qapitol-evals-kit 0.1.1

Local BYOK LLM and RAG evaluation metrics (Qapitol Evals Kit)

qapitol-evals-kit

Local BYOK LLM and RAG evaluation metrics from Qapitol. Your data never leaves your machine.

Usage guide

docs/USAGE.md — install, all 9 evaluators, batch evals, traces, multi-turn patterns, CLI, examples, CI, FAQ.

After pip install, the full guide is on GitHub (linked from PyPI Documentation); the wheel contains code + CLI only.

Install

PyPI:

pip install qapitol-evals-kit==0.1.1
pip install "qapitol-evals-kit[all]==0.1.1"   # OpenAI + Anthropic judges

From source (development):

pip install -e ".[dev,all]"

From GitHub (fallback):

pip install "qapitol-evals-kit @ git+https://github.com/QapitolAI/qapitol-evals-kit@v0.1.0"
pip install "qapitol-evals-kit @ git+https://github.com/QapitolAI/qapitol-evals-kit.git@main"

Repo: github.com/QapitolAI/qapitol-evals-kit · Releases: v0.1.0 · PyPI: qapitol-evals-kit

Quick start (no API key)

from qapitol.evals import ExactMatchEvaluator

score = ExactMatchEvaluator().evaluate({
    "output": "Paris",
    "expected": "Paris",
})
print(score.score, score.label)  # 1.0 match

Quick start (LLM judge)

export OPENAI_API_KEY=sk-...

from qapitol.evals import CoherenceEvaluator
from qapitol.evals.llm import LLM

llm = LLM(provider="openai", model="gpt-4o-mini")
score = CoherenceEvaluator(llm).evaluate({
    "input": "What is RAG?",
    "output": "RAG retrieves context then generates an answer.",
})
print(score.score, score.label, score.explanation)

CLI

qapitol-evals doctor
qapitol-evals run --metric coherence --input "What is AI?" --output "AI is ..."

Metrics (v0.1.0)

Type	Evaluators
Code	ExactMatchEvaluator, CustomAccuracyEvaluator
LLM	Coherence, Relevance, Correctness, Hallucination, Toxicity
RAG	Faithfulness, Answer Relevancy

Test

ruff check src tests
pytest tests/ -v

For agents working on this repo, see agent.md.

License

MIT