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Evaluation: Improve guardrails awareness

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@AkhileshNegi já está trabalhando nisso.

Desde 28/9/2026.

  • #1208 de @AkhileshNegi — aberto

Avaliação

Dificuldade
5/5
Tempo estimado
Mais de uma semana
Facilidade para iniciantes
15/100
Tipo de issue
Funcionalidade
Clareza
Claramente especificada
Status de atividade
Estagnada
Stack de tecnologia
fastapi, python
Domínio
ai, backend-api-design

Direção de pesquisa

The issue says the evaluation job's v2 path and GET /api/v1/evaluations/{id} results are involved; start by tracing that evaluation path and the execute_llm_call path. Check how guardrail data passes through retries, merging, scoring, and traces. Done means fast/v2 evaluations apply input and output guardrails and expose the requested per-row fields without recording eval traffic as llm_call rows; the issue reports this work as implemented in #1208.

Escrita pelo modelo de indexação a partir do texto da issue.

Descrição

enhancement

Is your feature request related to a problem?
Text evaluations (fast run mode / v2) currently ignore the stored input and output guardrails. This leads to eval scores that don't accurately reflect the system's behavior in production.

Describe the solution you'd like

  • Make fast/v2 evaluations guardrails-aware: check each dataset query against input_guardrails before the LLM call and the response against output_guardrails after.
  • Use the execute_llm_call path instead of a separate eval-only path, avoiding the recording of eval traffic as llm_call rows.
  • Include per-row guardrail status, input_to_llm, and output_from_llm in GET /api/v1/evaluations/{id} results.
  • Carry guardrail data through retry, merge, scoring, and traces.
Original issue

Is your feature request related to a problem?
Text evaluations (fast run mode / v2) ignore the input and output guardrails stored on the config. The golden dataset query is sent straight to the LLM and the generated response is never checked by output guardrails, so eval scores don't reflect how the config actually behaves in production via /llm/call.

Describe the solution you'd like

  • Make the fast/v2 evaluation run guardrails-aware: run each dataset query through the config's input_guardrails before the LLM call, and the response through output_guardrails after.
  • Reuse the execute_llm_call path instead of a separate eval-only LLM path, without recording eval traffic as llm_call rows / production metrics.
  • Surface per-row guardrail status, input_to_llm and output_from_llm in GET /api/v1/evaluations/{id} results.
  • Carry guardrail data through retry, merge, scoring and traces.

Original issue

Earlier doing text evals v2 used not to take into account the stored input and output validators ids and instead sending the input / output straight to openAI. Now the evaluation job (v2) is guardrails aware; meaning the query in the golden dataset is passed through input guardrails if they are stored; similar functionality for responses generated (if o/p guardrails are configured).

Comments: Implemented in #1208.

Linguagem predominante
Python
Estrelas
18
Forks
11
Merge médio
4d 3h
PRs com merge (30d)
13

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