# 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,
lossless_floats: bool = 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
lossless_floats: if True, preserve full detail on floats using `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.
"""
```
Raw data
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