/**
 * SelfConsistency — sample N answers, pick the majority.
 *
 * Paper: "Self-Consistency Improves Chain of Thought Reasoning in
 * Language Models" — Wang et al., 2022 (https://arxiv.org/abs/2203.11171).
 *
 * Pattern: Factory (GoF) → produces a `Runner` that composes `Parallel`
 *          of N `LLMCall` branches with a majority-vote merge function.
 * Role:    patterns/ layer. Pure composition of core primitives +
 *          core-flow compositions — no new abstractions.
 * Emits:   Everything `Parallel` + `LLMCall` emit (stream / composition /
 *          context). No pattern-specific event domain needed; consumers
 *          observe via the standard typed listeners.
 */
import type { LLMProvider } from '../adapters/types.js';
import type { Runner } from '../core/runner.js';
export interface SelfConsistencyOptions {
    readonly provider: LLMProvider;
    readonly model: string;
    readonly systemPrompt: string;
    /** Number of parallel samples. 3 / 5 are typical; paper uses up to 40. */
    readonly samples: number;
    /** Sampling temperature. Defaults to a higher value (0.7) to get diverse samples. */
    readonly temperature?: number;
    readonly maxTokens?: number;
    /**
     * Consumer-provided extractor: given a full LLM response, return the
     * "vote token" (e.g., the final answer stripped of the chain-of-thought
     * preamble). Defaults to returning the trimmed string.
     */
    readonly extract?: (response: string) => string;
    readonly name?: string;
    readonly id?: string;
}
/**
 * Build a SelfConsistency Runner. Given a system prompt, the Runner
 * runs `samples` parallel LLMCalls with the same input, extracts each
 * response's vote token, then returns the most-frequent token. Ties
 * are broken by the first response's extract.
 */
export declare function selfConsistency(opts: SelfConsistencyOptions): Runner<{
    message: string;
}, string>;
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