semanticheck 0.2.0

pytest-native semantic assertions for LLM and generative AI applications. No servers. No SaaS. Works with OpenAI, Anthropic, LiteLLM and any LLM client.

semanticheck

pytest-native semantic assertions for LLM and generative AI applications.

No servers. No SaaS. No config. Works with OpenAI, Anthropic, LiteLLM, or any LLM client.

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Installation

pip install semanticheck

With local embeddings (recommended, no API cost):

pip install "semanticheck[local]"

With OpenAI embeddings:

pip install "semanticheck[openai]"

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Quick Start

from semanticheck import assert_intent, assert_tone, assert_no_hallucination, assert_no_pii

def test_customer_support_reply():
    response = my_llm("Help me reset my password")

    assert_intent(response, "instructions for resetting a password")
    assert_tone(response, "friendly")
    assert_no_pii(response)
    assert_no_hallucination(response, known_facts=["Password reset links expire after 24 hours"])

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Assertions

Function	What it checks
assert_intent(response, expected_intent)	Semantic match to expected meaning
assert_tone(response, tone)	Tone: professional, casual, friendly, formal, etc.
assert_no_hallucination(response, known_facts)	No contradiction of known facts
assert_similar_to(response, reference)	Cosine similarity to a reference text
assert_token_budget(response, max_tokens)	Response stays within token limit
assert_schema(response, schema)	Valid JSON Schema or Pydantic model
assert_language(response, language)	Written in expected language
assert_no_pii(response)	No emails, SSNs, credit cards, phones, etc.
assert_readability(response, min_score=60)	Flesch Reading Ease score
assert_sentiment(response, "positive")	Sentiment polarity

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Baseline Regression Testing

import pytest
from semanticheck import record_baseline, compare_baseline

@pytest.mark.llm
def test_summarizer_regression(llm_record):
    response = my_summarizer(article)
    if llm_record:
        record_baseline("summarizer_v1", response)
    else:
        compare_baseline("summarizer_v1", response, threshold=0.85)

Run with:

pytest --record-baselines                 # record golden baselines
pytest                                    # compare against them
pytest --baseline-dir ./.baselines/llm    # choose baseline directory (recommended for monorepos)

Recommended ergonomic version (auto baseline naming via nodeid):

import pytest

@pytest.mark.llm
def test_summarizer_regression(llm_baseline):
    response = my_summarizer(article)
    llm_baseline.check(response, threshold=0.85)

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LocalJudge

Use a local model as a judge — zero API cost in CI:

from semanticheck import LocalJudge

judge = LocalJudge()  # uses Qwen2.5-0.5B by default
result = judge.evaluate(
    response="Paris is the capital of France.",
    criterion="Correctly answers a geography question about European capitals.",
)
assert result.passed
print(result.score, result.reasoning)

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pytest Plugin

semanticheck auto-registers as a pytest plugin. Available flags:

pytest --skip-llm           # skip all @pytest.mark.llm tests
pytest --llm-threshold 0.8  # override similarity threshold globally
pytest --record-baselines   # record golden baselines

Available markers:

@pytest.mark.llm        # LLM semantic test
@pytest.mark.llm_slow   # slow test using local judge inference

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Embedding Backends

Backend	How to activate	Cost
sentence-transformers	pip install semanticheck[local]	Free
OpenAI	Set OPENAI_API_KEY	Per token
Hash fallback	SEMANTICHECK_EMBED_BACKEND=fallback	Free (smoke tests only)

Override via env vars:

SEMANTICHECK_EMBED_BACKEND=openai
SEMANTICHECK_EMBED_MODEL=text-embedding-3-large

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License

MIT