agsamantha/node_modules/@langchain/openai/dist/chat_models.d.ts

import { type ClientOptions, OpenAI as OpenAIClient } from "openai";
import { CallbackManagerForLLMRun } from "@langchain/core/callbacks/manager";
import { AIMessageChunk, type BaseMessage } from "@langchain/core/messages";
import { ChatGenerationChunk, type ChatResult } from "@langchain/core/outputs";
import { BaseChatModel, BindToolsInput, LangSmithParams, type BaseChatModelParams } from "@langchain/core/language_models/chat_models";
import { type BaseFunctionCallOptions, type BaseLanguageModelInput, type StructuredOutputMethodOptions, type StructuredOutputMethodParams } from "@langchain/core/language_models/base";
import { z } from "zod";
import { Runnable } from "@langchain/core/runnables";
import type { AzureOpenAIInput, OpenAICallOptions, OpenAIChatInput, OpenAICoreRequestOptions, LegacyOpenAIInput, ChatOpenAIResponseFormat } from "./types.js";
import { OpenAIToolChoice } from "./utils/openai.js";
export type { AzureOpenAIInput, OpenAICallOptions, OpenAIChatInput };
interface TokenUsage {
    completionTokens?: number;
    promptTokens?: number;
    totalTokens?: number;
}
interface OpenAILLMOutput {
    tokenUsage: TokenUsage;
}
type OpenAIRoleEnum = "system" | "assistant" | "user" | "function" | "tool";
export declare function messageToOpenAIRole(message: BaseMessage): OpenAIRoleEnum;
export declare function _convertMessagesToOpenAIParams(messages: BaseMessage[]): OpenAIClient.Chat.Completions.ChatCompletionMessageParam[];
type ChatOpenAIToolType = BindToolsInput | OpenAIClient.ChatCompletionTool;
export interface ChatOpenAIStructuredOutputMethodOptions<IncludeRaw extends boolean> extends StructuredOutputMethodOptions<IncludeRaw> {
    /**
     * strict: If `true` and `method` = "function_calling", model output is
     * guaranteed to exactly match the schema. If `true`, the input schema
     * will also be validated according to
     * https://platform.openai.com/docs/guides/structured-outputs/supported-schemas.
     * If `false`, input schema will not be validated and model output will not
     * be validated.
     * If `undefined`, `strict` argument will not be passed to the model.
     *
     * @version 0.2.6
     * @note Planned breaking change in version `0.3.0`:
     * `strict` will default to `true` when `method` is
     * "function_calling" as of version `0.3.0`.
     */
    strict?: boolean;
}
export interface ChatOpenAICallOptions extends OpenAICallOptions, BaseFunctionCallOptions {
    tools?: ChatOpenAIToolType[];
    tool_choice?: OpenAIToolChoice;
    promptIndex?: number;
    response_format?: ChatOpenAIResponseFormat;
    seed?: number;
    /**
     * Additional options to pass to streamed completions.
     * If provided takes precedence over "streamUsage" set at initialization time.
     */
    stream_options?: {
        /**
         * Whether or not to include token usage in the stream.
         * If set to `true`, this will include an additional
         * chunk at the end of the stream with the token usage.
         */
        include_usage: boolean;
    };
    /**
     * Whether or not to restrict the ability to
     * call multiple tools in one response.
     */
    parallel_tool_calls?: boolean;
    /**
     * If `true`, model output is guaranteed to exactly match the JSON Schema
     * provided in the tool definition. If `true`, the input schema will also be
     * validated according to
     * https://platform.openai.com/docs/guides/structured-outputs/supported-schemas.
     *
     * If `false`, input schema will not be validated and model output will not
     * be validated.
     *
     * If `undefined`, `strict` argument will not be passed to the model.
     *
     * @version 0.2.6
     */
    strict?: boolean;
}
export interface ChatOpenAIFields extends Partial<OpenAIChatInput>, Partial<AzureOpenAIInput>, BaseChatModelParams {
    configuration?: ClientOptions & LegacyOpenAIInput;
}
/**
 * OpenAI chat model integration.
 *
 * Setup:
 * Install `@langchain/openai` and set an environment variable named `OPENAI_API_KEY`.
