Conversational-Agnostic Matchmaking Architecture for Contextual Search
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Computing systems face challenges in efficiently and accurately matching nuanced queries due to suboptimal context interpretation, leading to delays and excessive resource utilization.
Innovation Solution
A conversational agnostic matchmaking model architecture utilizing multiple machine learning models, including information retrieval, named entity recognition, and generative chat models, to dynamically adapt to user inquiries and enhance context-based search processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a computing system uses traditional search methods to match nuanced queries, then the system structure remains simple, but the accuracy and relevance of matches deteriorate due to inability to interpret context
Solution Approach 1:
The system segments the matchmaking process into distinct functional modules: a information retrieval model for initial document fetching, a named entity recognition model for context extraction, and a generative chat model for query refinement. Each model specializes in a specific aspect of the matching process, improving overall accuracy while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The orchestrating agent serves as a universal component that manages multiple specialized models (information retrieval, NER, generative chat) and coordinates their interactions. This multi-functional agent handles diverse query types and contexts, allowing the system to maintain simplicity in individual components while achieving high match accuracy through their coordinated operation.
2Reliability
If the system processes excessive prompts with suboptimal context interpretation, then more data can be searched, but resource consumption and latency increase significantly
Solution Approach 1:
The information retrieval model performs preliminary action by fetching relevant documents before the main matching process. This pre-retrieval step filters out obviously irrelevant data early, reducing the computational burden on subsequent models and lowering overall resource consumption while maintaining match reliability.
Solution Approach 2:
The named entity recognition model acts as an intermediary between the information retrieval model and the generative chat model. It extracts contextual entities from retrieved documents and uses them to refine queries, mediating the flow of information and ensuring that only relevant contextual data is processed further, thus optimizing resource usage.
3Adaptability or versatility
If multiple specialized models are used for context-based matchmaking, then match relevance improves, but system complexity increases
Solution Approach 1:
The system employs dynamic model selection and orchestration where the orchestrating agent adapts the interaction flow between specialized models based on the specific query and context. This dynamic coordination allows the system to handle diverse contextual requirements with multiple models while maintaining manageable complexity through adaptive, rather than rigid, architectural design.
4Productivity
If traditional search approaches are used, then the system is easier to operate, but latency increases due to excessive data processing
Solution Approach 1:
The named entity recognition model extracts only the essential contextual entities from retrieved documents, separating critical information from extraneous data. This extraction process filters out unnecessary processing overhead, reducing the time required for subsequent matching operations while maintaining high relevance through focused entity-based query refinement.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A system for matchmaking using a conversational agnostic matchmaking model is described. The system can receive a first query indicating a request for document objects and including criteria for selection of the document objects. The system can identify named entities from portions of the first query. The system can generate a second query to obtain the document objects, in response to the named entities being indicative of a context for the first query. The system can obtain the document objects according to the second query. The system can generate a reply to the first query including a description object and the documents, the description object based on the first query. The system can cause a user interface to present the reply to the first query and the description object.