import { Serialized } from "@langchain/core/load/serializable";
import { LangSmithParams } from "@langchain/core/language_models/chat_models";
import { StructuredOutputMethodOptions } from "@langchain/core/language_models/base";
import { BaseChatOpenAIFields, ChatOpenAI, ChatOpenAICallOptions } from "../chat_models.js";
import { AzureOpenAIChatInput, AzureOpenAIInput } from "../types.js";
export type { AzureOpenAIInput };
interface AzureChatOpenAIFields extends BaseChatOpenAIFields, Partial<AzureOpenAIChatInput> {
    /**
     * Whether to use the responses API for all requests. If `false` the responses API will be used
     * only when required in order to fulfill the request.
     */
    useResponsesApi?: boolean;
}
/**
 * Azure OpenAI chat model integration.
 *
 * Setup:
 * Install `@langchain/openai` and set the following environment variables:
 *
 * ```bash
 * npm install @langchain/openai
 * export AZURE_OPENAI_API_KEY="your-api-key"
 * export AZURE_OPENAI_API_DEPLOYMENT_NAME="your-deployment-name"
 * export AZURE_OPENAI_API_VERSION="your-version"
 * export AZURE_OPENAI_BASE_PATH="your-base-path"
 * ```
 *
 * ## [Constructor args](https://api.js.langchain.com/classes/langchain_openai.AzureChatOpenAI.html#constructor)
 *
 * ## [Runtime args](https://api.js.langchain.com/interfaces/langchain_openai.ChatOpenAICallOptions.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"],
 *   tools: [...],
 * });
 *
 * // When calling `.bindTools`, call options should be passed via the second argument
 * const llmWithTools = llm.bindTools(
 *   [...],
 *   {
 *     tool_choice: "auto",
 *   }
 * );
 * ```
 *
 * ## Examples
 *
 * <details open>
 * <summary><strong>Instantiate</strong></summary>
 *
 * ```typescript
 * import { AzureChatOpenAI } from '@langchain/openai';
 *
 * const llm = new AzureChatOpenAI({
 *   azureOpenAIApiKey: process.env.AZURE_OPENAI_API_KEY, // In Node.js defaults to process.env.AZURE_OPENAI_API_KEY
 *   azureOpenAIApiInstanceName: process.env.AZURE_OPENAI_API_INSTANCE_NAME, // In Node.js defaults to process.env.AZURE_OPENAI_API_INSTANCE_NAME
 *   azureOpenAIApiDeploymentName: process.env.AZURE_OPENAI_API_DEPLOYMENT_NAME, // In Node.js defaults to process.env.AZURE_OPENAI_API_DEPLOYMENT_NAME
 *   azureOpenAIApiVersion: process.env.AZURE_OPENAI_API_VERSION, // In Node.js defaults to process.env.AZURE_OPENAI_API_VERSION
 *   temperature: 0,
 *   maxTokens: undefined,
 *   timeout: undefined,
 *   maxRetries: 2,
 *   // apiKey: "...",
 *   // baseUrl: "...",
 *   // 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 {
 *   "id": "chatcmpl-9u4Mpu44CbPjwYFkTbeoZgvzB00Tz",
 *   "content": "J'adore la programmation.",
 *   "response_metadata": {
 *     "tokenUsage": {
 *       "completionTokens": 5,
 *       "promptTokens": 28,
 *       "totalTokens": 33
 *     },
 *     "finish_reason": "stop",
 *     "system_fingerprint": "fp_3aa7262c27"
 *   },
 *   "usage_metadata": {
 *     "input_tokens": 28,
 *     "output_tokens": 5,
 *     "total_tokens": 33
 *   }
 * }
 * ```
 * </details>
 *
 * <br />
 *
 * <details>
 * <summary><strong>Streaming Chunks</strong></summary>
 *
 * ```typescript
 * for await (const chunk of await llm.stream(input)) {
 *   console.log(chunk);
 * }
 * ```
 *
 * ```txt
 * AIMessageChunk {
 *   "id": "chatcmpl-9u4NWB7yUeHCKdLr6jP3HpaOYHTqs",
 *   "content": ""
 * }
 * AIMessageChunk {
 *   "content": "J"
 * }
 * AIMessageChunk {
 *   "content": "'adore"
 * }
 * AIMessageChunk {
 *   "content": " la"
 * }
 * AIMessageChunk {
 *   "content": " programmation",,
 * }
 * AIMessageChunk {
 *   "content": ".",,
 * }
 * AIMessageChunk {
 *   "content": "",
 *   "response_metadata": {
 *     "finish_reason": "stop",
 *     "system_fingerprint": "fp_c9aa9c0491"
 *   },
 * }
 * AIMessageChunk {
 *   "content": "",
 *   "usage_metadata": {
 *     "input_tokens": 28,
 *     "output_tokens": 5,
 *     "total_tokens": 33
 *   }
 * }
 * ```
 * </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 {
 *   "id": "chatcmpl-9u4PnX6Fy7OmK46DASy0bH6cxn5Xu",
 *   "content": "J'adore la programmation.",
 *   "response_metadata": {
 *     "prompt": 0,
 *     "completion": 0,
 *     "finish_reason": "stop",
 *   },
 *   "usage_metadata": {
 *     "input_tokens": 28,
 *     "output_tokens": 5,
