Dual-Model Data Retrieval Framework for Structured Query Responses
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Solution Overview
Problem
Current data retrieval methods, particularly those using artificial intelligence and machine learning, fail to accurately navigate the complexities of human language and context, leading to inefficiencies in extracting precise and relevant information from vast, context-rich data environments like the internet.
Innovation Solution
A combined model framework that employs a language model to generate semantically relevant keywords and a similarity model to extract relevant data from a hierarchical structured datastore, determining relevancy scores and filtering information nodes based on these keywords, with a large language model generating responses from the extracted data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional machine learning algorithms are used to process vast amounts of data, then data processing capacity is improved, but accuracy in understanding human language and context deteriorates
Solution Approach 1:
The system segments the data retrieval process into distinct stages: keyword generation by language model, hierarchical node traversal by similarity model, and response synthesis. This segmentation allows each component to specialize - the language model handles semantic understanding while the similarity model handles efficient data retrieval, resolving the contradiction between processing capacity and context accuracy.
Solution Approach 2:
The patent introduces keywords as an intermediary between the user query and the vast data corpus. The language model generates these keywords to capture semantic intent, which then guide the similarity model's traversal of hierarchical data structures. This intermediary mechanism enables accurate context understanding without requiring the system to process all raw data.
2Loss of information
If comprehensive data from all information nodes is processed, then information completeness is improved, but data processing time and computational load worsen
Solution Approach 1:
The system extracts only relevant information from the vast data corpus by using keywords to guide the similarity model's traversal of hierarchical structures. Rather than processing all information nodes, the system extracts data from nodes that match the semantic intent, achieving information completeness for the specific query while minimizing processing time.
Solution Approach 2:
The language model performs preliminary action by generating keywords before the actual data retrieval begins. These pre-generated keywords serve as filters that guide the subsequent traversal of hierarchical data structures, enabling the system to skip irrelevant branches and focus only on potentially relevant information nodes, thus reducing processing time while maintaining completeness.
3Ease of manufacture
If traditional search methods are used to navigate unstructured data, then ease of implementation is improved, but ability to handle complex data presentation and irregularities worsens
Solution Approach 1:
The system changes the parameter of data organization by imposing a hierarchical structure on the data corpus, with information nodes organized in layers. This hierarchical parameter transformation enables the similarity model to systematically traverse and compare data against keywords, providing adaptability to complex data presentations while maintaining implementation feasibility through structured approaches.
Data Source
AI summary
The described system provides a dual-model framework for data retrieval from complex data environments such as webpages on the internet. It combines a traditional similarity model that identifies relevant data from vast amounts of data and a large language model that delves deeper into the relevant data to uncover specifics. The models, in conjunction, provide a method for providing responses to structured queries about an entity. A source investigator receives a request for information about an entity alongside a set of keywords. A source datastore is identified for the entity and a similarity model is applied to the datastore to determine relevancy scores for data within. Data and/or nodes above a relevancy threshold are stored as relevant data. Then, using the large language model, the investigator generates responses to the structured queries based on the relevant data and provides responses to the user system.