forgesight-eval 0.1.1

ForgeSight eval — run-correlated eval scores + human feedback on the telemetry pipeline.

forgesight-eval

Run-correlated eval scores and human feedback for ForgeSight. Quality joins cost and structure on the same run_id, on the same pipeline, in the same backends — Langfuse scores, Phoenix evaluations, any OTLP sink — for two function calls.

pip install forgesight-eval

from forgesight import telemetry
from forgesight_eval import record_evaluation, record_feedback

with telemetry.agent_run("rag-answerer", metadata={"prompt_version": "v7"}) as run:
    answer = await answer_question(...)
    # an automated eval (LLM-as-judge / Ragas / DeepEval) → one call, correlated to the live run
    record_evaluation("faithfulness", score=0.91, label="pass",
                      explanation="All claims grounded.", evaluator="ragas")

# hours later, from a webhook — post-hoc human feedback, by run_id:
def on_thumbs_down(run_id: str, comment: str) -> None:
    record_feedback("user_satisfaction", run_id=run_id, label="thumbs_down",
                    score=0.0, comment=comment, source="human")

How it works

• record_evaluation attaches to the current run (ambient context) — a real-time eval nests as a child span under the run's open trace. record_feedback attaches to a past run by run_id — a standalone record carrying the id so the backend re-associates it.
• Both emit the OTel gen_ai.evaluation.* attributes (name / score.value / score.label / explanation) plus forgesight.evaluation.* extensions (source, realtime, evaluator), and publish an EVALUATION_RECORDED lifecycle event. Backends that speak the convention (Langfuse, Phoenix) display them as scores/evaluations with no per-backend code (P4).
• Non-blocking (P6): both build a record and enqueue — no network I/O on the agent or the webhook. Secure by default (P7): explanation / comment text is gated by capture_explanation and the global content-capture switch.

Configuration

modules:
  eval:
    enabled: true              # master switch (default false — install ≠ active)
    emit_as: "span"            # span | event
    capture_explanation: true  # still gated by the global content-capture switch (P7)
    score_schema:              # optional — validates score/label at the call site
      faithfulness:      { type: "numeric", min: 0.0, max: 1.0 }
      user_satisfaction: { type: "categorical", labels: ["thumbs_up", "thumbs_down"] }

At least one of score / label must be set. Schema'd dimensions are validated (numeric range, categorical membership); un-schema'd dimensions are accepted unvalidated (open set).

Out of scope

Running the evaluations (judges/metrics live in the agent or a library — this is the transport), a dashboard, and score storage/aggregation (the backend's job).

License

Apache-2.0