@langchain/openai
Version:
OpenAI integrations for LangChain.js
609 lines (607 loc) • 16.3 kB
JavaScript
const require_tools = require('../utils/tools.cjs');
const require_misc = require('../utils/misc.cjs');
const require_base = require('./base.cjs');
const require_completions = require('./completions.cjs');
const require_responses = require('./responses.cjs');
//#region src/chat_models/index.ts
/**
* OpenAI chat model integration.
*
* To use with Azure, import the `AzureChatOpenAI` class.
*
* 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 `.withConfig`, or the second arg in `.bindTools`, like shown in the examples below:
*
* ```typescript
* // When calling `.withConfig`, call options should be passed via the first argument
* const llmWithArgsBound = llm.withConfig({
* stop: ["\n"],
* tools: [...],
* });
*
* // When calling `.bindTools`, call options should be passed via the second argument
* const llmWithTools = llm.bindTools(
* [...],
* {
* tool_choice: "auto",
* }
* );
* ```
*
* ## Examples
*
* <details open>
* <summary><strong>Instantiate</strong></summary>
*
* ```typescript
* import { ChatOpenAI } from '@langchain/openai';
*
* const llm = new ChatOpenAI({
* model: "gpt-4o-mini",
* temperature: 0,
* maxTokens: undefined,
* timeout: undefined,
* maxRetries: 2,
* // apiKey: "...",
* // configuration: {
* // 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().nullable().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.withConfig({ response_format: { type: "json_object" } });
* const jsonLlmAiMsg = await jsonLlm.invoke(
* "Return a JSON object with key 'randomInts' and a value of 10 random ints in [0-99]"
* );
* console.log(jsonLlmAiMsg.content);
* ```
*
* ```txt
* {
* "randomInts": [23, 87, 45, 12, 78, 34, 56, 90, 11, 67]
* }
* ```
* </details>
*
* <br />
*
* <details>
* <summary><strong>Multimodal</strong></summary>
*
* ```typescript
* import { HumanMessage } from '@langchain/core/messages';
*
* const imageUrl = "https://example.com/image.jpg";
* const imageData = await fetch(imageUrl).then(res => res.arrayBuffer());
* const base64Image = Buffer.from(imageData).toString('base64');
*
* const message = new HumanMessage({
* content: [
* { type: "text", text: "describe the weather in this image" },
* {
* type: "image_url",
* image_url: { url: `data:image/jpeg;base64,${base64Image}` },
* },
* ]
* });
*
* const imageDescriptionAiMsg = await llm.invoke([message]);
* console.log(imageDescriptionAiMsg.content);
* ```
*
* ```txt
* The weather in the image appears to be clear and sunny. The sky is mostly blue with a few scattered white clouds, indicating fair weather. The bright sunlight is casting shadows on the green, grassy hill, suggesting it is a pleasant day with good visibility. There are no signs of rain or stormy conditions.
* ```
* </details>
*
* <br />
*
* <details>
* <summary><strong>Usage Metadata</strong></summary>
*
* ```typescript
* const aiMsgForMetadata = await llm.invoke(input);
* console.log(aiMsgForMetadata.usage_metadata);
* ```
*
* ```txt
* { input_tokens: 28, output_tokens: 5, total_tokens: 33 }
* ```
* </details>
*
* <br />
*
* <details>
* <summary><strong>Logprobs</strong></summary>
*
* ```typescript
* const logprobsLlm = new ChatOpenAI({ model: "gpt-4o-mini", logprobs: true });
* const aiMsgForLogprobs = await logprobsLlm.invoke(input);
* console.log(aiMsgForLogprobs.response_metadata.logprobs);
* ```
*
* ```txt
* {
* content: [
* {
* token: 'J',
* logprob: -0.000050616763,
* bytes: [Array],
* top_logprobs: []
* },
* {
* token: "'",
* logprob: -0.01868736,
* bytes: [Array],
* top_logprobs: []
* },
* {
* token: 'ad',
* logprob: -0.0000030545007,
* bytes: [Array],
* top_logprobs: []
* },
* { token: 'ore', logprob: 0, bytes: [Array], top_logprobs: [] },
* {
* token: ' la',
* logprob: -0.515404,
* bytes: [Array],
* top_logprobs: []
* },
* {
* token: ' programm',
* logprob: -0.0000118755715,
* bytes: [Array],
* top_logprobs: []
* },
* { token: 'ation', logprob: 0, bytes: [Array], top_logprobs: [] },
* {
* token: '.',
* logprob: -0.0000037697225,
* bytes: [Array],
* top_logprobs: []
* }
* ],
* refusal: null
* }
* ```
* </details>
*
* <br />
*
* <details>
* <summary><strong>Response Metadata</strong></summary>
*
* ```typescript
* const aiMsgForResponseMetadata = await llm.invoke(input);
* console.log(aiMsgForResponseMetadata.response_metadata);
* ```
*
* ```txt
* {
* tokenUsage: { completionTokens: 5, promptTokens: 28, totalTokens: 33 },
* finish_reason: 'stop',
* system_fingerprint: 'fp_3aa7262c27'
* }
* ```
* </details>
*
* <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 />
*
* <details>
* <summary><strong>Audio Outputs</strong></summary>
*
* ```typescript
* import { ChatOpenAI } from "@langchain/openai";
*
* const modelWithAudioOutput = new ChatOpenAI({
* model: "gpt-4o-audio-preview",
* // You may also pass these fields to `.withConfig` as a call argument.
