fix(document): serialize tag value in the correct priority (#60)

docarray/document/pydantic_model.py

@@ -7,7 +7,8 @@
 if TYPE_CHECKING:
     from ..types import ArrayType
 
-_ProtoValueType = Optional[Union[str, bool, float]]
+# this order must be preserved: https://pydantic-docs.helpmanual.io/usage/types/#unions
+_ProtoValueType = Optional[Union[bool, float, str]]
 _StructValueType = Union[
     _ProtoValueType, List[_ProtoValueType], Dict[str, _ProtoValueType]
 ]

tests/__init__.py

@@ -20,7 +20,7 @@ def random_docs(
 
         d = Document(id=doc_id)
         d.text = text
-        d.tags['id'] = doc_id
+        d.tags['id'] = f'myself id is: {doc_id}'
         if embedding:
             if sparse_embedding:
                 from scipy.sparse import coo_matrix
@@ -42,8 +42,8 @@ def random_docs(
                 c.embedding = np.random.random(
                     [embed_dim + np.random.randint(0, jitter)]
                 )
-            c.tags['parent_id'] = doc_id
-            c.tags['id'] = chunk_doc_id
+            c.tags['parent_id'] = f'my parent is: {id}'
+            c.tags['id'] = f'myself id is: {doc_id}'
             d.chunks.append(c)
             next_chunk_doc_id += 1
 

tests/unit/test_pydantic.py

@@ -2,6 +2,7 @@
 from typing import List, Optional
 
 import numpy as np
+import pytest
 from fastapi import FastAPI
 from pydantic import BaseModel
 from starlette.testclient import TestClient
@@ -116,3 +117,28 @@ def test_match_to_from_pydantic():
 def test_with_embedding_no_tensor():
     d = Document(embedding=np.random.rand(2, 2))
     PydanticDocument.parse_obj(d.to_pydantic_model().dict())
+
+
+@pytest.mark.parametrize(
+    'tag_value, tag_type',
+    [(3, float), (3.4, float), ('hello', str), (True, bool), (False, bool)],
+)
+@pytest.mark.parametrize('protocol', ['protobuf', 'jsonschema'])
+def test_tags_int_float_str_bool(tag_type, tag_value, protocol):
+    d = Document(tags={'hello': tag_value})
+    dd = d.to_dict(protocol=protocol)['tags']['hello']
+    assert dd == tag_value
+    assert isinstance(dd, tag_type)
+
+    # now nested tags in dict
+
+    d = Document(tags={'hello': {'world': tag_value}})
+    dd = d.to_dict(protocol=protocol)['tags']['hello']['world']
+    assert dd == tag_value
+    assert isinstance(dd, tag_type)
+
+    # now nested in list
+    d = Document(tags={'hello': [tag_value] * 10})
+    dd = d.to_dict(protocol=protocol)['tags']['hello'][-1]
+    assert dd == tag_value
+    assert isinstance(dd, tag_type)