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",t};e.buildCustomizationMenuUi=t;function n(e){let t='
",t}function s(e){let n=e.filter.currentValue||e.filter.defaultValue,t='${e.filter.label}
`,e.filter.options.forEach(s=>{let o=s.id===n;t+=``}),t+="${e.filter.label}
`,t+=`In the context of LLM applications, it’s important to track user feedback and evaluate the quality of your LLM application’s responses. While LLM Observability provides a few out-of-the-box evaluations for your traces, you can submit your own evaluations to LLM Observability in two ways: with Datadog’s SDK, or with the LLM Observability API. See Naming custom metrics for guidelines on how to choose an appropriate label for your evaluations.
The LLM Observability SDK provides the methods LLMObs.submit_evaluation_for()
and LLMObs.export_span()
to help your traced LLM application submit evaluations to LLM Observability. See the Python or NodeJS SDK documentation for more details.
from ddtrace.llmobs import LLMObs
from ddtrace.llmobs.decorators import llm
def my_harmfulness_eval(input: Any) -> float:
score = ... # custom harmfulness evaluation logic
return score
@llm(model_name="claude", name="invoke_llm", model_provider="anthropic")
def llm_call():
completion = ... # user application logic to invoke LLM
# joining an evaluation to a span via span ID and trace ID
span_context = LLMObs.export_span(span=None)
LLMObs.submit_evaluation(
span = span_context,
ml_app = "chatbot",
label="harmfulness",
metric_type="score", # can be score or categorical
value=my_harmfulness_eval(completion),
tags={"reasoning": "it makes sense", "type": "custom"},
)
You can use the evaluations API provided by LLM Observability to send evaluations associated with spans to Datadog. See the Evaluations API for more details on the API specifications.
{
"data": {
"type": "evaluation_metric",
"id": "456f4567-e89b-12d3-a456-426655440000",
"attributes": {
"metrics": [
{
"id": "cdfc4fc7-e2f6-4149-9c35-edc4bbf7b525",
"join_on": {
"tag": {
"key": "msg_id",
"value": "1123132"
}
},
"span_id": "20245611112024561111",
"trace_id": "13932955089405749200",
"ml_app": "weather-bot",
"timestamp_ms": 1609479200,
"metric_type": "score",
"label": "Accuracy",
"score_value": 3
}
]
}
}
}