import { TraceloopClient } from "../traceloop-client";
import { BaseDatasetEntity } from "../dataset/base-dataset";
import type { EvaluatorRunOptions, TriggerEvaluatorRequest, TriggerEvaluatorResponse, CreateCustomEvaluatorRequest, EvaluatorUpdateRequest, EvaluatorExecuteOptions, EvaluatorCreateResponse, EvaluatorUpdateResponse, EvaluatorExecuteResponse, EvaluatorSource, EvaluatorCatalogItem, EvaluatorData } from "../../interfaces/evaluator.interface";
import type { ExecutionResponse } from "../../interfaces/experiment.interface";
export declare class Evaluator extends BaseDatasetEntity {
    constructor(client: TraceloopClient);
    /**
     * Creates a new LLM-as-a-judge custom evaluator without binding it to any project or environment.
     * @param options The evaluator configuration including name, messages, provider, model, and schemas
     * @returns The created evaluator's ID and slug
     * @throws Error if the API request fails
     *
     * @example
     * const result = await client.evaluator.create({
     *   name: "Quality Evaluator",
     *   provider: "openai",
     *   model: "gpt-4o",
     *   messages: [
     *     { role: "system", content: "You are a strict quality evaluator." },
     *     { role: "user", content: "Evaluate: {{text}}" },
     *   ],
     *   inputSchema: [{ name: "text", type: "string" }],
     *   outputSchema: [{ name: "passed", type: "boolean" }],
     * });
     * console.log(result.id, result.slug);
     */
    create(options: CreateCustomEvaluatorRequest): Promise<EvaluatorCreateResponse>;
    /**
     * Lists all evaluators for the organization, optionally filtered by source.
     * @param source Optional filter — "custom" for user-created evaluators, "prebuilt" for Traceloop built-in evaluators, omit to get all
     * @returns Array of evaluators with their metadata and schemas
     * @throws Error if the API request fails or if an invalid source is provided
     *
     * @example
     * // Get all evaluators
     * const all = await client.evaluator.list();
     *
     * @example
     * // Get only custom evaluators
     * const custom = await client.evaluator.list("custom");
     */
    list(source?: EvaluatorSource): Promise<EvaluatorCatalogItem[]>;
    /**
     * Retrieves the full configuration of a single evaluator by ID or slug.
     * @param identifier The evaluator's ID (e.g. "cmb6nr...") or slug (e.g. "my-quality-evaluator")
     * @returns Full evaluator details including config, provider, model, messages, and schemas
     * @throws Error if the evaluator is not found or if the config is missing required fields
     *
     * @example
     * // Get by ID
     * const evaluator = await client.evaluator.get("cmb6nr...");
     *
     * @example
     * // Get by slug
     * const evaluator = await client.evaluator.get("my-quality-evaluator");
     * console.log(evaluator.provider, evaluator.model);
     */
    get(identifier: string): Promise<EvaluatorData>;
    /**
     * Partially updates a custom evaluator. Only the fields you provide are changed.
     * To update the LLM config (provider, model, messages, etc.), pass the full config object — it replaces the existing one.
     * @param identifier The evaluator's ID or slug
     * @param patch The fields to update — all fields are optional, but at least one must be provided
     * @returns The updated evaluator's ID
     * @throws Error if the evaluator is not found, if no fields are provided, or if the API request fails
     *
     * @example
     * // Update name only
     * await client.evaluator.update("my-quality-evaluator", { name: "Updated Name" });
     *
     * @example
     * // Update config and schemas
     * await client.evaluator.update("cmb6nr...", {
     *   provider: "anthropic",
     *   model: "claude-3-5-sonnet",
     *   messages: [{ role: "user", content: "Evaluate: {{text}}" }],
     *   inputSchema: [{ name: "text", type: "string" }],
     *   outputSchema: [{ name: "passed", type: "boolean" }],
     * });
     */
    update(identifier: string, patch: EvaluatorUpdateRequest): Promise<EvaluatorUpdateResponse>;
    /**
     * Runs an evaluator synchronously with the given input and returns the result.
     * The input keys must match the evaluator's input schema.
     * @param identifier The evaluator's ID or slug
     * @param options The execution options containing the input values
     * @returns The execution result shaped according to the evaluator's output schema
     * @throws Error if the evaluator is not found, if the input is empty, or if the API request fails
     *
     * @example
     * const result = await client.evaluator.run("my-quality-evaluator", {
     *   input: { text: "The sky is blue because of Rayleigh scattering." },
     * });
     * console.log(result.result); // { passed: true, reason: "Factually accurate." }
     */
    run(identifier: string, options: EvaluatorExecuteOptions): Promise<EvaluatorExecuteResponse>;
    private validateIdentifier;
    private buildPayload;
    private toEvaluatorData;
    /**
     * Run evaluators on experiment task results and wait for completion
     */
    runExperimentEvaluator(options: EvaluatorRunOptions): Promise<ExecutionResponse[]>;
    /**
     * Trigger evaluator execution without waiting for results
     */
    triggerExperimentEvaluator(request: TriggerEvaluatorRequest): Promise<TriggerEvaluatorResponse>;
    /**
     * Wait for execution result via stream URL (actually JSON endpoint)
     */
    waitForResult(executionId: string, streamUrl: string): Promise<ExecutionResponse[]>;
    /**
     * Validate evaluator run options
     */
    private validateEvaluatorOptions;
    /**
     * Create InputSchemaMapping from input object
     */
    private createInputSchemaMapping;
}
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