panini-lang 0.2.0

Python bindings for the Panini morphological extraction engine.

panini-lang

Python bindings for Panini — an LLM-powered morphological feature extraction engine for any language.

Installation

pip install panini-lang

No other dependencies required. The package ships pre-compiled for macOS, Linux, and Windows.

Quick Start

from panini import extract

result = extract(
    provider="openai",        # "openai" | "anthropic" | "google"
    model="gpt-4o",
    api_key="sk-...",
    language="fra",           # ISO 639-3 code
    text="Je ne sais pas",
    targets=["sais"],
)

print(result)
# {
#   "target_features": [...],
#   "context_features": [...]
# }

Async Support

import asyncio
from panini import async_extract

async def main():
    result = await async_extract(
        provider="anthropic",
        model="claude-opus-4-5",
        api_key="sk-ant-...",
        language="pol",
        text="Nie wiem",
        targets=["wiem"],
    )
    print(result)

asyncio.run(main())

Supported Languages

from panini import supported_languages

supported_languages()
# ['pol', 'tur', 'ara', 'fra']

Code	Language
pol	Polish
tur	Turkish
ara	Arabic
fra	French

Supported Providers

Provider	provider= value	Example model
OpenAI	"openai"	"gpt-4o"
Anthropic	"anthropic"	"claude-opus-4-5"
Google	"google"	"gemini-2.0-flash"

Analysis Components

By default, all compatible components are run. You can select specific ones:

result = extract(
    ...,
    components=["morphology", "pedagogical_explanation"],
)

Key	Description
morphology	POS tag, lemma, case/tense/gender/etc.
pedagogical_explanation	HTML explanation for language learners
multiword_expressions	Idioms, collocations, phrasal verbs
morpheme_segmentation	Morpheme-by-morpheme breakdown (agglutinative languages only)
leipzig_alignment	Leipzig-style interlinear glossing

Custom Prompts

Panini ships with built-in extraction prompts. You can override them by passing a YAML file path or a dict:

# From a YAML file
result = extract(..., prompts="/path/to/my_prompts.yml")

# From a dict
result = extract(..., prompts={
    "system_role": "You are a strict NLP extractor...",
    ...
})

# Inspect the built-in default prompts
from panini import get_default_prompts
print(get_default_prompts())

Full API Reference

extract(...) -> dict

Synchronous extraction.

Parameter	Type	Default	Description
provider	str	required	LLM provider: "openai", "anthropic", "google"
model	str	required	Model name (e.g. "gpt-4o")
api_key	str	required	Provider API key
language	str	required	ISO 639-3 language code
text	str	required	Sentence to analyse
targets	list[str]	required	Target words to extract features for
prompts	str | dict | None	None	Path to YAML, dict, or None for built-in defaults
temperature	float	0.2	LLM temperature
max_tokens	int	4096	Max output tokens
ui_language	str	"English"	Language for pedagogical explanations
components	list[str] | None	None	Components to run (all if None)

async_extract(...) -> dict

Asynchronous version of extract(). Same parameters, returns a coroutine.

supported_languages() -> list[str]

Returns the list of supported ISO 639-3 language codes.

get_default_prompts() -> dict

Returns the built-in extraction prompts as a Python dict.

version() -> str

Returns the current package version.

License

MIT