Add AI artifact cache layer and AI.* RESP commands (embeddings/prompts/RAG/dedup/invalidation)

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Motivation

• Provide a local, best-effort AI Artifact Cache layer for embeddings, prompts, RAG chunks, rerank buffers and responses with cache semantics (lossy, TTL/capacity-driven, warm restart).
• Use deterministic canonical keys and a content-addressed blob indirection to enable deduplication and compact metadata handling.
• Expose AI-native commands over RESP so existing clients like redis-cli can store, fetch, invalidate, and introspect AI artifacts.

Description

• Added a new AiArtifactCache layer (include/pomai_cache/ai_cache.hpp, src/server/ai_cache.cpp) that stores typed ArtifactMeta + payloads with a blob:<content_hash> indirection, best-effort refcounts, per-epoch/model/prefix bounded indexes, and introspection (stats, top_hot, top_costly, explain).
• Extended the RESP server (src/server/server_main.cpp) with AI commands: AI.PUT, AI.GET, AI.MGET, AI.EMB.PUT, AI.EMB.GET, AI.INVALIDATE EPOCH|MODEL|PREFIX, AI.STATS, AI.TOP HOT|COSTLY, and AI.EXPLAIN, and instantiated AiArtifactCache in the server loop.
• Implemented deterministic canonical key helpers (emb/prm/rag/rrk/rsp) and owner TTL defaults + type-based miss_cost guidance; updated engine owner miss-cost defaults in src/engine/engine.cpp to bias policy for AI artifact owners.
• Added AI benchmark bench/ai_artifact_bench.cpp and wired up CMake (CMakeLists.txt) plus tests (tests/test_ai_cache.cpp, extended tests/test_integration.cpp) and docs (docs/AI_CACHE.md, docs/AI_COMMANDS.md, docs/INVALIDATION.md, docs/BLOB_DEDUP.md), and updated README.md with quickstart examples and recommended AI config.

Testing

• Built the project (cmake -S . -B build -DCMAKE_BUILD_TYPE=Debug && cmake --build build -j) and all test targets compiled successfully. ✅
• Ran the full test suite (ctest --test-dir build --output-on-failure), and all tests including the new AI unit and integration tests passed. ✅
• Built and executed the AI benchmark target (pomai_cache_ai_bench) which produced a JSON summary (ops/s, p50/p95/p99/p999, hit_rate) for the embedding workload; the bench target compiles and runs but longer runs may require tuning in constrained CI environments (a longer timeout was observed during an extended run). ⚠️

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