import { GenerateContentRequest, SafetySetting, Part as GenerativeAIPart, ModelParams, RequestOptions, type CachedContent, Schema } from '@google/generative-ai';
import { CallbackManagerForLLMRun } from '@langchain/core/callbacks/manager';
import { AIMessageChunk, BaseMessage } from '@langchain/core/messages';
import { ChatGenerationChunk, ChatResult } from '@langchain/core/outputs';
import { BaseChatModel, type BaseChatModelCallOptions, type LangSmithParams, type BaseChatModelParams } from '@langchain/core/language_models/chat_models';
import { BaseLanguageModelInput, StructuredOutputMethodOptions } from '@langchain/core/language_models/base';
import { Runnable } from '@langchain/core/runnables';
import { InteropZodType } from '@langchain/core/utils/types';
import { GoogleGenerativeAIToolType } from './utils/types.js';
import { IMultiModalOption, IVisionChatModal } from '../../../src';
export type BaseMessageExamplePair = {
    input: BaseMessage;
    output: BaseMessage;
};
export interface GoogleGenerativeAIChatCallOptions extends BaseChatModelCallOptions {
    tools?: GoogleGenerativeAIToolType[];
    /**
     * Allowed functions to call when the mode is "any".
     * If empty, any one of the provided functions are called.
     */
    allowedFunctionNames?: string[];
    /**
     * Whether or not to include usage data, like token counts
     * in the streamed response chunks.
     * @default true
     */
    streamUsage?: boolean;
    /**
     * JSON schema to be returned by the model.
     */
    responseSchema?: Schema;
}
/**
 * An interface defining the input to the ChatGoogleGenerativeAI class.
 */
export interface GoogleGenerativeAIChatInput extends BaseChatModelParams, Pick<GoogleGenerativeAIChatCallOptions, 'streamUsage'> {
    /**
     * Model Name to use
     *
     * Note: The format must follow the pattern - `{model}`
     */
    model: string;
    /**
     * Controls the randomness of the output.
     *
     * Values can range from [0.0,2.0], inclusive. A value closer to 2.0
     * will produce responses that are more varied and creative, while
     * a value closer to 0.0 will typically result in less surprising
     * responses from the model.
     *
     * Note: The default value varies by model
     */
    temperature?: number;
    /**
     * Maximum number of tokens to generate in the completion.
     */
    maxOutputTokens?: number;
    /**
     * Top-p changes how the model selects tokens for output.
     *
     * Tokens are selected from most probable to least until the sum
     * of their probabilities equals the top-p value.
     *
     * For example, if tokens A, B, and C have a probability of
     * .3, .2, and .1 and the top-p value is .5, then the model will
     * select either A or B as the next token (using temperature).
     *
     * Note: The default value varies by model
     */
    topP?: number;
    /**
     * Top-k changes how the model selects tokens for output.
     *
     * A top-k of 1 means the selected token is the most probable among
     * all tokens in the model's vocabulary (also called greedy decoding),
     * while a top-k of 3 means that the next token is selected from
     * among the 3 most probable tokens (using temperature).
     *
     * Note: The default value varies by model
     */
    topK?: number;
    /**
     * The set of character sequences (up to 5) that will stop output generation.
     * If specified, the API will stop at the first appearance of a stop
     * sequence.
     *
     * Note: The stop sequence will not be included as part of the response.
     * Note: stopSequences is only supported for Gemini models
     */
    stopSequences?: string[];
    /**
     * A list of unique `SafetySetting` instances for blocking unsafe content. The API will block
     * any prompts and responses that fail to meet the thresholds set by these settings. If there
     * is no `SafetySetting` for a given `SafetyCategory` provided in the list, the API will use
     * the default safety setting for that category.
     */
    safetySettings?: SafetySetting[];
    /**
     * Google API key to use
     */
    apiKey?: string;
    /**
     * Google API version to use
     */
    apiVersion?: string;
    /**
     * Google API base URL to use
     */
    baseUrl?: string;
    /** Whether to stream the results or not */
    streaming?: boolean;
    /**
     * Whether or not to force the model to respond with JSON.
     * Available for `gemini-1.5` models and later.
     * @default false
     */
    json?: boolean;
    /**
     * Whether or not model supports system instructions.
