/**
 * CLI embedder — pluggable text-to-vector embedding for search commands
 * @generated by @constructive-io/graphql-codegen
 * DO NOT EDIT - changes will be overwritten
 */
export type EmbedderFunction = (text: string) => Promise<number[]>;
export interface EmbedderConfig {
    /** Provider name: 'ollama' or 'custom' */
    provider: string;
    /** Model identifier (e.g. 'nomic-embed-text') */
    model?: string;
    /** Base URL for the provider (e.g. 'http://localhost:11434' for Ollama) */
    baseUrl?: string;
}
/**
 * Resolve an embedder function from environment variables or appstash config.
 *
 * Resolution order:
 *   1. EMBEDDER_PROVIDER env var (+ EMBEDDER_MODEL, EMBEDDER_BASE_URL)
 *   2. appstash config keys: embedder.provider, embedder.model, embedder.baseUrl
 *   3. null (no embedder configured)
 *
 * @param store - Optional appstash config store for reading persisted config
 * @returns An EmbedderFunction or null if no embedder is configured
 */
export declare function resolveEmbedder(store?: {
    getVar: (key: string) => string | undefined;
}): EmbedderFunction | null;
/**
 * Auto-embed text values in vector where-clause fields.
 *
 * When --auto-embed is passed, any vector field in the where clause that
 * contains a text string (instead of a float array) will be converted to
 * an embedding vector using the configured embedder.
 *
 * @param where - The where clause object (mutated in place)
 * @param vectorFieldNames - Names of vector embedding fields (e.g. ['vectorEmbedding'])
 * @param embedder - The resolved embedder function
 * @returns The modified where clause
 */
export declare function autoEmbedWhere<T extends object>(where: T, vectorFieldNames: string[], embedder: EmbedderFunction): Promise<T>;
/**
 * Auto-embed text values in mutation input data (create/update).
 *
 * When --auto-embed is passed on create or update, any vector field in
 * the input data that contains a text string will be converted to an
 * embedding vector using the configured embedder.
 *
 * Usage:
 *   csdk article create --input.embedding "Machine learning concepts" --auto-embed
 *   csdk article update --id xxx --input.embedding "Updated description" --auto-embed
 *
 * This is a CLI-only convenience — in production, database triggers or
 * a job queue should handle embedding generation.
 *
 * @param data - The mutation input data object (mutated in place)
 * @param vectorFieldNames - Names of vector embedding fields (e.g. ['embedding'])
 * @param embedder - The resolved embedder function
 * @returns The modified data object with text values replaced by vectors
 */
export declare function autoEmbedInput<T extends object>(data: T, vectorFieldNames: string[], embedder: EmbedderFunction): Promise<T>;