 *     "total_tokens": 33
 *   }
 * }
 * ```
 * </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',
 *     id: 'call_uPU4FiFzoKAtMxfmPnfQL6UK'
 *   },
 *   {
 *     name: 'GetWeather',
 *     args: { location: 'New York, NY' },
 *     type: 'tool_call',
 *     id: 'call_UNkEwuQsHrGYqgDQuH9nPAtX'
 *   },
 *   {
 *     name: 'GetPopulation',
 *     args: { location: 'Los Angeles, CA' },
 *     type: 'tool_call',
 *     id: 'call_kL3OXxaq9OjIKqRTpvjaCH14'
 *   },
 *   {
 *     name: 'GetPopulation',
 *     args: { location: 'New York, NY' },
 *     type: 'tool_call',
 *     id: 'call_s9KQB1UWj45LLGaEnjz0179q'
 *   }
 * ]
 * ```
 * </details>
 *
 * <br />
 *
 * <details>
 * <summary><strong>Structured Output</strong></summary>
 *
 * ```typescript
 * import { z } from 'zod';
 *
 * 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().nullable().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 was the cat sitting on the computer?',
 *   punchline: 'Because it wanted to keep an eye on the mouse!',
 *   rating: 7
 * }
 * ```
 * </details>
 *
 * <br />
 *
 * <details>
 * <summary><strong>JSON Object Response Format</strong></summary>
 *
 * ```typescript
 * const jsonLlm = llm.withConfig({ response_format: { type: "json_object" } });
 * const jsonLlmAiMsg = await jsonLlm.invoke(
 *   "Return a JSON object with key 'randomInts' and a value of 10 random ints in [0-99]"
 * );
 * console.log(jsonLlmAiMsg.content);
 * ```
 *
 * ```txt
 * {
 *   "randomInts": [23, 87, 45, 12, 78, 34, 56, 90, 11, 67]
 * }
 * ```
 * </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: 28, output_tokens: 5, total_tokens: 33 }
 * ```
 * </details>
 *
 * <br />
 *
 * <details>
 * <summary><strong>Logprobs</strong></summary>
 *
 * ```typescript
 * const logprobsLlm = new ChatOpenAI({ model: "gpt-4o-mini", logprobs: true });
 * const aiMsgForLogprobs = await logprobsLlm.invoke(input);
 * console.log(aiMsgForLogprobs.response_metadata.logprobs);
 * ```
 *
 * ```txt
 * {
 *   content: [
 *     {
 *       token: 'J',
 *       logprob: -0.000050616763,
 *       bytes: [Array],
 *       top_logprobs: []
 *     },
 *     {
 *       token: "'",
 *       logprob: -0.01868736,
 *       bytes: [Array],
 *       top_logprobs: []
 *     },
 *     {
 *       token: 'ad',
 *       logprob: -0.0000030545007,
 *       bytes: [Array],
 *       top_logprobs: []
 *     },
 *     { token: 'ore', logprob: 0, bytes: [Array], top_logprobs: [] },
 *     {
 *       token: ' la',
 *       logprob: -0.515404,
 *       bytes: [Array],
 *       top_logprobs: []
 *     },
 *     {
 *       token: ' programm',
 *       logprob: -0.0000118755715,
 *       bytes: [Array],
 *       top_logprobs: []
 *     },
 *     { token: 'ation', logprob: 0, bytes: [Array], top_logprobs: [] },
 *     {
 *       token: '.',
 *       logprob: -0.0000037697225,
 *       bytes: [Array],
 *       top_logprobs: []
 *     }
 *   ],
 *   refusal: null
 * }
 * ```
 * </details>
 *
 * <br />
 *
 * <details>
 * <summary><strong>Response Metadata</strong></summary>
 *
 * ```typescript
 * const aiMsgForResponseMetadata = await llm.invoke(input);
 * console.log(aiMsgForResponseMetadata.response_metadata);
 * ```
 *
 * ```txt
 * {
 *   tokenUsage: { completionTokens: 5, promptTokens: 28, totalTokens: 33 },
 *   finish_reason: 'stop',
 *   system_fingerprint: 'fp_3aa7262c27'
 * }
 * ```
 * </details>
 */
export declare class AzureChatOpenAI<CallOptions extends ChatOpenAICallOptions = ChatOpenAICallOptions> extends ChatOpenAI<CallOptions> implements Partial<AzureOpenAIChatInput> {
    azureOpenAIApiVersion?: string;
    azureOpenAIApiKey?: string;
    azureADTokenProvider?: () => Promise<string>;
    azureOpenAIApiInstanceName?: string;
    azureOpenAIApiDeploymentName?: string;
    azureOpenAIBasePath?: string;
    azureOpenAIEndpoint?: string;
    _llmType(): string;
    get lc_aliases(): Record<string, string>;
    get lc_secrets(): {
        [key: string]: string;
    } | undefined;
    get lc_serializable_keys(): string[];
    getLsParams(options: this["ParsedCallOptions"]): LangSmithParams;
    constructor(fields?: AzureChatOpenAIFields);
    /** @internal */
    _getStructuredOutputMethod(config: StructuredOutputMethodOptions<boolean>): string | undefined;
    toJSON(): Serialized;
}