     * The following models support system instructions:
     * - All Gemini 1.5 Pro model versions
     * - All Gemini 1.5 Flash model versions
     * - Gemini 1.0 Pro version gemini-1.0-pro-002
     */
    convertSystemMessageToHumanContent?: boolean | undefined;
}
/**
 * Google Generative AI chat model integration.
 *
 * Setup:
 * Install `@langchain/google-genai` and set an environment variable named `GOOGLE_API_KEY`.
 *
 * ```bash
 * npm install @langchain/google-genai
 * export GOOGLE_API_KEY="your-api-key"
 * ```
 *
 * ## [Constructor args](https://api.js.langchain.com/classes/langchain_google_genai.ChatGoogleGenerativeAI.html#constructor)
 *
 * ## [Runtime args](https://api.js.langchain.com/interfaces/langchain_google_genai.GoogleGenerativeAIChatCallOptions.html)
 *
 * Runtime args can be passed as the second argument to any of the base runnable methods `.invoke`. `.stream`, `.batch`, etc.
 * They can also be passed via `.withConfig`, or the second arg in `.bindTools`, like shown in the examples below:
 *
 * ```typescript
 * // When calling `.withConfig`, call options should be passed via the first argument
 * const llmWithArgsBound = llm.withConfig({
 *   stop: ["\n"],
 * });
 *
 * // When calling `.bindTools`, call options should be passed via the second argument
 * const llmWithTools = llm.bindTools(
 *   [...],
 *   {
 *     stop: ["\n"],
 *   }
 * );
 * ```
 *
 * ## Examples
 *
 * <details open>
 * <summary><strong>Instantiate</strong></summary>
 *
 * ```typescript
 * import { ChatGoogleGenerativeAI } from '@langchain/google-genai';
 *
 * const llm = new ChatGoogleGenerativeAI({
 *   model: "gemini-1.5-flash",
 *   temperature: 0,
 *   maxRetries: 2,
 *   // apiKey: "...",
 *   // other params...
 * });
 * ```
 * </details>
 *
 * <br />
 *
 * <details>
 * <summary><strong>Invoking</strong></summary>
 *
 * ```typescript
 * const input = `Translate "I love programming" into French.`;
 *
 * // Models also accept a list of chat messages or a formatted prompt
 * const result = await llm.invoke(input);
 * console.log(result);
 * ```
 *
 * ```txt
 * AIMessage {
 *   "content": "There are a few ways to translate \"I love programming\" into French, depending on the level of formality and nuance you want to convey:\n\n**Formal:**\n\n* **J'aime la programmation.** (This is the most literal and formal translation.)\n\n**Informal:**\n\n* **J'adore programmer.** (This is a more enthusiastic and informal translation.)\n* **J'aime beaucoup programmer.** (This is a slightly less enthusiastic but still informal translation.)\n\n**More specific:**\n\n* **J'aime beaucoup coder.** (This specifically refers to writing code.)\n* **J'aime beaucoup développer des logiciels.** (This specifically refers to developing software.)\n\nThe best translation will depend on the context and your intended audience. \n",
 *   "response_metadata": {
 *     "finishReason": "STOP",
 *     "index": 0,
 *     "safetyRatings": [
 *       {
 *         "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
 *         "probability": "NEGLIGIBLE"
 *       },
 *       {
 *         "category": "HARM_CATEGORY_HATE_SPEECH",
 *         "probability": "NEGLIGIBLE"
 *       },
 *       {
 *         "category": "HARM_CATEGORY_HARASSMENT",
 *         "probability": "NEGLIGIBLE"
 *       },
 *       {
 *         "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
 *         "probability": "NEGLIGIBLE"
 *       }
 *     ]
 *   },
 *   "usage_metadata": {
 *     "input_tokens": 10,
 *     "output_tokens": 149,
 *     "total_tokens": 159
 *   }
 * }
 * ```
 * </details>
