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Add semantic-cache module: CallAdvisor over a Redis Stack vector index with a real in-process embedding model; threshold sweep (hit rate against wrong answers), 600-request replay with an illustrative cost model, tenant filter, expiry, look-alike and embedding-model-change traps; optional real local model

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Co-Authored-By: Claude Sonnet 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01G8ikz8xdWuTP5yun8DZ1hk
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asmhatre
2026-10-11 09:46:19 +00:00
co-authored by Claude Sonnet 5.5
parent cdee85d3f4
commit d51ef56468
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semantic-cache/output/06-tenant-isolation.txt
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# Same question, two tenants
shop-a asks "How do I reset my account password?" -> model call (calls so far: 1)
shop-b asks the identical question -> model call, no hit (model calls so far: 2)
shop-a asks again -> cache hit
entries in the index: 2 (one per tenant)
The filter is a tag on the document and an expression on the search: tenant == 'shop-b'.
nearest(shop-a, q) without asking as shop-b still finds: "How do I reset my account password?"