import type { Carte } from "./carte.js";
import type { UIAdapter } from "./ui-adapter.js";
export type PromptFormat = "markdown" | "xml" | "plain";
export interface GeneratePromptOptions {
    /**
     * Output serialisation. Defaults to `"markdown"`. XML tends to work slightly
     * better with Claude for structured content; markdown is the most portable
     * choice for GPT-family models.
     */
    format?: PromptFormat;
    /**
     * Append a guide that tells the LLM how to structure its response (the
     * `{ layout, panels: [...] }` shape, the `{ $bind: "..." }` ref syntax,
     * the "respond with a single JSON code block" instruction). Defaults to
     * `true` because without it the LLM has to guess the wrapper shape and
     * usually guesses wrong. Set `false` if you supply your own format
     * instructions or use a structured-output mode.
     */
    includePlanFormat?: boolean;
    /**
     * Restrict the prompt to a subset of carte entries by id. Ids absent
     * from the carte are silently skipped. RBAC still applies — the
     * effective set is `only ∩ rbac_allowed`.
     *
     * Useful for two-stage flows: a first LLM call picks relevant query ids
     * from a compact carte summary (see `generateCompactPrompt`), and the
     * second call gets a full prompt restricted to those ids.
     */
    only?: ReadonlyArray<string>;
}
export interface GenerateCompactPromptOptions {
    /**
     * Output serialisation. Defaults to `"markdown"`. Markdown is the natural
     * fit; xml and plain are provided for consistency with `generatePrompt`.
     */
    format?: PromptFormat;
}
/**
 * Serialises the carte and UI carte into a system-prompt fragment the LLM
 * can reason about. Carte entries are filtered by `access(ctx)` *before*
 * serialisation, so the LLM only sees entries the current context may use.
 *
 * The prompt text is the framework's interface to the model — keep it stable.
 */
export declare function generatePrompt<TCtx>(carte: Carte<TCtx>, uiAdapter: UIAdapter, ctx: TCtx, options?: GeneratePromptOptions): Promise<string>;
/**
 * Generates a compact one-line-per-entry carte summary for use as a
 * first-stage LLM call that selects relevant entries before full prompt
 * generation. The output framing instructs the model to return ONLY a JSON
 * array of query ids.
 *
 * Carte entries are filtered by `access(ctx)` before serialisation, same
 * as `generatePrompt`. Entries that have an `access` predicate (and pass it
 * for the current context) are tagged `[restricted]` so the caller knows the
 * list shape may differ across roles.
 *
 * Components aren't part of the compact prompt — the first stage selects
 * queries; the second-stage `generatePrompt({ only })` handles components.
 *
 * @example
 * // Stage 1: identify relevant entries
 * const compact = await generateCompactPrompt(carte, ctx);
 * const ids = JSON.parse(await callLLM(compact + "\n\nUser: " + userMessage));
 *
 * // Stage 2: full prompt for just those entries
 * const full = await generatePrompt(carte, uiAdapter, ctx, { only: ids });
 * const plan = JSON.parse(await callLLM(full + "\n\nUser: " + userMessage));
 *
 * // Validate and execute as normal
 * const parsed = parsePlan(plan, carte, uiAdapter, ctx);
 * const results = parsed.ok ? await executePlan(parsed.plan, carte) : [];
 */
export declare function generateCompactPrompt(carte: Carte<any>, ctx: unknown, options?: GenerateCompactPromptOptions): Promise<string>;
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