splunk-otel-genai-evals-deepeval 0.1.7

OpenTelemetry GenAI Utils

This package plugs the deepeval metrics suite into the OpenTelemetry GenAI evaluation pipeline. When it is installed a Deepeval evaluator is registered automatically and, unless explicitly disabled, is executed for every LLM/agent invocation alongside the builtin metrics.

Installation

Install the evaluator (and its runtime dependencies) from PyPI:

pip install opentelemetry-util-genai-evals-deepeval

The command pulls in opentelemetry-util-genai, deepeval and openai automatically so the evaluator is ready to use right after installation.

Requirements

• opentelemetry-util-genai together with deepeval and openai – these are installed automatically when you install this package.

• An LLM provider supported by Deepeval. By default the evaluator uses OpenAI’s gpt-4o-mini model because it offers the best balance of latency and cost for judge workloads right now, so make sure OPENAI_API_KEY is available. To override the model, set DEEPEVAL_EVALUATION_MODEL (or DEEPEVAL_MODEL / OPENAI_MODEL) to a different deployment along with the corresponding provider credentials.

• (Optional) DEEPEVAL_API_KEY if your Deepeval account requires it.

Configuration

Use OTEL_INSTRUMENTATION_GENAI_EVALS_EVALUATORS to select the metrics that should run. Leaving the variable unset enables every registered evaluator with its default metric set. Examples:

• OTEL_INSTRUMENTATION_GENAI_EVALS_EVALUATORS=Deepeval – run the default Deepeval bundle (Bias, Toxicity, Answer Relevancy, Faithfulness).

• OTEL_INSTRUMENTATION_GENAI_EVALS_EVALUATORS=Deepeval(LLMInvocation(bias(threshold=0.75))) – override the Bias threshold for LLM invocations and skip the remaining metrics.

• OTEL_INSTRUMENTATION_GENAI_EVALS_EVALUATORS=none – disable the evaluator entirely.

Results are emitted through the standard GenAI evaluation emitters (events, metrics, spans). Each metric includes helper attributes such as deepeval.success, deepeval.threshold and any evaluation model metadata returned by Deepeval. Metrics that cannot run because required inputs are missing (for example Faithfulness without a retrieval_context) are marked as label="skipped" and carry a deepeval.error attribute so you can wire the necessary data or disable that metric explicitly.