PR fixes

Author: alonre24

docs/commands.md

@@ -79,7 +79,7 @@ Depending on the specified reply format:
     1. The tensor's shape as an Array consisting of an item per dimension
  * **BLOB**: the tensor's binary data as a String. If used together with the **META** option, the binary data string will put after the metadata in the array reply.
  * **VALUES**: Array containing the numerical representation of the tensor's data. If used together with the **META** option, the binary data string will put after the metadata in the array reply.
- * Default: META+BLOB are returned by default, in case that non of the arguments above is specified.  
+* Default: **META** and **BLOB** are returned by default, in case that non of the arguments above is specified. 
 
 
 **Examples**

src/tensor.c

@@ -851,8 +851,9 @@ uint ParseTensorGetArgs(RedisModuleCtx *ctx, RedisModuleString **argv, int argc)
         RedisModule_WrongArity(ctx);
         return fmt;
     }
-    if (argc == 2)
-        fmt = TENSOR_BLOB | TENSOR_META;
+    if (argc == 2) {
+        return TENSOR_BLOB | TENSOR_META;
+    }
     for (int i = 2; i < argc; i++) {
         const char *fmtstr = RedisModule_StringPtrLen(argv[i], NULL);
         if (!strcasecmp(fmtstr, "BLOB")) {

tests/flow/tests_common.py

@@ -205,7 +205,10 @@ def test_common_tensorget(env):
         env.assertEqual(datatype.encode('utf-8'), tensor_dtype)
         env.assertEqual([2], tensor_dim)
 
-        # Confirm that default reply format is META BLOB
+    # Confirm that default reply format is META BLOB
+    for datatype in tested_datatypes:
+        _, tensor_dtype, _, tensor_dim, _, tensor_blob = con.execute_command('AI.TENSORGET', 'tensor_{0}'.format(datatype),
+                                                                             'META', 'BLOB')
         _, tensor_dtype_default, _, tensor_dim_default, _, tensor_blob_default = con.execute_command('AI.TENSORGET',
                                                                                                      'tensor_{0}'.format(datatype))
         env.assertEqual(tensor_dtype, tensor_dtype_default)