Conversation Service Response Caching via Fuzzy Indexing
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Solution Overview
Problem
Existing virtual assistant systems face challenges in efficiently managing AI model calls, leading to high user latencies and increased computational resources, due to the need for repeated evaluations through multiple dialogue flows and the limitations of rudimentary caching mechanisms that become outdated with AI model changes.
Innovation Solution
A modular approach is implemented to dynamically cache and efficiently recall the model execution path and results from AI model calls during conversational dialogue flow processing, using a fine-tuned fuzzy caching strategy to maximize cached response hits and reduce the need for repeated AI model calls.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple AI models are called for each user utterance through multiple dialogue flows, then the quality and accuracy of responses is improved, but user latency increases significantly and computational resources are drastically increased
Solution Approach 1:
The system performs preliminary actions by pre-processing user utterances into standardized intent categories and pre-computing response templates before actual AI model calls are needed. This allows the system to quickly match incoming utterances to pre-defined response patterns, reducing the need for real-time AI model invocations and thereby decreasing user latency while maintaining response quality.
Solution Approach 2:
The system creates copies of successful response patterns and stores them in a cache database. When a similar utterance arrives, the system retrieves and reuses the cached response instead of calling AI models again. This copying approach maintains response quality by preserving proven effective responses while dramatically reducing computational resources and latency for repeated queries.
2Reliability
If multiple AI models are called for each user utterance, then response accuracy is improved, but computational resources and execution costs are drastically increased
Solution Approach 1:
The system stores accurate response copies in a cache database after initial AI model generation. Subsequent similar queries retrieve these pre-computed responses, eliminating the need for repeated AI model calls. This significantly reduces computational resource consumption and execution costs while maintaining response accuracy through reuse of proven effective responses.
Solution Approach 2:
The system changes the parameter of response generation from real-time AI model inference to pre-computed cached responses. By transforming the temporal aspect of response generation, the system maintains accuracy through cached results while reducing computational resource usage during peak demand periods.
3Loss of time
If rudimentary caching is used to store single responses for specific intents, then some repeated queries are answered faster, but the cache becomes outdated when AI models change leading to incorrect responses
Solution Approach 1:
The system implements dynamic cache management where cached responses are associated with specific AI model versions and intent categories. When AI models are updated, the system dynamically invalidates or updates cached responses corresponding to the old model versions. This dynamic approach maintains response relevance by ensuring cached responses correspond to current, accurate AI model knowledge while preserving fast retrieval times for valid cached entries.
Data Source
AI summary
Methods and systems for efficient caching and retrieval of responses in conversation service applications includes a server that captures an utterance and converts the utterance into an utterance index key. The server searches a first response cache to determine whether the utterance index key matches a response index key. When there is a match, the server transmits a response that matches the utterance index key to a client device. When there is not a match, the server converts the utterance into an utterance embedding and searches a second response cache to identify a response embedding. The server captures a fuzzy response index key associated with the closest matching response embedding and searches the first response cache to identify a response index key that matches the fuzzy response index key.