This repository was archived by the owner on Sep 9, 2026. It is now read-only.
Repository navigation
Expand file tree
/
Copy pathcreate_dynamic_doc_class.py
More file actions
237 lines (212 loc) · 8.84 KB
/
Copy pathcreate_dynamic_doc_class.py
File metadata and controls
237 lines (212 loc) · 8.84 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
from docarray import DocList, BaseDoc
from docarray.typing import AnyTensor
from pydantic import create_model
from typing import Dict, List, Any, Union, Optional, Type
from docarray.utils._internal._typing import safe_issubclass
def create_pure_python_type_model(model: Any) -> BaseDoc:
"""
Take a Pydantic model and cast DocList fields into List fields.
This may be necessary due to limitations in Pydantic:
https://github.com/docarray/docarray/issues/1521
https://github.com/pydantic/pydantic/issues/1457
---
```python
from docarray import BaseDoc
class MyDoc(BaseDoc):
tensor: Optional[AnyTensor]
url: ImageUrl
title: str
texts: DocList[TextDoc]
MyDocCorrected = create_new_model_cast_doclist_to_list(CustomDoc)
```
---
:param model: The input model
:return: A new subclass of BaseDoc, where every DocList type in the schema is replaced by List.
"""
fields: Dict[str, Any] = {}
for field_name, field in model.__annotations__.items():
try:
if safe_issubclass(field, DocList):
t: Any = field.doc_type
fields[field_name] = (List[t], {})
else:
fields[field_name] = (field, {})
except TypeError:
fields[field_name] = (field, {})
return create_model(
model.__name__, __base__=model, __validators__=model.__validators__, **fields
)
def _get_field_type_from_schema(
field_schema: Dict[str, Any],
field_name: str,
root_schema: Dict[str, Any],
cached_models: Dict[str, Any],
is_tensor: bool = False,
num_recursions: int = 0,
) -> type:
"""
Private method used to extract the corresponding field type from the schema.
:param field_schema: The schema from which to extract the type
:param field_name: The name of the field to be created
:param root_schema: The schema of the root object, important to get references
:param cached_models: Parameter used when this method is called recursively to reuse partial nested classes.
:param is_tensor: Boolean used to tell between tensor and list
:param num_recursions: Number of recursions to properly handle nested types (Dict, List, etc ..)
:return: A type created from the schema
"""
field_type = field_schema.get('type', None)
tensor_shape = field_schema.get('tensor/array shape', None)
ret: Any
if 'anyOf' in field_schema:
any_of_types = []
for any_of_schema in field_schema['anyOf']:
if '$ref' in any_of_schema:
obj_ref = any_of_schema.get('$ref')
ref_name = obj_ref.split('/')[-1]
any_of_types.append(
create_base_doc_from_schema(
root_schema['definitions'][ref_name],
ref_name,
cached_models=cached_models,
)
)
else:
any_of_types.append(
_get_field_type_from_schema(
any_of_schema,
field_name,
root_schema=root_schema,
cached_models=cached_models,
is_tensor=tensor_shape is not None,
num_recursions=0,
)
) # No Union of Lists
ret = Union[tuple(any_of_types)]
for rec in range(num_recursions):
ret = List[ret]
elif field_type == 'string':
ret = str
for rec in range(num_recursions):
ret = List[ret]
elif field_type == 'integer':
ret = int
for rec in range(num_recursions):
ret = List[ret]
elif field_type == 'number':
if num_recursions <= 1:
# This is a hack because AnyTensor is more generic than a simple List and it comes as simple List
if is_tensor:
ret = AnyTensor
else:
ret = List[float]
else:
ret = float
for rec in range(num_recursions):
ret = List[ret]
elif field_type == 'boolean':
ret = bool
for rec in range(num_recursions):
ret = List[ret]
elif field_type == 'object' or field_type is None:
doc_type: Any
if 'additionalProperties' in field_schema: # handle Dictionaries
additional_props = field_schema['additionalProperties']
if additional_props.get('type') == 'object':
doc_type = create_base_doc_from_schema(
additional_props, field_name, cached_models=cached_models
)
ret = Dict[str, doc_type]
else:
ret = Dict[str, Any]
else:
obj_ref = field_schema.get('$ref') or field_schema.get('allOf', [{}])[
0
].get('$ref', None)
if num_recursions == 0: # single object reference
if obj_ref:
ref_name = obj_ref.split('/')[-1]
ret = create_base_doc_from_schema(
root_schema['definitions'][ref_name],
ref_name,
cached_models=cached_models,
)
else:
ret = Any
else: # object reference in definitions
if obj_ref:
ref_name = obj_ref.split('/')[-1]
doc_type = create_base_doc_from_schema(
root_schema['definitions'][ref_name],
ref_name,
cached_models=cached_models,
)
ret = DocList[doc_type]
else:
doc_type = create_base_doc_from_schema(
field_schema, field_name, cached_models=cached_models
)
ret = DocList[doc_type]
elif field_type == 'array':
ret = _get_field_type_from_schema(
field_schema=field_schema.get('items', {}),
field_name=field_name,
root_schema=root_schema,
cached_models=cached_models,
is_tensor=tensor_shape is not None,
num_recursions=num_recursions + 1,
)
else:
if num_recursions > 0:
raise ValueError(
f"Unknown array item type: {field_type} for field_name {field_name}"
)
else:
raise ValueError(
f"Unknown field type: {field_type} for field_name {field_name}"
)
return ret
def create_base_doc_from_schema(
schema: Dict[str, Any], base_doc_name: str, cached_models: Optional[Dict] = None
) -> Type:
"""
Dynamically create a `BaseDoc` subclass from a `schema` of another `BaseDoc`.
This method is intended to dynamically create a `BaseDoc` compatible with the schema
of another BaseDoc. This is useful when that other `BaseDoc` is not available in the current scope. For instance, you may have stored the schema
as a JSON, or sent it to another service, etc.
Due to this Pydantic limitation (https://github.com/docarray/docarray/issues/1521, https://github.com/pydantic/pydantic/issues/1457), we need to make sure that the
input schema uses `List` and not `DocList`. Therefore this is recommended to be used in combination with `create_new_model_cast_doclist_to_list`
to make sure that `DocLists` in schema are converted to `List`.
---
```python
from docarray import BaseDoc
class MyDoc(BaseDoc):
tensor: Optional[AnyTensor]
url: ImageUrl
title: str
texts: DocList[TextDoc]
MyDocCorrected = create_pure_python_type_model(CustomDoc)
new_my_doc_cls = create_base_doc_from_schema(CustomDocCopy.schema(), 'MyDoc')
```
---
:param schema: The schema of the original `BaseDoc` where DocLists are passed as regular Lists of Documents.
:param base_doc_name: The name of the new pydantic model created.
:param cached_models: Parameter used when this method is called recursively to reuse partial nested classes.
:return: A BaseDoc class dynamically created following the `schema`.
"""
cached_models = cached_models if cached_models is not None else {}
fields: Dict[str, Any] = {}
if base_doc_name in cached_models:
return cached_models[base_doc_name]
for field_name, field_schema in schema.get('properties', {}).items():
field_type = _get_field_type_from_schema(
field_schema=field_schema,
field_name=field_name,
root_schema=schema,
cached_models=cached_models,
is_tensor=False,
num_recursions=0,
)
fields[field_name] = (field_type, field_schema.get('description'))
model = create_model(base_doc_name, __base__=BaseDoc, **fields)
cached_models[base_doc_name] = model
return model