/**
 * @license
 * Copyright 2024 Google LLC
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     https://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
import { CachedContent, Content, CountTokensRequest, CountTokensResponse, GenerateContentRequest, GenerateContentResult, GetGenerativeModelParams, StartChatParams, StreamGenerateContentResult } from '../types/content';
import { ChatSession, ChatSessionPreview } from './chat_session';
/**
 * The `GenerativeModel` class is the base class for the generative models on
 * Vertex AI.
 * NOTE: Don't instantiate this class directly. Use
 * `vertexai.getGenerativeModel()` instead.
 */
export declare class GenerativeModel {
    private readonly model;
    private readonly generationConfig?;
    private readonly safetySettings?;
    private readonly tools?;
    private readonly toolConfig?;
    private readonly requestOptions?;
    private readonly systemInstruction?;
    private readonly project;
    private readonly location;
    private readonly googleAuth;
    private readonly publisherModelEndpoint;
    private readonly resourcePath;
    private readonly apiEndpoint?;
    /**
     * @constructor
     * @param getGenerativeModelParams - {@link GetGenerativeModelParams}
     */
    constructor(getGenerativeModelParams: GetGenerativeModelParams);
    /**
     * Gets access token from GoogleAuth. Throws {@link GoogleAuthError} when
     * fails.
     * @returns Promise of token string.
     */
    private fetchToken;
    /**
     * Makes an async call to generate content.
     *
     * The response will be returned in {@link
     * GenerateContentResult.response}.
     *
     * @example
     * ```
     * const request = {
     *   contents: [{role: 'user', parts: [{text: 'How are you doing today?'}]}],
     * };
     * const result = await generativeModel.generateContent(request);
     * console.log('Response: ', JSON.stringify(result.response));
     * ```
     *
     * @param request - A GenerateContentRequest object with the request contents.
     * @returns The GenerateContentResponse object with the response candidates.
     */
    generateContent(request: GenerateContentRequest | string): Promise<GenerateContentResult>;
    /**
     * Makes an async stream request to generate content.
     *
     * The response is returned chunk by chunk as it's being generated in {@link
     * StreamGenerateContentResult.stream}. After all chunks of the response are
     * returned, the aggregated response is available in
     * {@link StreamGenerateContentResult.response}.
     *
     * @example
     * ```
     * const request = {
     *   contents: [{role: 'user', parts: [{text: 'How are you doing today?'}]}],
     * };
     * const streamingResult = await generativeModel.generateContentStream(request);
     * for await (const item of streamingResult.stream) {
     *   console.log('stream chunk: ', JSON.stringify(item));
     * }
     * const aggregatedResponse = await streamingResult.response;
     * console.log('aggregated response: ', JSON.stringify(aggregatedResponse));
     * ```
     *
     * @param request - {@link GenerateContentRequest}
     * @returns Promise of {@link StreamGenerateContentResult}
     */
    generateContentStream(request: GenerateContentRequest | string): Promise<StreamGenerateContentResult>;
    /**
     * Makes an async request to count tokens.
     *
     * The `countTokens` function returns the token count and the number of
     * billable characters for a prompt.
     *
     * @example
     * ```
     * const request = {
     *   contents: [{role: 'user', parts: [{text: 'How are you doing today?'}]}],
     * };
     * const resp = await generativeModel.countTokens(request);
     * console.log('count tokens response: ', resp);
     * ```
     *
     * @param request - A CountTokensRequest object with the request contents.
     * @returns The CountTokensResponse object with the token count.
     */
    countTokens(request: CountTokensRequest): Promise<CountTokensResponse>;
    /**
     * Instantiates a {@link ChatSession}.
     *
     * The {@link ChatSession} class is a stateful class that holds the state of
     * the conversation with the model and provides methods to interact with the
     * model in chat mode. Calling this method doesn't make any calls to a remote
     * endpoint. To make remote call, use {@link ChatSession.sendMessage} or
     * @link ChatSession.sendMessageStream}.
     *
     * @example
     * ```
     * const chat = generativeModel.startChat();
     * const result1 = await chat.sendMessage("How can I learn more about Node.js?");
     * const response1 = await result1.response;
     * console.log('Response: ', JSON.stringify(response1));
     *
     * const result2 = await chat.sendMessageStream("What about python?");
     * const response2 = await result2.response;
     * console.log('Response: ', JSON.stringify(await response2));
     * ```
     *
     * @param request - {@link StartChatParams}
     * @returns {@link ChatSession}
     */
    startChat(request?: StartChatParams): ChatSession;
}
/**
 * The `GenerativeModelPreview` class is the base class for the generative models
 * that are in preview.
