Context-Based Query Routing for Multi-Agent Response Accuracy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing large language models (LLMs) often struggle with generating accurate responses to complex and multi-part user queries due to varying configurations and training processes, leading to inefficiencies and latency in response generation.
Innovation Solution
A method and system that decomposes user queries into sub-queries based on context, routing each sub-query to a specific agent configured for that context, using a plurality of agents each trained for unique contexts, and combining their responses for an accurate and efficient answer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single large language model is used to handle all user queries, then the system structure is simple, but the response accuracy for complex and multi-part queries deteriorates
Solution Approach 1:
The patent segments the single LLM system into multiple specialized agents, each trained on specific contexts or query types. The query processing system is divided into: (1) query analysis component that identifies sub-queries and contexts, (2) agent selection component that routes queries to appropriate agents, and (3) response synthesis component that combines agent responses. This segmentation allows each agent to specialize in specific domains while maintaining overall system functionality.
Solution Approach 2:
The patent creates a universal query processing framework that can handle diverse query types through multiple agents. Each agent is trained on specific contexts but the overall system provides universal coverage across different query domains. The framework includes universal components for query analysis, agent selection, and response synthesis that work across all specialized agents.
2Measurement precision
If multiple specialized agents are used to handle different query contexts, then the response accuracy improves, but the system complexity increases
Solution Approach 1:
The patent introduces intermediary components that mediate between users and multiple specialized agents. The query analysis component acts as an intermediary that decomposes complex queries into sub-queries and identifies relevant contexts. The agent selection component serves as a mediator that routes queries to appropriate agents based on context matching. These intermediaries simplify the interaction complexity by providing a unified interface despite multiple underlying agents.
Solution Approach 2:
The system segments query processing into distinct functional modules: query analysis, context identification, agent selection, and response synthesis. Each module handles a specific aspect of the processing pipeline, reducing overall system complexity by breaking down the monolithic LLM approach into manageable, specialized components.
3Measurement precision
If multiple specialized agents are deployed for different query types, then the response accuracy for complex queries improves, but the processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-training each agent on specific contexts and query types before runtime. The query analysis component pre-identifies sub-queries and contexts, and the agent selection component pre-establishes matching criteria between queries and agents. This preliminary preparation enables faster runtime processing by avoiding ad-hoc analysis and direct routing decisions.
Solution Approach 2:
The patent segments the processing workload across multiple agents that can operate in parallel. Instead of one LLM processing the entire query sequentially, different sub-queries are routed to different agents simultaneously, reducing overall processing time through parallel execution while maintaining high accuracy through specialized handling.
4Ease of operation
If a single LLM processes all queries, then the system is easy to operate, but the handling of multi-part queries with varying contexts becomes inefficient
Solution Approach 1:
The patent implements self-service mechanisms where the system automatically analyzes queries, identifies contexts, selects appropriate agents, and synthesizes responses without requiring manual intervention. The query analysis component autonomously decomposes queries and identifies relevant contexts, the agent selection component automatically routes queries based on context matching, and the response synthesis component seamlessly integrates agent responses. This automation maintains ease of operation while dramatically improving productivity for complex multi-part queries.
Data Source
AI summary
Aspects of the present disclosure relate to an online resource that can initiate a conversation between a user and an automated assistant provided by the online resource. The online resource identifies a plurality of queries from the user during the conversation between the user and the automated assistant and determines a context for each query. The online resource selects an agent for each query based on its context, and then sends the queries to their respective selected agents to generate responses. The online resource combines the responses received from the selected agents to form an answer to the query, and then provides the answer to the user.