554 lines
16 KiB
JavaScript
554 lines
16 KiB
JavaScript
"use strict";
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Object.defineProperty(exports, "__esModule", { value: true });
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exports.AzureChatOpenAI = void 0;
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const openai_1 = require("openai");
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const chat_models_js_1 = require("../chat_models.cjs");
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const azure_js_1 = require("../utils/azure.cjs");
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/**
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* Azure OpenAI chat model integration.
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*
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* Setup:
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* Install `@langchain/openai` and set the following environment variables:
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*
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* ```bash
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* npm install @langchain/openai
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* export AZURE_OPENAI_API_KEY="your-api-key"
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* export AZURE_OPENAI_API_DEPLOYMENT_NAME="your-deployment-name"
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* export AZURE_OPENAI_API_VERSION="your-version"
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* export AZURE_OPENAI_BASE_PATH="your-base-path"
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* ```
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*
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* ## [Constructor args](https://api.js.langchain.com/classes/langchain_openai.AzureChatOpenAI.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 { AzureChatOpenAI } from '@langchain/openai';
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*
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* const llm = new AzureChatOpenAI({
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* azureOpenAIApiKey: process.env.AZURE_OPENAI_API_KEY, // In Node.js defaults to process.env.AZURE_OPENAI_API_KEY
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* azureOpenAIApiInstanceName: process.env.AZURE_OPENAI_API_INSTANCE_NAME, // In Node.js defaults to process.env.AZURE_OPENAI_API_INSTANCE_NAME
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* azureOpenAIApiDeploymentName: process.env.AZURE_OPENAI_API_DEPLOYMENT_NAME, // In Node.js defaults to process.env.AZURE_OPENAI_API_DEPLOYMENT_NAME
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* azureOpenAIApiVersion: process.env.AZURE_OPENAI_API_VERSION, // In Node.js defaults to process.env.AZURE_OPENAI_API_VERSION
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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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* // 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([GetWeather, GetPopulation]);
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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, { name: "Joke" });
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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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class AzureChatOpenAI extends chat_models_js_1.ChatOpenAI {
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_llmType() {
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return "azure_openai";
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}
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get lc_aliases() {
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return {
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openAIApiKey: "openai_api_key",
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openAIApiVersion: "openai_api_version",
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openAIBasePath: "openai_api_base",
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deploymentName: "deployment_name",
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azureOpenAIEndpoint: "azure_endpoint",
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azureOpenAIApiVersion: "openai_api_version",
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azureOpenAIBasePath: "openai_api_base",
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azureOpenAIApiDeploymentName: "deployment_name",
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};
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}
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constructor(fields) {
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const newFields = fields ? { ...fields } : fields;
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if (newFields) {
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// don't rewrite the fields if they are already set
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newFields.azureOpenAIApiDeploymentName =
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newFields.azureOpenAIApiDeploymentName ?? newFields.deploymentName;
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newFields.azureOpenAIApiKey =
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newFields.azureOpenAIApiKey ?? newFields.openAIApiKey;
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newFields.azureOpenAIApiVersion =
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newFields.azureOpenAIApiVersion ?? newFields.openAIApiVersion;
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}
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super(newFields);
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}
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getLsParams(options) {
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const params = super.getLsParams(options);
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params.ls_provider = "azure";
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return params;
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}
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_getClientOptions(options) {
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if (!this.client) {
