first commit

sem_cache.py

@@ -0,0 +1,60 @@
+import sys
+import numpy as np
+from sentence_transformers import SentenceTransformer
+
+def main():
+    print("=" * 60)
+    print("LOCAL SEMANTIC CACHE SIMULATOR")
+    print("=" * 60)
+    
+    # Initialize lightweight local embedding model
+    print("Loading local embedding model (all-MiniLM-L6-v2)...")
+    try:
+        model = SentenceTransformer("all-MiniLM-L6-v2")
+    except Exception as e:
+        print(f"Error loading model: {e}")
+        sys.exit(1)
+        
+    print("\nModel loaded successfully.")
+    print("-" * 60)
+    
+    # Enforce strict threshold to avoid false-positives
+    THRESHOLD = 0.92
+    
+    # User input
+    sentence_1 = input("Enter Sentence 1 (The Baseline Cached Query):\n> ").strip()
+    sentence_2 = input("\nEnter Sentence 2 (The New Incoming Query):\n> ").strip()
+    
+    if not sentence_1 or not sentence_2:
+        print("\n[Error] Both sentences must contain text.")
+        sys.exit(1)
+        
+    print("\nGenerating local embeddings and performing vector math...")
+    
+    # Generate embeddings (Returns 1D NumPy array for each string)
+    emb1 = model.encode(sentence_1)
+    emb2 = model.encode(sentence_2)
+    
+    # Calculate Cosine Similarity w NumPy vector operations
+    dot_product = np.dot(emb1, emb2)
+    norm_1 = np.linalg.norm(emb1)
+    norm_2 = np.linalg.norm(emb2)
+    
+    similarity_score = dot_product / (norm_1 * norm_2)
+    
+    print("-" * 60)
+    print(f"RESULTS:")
+    print(f"-> Calculated Cosine Similarity: {similarity_score:.4f}")
+    print(f"-> Target Safety Threshold:     {THRESHOLD:.4f}")
+    
+    # Evaluate Cache Efficacy
+    if similarity_score >= THRESHOLD:
+        print("\n[CACHE HIT] Returning cached response.")
+    else:
+        print("\n[CACHE MISS] Score below threshold. Request routed to LLM.")
+    print("=" * 60)
+
+if __name__ == "__main__":
+    main()
+
+# To do: see README.md