tagnify 0.2.0

LLM-powered automated data labeling with schema-first, confidence-scored, and local-model ready

Tagnify

LLM-powered automated data labeling schema-first, confidence-scored, local-model ready.

from tagnify import Tagnify, Schema, Example

schema = Schema(
    labels=["positive", "negative", "neutral"],
    examples=[Example(text="Great product!", label="positive")]
)

tagnify = Tagnify(model="qwen2.5:7b")
result = tagnify.label("This was a disappointing experience.", schema)

print(result.label)       # "negative"
print(result.confidence)  # 0.91

Features

• Schema-first design — labels and few-shot examples are defined upfront; examples are mandatory, not optional
• Confidence scoring — every label includes a confidence score; low-confidence results are automatically flagged
• Automatic retries — invalid or malformed model output triggers a retry with a stronger prompt, up to 3 attempts
• Reasoning traces — optionally request a one-line explanation for each label (reasoning=True)
• Pluggable backends — run locally for free via Ollama, or plug in your own LLM API

Installation

pip install tagnify

Requires Ollama running locally for the default backend.

Custom Backends

Have your own LLM API an internal company model, a provider Tagnify doesn't support yet, anything that isn't Ollama? Implement BaseBackend and wire it in with Tagnify.with_backend():

from tagnify import Tagnify, Schema, Example
from tagnify.backends.base import BaseBackend
from tagnify.exceptions import BackendError
import httpx

class MyCompanyBackend(BaseBackend):
    def __init__(self, endpoint: str, api_key: str, model: str):
        self.endpoint = endpoint
        self.api_key = api_key
        self.model = model

    def complete(self, prompt: str) -> str:
        try:
            response = httpx.post(
                self.endpoint,
                headers={"Authorization": f"Bearer {self.api_key}"},
                json={"model": self.model, "prompt": prompt},
                timeout=60.0,
            )
            response.raise_for_status()
        except httpx.HTTPError as e:
            raise BackendError(f"Backend call failed: {e}") from e
        return response.json()["text"]

backend = MyCompanyBackend(endpoint="...", api_key="...", model="...")
tagnify = Tagnify.with_backend(backend)

result = tagnify.label("This was a disappointing experience.", schema)

Everything downstream — retries, parsing, validation, confidence scoring — works identically, regardless of where complete() gets its text from. Wrap your own network/API errors in BackendError so the retry logic behaves correctly: it's treated as an infrastructure failure and is not retried, unlike a malformed model response which is.

Documentation

Full docs at docs.tagnify.io — coming soon.

License

MIT