Update app.py

app.py

@@ -81,30 +81,12 @@ async def t5answer(request: T5QuestionRequest):
 @app.post("/modify_query2", response_model=ModifyQueryResponse)
 async def modify_query2(request: ModifyQueryRequest):
     try:
-        embeddings = optimize_embedding([request.query_string])
+        embeddings = model.encode([request.query_string])
         return ModifyQueryResponse(embeddings=embeddings[0].tolist())
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
 
 
-def optimize_embedding(texts, precision='uint8'):
-    # Step 1: Generate embeddings with 384 dimensions
-    embeddings = model.encode(texts)
-
-    # Step 2: Normalize embeddings to [0, 1] range
-    embeddings_min = embeddings.min(axis=1, keepdims=True)
-    embeddings_max = embeddings.max(axis=1, keepdims=True)
-    normalized_embeddings = (embeddings - embeddings_min) / (embeddings_max - embeddings_min + 1e-8)
-
-    # Step 3: Scale normalized embeddings to fit within the range of uint8 or uint16
-    if precision == 'uint8':
-        scaled_embeddings = (normalized_embeddings * 255).astype('uint8')
-    elif precision == 'uint16':
-        scaled_embeddings = (normalized_embeddings * 65535).astype('uint16')
-    else:
-        raise ValueError("Unsupported precision. Use 'uint8' or 'uint16'.")
-
-    return scaled_embeddings
 
 if __name__ == "__main__":
     import uvicorn