web search agent, image search agent, SQL query agent, calculator agent, etc.), where each agent handles specific types of queries. This segmentation allows diverse data handling while maintaining manageable complexity through clear module boundaries and specialized functionality.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent implements a universal orchestration layer that manages multiple specialized agents through a common interface and standardized protocols. This universal coordinator handles resource allocation, query routing, and result aggregation, enabling the system to handle diverse query types without proportionally increasing coordination complexity.

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Measurement precision

If heuristic search algorithms are used for information retrieval, then the system can operate with simple architecture, but precision and reliability of results deteriorate

Engineering Contradiction:

Improveprecision of information retrieval resultsVSAvoidsystem architecture complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system implements feedback mechanisms where the orchestration layer evaluates results from specialized agents and refines query formulations based on result quality. This feedback loop continuously improves precision by adjusting search strategies based on actual performance, moving beyond static heuristic approaches.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent introduces an intermediary orchestration layer between the user query and the search agents that translates natural language queries into agent-specific search protocols. This intermediary enhances precision by adding semantic understanding and query optimization without requiring complex changes to the underlying search algorithms.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Productivity

If conventional isolated modules are used for data processing, then each module can be simple and task-specific, but integration into a unified result requires substantial coordination

Engineering Contradiction:

Improveefficiency of isolated module operationVSAvoiddependability of unified result integration

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent merges the output aggregation and result validation functions into the orchestration layer, combining results from multiple specialized agents into a unified response. This merging process includes cross-validation of results, conflict resolution, and consistency checking, thereby enhancing the reliability of the integrated output while preserving the efficiency of isolated module operations.

Inventive Principle:

Principle #5Merging (Combining)

Data Source

PatentUS12536212B1Systems and methods for error correction in large language model-based database querying

Publication Date: 2026.01.27 AJ PRESS LLC

AI summary

A system and method for querying, processing, and ranking governmental data across heterogeneous databases may include integrating large language models (LLMs) and multi-agent orchestration to enhance accuracy and mitigate bias. The system receives a natural language query, processes the query using an agent orchestration LLM to generate search query instructions, and executes specialized data record processing agents to retrieve data from diverse sources, including relational, vector, and graph databases. A plurality of candidate natural language responses is generated and validated by data verification LLMs based on relevance and accuracy metrics to reduce hallucinations. The system further employs cross-source validation, statistical and linguistic consistency analyses, and weighted scoring to refine responses, which may include textual summaries, tables, or downloadable files presented via a graphical user interface. Accordingly, the approach ensures precise and contextually relevant results, particularly for applications involving sensitive or regulated data such as legislative and governmental records.