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Overview
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 Python SDK, or with the LLM Observability API. See Naming custom metrics for guidelines on how to choose an appropriate label for your evaluations.
Submitting evaluations with the SDK
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 submitting evaluations in the SDK documentation for details.
Example
fromddtrace.llmobsimportLLMObsfromddtrace.llmobs.decoratorsimportllm@llm(model_name="claude",name="invoke_llm",model_provider="anthropic")defllm_call():completion=...# user application logic to invoke LLM# tag your span with a `msg_id`msg_id=get_msg_id()LLMObs.annotate(tags={'msg_id':msg_id})# submit an evaluation on a span tagged with a matching `msg_id`LLMObs.submit_evaluation_for(span_with_tag_value={"tag_key":"msg_id","tag_value":msg_id},ml_app="chatbot",label="harmfulness",metric_type="score",value=10,tags={"evaluation_provider":"ragas"},)
Submitting evaluations with the API
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.