 * NOTE: Don't instantiate this class directly. Use
 * `vertexai.preview.getGenerativeModel()` instead.
 */
export declare class GenerativeModelPreview {
    private readonly model;
    private readonly generationConfig?;
    private readonly safetySettings?;
    private readonly tools?;
    private readonly toolConfig?;
    private readonly requestOptions?;
    private readonly systemInstruction?;
    private readonly project;
    private readonly location;
    private readonly googleAuth;
    private readonly publisherModelEndpoint;
    private readonly resourcePath;
    private readonly apiEndpoint?;
    private readonly cachedContent?;
    /**
     * @constructor
     * @param getGenerativeModelParams - {@link GetGenerativeModelParams}
     */
    constructor(getGenerativeModelParams: GetGenerativeModelParams);
    /**
     * Gets access token from GoogleAuth. Throws {@link GoogleAuthError} when
     * fails.
     * @returns Promise of token string.
     */
    private fetchToken;
    /**
     * Makes an async call to generate content.
     *
     * The response will be returned in {@link GenerateContentResult.response}.
     *
     * @example
     * ```
     * const request = {
     *   contents: [{role: 'user', parts: [{text: 'How are you doing today?'}]}],
     * };
     * const result = await generativeModelPreview.generateContent(request);
     * console.log('Response: ', JSON.stringify(result.response));
     * ```
     *
     * @param request - A GenerateContentRequest object with the request contents.
     * @returns The GenerateContentResponse object with the response candidates.
     */
    generateContent(request: GenerateContentRequest | string): Promise<GenerateContentResult>;
    /**
     * Makes an async stream request to generate content.
     *
     * The response is returned chunk by chunk as it's being generated in {@link
     * StreamGenerateContentResult.stream}. After all chunks of the response are
     * returned, the aggregated response is available in
     * {@link StreamGenerateContentResult.response}.
     *
     * @example
     * ```
     * const request = {
     *   contents: [{role: 'user', parts: [{text: 'How are you doing today?'}]}],
     * };
     * const streamingResult = await generativeModelPreview.generateContentStream(request);
     * for await (const item of streamingResult.stream) {
     *   console.log('stream chunk: ', JSON.stringify(item));
     * }
     * const aggregatedResponse = await streamingResult.response;
     * console.log('aggregated response: ', JSON.stringify(aggregatedResponse));
     * ```
     *
     * @param request - {@link GenerateContentRequest}
     * @returns Promise of {@link StreamGenerateContentResult}
     */
    generateContentStream(request: GenerateContentRequest | string): Promise<StreamGenerateContentResult>;
    /**
     * Makes an async request to count tokens.
     *
     * The `countTokens` function returns the token count and the number of
     * billable characters for a prompt.
     *
     * @example
     * ```
     * const request = {
     *   contents: [{role: 'user', parts: [{text: 'How are you doing today?'}]}],
     * };
     * const resp = await generativeModelPreview.countTokens(request);
     * console.log('count tokens response: ', resp);
     * ```
     *
     * @param request - A CountTokensRequest object with the request contents.
     * @returns The CountTokensResponse object with the token count.
     */
    countTokens(request: CountTokensRequest): Promise<CountTokensResponse>;
    /**
     * Instantiates a {@link ChatSessionPreview}.
     *
     * The {@link ChatSessionPreview} class is a stateful class that holds the state of
     * the conversation with the model and provides methods to interact with the
     * model in chat mode. Calling this method doesn't make any calls to a remote
     * endpoint. To make remote call, use {@link ChatSessionPreview.sendMessage} or
     * {@link ChatSessionPreview.sendMessageStream}.
     *
     * @example
     * ```
     * const chat = generativeModelPreview.startChat();
     * const result1 = await chat.sendMessage("How can I learn more about Node.js?");
     * const response1 = await result1.response;
     * console.log('Response: ', JSON.stringify(response1));
     *
     * const result2 = await chat.sendMessageStream("What about python?");
     * const response2 = await result2.response;
     * console.log('Response: ', JSON.stringify(await response2));
     * ```
     *
     * @param request - {@link StartChatParams}
     * @returns {@link ChatSessionPreview}
     */
    startChat(request?: StartChatParams): ChatSessionPreview;
    getModelName(): string;
    getCachedContent(): CachedContent | undefined;
    getSystemInstruction(): Content | undefined;
}