What is the most cost-effective way to cache LLM responses in our application to avoid paying for identical queries from different users?
Download run
Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
What is the most cost-effective way to cache LLM responses in our application to avoid paying for identical queries from different users?
2 real searches · 1 real-search pass
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 (2)
Classes come from the query-classifier (a local Laya model). 2 of 2 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT2 real searches · gpt-6
Redis semantic cache documentation vector similarity caching LLM responses pgvector caching architecture Google ↗
PostgreSQL pgvector documentation vector similarity index HNSW Google ↗
- Redis semantic cache · redis.io
- LangCache concepts · redis.io
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
Redis semantic cache documentation vector similarity caching LLM responses pgvector caching architecture OBSERVED | Google ↗ AI Mode ↗ | — | Other / navigational | — | — | 1x of 1 | |
PostgreSQL pgvector documentation vector similarity index HNSW OBSERVED | Google ↗ AI Mode ↗ | — | Other / navigational | — | — | 1x of 1 |