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

Co-Authored-By: Claude Sonnet 5.5 <[email protected]> Claude-Session: https://claude.ai/code/session_01G8ikz8xdWuTP5yun8DZ1hk
2026-10-11 09:46:19 +00:00
parent cdee85d3f4
commit d51ef56468
24 changed files with 1383 additions and 0 deletions
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+package com.ankurm.semanticcache;
+
+import org.springframework.ai.embedding.EmbeddingModel;
+import org.springframework.ai.transformers.TransformersEmbeddingModel;
+import org.springframework.core.io.DefaultResourceLoader;
+
+/**
+ * all-MiniLM-L6-v2 (384 dimensions) running inside the JVM. Spring AI's default download location for
+ * the model is a GitHub media URL; here both files come from Hugging Face and are cached on disk, so the
+ * first run downloads about 90 MB and later runs are offline.
+ */
+public final class Embedder {
+
+    static final String HF = "https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2/resolve/main/";
+
+    private Embedder() { }
+
+    public static EmbeddingModel miniLm() {
+        try {
+            TransformersEmbeddingModel m = new TransformersEmbeddingModel();
+            m.setTokenizerResource(HF + "tokenizer.json");
+            m.setModelResource(HF + "onnx/model.onnx");
+            m.setResourceCacheDirectory(System.getProperty("java.io.tmpdir") + "/minilm-cache");
+            m.setTokenizerOptions(java.util.Map.of("padding", "true", "truncation", "true", "maxLength", "256"));
+            m.afterPropertiesSet();
+            return m;
+        } catch (Exception e) {
+            throw new IllegalStateException(e);
+        }
+    }
+
+    public static double cosine(float[] a, float[] b) {
+        double dot = 0, na = 0, nb = 0;
+        for (int i = 0; i < a.length; i++) {
+            dot += a[i] * b[i];
+            na += a[i] * a[i];
+            nb += b[i] * b[i];
+        }
+        return dot / (Math.sqrt(na) * Math.sqrt(nb) + 1e-12);
+    }
+}