651 lines
20 KiB
TypeScript
651 lines
20 KiB
TypeScript
import { type ClientOptions, OpenAI as OpenAIClient } from "openai";
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import { CallbackManagerForLLMRun } from "@langchain/core/callbacks/manager";
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import { AIMessageChunk, type BaseMessage } from "@langchain/core/messages";
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import { ChatGenerationChunk, type ChatResult } from "@langchain/core/outputs";
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import { BaseChatModel, BindToolsInput, LangSmithParams, type BaseChatModelParams } from "@langchain/core/language_models/chat_models";
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import { type BaseFunctionCallOptions, type BaseLanguageModelInput, type StructuredOutputMethodOptions, type StructuredOutputMethodParams } from "@langchain/core/language_models/base";
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import { z } from "zod";
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import { Runnable } from "@langchain/core/runnables";
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import type { AzureOpenAIInput, OpenAICallOptions, OpenAIChatInput, OpenAICoreRequestOptions, LegacyOpenAIInput, ChatOpenAIResponseFormat } from "./types.js";
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import { OpenAIToolChoice } from "./utils/openai.js";
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export type { AzureOpenAIInput, OpenAICallOptions, OpenAIChatInput };
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interface TokenUsage {
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completionTokens?: number;
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promptTokens?: number;
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totalTokens?: number;
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}
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interface OpenAILLMOutput {
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tokenUsage: TokenUsage;
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}
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type OpenAIRoleEnum = "system" | "assistant" | "user" | "function" | "tool";
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export declare function messageToOpenAIRole(message: BaseMessage): OpenAIRoleEnum;
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export declare function _convertMessagesToOpenAIParams(messages: BaseMessage[]): OpenAIClient.Chat.Completions.ChatCompletionMessageParam[];
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type ChatOpenAIToolType = BindToolsInput | OpenAIClient.ChatCompletionTool;
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export interface ChatOpenAIStructuredOutputMethodOptions<IncludeRaw extends boolean> extends StructuredOutputMethodOptions<IncludeRaw> {
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/**
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* strict: If `true` and `method` = "function_calling", model output is
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* guaranteed to exactly match the schema. If `true`, the input schema
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* will also be validated according to
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* https://platform.openai.com/docs/guides/structured-outputs/supported-schemas.
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* If `false`, input schema will not be validated and model output will not
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* be validated.
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* If `undefined`, `strict` argument will not be passed to the model.
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*
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* @version 0.2.6
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* @note Planned breaking change in version `0.3.0`:
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* `strict` will default to `true` when `method` is
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* "function_calling" as of version `0.3.0`.
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*/
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strict?: boolean;
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}
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export interface ChatOpenAICallOptions extends OpenAICallOptions, BaseFunctionCallOptions {
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tools?: ChatOpenAIToolType[];
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tool_choice?: OpenAIToolChoice;
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promptIndex?: number;
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response_format?: ChatOpenAIResponseFormat;
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seed?: number;
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/**
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* Additional options to pass to streamed completions.
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* If provided takes precedence over "streamUsage" set at initialization time.
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*/
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stream_options?: {
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/**
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* Whether or not to include token usage in the stream.
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* If set to `true`, this will include an additional
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* chunk at the end of the stream with the token usage.
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*/
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include_usage: boolean;
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};
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/**
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* Whether or not to restrict the ability to
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* call multiple tools in one response.
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*/
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parallel_tool_calls?: boolean;
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/**
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* If `true`, model output is guaranteed to exactly match the JSON Schema
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* provided in the tool definition. If `true`, the input schema will also be
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* validated according to
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* https://platform.openai.com/docs/guides/structured-outputs/supported-schemas.
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*
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* If `false`, input schema will not be validated and model output will not
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* be validated.
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*
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* If `undefined`, `strict` argument will not be passed to the model.
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*
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* @version 0.2.6
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*/
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strict?: boolean;
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}
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export interface ChatOpenAIFields extends Partial<OpenAIChatInput>, Partial<AzureOpenAIInput>, BaseChatModelParams {
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configuration?: ClientOptions & LegacyOpenAIInput;
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}
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/**
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* OpenAI chat model integration.