 *
 * ```bash
 * npm install @langchain/openai
 * export OPENAI_API_KEY="your-api-key"
 * ```
 *
 * ## [Constructor args](https://api.js.langchain.com/classes/langchain_openai.ChatOpenAI.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 `.bind`, or the second arg in `.bindTools`, like shown in the examples below:
 *
 * ```typescript
 * // When calling `.bind`, call options should be passed via the first argument
 * const llmWithArgsBound = llm.bind({
 *   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 { ChatOpenAI } from '@langchain/openai';
 *
 * const llm = new ChatOpenAI({
 *   model: "gpt-4o",
 *   temperature: 0,
 *   maxTokens: undefined,
 *   timeout: undefined,
 *   maxRetries: 2,
 *   // apiKey: "...",
 *   // baseUrl: "...",
 *   // organization: "...",
 *   // 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],
 *   {
 *     // strict: true  // enforce tool args schema is respected
 *   }
 * );
 * 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().optional().describe("How funny the joke is, from 1 to 10")
 * }).describe('Joke to tell user.');
 *
 * const structuredLlm = llm.withStructuredOutput(Joke, {
 *   name: "Joke",
 *   strict: true, // Optionally enable OpenAI structured outputs
 * });
 * 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.bind({ 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({ 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>
 *
 * <br />
 *
 * <details>
 * <summary><strong>JSON Schema Structured Output</strong></summary>
 *
 * ```typescript
 * const llmForJsonSchema = new ChatOpenAI({
 *   model: "gpt-4o-2024-08-06",
 * }).withStructuredOutput(
 *   z.object({
 *     command: z.string().describe("The command to execute"),
 *     expectedOutput: z.string().describe("The expected output of the command"),
 *     options: z
 *       .array(z.string())
 *       .describe("The options you can pass to the command"),
 *   }),
 *   {
 *     method: "jsonSchema",
 *     strict: true, // Optional when using the `jsonSchema` method
 *   }
 * );
 *
 * const jsonSchemaRes = await llmForJsonSchema.invoke(
 *   "What is the command to list files in a directory?"
 * );
 * console.log(jsonSchemaRes);
 * ```
 *
 * ```txt
 * {
 *   command: 'ls',
 *   expectedOutput: 'A list of files and subdirectories within the specified directory.',
 *   options: [
 *     '-a: include directory entries whose names begin with a dot (.).',
 *     '-l: use a long listing format.',
 *     '-h: with -l, print sizes in human readable format (e.g., 1K, 234M, 2G).',
 *     '-t: sort by time, newest first.',
 *     '-r: reverse order while sorting.',
 *     '-S: sort by file size, largest first.',
 *     '-R: list subdirectories recursively.'
 *   ]
 * }
 * ```
 * </details>
 *
 * <br />
 */
export declare class ChatOpenAI<CallOptions extends ChatOpenAICallOptions = ChatOpenAICallOptions> extends BaseChatModel<CallOptions, AIMessageChunk> implements OpenAIChatInput, AzureOpenAIInput {
    static lc_name(): string;
    get callKeys(): string[];
    lc_serializable: boolean;
    get lc_secrets(): {
        [key: string]: string;
    } | undefined;
    get lc_aliases(): Record<string, string>;
    temperature: number;
    topP: number;
    frequencyPenalty: number;
    presencePenalty: number;
    n: number;
    logitBias?: Record<string, number>;
    modelName: string;
    model: string;
    modelKwargs?: OpenAIChatInput["modelKwargs"];
    stop?: string[];
    stopSequences?: string[];
    user?: string;
    timeout?: number;
    streaming: boolean;
    streamUsage: boolean;
    maxTokens?: number;
    logprobs?: boolean;
    topLogprobs?: number;
    openAIApiKey?: string;
    apiKey?: string;
    azureOpenAIApiVersion?: string;
    azureOpenAIApiKey?: string;
    azureADTokenProvider?: () => Promise<string>;
    azureOpenAIApiInstanceName?: string;
    azureOpenAIApiDeploymentName?: string;
    azureOpenAIBasePath?: string;
    azureOpenAIEndpoint?: string;
    organization?: string;
    __includeRawResponse?: boolean;
    protected client: OpenAIClient;
    protected clientConfig: ClientOptions;
    /**
     * Whether the model supports the `strict` argument when passing in tools.
     * If `undefined` the `strict` argument will not be passed to OpenAI.
