# jiter
[](https://github.com/pydantic/jiter/actions?query=event%3Apush+branch%3Amain+workflow%3ACI)
[](https://pypi.python.org/pypi/jiter)
[](https://github.com/pydantic/jiter)
[](https://github.com/pydantic/jiter/blob/main/LICENSE)
This is a standalone version of the JSON parser used in `pydantic-core`. The recommendation is to only use this package directly if you do not use `pydantic`.
The API is extremely minimal:
```python
def from_json(
json_data: bytes,
/,
*,
allow_inf_nan: bool = True,
cache_mode: Literal[True, False, "all", "keys", "none"] = "all",
partial_mode: Literal[True, False, "off", "on", "trailing-strings"] = False,
catch_duplicate_keys: bool = False,
float_mode: Literal["float", "decimal", "lossless-float"] = False,
) -> Any:
"""
Parse input bytes into a JSON object.
Arguments:
json_data: The JSON data to parse
allow_inf_nan: Whether to allow infinity (`Infinity` an `-Infinity`) and `NaN` values to float fields.
Defaults to True.
cache_mode: cache Python strings to improve performance at the cost of some memory usage
- True / 'all' - cache all strings
- 'keys' - cache only object keys
- False / 'none' - cache nothing
partial_mode: How to handle incomplete strings:
- False / 'off' - raise an exception if the input is incomplete
- True / 'on' - allow incomplete JSON but discard the last string if it is incomplete
- 'trailing-strings' - allow incomplete JSON, and include the last incomplete string in the output
catch_duplicate_keys: if True, raise an exception if objects contain the same key multiple times
float_mode: How to return floats: as a `float`, `Decimal` or `LosslessFloat`
Returns:
Python object built from the JSON input.
"""
def cache_clear() -> None:
"""
Reset the string cache.
"""
def cache_usage() -> int:
"""
get the size of the string cache.
Returns:
Size of the string cache in bytes.
"""
```
## Examples
The main function provided by Jiter is `from_json()`, which accepts a bytes object containing JSON and returns a Python dictionary, list or other value.
```python
import jiter
json_data = b'{"name": "John", "age": 30}'
parsed_data = jiter.from_json(json_data)
print(parsed_data) # Output: {'name': 'John', 'age': 30}
```
### Handling Partial JSON
Incomplete JSON objects can be parsed using the `partial_mode=` parameter.
```python
import jiter
partial_json = b'{"name": "John", "age": 30, "city": "New Yor'
# Raise error on incomplete JSON
try:
jiter.from_json(partial_json, partial_mode=False)
except ValueError as e:
print(f"Error: {e}")
# Parse incomplete JSON, discarding incomplete last field
result = jiter.from_json(partial_json, partial_mode=True)
print(result) # Output: {'name': 'John', 'age': 30}
# Parse incomplete JSON, including incomplete last field
result = jiter.from_json(partial_json, partial_mode='trailing-strings')
print(result) # Output: {'name': 'John', 'age': 30, 'city': 'New Yor'}
```
### Catching Duplicate Keys
The `catch_duplicate_keys=True` option can be used to raise a `ValueError` if an object contains duplicate keys.
```python
import jiter
json_with_dupes = b'{"foo": 1, "foo": 2}'
# Default behavior (last value wins)
result = jiter.from_json(json_with_dupes)
print(result) # Output: {'foo': 2}
# Catch duplicate keys
try:
jiter.from_json(json_with_dupes, catch_duplicate_keys=True)
except ValueError as e:
print(f"Error: {e}")
```
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"description": "# jiter\n\n[](https://github.com/pydantic/jiter/actions?query=event%3Apush+branch%3Amain+workflow%3ACI)\n[](https://pypi.python.org/pypi/jiter)\n[](https://github.com/pydantic/jiter)\n[](https://github.com/pydantic/jiter/blob/main/LICENSE)\n\nThis is a standalone version of the JSON parser used in `pydantic-core`. The recommendation is to only use this package directly if you do not use `pydantic`.\n\nThe API is extremely minimal:\n\n```python\ndef from_json(\n json_data: bytes,\n /,\n *,\n allow_inf_nan: bool = True,\n cache_mode: Literal[True, False, \"all\", \"keys\", \"none\"] = \"all\",\n partial_mode: Literal[True, False, \"off\", \"on\", \"trailing-strings\"] = False,\n catch_duplicate_keys: bool = False,\n float_mode: Literal[\"float\", \"decimal\", \"lossless-float\"] = False,\n) -> Any:\n \"\"\"\n Parse input bytes into a JSON object.\n\n Arguments:\n json_data: The JSON data to parse\n allow_inf_nan: Whether to allow infinity (`Infinity` an `-Infinity`) and `NaN` values to float fields.\n Defaults to True.\n cache_mode: cache Python strings to improve performance at the cost of some memory usage\n - True / 'all' - cache all strings\n - 'keys' - cache only object keys\n - False / 'none' - cache nothing\n partial_mode: How to handle incomplete strings:\n - False / 'off' - raise an exception if the input is incomplete\n - True / 'on' - allow incomplete JSON but discard the last string if it is incomplete\n - 'trailing-strings' - allow incomplete JSON, and include the last incomplete string in the output\n catch_duplicate_keys: if True, raise an exception if objects contain the same key multiple times\n float_mode: How to return floats: as a `float`, `Decimal` or `LosslessFloat`\n\n Returns:\n Python object built from the JSON input.\n \"\"\"\n\ndef cache_clear() -> None:\n \"\"\"\n Reset the string cache.\n \"\"\"\n\ndef cache_usage() -> int:\n \"\"\"\n get the size of the string cache.\n\n Returns:\n Size of the string cache in bytes.\n \"\"\"\n```\n## Examples\n\nThe main function provided by Jiter is `from_json()`, which accepts a bytes object containing JSON and returns a Python dictionary, list or other value.\n\n```python\nimport jiter\n\njson_data = b'{\"name\": \"John\", \"age\": 30}'\nparsed_data = jiter.from_json(json_data)\nprint(parsed_data) # Output: {'name': 'John', 'age': 30}\n```\n\n### Handling Partial JSON\n\nIncomplete JSON objects can be parsed using the `partial_mode=` parameter.\n\n```python\nimport jiter\n\npartial_json = b'{\"name\": \"John\", \"age\": 30, \"city\": \"New Yor'\n\n# Raise error on incomplete JSON\ntry:\n jiter.from_json(partial_json, partial_mode=False)\nexcept ValueError as e:\n print(f\"Error: {e}\")\n\n# Parse incomplete JSON, discarding incomplete last field\nresult = jiter.from_json(partial_json, partial_mode=True)\nprint(result) # Output: {'name': 'John', 'age': 30}\n\n# Parse incomplete JSON, including incomplete last field\nresult = jiter.from_json(partial_json, partial_mode='trailing-strings')\nprint(result) # Output: {'name': 'John', 'age': 30, 'city': 'New Yor'}\n```\n\n### Catching Duplicate Keys\n\nThe `catch_duplicate_keys=True` option can be used to raise a `ValueError` if an object contains duplicate keys.\n\n```python\nimport jiter\n\njson_with_dupes = b'{\"foo\": 1, \"foo\": 2}'\n\n# Default behavior (last value wins)\nresult = jiter.from_json(json_with_dupes)\nprint(result) # Output: {'foo': 2}\n\n# Catch duplicate keys\ntry:\n jiter.from_json(json_with_dupes, catch_duplicate_keys=True)\nexcept ValueError as e:\n print(f\"Error: {e}\")\n```\n\n",
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