Simple, Pythonic building blocks to evaluate LLM applications.
pip install langcheckHaving installation issues? See the FAQ.
Use LangCheck's suite of metrics to evaluate LLM-generated text.
import langcheck
# Generate text with any LLM library
generated_outputs = [
'Black cat the',
'The black cat is sitting',
'The big black cat is sitting on the fence'
]
# Check text quality and get results as a DataFrame (threshold is optional)
langcheck.metrics.fluency(generated_outputs) > 0.5
It's easy to turn LangCheck metrics into unit tests, just use assert:
assert langcheck.metrics.fluency(generated_outputs) > 0.5LangCheck includes several types of metrics to evaluate LLM applications. Some examples:
| Type of Metric | Examples | Languages |
|---|---|---|
| Reference-Free Text Quality Metrics | toxicity(generated_outputs)sentiment(generated_outputs)ai_disclaimer_similarity(generated_outputs) |
EN, JA, ZH, DE |
| Reference-Based Text Quality Metrics | semantic_similarity(generated_outputs, reference_outputs)rouge2(generated_outputs, reference_outputs) |
EN, JA, ZH, DE |
| Source-Based Text Quality Metrics | factual_consistency(generated_outputs, sources) |
EN, JA, ZH, DE |
| Text Structure Metrics | is_float(generated_outputs, min=0, max=None)is_json_object(generated_outputs) |
All Languages |
LangCheck comes with built-in, interactive visualizations of metrics.
# Choose some metrics
fluency_values = langcheck.metrics.fluency(generated_outputs)
sentiment_values = langcheck.metrics.sentiment(generated_outputs)
# Interactive scatter plot of one metric
fluency_values.scatter()
# Interactive scatter plot of two metrics
langcheck.plot.scatter(fluency_values, sentiment_values)
# Interactive histogram of a single metric
fluency_values.histogram()
Text augmentations can automatically generate reworded prompts, typos, gender changes, and more to evaluate model robustness.
For example, to measure how the model responds to different genders:
male_prompts = langcheck.augment.gender(prompts, to_gender='male')
female_prompts = langcheck.augment.gender(prompts, to_gender='female')
male_generated_outputs = [my_llm_app(prompt) for prompt in male_prompts]
female_generated_outputs = [my_llm_app(prompt) for prompt in female_prompts]
langcheck.metrics.sentiment(male_generated_outputs)
langcheck.metrics.sentiment(female_generated_outputs)You can write test cases for your LLM application using LangCheck metrics.
For example, if you only have a list of prompts to test against:
from langcheck.utils import load_json
# Run the LLM application once to generate text
prompts = load_json('test_prompts.json')
generated_outputs = [my_llm_app(prompt) for prompt in prompts]
# Unit tests
def test_toxicity(generated_outputs):
assert langcheck.metrics.toxicity(generated_outputs) < 0.1
def test_fluency(generated_outputs):
assert langcheck.metrics.fluency(generated_outputs) > 0.9
def test_json_structure(generated_outputs):
assert langcheck.metrics.validation_fn(
generated_outputs, lambda x: 'myKey' in json.loads(x)).all()You can monitor the quality of your LLM outputs in production with LangCheck metrics.
Just save the outputs and pass them into LangCheck.
production_outputs = load_json('llm_logs_2023_10_02.json')['outputs']
# Evaluate and display toxic outputs in production logs
langcheck.metrics.toxicity(production_outputs) > 0.75
# Or if your app outputs structured text
langcheck.metrics.is_json_array(production_outputs)You can provide guardrails on LLM outputs with LangCheck metrics.
Just filter candidate outputs through LangCheck.
# Get a candidate output from the LLM app
raw_output = my_llm_app(random_user_prompt)
# Filter the output before it reaches the user
while langcheck.metrics.contains_any_strings(raw_output, blacklist_words).any():
raw_output = my_llm_app(random_user_prompt)
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent