Add the following two lines to your application code:

import openlit

# The 'All' evaluator runs checks for Hallucination, Bias, and Toxicity
evals = openlit.evals.All()
result = evals.measure()

import openlit

# openlit can also read the OPENAI_API_KEY variable directy from env if not specified via function argument
openai_api_key=os.getenv("OPENAI_API_KEY")

# The 'All' evaluator runs checks for Hallucination, Bias, and Toxicity
evals = openlit.evals.All(provider="openai", api_key=openai_api_key)

contexts = ["Einstein won the Nobel Prize for his discovery of the photoelectric effect in 1921"]
prompt = "When and why did Einstein win the Nobel Prize?"
text = "Einstein won the Nobel Prize in 1969 for his discovery of the photoelectric effect"

result = evals.measure(prompt=prompt, contexts=contexts, text=text)

verdict='yes' evaluation='hallucination' score=0.9 classification='factual_inaccuracy' explanation='The text incorrectly states that Einstein won the Nobel Prize in 1969, while the context specifies that he won it in 1921 for his discovery of the photoelectric effect, leading to a significant factual inconsistency.'

import openlit

# The 'All' evaluator runs checks for Hallucination, Bias, and Toxicity
evals = openlit.evals.All()
result = evals.measure()

import openlit

# openlit can also read the OPENAI_API_KEY variable directy from env if not specified via function argument
openai_api_key=os.getenv("OPENAI_API_KEY")

# The 'All' evaluator runs checks for Hallucination, Bias, and Toxicity
evals = openlit.evals.All(provider="openai", api_key=openai_api_key)

contexts = ["Einstein won the Nobel Prize for his discovery of the photoelectric effect in 1921"]
prompt = "When and why did Einstein win the Nobel Prize?"
text = "Einstein won the Nobel Prize in 1969 for his discovery of the photoelectric effect"

result = evals.measure(prompt=prompt, contexts=contexts, text=text)

verdict='yes' evaluation='hallucination' score=0.9 classification='factual_inaccuracy' explanation='The text incorrectly states that Einstein won the Nobel Prize in 1969, while the context specifies that he won it in 1921 for his discovery of the photoelectric effect, leading to a significant factual inconsistency.'

The “All” evaluator is great for checking text for Hallucination, Bias, and Toxicity simultaneously. For more efficient, targeted evaluations, you can use specific evaluators like openlit.evals.Hallucination(), openlit.evals.Bias(), or openlit.evals.Toxicity().

For details on how it works, and to see the supported providers, models and parameters to pass, check our Evaluations Guide.

The openlit.evals module integrates with OpenTelemetry to track evaluation metrics as a counter, including score details and evaluation metadata. To enable metric collection, initialize OpenLIT with metrics tracking:

import openlit

# Initialize OpenLIT for metrics collection
openlit.init()

# Then, initialize the evaluator with metric tracking enabled
evals = openlit.evals.All(collect_metrics=True)

These metrics can be sent to any OpenTelemetry-compatible backend. For configuration details, check out our Connections Guide to choose your preferred destination for these metrics.