* modalities: ["text", "audio"], // Specifies that the model should output audio.
* audio: {
* voice: "alloy",
* format: "wav",
* },
* });
*
* const audioOutputResult = await modelWithAudioOutput.invoke("Tell me a joke about cats.");
* const castMessageContent = audioOutputResult.content[0] as Record<string, any>;
*
* console.log({
* ...castMessageContent,
* data: castMessageContent.data.slice(0, 100) // Sliced for brevity
* })
* ```
*
* ```txt
* {
* id: 'audio_67117718c6008190a3afad3e3054b9b6',
* data: 'UklGRqYwBgBXQVZFZm10IBAAAAABAAEAwF0AAIC7AAACABAATElTVBoAAABJTkZPSVNGVA4AAABMYXZmNTguMjkuMTAwAGRhdGFg',
* expires_at: 1729201448,
* transcript: 'Sure! Why did the cat sit on the computer? Because it wanted to keep an eye on the mouse!'
* }
* ```
* </details>
*
* <br />
*
* <details>
* <summary><strong>Audio Outputs</strong></summary>
*
* ```typescript
* import { ChatOpenAI } from "@langchain/openai";
*
* const modelWithAudioOutput = new ChatOpenAI({
* model: "gpt-4o-audio-preview",
* // You may also pass these fields to `.withConfig` as a call argument.
* modalities: ["text", "audio"], // Specifies that the model should output audio.
* audio: {
* voice: "alloy",
* format: "wav",
* },
* });
*
* const audioOutputResult = await modelWithAudioOutput.invoke("Tell me a joke about cats.");
* const castAudioContent = audioOutputResult.additional_kwargs.audio as Record<string, any>;
*
* console.log({
* ...castAudioContent,
* data: castAudioContent.data.slice(0, 100) // Sliced for brevity
* })
* ```
*
* ```txt
* {
* id: 'audio_67117718c6008190a3afad3e3054b9b6',
* data: 'UklGRqYwBgBXQVZFZm10IBAAAAABAAEAwF0AAIC7AAACABAATElTVBoAAABJTkZPSVNGVA4AAABMYXZmNTguMjkuMTAwAGRhdGFg',
* expires_at: 1729201448,
* transcript: 'Sure! Why did the cat sit on the computer? Because it wanted to keep an eye on the mouse!'
* }
* ```
* </details>
*
* <br />
*/
var ChatOpenAI = class ChatOpenAI extends require_base.BaseChatOpenAI {
/**
* Whether to use the responses API for all requests. If `false` the responses API will be used
* only when required in order to fulfill the request.
*/
useResponsesApi = false;
responses;
completions;
get lc_serializable_keys() {
return [...super.lc_serializable_keys, "useResponsesApi"];
}
get callKeys() {
return [...super.callKeys, "useResponsesApi"];
}
constructor(fields) {
super(fields);
this.fields = fields;
this.useResponsesApi = fields?.useResponsesApi ?? false;
this.responses = fields?.responses ?? new require_responses.ChatOpenAIResponses(fields);
this.completions = fields?.completions ?? new require_completions.ChatOpenAICompletions(fields);
}
_useResponsesApi(options) {
const usesBuiltInTools = options?.tools?.some(require_tools.isBuiltInTool);
const hasResponsesOnlyKwargs = options?.previous_response_id != null || options?.text != null || options?.truncation != null || options?.include != null || options?.reasoning?.summary != null || this.reasoning?.summary != null;
const hasCustomTools = options?.tools?.some(require_tools.isOpenAICustomTool) || options?.tools?.some(require_tools.isCustomTool);
return this.useResponsesApi || usesBuiltInTools || hasResponsesOnlyKwargs || hasCustomTools || require_misc._modelPrefersResponsesAPI(this.model);
}
getLsParams(options) {
const optionsWithDefaults = this._combineCallOptions(options);
if (this._useResponsesApi(options)) return this.responses.getLsParams(optionsWithDefaults);
return this.completions.getLsParams(optionsWithDefaults);
}
invocationParams(options) {
const optionsWithDefaults = this._combineCallOptions(options);
if (this._useResponsesApi(options)) return this.responses.invocationParams(optionsWithDefaults);
return this.completions.invocationParams(optionsWithDefaults);
}
/** @ignore */
async _generate(messages, options, runManager) {
if (this._useResponsesApi(options)) return this.responses._generate(messages, options);
return this.completions._generate(messages, options, runManager);
}
async *_streamResponseChunks(messages, options, runManager) {
if (this._useResponsesApi(options)) {
yield* this.responses._streamResponseChunks(messages, this._combineCallOptions(options), runManager);
return;
}
yield* this.completions._streamResponseChunks(messages, this._combineCallOptions(options), runManager);
}
withConfig(config) {
const newModel = new ChatOpenAI(this.fields);
newModel.defaultOptions = {
...this.defaultOptions,
...config
};
return newModel;
}
};
//#endregion
exports.ChatOpenAI = ChatOpenAI;
//# sourceMappingURL=index.cjs.map