imporved correct embedding

app.py

@@ -39,7 +39,7 @@ def get_embedding(text):
 def cosine_similarity(vec1, vec2):
     return float(np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2)))
 
-def search_cache(user_id, user_input, threshold=0.8):
+def search_cache(user_id, user_input, threshold=0.9):  # stricter threshold
     query_vec = get_embedding(user_input)
     best_key, best_score, best_val = None, -1, None
     cache_key = f"cache:{user_id}"
@@ -58,11 +58,22 @@ def search_cache(user_id, user_input, threshold=0.8):
 def store_cache(user_id, user_input, output):
     vec = get_embedding(user_input).tolist()
     cache_key = f"cache:{user_id}"
-    redis_client.hset(cache_key, user_input, json.dumps({
+
+    # Store extra context: include language keyword if present
+    context_input = user_input.lower()
+    if "java" in context_input:
+        context_input = "JAVA: " + context_input
+    elif "python" in context_input:
+        context_input = "PYTHON: " + context_input
+    elif "c++" in context_input or "cpp" in context_input:
+        context_input = "CPP: " + context_input
+    elif "c " in context_input:
+        context_input = "C: " + context_input
+
+    redis_client.hset(cache_key, context_input, json.dumps({
         "embedding": vec,
         "output": output
     }))
-    # ⏳ Reset TTL every time user stores something
     redis_client.expire(cache_key, CACHE_TTL)
 
 def clear_user_cache(user_id):