/**
 * @license
 * Copyright 2025 Steven Roussey <sroussey@gmail.com>
 * SPDX-License-Identifier: Apache-2.0
 */
import type { IRunConfig, TaskConfig } from "@workglow/task-graph";
import { CreateWorkflow } from "@workglow/task-graph";
import { DataPortSchema, FromSchema, TypedArraySchemaOptions } from "@workglow/util/schema";
import type { ModelConfig } from "../model/ModelSchema";
import { AiTask } from "./base/AiTask";
export declare const TextEmbeddingInputSchema: {
    readonly type: "object";
    readonly properties: {
        readonly text: {
            readonly anyOf: readonly [{
                readonly type: "string";
                readonly title: "Text";
                readonly description: "The text to embed";
            }, {
                readonly type: "array";
                readonly items: {
                    readonly type: "string";
                    readonly title: "Text";
                    readonly description: "The text to embed";
                };
            }];
        };
        readonly model: {
            readonly oneOf: readonly [{
                readonly title: "Model";
                readonly description: `The model ${string}`;
            } & Record<string, never> & {
                readonly format: import(".").TypeModelSemantic;
                readonly type: "string";
            }, {
                readonly type: "object";
                readonly properties: {
                    readonly model_id: {
                        readonly type: "string";
                    };
                    readonly capabilities: {
                        readonly type: "array";
                        readonly items: {
                            readonly type: "string";
                        };
                        readonly "x-ui-editor": "multiselect";
                    };
                    readonly title: {
                        readonly type: "string";
                    };
                    readonly description: {
                        readonly type: "string";
                        readonly "x-ui-editor": "textarea";
                    };
                    readonly provider: {
                        readonly type: "string";
                    };
                    readonly provider_config: {
                        readonly type: "object";
                        readonly properties: {
                            readonly credential_key: {
                                readonly type: "string";
                                readonly format: "credential";
                                readonly "x-ui-hidden": true;
                            };
                            readonly native_dimensions: {
                                readonly type: "integer";
                                readonly description: "Native output vector dimensions for embedding models";
                            };
                            readonly mrl: {
                                readonly type: "boolean";
                                readonly description: "Whether the model supports Matryoshka Representation Learning";
                            };
                        };
                        readonly additionalProperties: true;
                        readonly default: {};
                    };
                    readonly metadata: {
                        readonly type: "object";
                        readonly default: {};
                        readonly "x-ui-hidden": true;
                    };
                };
                readonly required: readonly ["provider", "provider_config"];
                readonly format: "model";
                readonly additionalProperties: true;
            } & Record<string, never> & {
                readonly format: import(".").TypeModelSemantic;
            }];
        } & Record<string, never> & {
            readonly format: import(".").TypeModelSemantic;
        };
    };
    readonly required: readonly ["text", "model"];
    readonly additionalProperties: false;
};
export declare const TextEmbeddingOutputSchema: {
    readonly type: "object";
    readonly properties: {
        readonly vector: {
            readonly anyOf: readonly [{
                readonly type: "array";
                readonly format: "TypedArray";
                readonly title: "Typed Array";
                readonly description: "A typed array (Float32Array, Int8Array, etc.)";
            }, {
                readonly type: "array";
                readonly items: {
                    readonly type: "array";
                    readonly format: "TypedArray";
                    readonly title: "Typed Array";
                    readonly description: "A typed array (Float32Array, Int8Array, etc.)";
                };
            }];
        };
    };
    readonly required: readonly ["vector"];
    readonly additionalProperties: false;
};
export type TextEmbeddingTaskInput = {
    model: string | ModelConfig;
    text: string | string[];
};
export type TextEmbeddingTaskOutput = FromSchema<typeof TextEmbeddingOutputSchema, TypedArraySchemaOptions>;
export type TextEmbeddingTaskConfig = TaskConfig<TextEmbeddingTaskInput>;
/**
 * A task that generates vector embeddings for text using a specified embedding model.
 * Embeddings are numerical representations of text that capture semantic meaning,
 * useful for similarity comparisons and semantic search.
 *
 * @extends AiTask
 */
export declare class TextEmbeddingTask extends AiTask<TextEmbeddingTaskInput, TextEmbeddingTaskOutput, TextEmbeddingTaskConfig> {
    static type: string;
    /** Capabilities required of the model; gated in {@link AiTask.execute}. */
    static readonly requires: ["text.embedding"];
    static category: string;
    static title: string;
    static description: string;
    static inputSchema(): DataPortSchema;
    static outputSchema(): DataPortSchema;
}
export declare const textEmbedding: (input: TextEmbeddingTaskInput, config?: TextEmbeddingTaskConfig, runConfig?: Partial<IRunConfig>) => Promise<{
    vector: import("@workglow/util/worker").TypedArray[] | import("@workglow/util/worker").TypedArray;
}>;
declare module "@workglow/task-graph" {
    interface Workflow {
        textEmbedding: CreateWorkflow<TextEmbeddingTaskInput, TextEmbeddingTaskOutput, TextEmbeddingTaskConfig>;
    }
}
//# sourceMappingURL=TextEmbeddingTask.d.ts.map