/**
 * ollama_embed — FLAGSHIP TOOL.
 *
 * Batch-aware vector embeddings. Powers concept-search over memory/,
 * canon, doctrine, protocols — the bridge from filename search to idea search.
 *
 * Tier: Embed. Pin model version alongside vectors so drift is detectable.
 */
import { z } from "zod";
import type { Envelope } from "../envelope.js";
import type { RunContext } from "../runContext.js";
export declare const embedSchema: z.ZodObject<{
    input: z.ZodUnion<readonly [z.ZodString, z.ZodArray<z.ZodString>]>;
}, z.core.$strip>;
export type EmbedInput = z.infer<typeof embedSchema>;
export interface EmbedResult {
    embeddings: number[][];
    model_version: string;
    count: number;
    dim: number;
}
/**
 * Payload-size warning threshold. Exported for tests so the invariant
 * "warnings fire when payload crosses 500KB" stays codified and not a
 * floating magic number in an assertion.
 *
 * 500 KB corresponds to roughly 130 768-dim float32 vectors serialized
 * as JSON text — well under Node/MCP transport limits, but past the
 * point where batch-level concept search via ollama_embed_search is a
 * strictly better shape.
 */
export declare const EMBED_PAYLOAD_WARN_BYTES: number;
/**
 * Compute the approximate JSON-wire size of the embeddings array. We
 * don't serialize the full envelope to measure — just the vectors, since
 * they're what blows the budget at scale. Each number is counted as a
 * fixed 9-byte estimate (e.g. "-0.123456,") which tracks real JSON
 * payloads to within a few percent.
 */
export declare function estimateEmbeddingsBytes(embeddings: number[][]): number;
export declare function handleEmbed(input: EmbedInput, ctx: RunContext): Promise<Envelope<EmbedResult>>;
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