Real ChatGPT answer · index seedHTLckXS3NO0completeB2B software · AI and automation toolsinformational
How can we implement semantic caching to reduce redundant API calls and assist with managing LLM token budgets for our customer support chatbot?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
How can we implement semantic caching to reduce redundant API calls and assist with managing LLM token budgets for our customer support chatbot?
1 real search · 1 real-search pass
semantic caching LLM vector similarity cache Redis documentation threshold metadata filtering token cost best practicesGoogle ↗
OBSERVEDComparison
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
ChatGPT real searches (1)
Classes come from the query-classifier (a local Laya model). 1 of 1 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT1 real searches · gpt-6
semantic caching LLM vector similarity cache Redis documentation threshold metadata filtering token cost best practices Google ↗
- LLM Cache · redis.io
- LangCache concepts · redis.io
- Best practices · docs.aws.amazon.com
- Overview of semantic caching · docs.aws.amazon.com