 *
 * <br />
 *
 * <details>
 * <summary><strong>Streaming Chunks</strong></summary>
 *
 * ```typescript
 * for await (const chunk of await llm.stream(input)) {
 *   console.log(chunk);
 * }
 * ```
 *
 * ```txt
 * AIMessageChunk {
 *   "content": "There",
 *   "response_metadata": {
 *     "index": 0
 *   }
 *   "usage_metadata": {
 *     "input_tokens": 10,
 *     "output_tokens": 1,
 *     "total_tokens": 11
 *   }
 * }
 * AIMessageChunk {
 *   "content": " are a few ways to translate \"I love programming\" into French, depending on",
 * }
 * AIMessageChunk {
 *   "content": " the level of formality and nuance you want to convey:\n\n**Formal:**\n\n",
 * }
 * AIMessageChunk {
 *   "content": "* **J'aime la programmation.** (This is the most literal and formal translation.)\n\n**Informal:**\n\n* **J'adore programmer.** (This",
 * }
 * AIMessageChunk {
 *   "content": " is a more enthusiastic and informal translation.)\n* **J'aime beaucoup programmer.** (This is a slightly less enthusiastic but still informal translation.)\n\n**More",
 * }
 * AIMessageChunk {
 *   "content": " specific:**\n\n* **J'aime beaucoup coder.** (This specifically refers to writing code.)\n* **J'aime beaucoup développer des logiciels.** (This specifically refers to developing software.)\n\nThe best translation will depend on the context and",
 * }
 * AIMessageChunk {
 *   "content": " your intended audience. \n",
 * }
 * ```
 * </details>
 *
 * <br />
 *
 * <details>
 * <summary><strong>Aggregate Streamed Chunks</strong></summary>
 *
 * ```typescript
 * import { AIMessageChunk } from '@langchain/core/messages';
 * import { concat } from '@langchain/core/utils/stream';
 *
 * const stream = await llm.stream(input);
 * let full: AIMessageChunk | undefined;
 * for await (const chunk of stream) {
 *   full = !full ? chunk : concat(full, chunk);
 * }
 * console.log(full);
 * ```
 *
 * ```txt
 * AIMessageChunk {
 *   "content": "There are a few ways to translate \"I love programming\" into French, depending on the level of formality and nuance you want to convey:\n\n**Formal:**\n\n* **J'aime la programmation.** (This is the most literal and formal translation.)\n\n**Informal:**\n\n* **J'adore programmer.** (This is a more enthusiastic and informal translation.)\n* **J'aime beaucoup programmer.** (This is a slightly less enthusiastic but still informal translation.)\n\n**More specific:**\n\n* **J'aime beaucoup coder.** (This specifically refers to writing code.)\n* **J'aime beaucoup développer des logiciels.** (This specifically refers to developing software.)\n\nThe best translation will depend on the context and your intended audience. \n",
 *   "usage_metadata": {
 *     "input_tokens": 10,
 *     "output_tokens": 277,
 *     "total_tokens": 287
 *   }
 * }
 * ```
 * </details>
 *
 * <br />
 *
 * <details>
 * <summary><strong>Bind tools</strong></summary>
 *
 * ```typescript
 * import { z } from 'zod';
 *
 * const GetWeather = {
 *   name: "GetWeather",
 *   description: "Get the current weather in a given location",
 *   schema: z.object({
 *     location: z.string().describe("The city and state, e.g. San Francisco, CA")
 *   }),
 * }
 *
 * const GetPopulation = {
 *   name: "GetPopulation",
 *   description: "Get the current population in a given location",
 *   schema: z.object({
 *     location: z.string().describe("The city and state, e.g. San Francisco, CA")
 *   }),
 * }
 *
 * const llmWithTools = llm.bindTools([GetWeather, GetPopulation]);
 * const aiMsg = await llmWithTools.invoke(
 *   "Which city is hotter today and which is bigger: LA or NY?"