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*
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* Setup:
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* Install `@langchain/openai` and set an environment variable named `OPENAI_API_KEY`.
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*
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* ```bash
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* npm install @langchain/openai
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* export OPENAI_API_KEY="your-api-key"
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* ```
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*
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* ## [Constructor args](https://api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html#constructor)
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*
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* ## [Runtime args](https://api.js.langchain.com/interfaces/langchain_openai.ChatOpenAICallOptions.html)
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*
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* Runtime args can be passed as the second argument to any of the base runnable methods `.invoke`. `.stream`, `.batch`, etc.
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* They can also be passed via `.bind`, or the second arg in `.bindTools`, like shown in the examples below:
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*
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* ```typescript
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* // When calling `.bind`, call options should be passed via the first argument
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* const llmWithArgsBound = llm.bind({
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* stop: ["\n"],
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* tools: [...],
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* });
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*
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* // When calling `.bindTools`, call options should be passed via the second argument
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* const llmWithTools = llm.bindTools(
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* [...],
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* {
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* tool_choice: "auto",
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* }
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* );
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* ```
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*
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* ## Examples
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*
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* <details open>
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* <summary><strong>Instantiate</strong></summary>
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*
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* ```typescript
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* import { ChatOpenAI } from '@langchain/openai';
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*
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* const llm = new ChatOpenAI({
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* model: "gpt-4o",
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* temperature: 0,
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* maxTokens: undefined,
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* timeout: undefined,
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* maxRetries: 2,
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* // apiKey: "...",
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* // baseUrl: "...",
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* // organization: "...",
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* // other params...
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* });
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* ```
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* </details>
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*
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* <br />
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*
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* <details>
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* <summary><strong>Invoking</strong></summary>
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*
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* ```typescript
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* const input = `Translate "I love programming" into French.`;
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*
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* // Models also accept a list of chat messages or a formatted prompt
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* const result = await llm.invoke(input);
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* console.log(result);
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* ```
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*
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* ```txt
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* AIMessage {
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* "id": "chatcmpl-9u4Mpu44CbPjwYFkTbeoZgvzB00Tz",
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* "content": "J'adore la programmation.",
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* "response_metadata": {
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* "tokenUsage": {
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* "completionTokens": 5,
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* "promptTokens": 28,
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* "totalTokens": 33
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* },
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* "finish_reason": "stop",
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* "system_fingerprint": "fp_3aa7262c27"
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* },
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* "usage_metadata": {
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* "input_tokens": 28,
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* "output_tokens": 5,
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* "total_tokens": 33
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* }
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* }
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* ```
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* </details>
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*
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* <br />
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*
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* <details>
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* <summary><strong>Streaming Chunks</strong></summary>
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*
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* ```typescript
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* for await (const chunk of await llm.stream(input)) {
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* console.log(chunk);
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* }
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* ```
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*
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* ```txt
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* AIMessageChunk {
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* "id": "chatcmpl-9u4NWB7yUeHCKdLr6jP3HpaOYHTqs",
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* "content": ""
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* }
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* AIMessageChunk {
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* "content": "J"
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* }
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* AIMessageChunk {
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* "content": "'adore"
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* }
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* AIMessageChunk {
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* "content": " la"
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* }
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* AIMessageChunk {
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* "content": " programmation",,
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* }
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* AIMessageChunk {
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* "content": ".",,
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* }
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* AIMessageChunk {
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* "content": "",
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* "response_metadata": {
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* "finish_reason": "stop",
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* "system_fingerprint": "fp_c9aa9c0491"
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* },
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* }
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* AIMessageChunk {
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* "content": "",
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* "usage_metadata": {
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* "input_tokens": 28,
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* "output_tokens": 5,
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* "total_tokens": 33
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* }
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* }
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* ```
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* </details>
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*
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* <br />
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*
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* <details>
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* <summary><strong>Aggregate Streamed Chunks</strong></summary>
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*
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* ```typescript
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* import { AIMessageChunk } from '@langchain/core/messages';
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* import { concat } from '@langchain/core/utils/stream';
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*
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* const stream = await llm.stream(input);
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* let full: AIMessageChunk | undefined;
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* for await (const chunk of stream) {
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* full = !full ? chunk : concat(full, chunk);
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* }
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* console.log(full);
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* ```
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*
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* ```txt
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* AIMessageChunk {
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* "id": "chatcmpl-9u4PnX6Fy7OmK46DASy0bH6cxn5Xu",
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* "content": "J'adore la programmation.",
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* "response_metadata": {
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* "prompt": 0,
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* "completion": 0,
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* "finish_reason": "stop",
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* },
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* "usage_metadata": {
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* "input_tokens": 28,
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* "output_tokens": 5,
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* "total_tokens": 33
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* }
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* }
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* ```
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* </details>
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*
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* <br />
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*
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* <details>
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* <summary><strong>Bind tools</strong></summary>
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*
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* ```typescript
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* import { z } from 'zod';
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*
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* const GetWeather = {
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* name: "GetWeather",
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* description: "Get the current weather in a given location",
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* schema: z.object({
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* location: z.string().describe("The city and state, e.g. San Francisco, CA")
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* }),
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* }
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*
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* const GetPopulation = {
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* name: "GetPopulation",
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* description: "Get the current population in a given location",
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* schema: z.object({
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* location: z.string().describe("The city and state, e.g. San Francisco, CA")
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* }),
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* }
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*
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* const llmWithTools = llm.bindTools(
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* [GetWeather, GetPopulation],
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* {
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* // strict: true // enforce tool args schema is respected
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* }
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* );
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* const aiMsg = await llmWithTools.invoke(
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* "Which city is hotter today and which is bigger: LA or NY?"
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* );
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* console.log(aiMsg.tool_calls);
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* ```
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*
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* ```txt
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* [
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* {
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* name: 'GetWeather',
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* args: { location: 'Los Angeles, CA' },
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* type: 'tool_call',
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* id: 'call_uPU4FiFzoKAtMxfmPnfQL6UK'
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* },
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* {
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* name: 'GetWeather',
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* args: { location: 'New York, NY' },
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* type: 'tool_call',
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* id: 'call_UNkEwuQsHrGYqgDQuH9nPAtX'
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* },
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* {
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* name: 'GetPopulation',
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* args: { location: 'Los Angeles, CA' },
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* type: 'tool_call',
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* id: 'call_kL3OXxaq9OjIKqRTpvjaCH14'
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* },
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* {
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* name: 'GetPopulation',
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* args: { location: 'New York, NY' },
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* type: 'tool_call',
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* id: 'call_s9KQB1UWj45LLGaEnjz0179q'
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* }
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* ]
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* ```
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* </details>
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*
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* <br />
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*
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* <details>
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* <summary><strong>Structured Output</strong></summary>
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*
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* ```typescript
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* import { z } from 'zod';
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*
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* const Joke = z.object({
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* setup: z.string().describe("The setup of the joke"),
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* punchline: z.string().describe("The punchline to the joke"),
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* rating: z.number().optional().describe("How funny the joke is, from 1 to 10")
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* }).describe('Joke to tell user.');
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*
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* const structuredLlm = llm.withStructuredOutput(Joke, {
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* name: "Joke",
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* strict: true, // Optionally enable OpenAI structured outputs
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* });
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* const jokeResult = await structuredLlm.invoke("Tell me a joke about cats");
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* console.log(jokeResult);
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* ```
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*
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* ```txt
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* {
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* setup: 'Why was the cat sitting on the computer?',
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* punchline: 'Because it wanted to keep an eye on the mouse!',
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* rating: 7
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* }
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* ```
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* </details>
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*
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* <br />
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*
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* <details>
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* <summary><strong>JSON Object Response Format</strong></summary>
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*
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* ```typescript
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* const jsonLlm = llm.bind({ response_format: { type: "json_object" } });
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* const jsonLlmAiMsg = await jsonLlm.invoke(
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* "Return a JSON object with key 'randomInts' and a value of 10 random ints in [0-99]"
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* );
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* console.log(jsonLlmAiMsg.content);
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* ```
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*
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* ```txt
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* {
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* "randomInts": [23, 87, 45, 12, 78, 34, 56, 90, 11, 67]
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* }
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* ```
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* </details>
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*
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* <br />
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*
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* <details>
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* <summary><strong>Multimodal</strong></summary>
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*
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* ```typescript
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* import { HumanMessage } from '@langchain/core/messages';
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*
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* const imageUrl = "https://example.com/image.jpg";
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* const imageData = await fetch(imageUrl).then(res => res.arrayBuffer());
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* const base64Image = Buffer.from(imageData).toString('base64');
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*
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* const message = new HumanMessage({
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* content: [
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* { type: "text", text: "describe the weather in this image" },
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* {
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* type: "image_url",
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* image_url: { url: `data:image/jpeg;base64,${base64Image}` },
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* },
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* ]
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* });
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*
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* const imageDescriptionAiMsg = await llm.invoke([message]);
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* console.log(imageDescriptionAiMsg.content);
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* ```
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*
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* ```txt
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* 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.
