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Caching LLM Calls for Dynamic Queries
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Caching LLM Calls for Dynamic Queries

HardPython

Problem

Implement a caching layer that reduces redundant LLM calls while maintaining response relevance for dynamic queries.

Implement get_cached_response using normalized-token similarity, a relevance threshold, TTL expiration, and LRU eviction. On a cache miss, obtain the response from the supplied llm_responses mapping, store it, and return the updated cache.

The function receives a cache list and returns an object containing the response, whether the result was cached, and the updated cache. Cache entries use query, response, created_at, and last_used fields.

Constraints

  • 1 <= capacity <= 10^4
  • 0 <= ttl
  • 0.0 <= similarity_threshold <= 1.0
  • The cache contains at most 10^4 entries
  • Each cache entry contains query, response, created_at, and last_used
  • Every normalized cache-miss query exists in llm_responses
  • Use token Jaccard similarity; no external embedding model is required

Function Signature

def get_cached_response(query, now, cache, ttl, similarity_threshold, capacity, llm_responses):
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