Modular LLM Agents for Context-Aware Query Processing
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Solution Overview
Problem
Current systems for large language models (LLMs) face limitations in handling general inquiries, managing context for follow-up questions, and efficiently utilizing computing resources, leading to irrelevant and inaccurate responses.
Innovation Solution
A modular agent system for LLMs that mimics human cognitive processes, utilizing modules for query evaluation, environment information, knowledge retrieval, memory management, and intuition to generate contextually relevant responses, conserving resources by limiting unnecessary database interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a traditional LLM system processes all queries through the large language model, then response coverage is improved, but computational resource consumption increases
Solution Approach 1:
The system segments query processing into multiple specialized modules: an evaluation module that assesses query characteristics, a planning module that determines processing strategy, and an intuition module that handles straightforward queries without full LLM processing. This segmentation allows the system to apply computational resources selectively rather than uniformly to all queries.
Solution Approach 2:
The evaluation module and planning module serve as intermediaries between the user query and the large language model. These intermediary components filter and preprocess queries, determining which ones require full LLM processing and which can be handled more efficiently, thereby reducing unnecessary computational resource consumption.
2Reliability
If the system retrieves extensive context information for all queries, then response relevance is improved, but processing time increases
Solution Approach 1:
The evaluation module performs preliminary assessment of queries before full processing begins. By evaluating query characteristics in advance, the system can determine the appropriate level of context retrieval needed, avoiding unnecessary extensive searches for simple queries and thus reducing processing time while maintaining relevance for complex queries.
Solution Approach 2:
The system applies partial context retrieval for straightforward queries that don't require extensive background information, while reserving full context retrieval for complex queries that benefit from comprehensive information. This selective approach reduces average processing time while maintaining response relevance where needed.
3Loss of information
If the system stores all query data indefinitely, then knowledge completeness is improved, but storage resource consumption increases
Solution Approach 1:
The memory module implements a strategy of discarding query data after it has been processed and the lessons learned have been extracted. The system recovers valuable patterns and knowledge through the intuition module's learning mechanisms, while discarding the raw query data that is no longer needed, thus maintaining knowledge completeness without indefinite storage of all query data.
Solution Approach 2:
The system changes the state of stored information from raw query data to processed knowledge representations. By transforming query data into extracted insights and patterns through evaluation and intuition modules, the system maintains essential knowledge while reducing storage requirements by eliminating redundant raw data.
Data Source
AI summary
A device may receive a query from a user device, evaluate the query to generate query evaluation results, and generate an action plan for the query. The device may utilize a tools module to generate environment information, and may utilize a knowledge module to generate knowledge information. The device may utilize a memory module to generate memory information, and may utilize an intuition module to determine logical inferences about the query. The device may process the action plan for the query and the logical inferences about the query, with a large language model, to generate a response to the query, and may determine whether the response answers the query. The device may utilize a reflect module to modify the response and generate a final response based on determining that the response answers the query, and may provide the final response to the user device.