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* ```
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* </details>
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*
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* <br />
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*
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* <details>
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* <summary><strong>Usage Metadata</strong></summary>
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*
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* ```typescript
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* const aiMsgForMetadata = await llm.invoke(input);
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* console.log(aiMsgForMetadata.usage_metadata);
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* ```
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*
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* ```txt
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* { input_tokens: 28, output_tokens: 5, total_tokens: 33 }
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* ```
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* </details>
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*
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* <br />
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*
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* <details>
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* <summary><strong>Logprobs</strong></summary>
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*
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* ```typescript
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* const logprobsLlm = new ChatOpenAI({ logprobs: true });
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* const aiMsgForLogprobs = await logprobsLlm.invoke(input);
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* console.log(aiMsgForLogprobs.response_metadata.logprobs);
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* ```
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*
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* ```txt
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* {
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* content: [
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* {
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* token: 'J',
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* logprob: -0.000050616763,
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* bytes: [Array],
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* top_logprobs: []
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* },
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* {
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* token: "'",
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* logprob: -0.01868736,
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* bytes: [Array],
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* top_logprobs: []
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* },
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* {
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* token: 'ad',
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* logprob: -0.0000030545007,
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* bytes: [Array],
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* top_logprobs: []
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* },
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* { token: 'ore', logprob: 0, bytes: [Array], top_logprobs: [] },
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* {
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* token: ' la',
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* logprob: -0.515404,
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* bytes: [Array],
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* top_logprobs: []
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* },
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* {
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* token: ' programm',
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* logprob: -0.0000118755715,
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* bytes: [Array],
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* top_logprobs: []
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* },
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* { token: 'ation', logprob: 0, bytes: [Array], top_logprobs: [] },
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* {
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* token: '.',
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* logprob: -0.0000037697225,
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* bytes: [Array],
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* top_logprobs: []
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* }
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* ],
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* refusal: null
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* }
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* ```
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* </details>
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*
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* <br />
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*
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* <details>
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* <summary><strong>Response Metadata</strong></summary>
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*
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* ```typescript
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* const aiMsgForResponseMetadata = await llm.invoke(input);
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* console.log(aiMsgForResponseMetadata.response_metadata);
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* ```
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*
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* ```txt
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* {
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* tokenUsage: { completionTokens: 5, promptTokens: 28, totalTokens: 33 },
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* finish_reason: 'stop',
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* system_fingerprint: 'fp_3aa7262c27'
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* }
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* ```
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* </details>
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*
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* <br />
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*
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* <details>
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* <summary><strong>JSON Schema Structured Output</strong></summary>
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*
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* ```typescript
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* const llmForJsonSchema = new ChatOpenAI({
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* model: "gpt-4o-2024-08-06",
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* }).withStructuredOutput(
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* z.object({
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* command: z.string().describe("The command to execute"),
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* expectedOutput: z.string().describe("The expected output of the command"),
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* options: z
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* .array(z.string())
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* .describe("The options you can pass to the command"),
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* }),
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* {
|
|
* 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;
|
|
}>;
|
|
}
|