Semantic Cache for Generative AI Model Outputs
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Solution Overview
Problem
Existing cache systems for generative AI systems are inefficient as they store only the final output, leading to high GPU costs and longer response times when individual AI model outputs change, and they require exact matches for query retrieval, wasting resources on similar queries.
Innovation Solution
Implement a caching system that stores the outputs of individual AI models and uses semantic encoding to perform semantic lookups, allowing for the validation of cached outputs and reducing the need to re-run all AI models for each query.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the cache stores only the final output from the orchestrator, then the cache size is reduced, but all AI models must be re-run when any individual model output changes, increasing GPU costs and response time
Solution Approach 1:
The patent segments the final output into individual model outputs, caching each model's output separately rather than only the final orchestrator output. This allows selective invalidation and reuse of individual model outputs without requiring all models to re-run, thereby reducing response time while managing cache size through targeted storage of only necessary model outputs.
Solution Approach 2:
The patent performs preliminary actions by pre-computing and caching individual model outputs before they are needed by the orchestrator. When a query arrives, the system checks which model outputs are valid and can be reused, avoiding the need to re-run all models from scratch and thus reducing overall response time.
2Speed
If the cache uses exact match for query retrieval, then the cache lookup is simple and fast, but semantically similar queries are not matched, wasting computational resources
Solution Approach 1:
The patent changes the parameter used for cache lookup from exact string match to semantic similarity based on vector embeddings. Queries and cached outputs are represented as vectors in a semantic space, and lookup uses similarity thresholds rather than exact matches. This allows semantically similar queries to be matched, improving resource efficiency while maintaining acceptable lookup performance through optimized similarity search algorithms.
3Reliability
If the system re-runs all AI models for each query, then the output is always fresh and accurate, but GPU resource consumption increases significantly
Solution Approach 1:
The patent implements a feedback mechanism where the system tracks which model outputs are valid and when they were generated. When a query arrives, the system uses this feedback information to determine which model outputs can be safely reused and which need to be re-computed. This selective approach maintains output freshness and accuracy for changed models while avoiding unnecessary re-runs of unchanged models, thereby reducing GPU resource consumption.
Solution Approach 2:
The system performs preliminary validation of model output freshness by checking timestamps and validity metadata before serving queries. This preliminary action allows the system to reuse valid cached outputs without re-running models, while only re-computing models that have changed or expired, thus maintaining reliability while reducing GPU usage.
Data Source
AI summary
A generative artificial intelligence (AI) system has a plurality of different AI models. A prompt is provided to each of the different AI models and each of the different AI models generates an output. The outputs from the AI models are provided to an orchestrator. The orchestrator selects from among the different model outputs to generate a response. A cache system generates a cache entry, corresponding to the query, for each of the model outputs. When a subsequent query is received, the cache is searched based upon the subsequent query to determine whether any matching cache entries are found. The individual model outputs corresponding to a matching cache entry are output to the individual AI models for validation.