import { DatasetContract, DatasetEntry, DatasetOptions } from "./dataset.type.mjs";
import { judge } from "./judge-scorer.mjs";
import { fromJSON, toJSON } from "./report-json.mjs";
import { toJUnit } from "./report-junit.mjs";
import { EvalPredicate, contains, exact, predicate } from "./scorers.mjs";
import { runEval } from "./eval-runner.mjs";
import { dataset } from "./dataset.mjs";
import { diff } from "./regression.mjs";

//#region ../ai/src/eval/index.d.ts
/**
 * Built-in eval scorer factories plus the CI reporters, surfaced on
 * `ai.eval.*`.
 *
 * Scorers:
 * - `exact()` — pass when output equals the case `expected` (trimmed,
 *   case-insensitive; structured values compared by canonical JSON).
 * - `contains()` — pass when `expected` appears as a substring.
 * - `predicate(fn)` — wrap an arbitrary boolean assertion.
 * - `judge(config)` — LLM-as-judge scoring against a rubric.
 *
 * Reporters / serialization (pure functions over a finished `EvalReport`):
 * - `toJUnit(report)` — JUnit-XML artifact for CI ingestion.
 * - `toJSON(report)` / `fromJSON(serialized)` — round-trippable snapshot;
 *   today's report becomes tomorrow's `baseline`.
 *
 * @example
 * await myAgent.eval({
 *   cases: [{ name: "q", input: "Capital of Egypt?", expected: "Cairo" }],
 *   scorers: [ai.eval.contains()],
 * });
 *
 * @example
 * const report = await myAgent.eval({ cases: ds, scorers: [ai.eval.exact()] });
 * await writeFile("./report.junit.xml", ai.eval.toJUnit(report));
 */
declare const evalScorers: {
  exact: typeof exact;
  contains: typeof contains;
  predicate: typeof predicate;
  judge: typeof judge;
  toJUnit: typeof toJUnit;
  toJSON: typeof toJSON;
  fromJSON: typeof fromJSON;
};
//#endregion
export { evalScorers };
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