     */
    supportsStrictToolCalling?: boolean;
    constructor(fields?: ChatOpenAIFields, 
    /** @deprecated */
    configuration?: ClientOptions & LegacyOpenAIInput);
    getLsParams(options: this["ParsedCallOptions"]): LangSmithParams;
    bindTools(tools: ChatOpenAIToolType[], kwargs?: Partial<CallOptions>): Runnable<BaseLanguageModelInput, AIMessageChunk, CallOptions>;
    private createResponseFormat;
    /**
     * Get the parameters used to invoke the model
     */
    invocationParams(options?: this["ParsedCallOptions"], extra?: {
        streaming?: boolean;
    }): Omit<OpenAIClient.Chat.ChatCompletionCreateParams, "messages">;
    /** @ignore */
    _identifyingParams(): Omit<OpenAIClient.Chat.ChatCompletionCreateParams, "messages"> & {
        model_name: string;
    } & ClientOptions;
    _streamResponseChunks(messages: BaseMessage[], options: this["ParsedCallOptions"], runManager?: CallbackManagerForLLMRun): AsyncGenerator<ChatGenerationChunk>;
    /**
     * Get the identifying parameters for the model
     *
     */
    identifyingParams(): Omit<OpenAIClient.Chat.Completions.ChatCompletionCreateParams, "messages"> & {
        model_name: string;
    } & ClientOptions;
    /** @ignore */
    _generate(messages: BaseMessage[], options: this["ParsedCallOptions"], runManager?: CallbackManagerForLLMRun): Promise<ChatResult>;
    /**
     * Estimate the number of tokens a prompt will use.
     * Modified from: https://github.com/hmarr/openai-chat-tokens/blob/main/src/index.ts
     */
    private getEstimatedTokenCountFromPrompt;
    /**
     * Estimate the number of tokens an array of generations have used.
     */
    private getNumTokensFromGenerations;
    getNumTokensFromMessages(messages: BaseMessage[]): Promise<{
        totalCount: number;
        countPerMessage: number[];
    }>;
    /**
     * Calls the OpenAI API with retry logic in case of failures.
     * @param request The request to send to the OpenAI API.
     * @param options Optional configuration for the API call.
     * @returns The response from the OpenAI API.
     */
    completionWithRetry(request: OpenAIClient.Chat.ChatCompletionCreateParamsStreaming, options?: OpenAICoreRequestOptions): Promise<AsyncIterable<OpenAIClient.Chat.Completions.ChatCompletionChunk>>;
    completionWithRetry(request: OpenAIClient.Chat.ChatCompletionCreateParamsNonStreaming, options?: OpenAICoreRequestOptions): Promise<OpenAIClient.Chat.Completions.ChatCompletion>;
    /**
     * Call the beta chat completions parse endpoint. This should only be called if
     * response_format is set to "json_object".
     * @param {OpenAIClient.Chat.ChatCompletionCreateParamsNonStreaming} request
     * @param {OpenAICoreRequestOptions | undefined} options
     */
    betaParsedCompletionWithRetry(request: OpenAIClient.Chat.ChatCompletionCreateParamsNonStreaming, options?: OpenAICoreRequestOptions): Promise<ReturnType<OpenAIClient["beta"]["chat"]["completions"]["parse"]>>;
    protected _getClientOptions(options: OpenAICoreRequestOptions | undefined): OpenAICoreRequestOptions;
    _llmType(): string;
    /** @ignore */
    _combineLLMOutput(...llmOutputs: OpenAILLMOutput[]): OpenAILLMOutput;
    withStructuredOutput<RunOutput extends Record<string, any> = Record<string, any>>(outputSchema: StructuredOutputMethodParams<RunOutput, false> | z.ZodType<RunOutput> | Record<string, any>, config?: ChatOpenAIStructuredOutputMethodOptions<false>): Runnable<BaseLanguageModelInput, RunOutput>;
    withStructuredOutput<RunOutput extends Record<string, any> = Record<string, any>>(outputSchema: StructuredOutputMethodParams<RunOutput, true> | z.ZodType<RunOutput> | Record<string, any>, config?: ChatOpenAIStructuredOutputMethodOptions<true>): Runnable<BaseLanguageModelInput, {
        raw: BaseMessage;
        parsed: RunOutput;
    }>;
}