 * );
 * console.log(aiMsg.tool_calls);
 * ```
 *
 * ```txt
 * [
 *   {
 *     name: 'GetWeather',
 *     args: { location: 'Los Angeles, CA' },
 *     type: 'tool_call'
 *   },
 *   {
 *     name: 'GetWeather',
 *     args: { location: 'New York, NY' },
 *     type: 'tool_call'
 *   },
 *   {
 *     name: 'GetPopulation',
 *     args: { location: 'Los Angeles, CA' },
 *     type: 'tool_call'
 *   },
 *   {
 *     name: 'GetPopulation',
 *     args: { location: 'New York, NY' },
 *     type: 'tool_call'
 *   }
 * ]
 * ```
 * </details>
 *
 * <br />
 *
 * <details>
 * <summary><strong>Structured Output</strong></summary>
 *
 * ```typescript
 * const Joke = z.object({
 *   setup: z.string().describe("The setup of the joke"),
 *   punchline: z.string().describe("The punchline to the joke"),
 *   rating: z.number().optional().describe("How funny the joke is, from 1 to 10")
 * }).describe('Joke to tell user.');
 *
 * const structuredLlm = llm.withStructuredOutput(Joke, { name: "Joke" });
 * const jokeResult = await structuredLlm.invoke("Tell me a joke about cats");
 * console.log(jokeResult);
 * ```
 *
 * ```txt
 * {
 *   setup: "Why don\\'t cats play poker?",
 *   punchline: "Why don\\'t cats play poker? Because they always have an ace up their sleeve!"
 * }
 * ```
 * </details>
 *
 * <br />
 *
 * <details>
 * <summary><strong>Multimodal</strong></summary>
 *
 * ```typescript
 * import { HumanMessage } from '@langchain/core/messages';
 *
 * const imageUrl = "https://example.com/image.jpg";
 * const imageData = await fetch(imageUrl).then(res => res.arrayBuffer());
 * const base64Image = Buffer.from(imageData).toString('base64');
 *
 * const message = new HumanMessage({
 *   content: [
 *     { type: "text", text: "describe the weather in this image" },
 *     {
 *       type: "image_url",
 *       image_url: { url: `data:image/jpeg;base64,${base64Image}` },
 *     },
 *   ]
 * });
 *
 * const imageDescriptionAiMsg = await llm.invoke([message]);
 * console.log(imageDescriptionAiMsg.content);
 * ```
 *
 * ```txt
 * The weather in the image appears to be clear and sunny. The sky is mostly blue with a few scattered white clouds, indicating fair weather. The bright sunlight is casting shadows on the green, grassy hill, suggesting it is a pleasant day with good visibility. There are no signs of rain or stormy conditions.
 * ```
 * </details>
 *
 * <br />
 *
 * <details>
 * <summary><strong>Usage Metadata</strong></summary>
 *
 * ```typescript
 * const aiMsgForMetadata = await llm.invoke(input);
 * console.log(aiMsgForMetadata.usage_metadata);
 * ```
 *
 * ```txt
 * { input_tokens: 10, output_tokens: 149, total_tokens: 159 }
 * ```
 * </details>
 *
 * <br />
 *
 * <details>
 * <summary><strong>Response Metadata</strong></summary>
 *
 * ```typescript
 * const aiMsgForResponseMetadata = await llm.invoke(input);
 * console.log(aiMsgForResponseMetadata.response_metadata);
 * ```
 *
 * ```txt
 * {
 *   finishReason: 'STOP',
 *   index: 0,
 *   safetyRatings: [
 *     {
 *       category: 'HARM_CATEGORY_SEXUALLY_EXPLICIT',
 *       probability: 'NEGLIGIBLE'
 *     },
 *     {
 *       category: 'HARM_CATEGORY_HATE_SPEECH',
 *       probability: 'NEGLIGIBLE'
 *     },
 *     { category: 'HARM_CATEGORY_HARASSMENT', probability: 'NEGLIGIBLE' },
 *     {
 *       category: 'HARM_CATEGORY_DANGEROUS_CONTENT',
 *       probability: 'NEGLIGIBLE'
 *     }
 *   ]
 * }
 * ```
 * </details>
 *
 * <br />
 *
 * <details>
 * <summary><strong>Document Messages</strong></summary>
 *
 * This example will show you how to pass documents such as PDFs to Google
 * Generative AI through messages.
 *
 * ```typescript
 * const pdfPath = "/Users/my_user/Downloads/invoice.pdf";
 * const pdfBase64 = await fs.readFile(pdfPath, "base64");
 *
 * const response = await llm.invoke([
 *   ["system", "Use the provided documents to answer the question"],
 *   [
 *     "user",
 *     [
 *       {
 *         type: "application/pdf", // If the `type` field includes a single slash (`/`), it will be treated as inline data.
