Query Clustering for Energy-Efficient ML Response Generation
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Solution Overview
Problem
Existing systems face challenges in efficiently and reliably generating compute and energy-efficient responses for complex tasks across various processing frameworks, leading to inefficient use of resources and storage space.
Innovation Solution
A machine learning-based system generates embeddings for queries, forms clusters based on vector representations, and provides pre-generated query-response pairs for frequently asked questions, allowing efficient matching of incoming requests with suitable responses while reducing storage and computational demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If pre-generated query-response pairs are used for frequently asked questions, then computational resources and energy are saved, but the system complexity increases due to embedding generation and cluster management
Solution Approach 1:
The system performs preliminary actions by generating embeddings for queries and pre-generating query-response pairs for frequently asked questions before actual user requests arrive. Historical queries are processed in advance, clustered by similarity, and stored as reusable templates, allowing the system to quickly match and retrieve pre-generated responses rather than generating new responses for every query.
Solution Approach 2:
The system segments the query processing by dividing queries into different categories: frequently asked questions that have pre-generated responses, and new queries that require real-time generation. This segmentation allows the system to apply different handling strategies - retrieval for FAQ clusters and generation for novel queries - optimizing resource usage for each segment.
2Reliability
If real-time generation is performed for new queries, then accuracy and quality are maintained, but computational resources and energy consumption increase
Solution Approach 1:
The system applies partial action by generating responses only when necessary - specifically for queries that do not match existing FAQ clusters. For matching queries, the system retrieves pre-generated responses without performing full generation, thus using minimal computational resources while maintaining accuracy for the portion of queries that require it.
Solution Approach 2:
The system changes the operational parameter from full response generation to selective retrieval based on query similarity matching. By adjusting the threshold for cluster matching and dynamically determining whether to retrieve or generate responses, the system optimizes the balance between energy consumption and response quality.
3Reliability
If all query-response pairs are stored individually, then response accuracy is maintained, but storage space requirements increase
Solution Approach 1:
The system merges similar queries into clusters based on embedding similarity, storing one representative query-response pair for each cluster instead of individual pairs for every possible query variation. This consolidation reduces storage requirements while maintaining response accuracy through similarity matching against cluster representatives.
Solution Approach 2:
Instead of storing and processing every unique query individually, the system creates representative copies (cluster centers) that capture the essence of multiple similar queries. These copied representations are stored in the embedding space, allowing the system to serve multiple query variants from a single stored response template.
Data Source
AI summary
The technical solutions provide ML-based generation of queries and responses for operational frameworks of entities. A processor can identify queries of a processing frameworks for an entity and construct embeddings for the queries. Each embedding can correspond to a vector of a query of the plurality of queries that form a plurality of clusters in a vector space. The processor can generate, for each cluster, using API calls to ML models, a cluster query and a cluster response for the cluster query and store, in a storage, a mapping of each cluster query to each cluster response. The processor can receive, from a client, a request and select, using the mapping, the cluster response based on a relation between the cluster query and the request. The processor can provide, to the client, the selected cluster response responsive to the request.