Multi-Agent Conversation Setup for Accurate Information Retrieval
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Solution Overview
Problem
Existing conversation systems with agents often struggle to provide accurate and efficient information acquisition when the agent is unable to respond effectively to user queries, leading to decreased efficiency in information retrieval.
Innovation Solution
A method is introduced where a second agent is created during a conversation with a first agent, forming a group chat to compensate for the first agent's limitations, using setting information generated based on the conversation context, including topic analysis and machine learning models to enhance information relevance and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single agent is used for conversation, then the system structure is simple, but the information acquisition efficiency decreases when the agent cannot respond effectively
Solution Approach 1:
The system segments the conversation handling capability by creating multiple specialized agents (first agent and second agent) with different functions. When the first agent cannot respond effectively, a second agent is generated to handle specific aspects of the conversation, thereby improving information acquisition efficiency while maintaining manageable system complexity through modular design.
Solution Approach 2:
The system dynamically adjusts its structure by generating a second agent based on conversation context analysis. The decision to create an additional agent is made dynamically during the conversation process when the first agent's response effectiveness is insufficient, allowing the system to adapt its complexity to the actual information needs without permanently increasing structural complexity.
2Measurement precision
If multiple agents are created to improve response accuracy, then the information retrieval efficiency improves, but the system complexity increases
Solution Approach 1:
The system applies local quality by creating a second agent with specialized knowledge or skills that complement the first agent. Rather than making all agents identical, the second agent is tailored with specific attributes, knowledge domains, or response styles that address particular gaps in the first agent's capabilities, thereby improving response accuracy in specific areas without unnecessarily complicating the entire system.
Solution Approach 2:
The system introduces an intermediary mechanism (the second agent) that mediates between the user's information needs and the available knowledge base. When the first agent cannot provide accurate responses, the second agent acts as an intermediary to fill the gap, improving overall response accuracy while the intermediary nature allows for controlled integration that manages system complexity.
3Loss of information
If a second agent is generated based on conversation context, then the information relevance improves, but the processing time increases
Solution Approach 1:
The system performs preliminary action by analyzing the conversation context in advance to determine when a second agent is needed. By proactively evaluating the conversation state and predicting when the first agent may be insufficient, the system can prepare for and quickly generate the second agent at the appropriate moment, reducing the actual processing time delay while maintaining high information relevance.
Solution Approach 2:
The system uses feedback mechanisms to monitor the first agent's response effectiveness in real-time. When feedback indicates that the first agent is not providing relevant or effective responses, the system triggers the generation of a second agent. This feedback-driven approach ensures that the additional processing time is only incurred when necessary, optimizing the balance between information relevance and processing time.
Data Source
AI summary
The disclosure relates to a conversation method, an electronic device, a storage medium, and a product, which relates to the field of computer technology. The conversation method includes: displaying a conversation between a user and a first agent; generating setting information for a second agent to be created based on the conversation; creating the second agent according to the setting information, wherein the second agent is configured to participate in the conversation between the user and the first agent based on the setting information for the second agent; and displaying the conversation among the user, the first agent and the second agent.