Evaluation: Improve guardrails awareness #1246

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.