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언제든지 연락주시기 바랍니다.Overview
DeepEval is an open source framework that provides ready-to-use LLM metrics and allows for customizable LLM evaluations. For more information, see DeepEval’s documentation.
You can use LLM Observability to run DeepEval evaluations in Experiments. DeepEval evaluation results appear as evaluator results tied to each instance in an LLM Observability dataset.
Setup
- Set up an LLM Observability Experiment and an LLM Observability Dataset.
- Provide a DeepEval evaluator to the
evaluators parameter in an LLMObs Experiment as demonstrated in the following code sample. For a working example, see Datadog’s DeepEval demo in GitHub.
from deepeval.metrics import GEval
from deepeval.test_case import LLMTestCaseParams
from ddtrace.llmobs import LLMObs
LLMObs.enable(
api_key="<YOUR_API_KEY>", # defaults to DD_API_KEY environment variable
app_key="<YOUR_APP_KEY>", # defaults to DD_APP_KEY environment variable
site="datadoghq.com", # defaults to DD_SITE environment variable
project_name="<YOUR_PROJECT>" # defaults to DD_LLMOBS_PROJECT_NAME environment variable, or "default-project" if the environment variable is not set
)
# this can be any DeepEval evaluator
deepeval_evaluator = GEval(
name="<EVAL_NAME>",
criteria="<CRITERIA>",
evaluation_steps=[
"<EVALUATION STEP 1",
"...",
"<EVALUATION STEP2>"
],
evaluation_params=[LLMTestCaseParams.INPUT, LLMTestCaseParams.ACTUAL_OUTPUT, LLMTestCaseParams.EXPECTED_OUTPUT],
async_mode=True,
)
dataset = LLMObs.create_dataset(
dataset_name="capitals-of-the-world",
project_name="capitals-project", # optional, defaults to project_name used in LLMObs.enable
description="Questions about world capitals",
records=[
{
"input_data": {
"question": "What is the capital of China?"
}, # required, JSON or string
"expected_output": "Beijing", # optional, JSON or string
"metadata": {"difficulty": "easy"}, # optional, JSON
},
{
"input_data": {
"question": "Which city serves as the capital of South Africa?"
},
"expected_output": "Pretoria",
"metadata": {"difficulty": "medium"},
},
],
)
def task(input_data: Dict[str, Any], config: Optional[Dict[str, Any]] = None) -> str:
question = input_data['question']
# Your LLM or processing logic here
return "Beijing" if "China" in question else "Unknown"
def num_exact_matches(inputs, outputs, expected_outputs, evaluators_results):
return evaluators_results["<EVAL_NAME>"].count(True)
experiment = LLMObs.experiment(
name="<EXPERIMENT_NAME>",
task=my_task,
dataset=dataset,
evaluators=[deepeval_evaluator],
summary_evaluators=[num_exact_matches], # optional
description="<EXPERIMENT_DESCRIPTION>",
)
results = experiment.run(jobs=4, raise_errors=True)
print(f"View experiment: {experiment.url}")
Usage
After you run an experiment with a DeepEval evaluation, you can view the DeepEval evaluation results per instance in the corresponding experiment run in Datadog. In the experiment below, a DeepEval evaluator with the name “Correctness” was run:
Further reading
