Personal Agent Context Retrieval for Higher-Quality Query Responses
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Solution Overview
Problem
Existing computer-implemented services face challenges in providing responses that meet user expectations due to inadequate query generation and resource-intensive data retrieval from distributed sources, often resulting in reduced response quality and increased resource consumption.
Innovation Solution
Utilizing personal agents to manage contextual information through static and dynamic data chunks from user-specific data sources, including retrieval-augmented generation (RAG) repositories, to enhance query responses using inference models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is retrieved from distributed data sources to improve query response quality, then response quality is improved, but resource consumption increases
Solution Approach 1:
The system performs preliminary actions by proactively retrieving and storing contextual information from distributed data sources before queries are submitted. Personal agents continuously gather and organize data chunks, user profiles, and contextual metadata in advance, so that when a query arrives, the relevant information is already prepared and cached, eliminating the need for resource-intensive real-time data retrieval.
Solution Approach 2:
The system applies local quality by creating user-specific local contexts through personal agents that manage individualized data sources. Each user has a personalized context library containing relevant contextual information tailored to their preferences, history, and behavior patterns. This localized approach ensures high response quality for each user without requiring the system to retrieve and process all available data from distributed sources for every query.
2Measurement precision
If contextual information is obtained from multiple data sources to meet user expectations, then response accuracy is improved, but system complexity increases
Solution Approach 1:
The system introduces personal agents as intermediary components that mediate between users and the complex distributed data sources. These agents simplify the interaction by automatically managing data retrieval, contextualization, and presentation. The agents translate user queries into appropriate data source queries, filter and organize results, and present synthesized information, thereby hiding the underlying system complexity from users while maintaining high response accuracy.
Solution Approach 2:
The system segments the complex task of obtaining contextual information from multiple data sources into smaller, manageable sub-tasks handled by specialized personal agents. Each agent is responsible for specific data sources, user profiles, or types of contextual information. This segmentation allows the system to manage complexity by distributing responsibilities across multiple independent agents rather than requiring a single monolithic system to handle all data retrieval and processing.
3Loss of energy
If user-specific data sources are managed by personal agents to reduce resource expenditure, then resource efficiency is improved, but data source management complexity increases
Solution Approach 1:
The system implements self-service by enabling personal agents to autonomously manage their assigned data sources without requiring constant human intervention or centralized control. Agents automatically retrieve contextual information, update user profiles, organize data chunks, and adapt to changing user preferences independently. This self-service capability reduces the operational overhead and resource expenditure associated with manually managing data sources while the agents handle the complexity of data source management through automated decision-making and adaptation.
Data Source
AI summary
Methods and systems for providing customized services to users of data processing systems are disclosed. To do so, a query may be obtained from a user. Contextual information for the query may be attempted to be obtained using a first data source associated with the user. If the portion of the contextual information is stored as a static data chunk in the first data source, the static data chunk may be obtained. If the portion of the contextual information is stored as a reference in the first data source, the reference may be obtained. The reference may indicate that the portion of the contextual information is available as a dynamic data chunk from another data source. The portion of the contextual information may be obtained using the static data chunk and/or the dynamic data chunk and the query may be serviced using at least the portion of the contextual information.