/**
 * ollama_corpus_answer — FLAGSHIP TOOL.
 *
 * Job-shape: given a NAMED CORPUS and a question, retrieve via
 * searchCorpus, then synthesize an answer grounded in the retrieved
 * chunks only. Distinct from ollama_research (which takes source_paths
 * the caller explicitly hands in).
 *
 * Tier: Deep for synthesis; Embed for retrieval (via searchCorpus).
 *
 * Laws:
 * - Retrieved chunks only. The synthesis prompt never receives outside
 *   context, and the system prompt forbids outside knowledge.
 * - Chunk-grounded citations. The model cites by source number; we
 *   validate every number is in [1, N] and strip the rest, then map
 *   back to (path, chunk_index, heading_path, title) from retrieval.
 * - Weak retrieval degrades honestly. Zero hits short-circuit without
 *   invoking the model; thin retrieval (< 2 hits) flags `weak: true`
 *   and adds a coverage note so the caller doesn't mistake a narrow
 *   answer for a confident one.
 * - Raw chunk text never crosses the MCP boundary. Only the structured
 *   citation shape and the final answer are returned.
 */
import { z } from "zod";
import type { Envelope } from "../envelope.js";
import { type SearchMode, type CorpusHit } from "../corpus/searcher.js";
import type { RunContext } from "../runContext.js";
export declare const corpusAnswerSchema: z.ZodObject<{
    corpus: z.ZodString;
    question: z.ZodString;
    mode: z.ZodOptional<z.ZodEnum<{
        semantic: "semantic";
        lexical: "lexical";
        hybrid: "hybrid";
        fact: "fact";
        title_path: "title_path";
    }>>;
    top_k: z.ZodOptional<z.ZodNumber>;
    max_words: z.ZodOptional<z.ZodNumber>;
    min_top_score: z.ZodOptional<z.ZodNumber>;
    model: z.ZodOptional<z.ZodString>;
}, z.core.$strip>;
export type CorpusAnswerInput = z.infer<typeof corpusAnswerSchema>;
export interface CorpusAnswerCitation {
    path: string;
    chunk_index: number;
    heading_path: string[];
    title: string | null;
    /** Retrieval relevance score for this chunk, propagated from the hit. */
    score: number;
}
export interface RetrievalStat {
    retrieved: number;
    total_in_corpus: number;
    top_score: number;
    weak: boolean;
}
export interface CorpusAnswerResult {
    answer: string;
    citations: CorpusAnswerCitation[];
    covered_sources: string[];
    omitted_sources: string[];
    coverage_notes: string[];
    mode: SearchMode;
    retrieval: RetrievalStat;
    /**
     * True when the tool refused to invoke the model because retrieval did
     * not clear a relevance bar — empty corpus, 0 hits, or top score below
     * the caller-supplied `min_top_score` floor. Distinct from `weak`: weak
     * means thin grounding (e.g. 1 hit); abstained means no synthesis at all.
     */
    abstained: boolean;
}
export declare function validateAndMapCitations(numbers: number[], hits: CorpusHit[]): {
    valid: CorpusAnswerCitation[];
    stripped: number;
};
export declare function computeCoverage(hits: CorpusHit[], citations: CorpusAnswerCitation[], weak: boolean, stripped: number): {
    covered: string[];
    omitted: string[];
    notes: string[];
};
export declare function handleCorpusAnswer(input: CorpusAnswerInput, ctx: RunContext): Promise<Envelope<CorpusAnswerResult>>;
//# sourceMappingURL=corpusAnswer.d.ts.map