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const openAIEndpointConfig = {
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azureOpenAIApiDeploymentName: this.azureOpenAIApiDeploymentName,
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azureOpenAIApiInstanceName: this.azureOpenAIApiInstanceName,
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azureOpenAIApiKey: this.azureOpenAIApiKey,
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azureOpenAIBasePath: this.azureOpenAIBasePath,
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azureADTokenProvider: this.azureADTokenProvider,
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baseURL: this.clientConfig.baseURL,
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azureOpenAIEndpoint: this.azureOpenAIEndpoint,
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};
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const endpoint = (0, azure_js_1.getEndpoint)(openAIEndpointConfig);
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const params = {
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...this.clientConfig,
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baseURL: endpoint,
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timeout: this.timeout,
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maxRetries: 0,
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};
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if (!this.azureADTokenProvider) {
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params.apiKey = openAIEndpointConfig.azureOpenAIApiKey;
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}
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if (!params.baseURL) {
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delete params.baseURL;
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}
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params.defaultHeaders = {
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...params.defaultHeaders,
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"User-Agent": params.defaultHeaders?.["User-Agent"]
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? `${params.defaultHeaders["User-Agent"]}: langchainjs-azure-openai-v2`
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: `langchainjs-azure-openai-v2`,
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};
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this.client = new openai_1.AzureOpenAI({
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apiVersion: this.azureOpenAIApiVersion,
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azureADTokenProvider: this.azureADTokenProvider,
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deployment: this.azureOpenAIApiDeploymentName,
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...params,
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});
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}
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const requestOptions = {
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...this.clientConfig,
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...options,
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};
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if (this.azureOpenAIApiKey) {
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requestOptions.headers = {
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"api-key": this.azureOpenAIApiKey,
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...requestOptions.headers,
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};
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requestOptions.query = {
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"api-version": this.azureOpenAIApiVersion,
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...requestOptions.query,
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};
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}
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return requestOptions;
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}
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// eslint-disable-next-line @typescript-eslint/no-explicit-any
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toJSON() {
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const json = super.toJSON();
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function isRecord(obj) {
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return typeof obj === "object" && obj != null;
|
|
}
|
|
if (isRecord(json) && isRecord(json.kwargs)) {
|
|
delete json.kwargs.azure_openai_base_path;
|
|
delete json.kwargs.azure_openai_api_deployment_name;
|
|
delete json.kwargs.azure_openai_api_key;
|
|
delete json.kwargs.azure_openai_api_version;
|
|
delete json.kwargs.azure_open_ai_base_path;
|
|
if (!json.kwargs.azure_endpoint && this.azureOpenAIEndpoint) {
|
|
json.kwargs.azure_endpoint = this.azureOpenAIEndpoint;
|
|
}
|
|
if (!json.kwargs.azure_endpoint && this.azureOpenAIBasePath) {
|
|
const parts = this.azureOpenAIBasePath.split("/openai/deployments/");
|
|
if (parts.length === 2 && parts[0].startsWith("http")) {
|
|
const [endpoint] = parts;
|
|
json.kwargs.azure_endpoint = endpoint;
|
|
}
|
|
}
|
|
if (!json.kwargs.azure_endpoint && this.azureOpenAIApiInstanceName) {
|
|
json.kwargs.azure_endpoint = `https://${this.azureOpenAIApiInstanceName}.openai.azure.com/`;
|
|
}
|
|
if (!json.kwargs.deployment_name && this.azureOpenAIApiDeploymentName) {
|
|
json.kwargs.deployment_name = this.azureOpenAIApiDeploymentName;
|
|
}
|
|
if (!json.kwargs.deployment_name && this.azureOpenAIBasePath) {
|
|
const parts = this.azureOpenAIBasePath.split("/openai/deployments/");
|
|
if (parts.length === 2) {
|
|
const [, deployment] = parts;
|
|
json.kwargs.deployment_name = deployment;
|
|
}
|
|
}
|
|
if (json.kwargs.azure_endpoint &&
|
|
json.kwargs.deployment_name &&
|
|
json.kwargs.openai_api_base) {
|
|
delete json.kwargs.openai_api_base;
|
|
}
|
|
if (json.kwargs.azure_openai_api_instance_name &&
|
|
json.kwargs.azure_endpoint) {
|
|
delete json.kwargs.azure_openai_api_instance_name;
|
|
}
|
|
}
|
|
return json;
|
|
}
|
|
}
|
|
exports.AzureChatOpenAI = AzureChatOpenAI;
|