 *         data: pdfBase64,
 *       },
 *       {
 *         type: "text",
 *         text: "Summarize the contents of this PDF",
 *       },
 *     ],
 *   ],
 * ]);
 *
 * console.log(response.content);
 * ```
 *
 * ```txt
 * This is a billing invoice from Twitter Developers for X API Basic Access. The transaction date is January 7, 2025,
 * and the amount is $194.34, which has been paid. The subscription period is from January 7, 2025 21:02 to February 7, 2025 00:00 (UTC).
 * The tax is $0.00, with a tax rate of 0%. The total amount is $194.34. The payment was made using a Visa card ending in 7022,
 * expiring in 12/2026. The billing address is Brace Sproul, 1234 Main Street, San Francisco, CA, US 94103. The company being billed is
 * X Corp, located at 865 FM 1209 Building 2, Bastrop, TX, US 78602. Terms and conditions apply.
 * ```
 * </details>
 *
 * <br />
 */
export declare class LangchainChatGoogleGenerativeAI extends BaseChatModel<GoogleGenerativeAIChatCallOptions, AIMessageChunk> implements GoogleGenerativeAIChatInput {
    static lc_name(): string;
    lc_serializable: boolean;
    get lc_secrets(): {
        [key: string]: string;
    } | undefined;
    lc_namespace: string[];
    get lc_aliases(): {
        apiKey: string;
    };
    model: string;
    temperature?: number;
    maxOutputTokens?: number;
    topP?: number;
    topK?: number;
    stopSequences: string[];
    safetySettings?: SafetySetting[];
    apiKey?: string;
    streaming: boolean;
    json?: boolean;
    streamUsage: boolean;
    convertSystemMessageToHumanContent: boolean | undefined;
    private client;
    get _isMultimodalModel(): boolean;
    constructor(fields: GoogleGenerativeAIChatInput);
    useCachedContent(cachedContent: CachedContent, modelParams?: ModelParams, requestOptions?: RequestOptions): void;
    get useSystemInstruction(): boolean;
    get computeUseSystemInstruction(): boolean;
    getLsParams(options: this['ParsedCallOptions']): LangSmithParams;
    _combineLLMOutput(): never[];
    _llmType(): string;
    bindTools(tools: GoogleGenerativeAIToolType[], kwargs?: Partial<GoogleGenerativeAIChatCallOptions>): Runnable<BaseLanguageModelInput, AIMessageChunk, GoogleGenerativeAIChatCallOptions>;
    invocationParams(options?: this['ParsedCallOptions']): Omit<GenerateContentRequest, 'contents'>;
    _generate(messages: BaseMessage[], options: this['ParsedCallOptions'], runManager?: CallbackManagerForLLMRun): Promise<ChatResult>;
    _streamResponseChunks(messages: BaseMessage[], options: this['ParsedCallOptions'], runManager?: CallbackManagerForLLMRun): AsyncGenerator<ChatGenerationChunk>;
    completionWithRetry(request: string | GenerateContentRequest | (string | GenerativeAIPart)[], options?: this['ParsedCallOptions']): Promise<import("@google/generative-ai").GenerateContentResult>;
    withStructuredOutput<RunOutput extends Record<string, any> = Record<string, any>>(outputSchema: InteropZodType<RunOutput> | Record<string, any>, config?: StructuredOutputMethodOptions<false>): Runnable<BaseLanguageModelInput, RunOutput>;
    withStructuredOutput<RunOutput extends Record<string, any> = Record<string, any>>(outputSchema: InteropZodType<RunOutput> | Record<string, any>, config?: StructuredOutputMethodOptions<true>): Runnable<BaseLanguageModelInput, {
        raw: BaseMessage;
        parsed: RunOutput;
    }>;
}
export declare class ChatGoogleGenerativeAI extends LangchainChatGoogleGenerativeAI implements IVisionChatModal {
    configuredModel: string;
    configuredMaxToken?: number;
    multiModalOption: IMultiModalOption;
    id: string;
    constructor(id: string, fields: GoogleGenerativeAIChatInput);
    revertToOriginalModel(): void;
    setMultiModalOption(multiModalOption: IMultiModalOption): void;
    setVisionModel(): void;
}