@langchain/openai
Version:
OpenAI integrations for LangChain.js
1,489 lines • 83 kB
JavaScript
import { OpenAI as OpenAIClient } from "openai";
import { AIMessage, AIMessageChunk, ChatMessage, ChatMessageChunk, FunctionMessageChunk, HumanMessageChunk, SystemMessageChunk, ToolMessageChunk, isAIMessage, } from "@langchain/core/messages";
import { ChatGenerationChunk, } from "@langchain/core/outputs";
import { getEnvironmentVariable } from "@langchain/core/utils/env";
import { BaseChatModel, } from "@langchain/core/language_models/chat_models";
import { isOpenAITool, } from "@langchain/core/language_models/base";
import { RunnableLambda, RunnablePassthrough, RunnableSequence, } from "@langchain/core/runnables";
import { JsonOutputParser, StructuredOutputParser, } from "@langchain/core/output_parsers";
import { JsonOutputKeyToolsParser, convertLangChainToolCallToOpenAI, makeInvalidToolCall, parseToolCall, } from "@langchain/core/output_parsers/openai_tools";
import { zodToJsonSchema } from "zod-to-json-schema";
import { zodResponseFormat } from "openai/helpers/zod";
import { getEndpoint } from "./utils/azure.js";
import { formatToOpenAIToolChoice, wrapOpenAIClientError, } from "./utils/openai.js";
import { formatFunctionDefinitions, } from "./utils/openai-format-fndef.js";
import { _convertToOpenAITool } from "./utils/tools.js";
function extractGenericMessageCustomRole(message) {
if (message.role !== "system" &&
message.role !== "developer" &&
message.role !== "assistant" &&
message.role !== "user" &&
message.role !== "function" &&
message.role !== "tool") {
console.warn(`Unknown message role: ${message.role}`);
}
return message.role;
}
export function messageToOpenAIRole(message) {
const type = message._getType();
switch (type) {
case "system":
return "system";
case "ai":
return "assistant";
case "human":
return "user";
case "function":
return "function";
case "tool":
return "tool";
case "generic": {
if (!ChatMessage.isInstance(message))
throw new Error("Invalid generic chat message");
return extractGenericMessageCustomRole(message);
}
default:
throw new Error(`Unknown message type: ${type}`);
}
}
// Used in LangSmith, export is important here
export function _convertMessagesToOpenAIParams(messages, model) {
// TODO: Function messages do not support array content, fix cast
return messages.flatMap((message) => {
let role = messageToOpenAIRole(message);
if (role === "system" && isReasoningModel(model)) {
role = "developer";
}
// eslint-disable-next-line @typescript-eslint/no-explicit-any
const completionParam = {
role,
content: message.content,
};
if (message.name != null) {
completionParam.name = message.name;
}
if (message.additional_kwargs.function_call != null) {
completionParam.function_call = message.additional_kwargs.function_call;
completionParam.content = "";
}
if (isAIMessage(message) && !!message.tool_calls?.length) {
completionParam.tool_calls = message.tool_calls.map(convertLangChainToolCallToOpenAI);
completionParam.content = "";
}
else {
if (message.additional_kwargs.tool_calls != null) {
completionParam.tool_calls = message.additional_kwargs.tool_calls;
}
if (message.tool_call_id != null) {
completionParam.tool_call_id = message.tool_call_id;
}
}
if (message.additional_kwargs.audio &&
typeof message.additional_kwargs.audio === "object" &&
"id" in message.additional_kwargs.audio) {
const audioMessage = {
role: "assistant",
audio: {
id: message.additional_kwargs.audio.id,
},
};
return [completionParam, audioMessage];
}
return completionParam;
});
}
const _FUNCTION_CALL_IDS_MAP_KEY = "__openai_function_call_ids__";
function _convertMessagesToOpenAIResponsesParams(messages, model) {
return messages.flatMap((lcMsg) => {
let role = messageToOpenAIRole(lcMsg);
if (role === "system" && isReasoningModel(model))
role = "developer";
if (role === "function") {
throw new Error("Function messages are not supported in Responses API");
}
if (role === "tool") {
const toolMessage = lcMsg;
// Handle computer call output
if (toolMessage.additional_kwargs?.type === "computer_call_output") {
const output = (() => {
if (typeof toolMessage.content === "string") {
return {
type: "computer_screenshot",
image_url: toolMessage.content,
};
}
if (Array.isArray(toolMessage.content)) {
const oaiScreenshot = toolMessage.content.find((i) => i.type === "computer_screenshot");
if (oaiScreenshot)
return oaiScreenshot;
const lcImage = toolMessage.content.find((i) => i.type === "image_url");
if (lcImage) {
return {
type: "computer_screenshot",
image_url: typeof lcImage.image_url === "string"
? lcImage.image_url
: lcImage.image_url.url,
};
}
}
throw new Error("Invalid computer call output");
})();
return {
type: "computer_call_output",
output,
call_id: toolMessage.tool_call_id,
};
}
return {
type: "function_call_output",
call_id: toolMessage.tool_call_id,
id: toolMessage.id,
output: typeof toolMessage.content !== "string"
? JSON.stringify(toolMessage.content)
: toolMessage.content,
};
}
if (role === "assistant") {
const input = [];
// reasoning items
if (lcMsg.additional_kwargs.reasoning != null) {
const isReasoningItem = (item) => typeof item === "object" &&
item != null &&
"type" in item &&
item.type === "reasoning";
if (isReasoningItem(lcMsg.additional_kwargs.reasoning)) {
input.push(lcMsg.additional_kwargs.reasoning);
}
}
// ai content
let { content } = lcMsg;
if (lcMsg.additional_kwargs.refusal != null) {
if (typeof content === "string") {
content = [{ type: "output_text", text: content, annotations: [] }];
}
content = [
...content,
{ type: "refusal", refusal: lcMsg.additional_kwargs.refusal },
];
}
input.push({
type: "message",
role: "assistant",
content: typeof content === "string"
? content
: content.flatMap((item) => {
if (item.type === "text") {
return {
type: "output_text",
text: item.text,
// @ts-expect-error TODO: add types for `annotations`
annotations: item.annotations ?? [],
};
}
if (item.type === "output_text" || item.type === "refusal") {
return item;
}
return [];
}),
});
// function tool calls and computer use tool calls
const functionCallIds =
// eslint-disable-next-line @typescript-eslint/no-use-before-define
lcMsg.additional_kwargs[_FUNCTION_CALL_IDS_MAP_KEY];
if (isAIMessage(lcMsg) && !!lcMsg.tool_calls?.length) {
input.push(...lcMsg.tool_calls.map((toolCall) => ({
type: "function_call",
name: toolCall.name,
arguments: JSON.stringify(toolCall.args),
call_id: toolCall.id,
// @ts-expect-error Might come from a non-Responses API message
id: functionCallIds?.[toolCall.id],
})));
}
else if (lcMsg.additional_kwargs.tool_calls != null) {
input.push(...lcMsg.additional_kwargs.tool_calls.map((toolCall) => ({
type: "function_call",
name: toolCall.function.name,
call_id: toolCall.id,
// @ts-expect-error Might come from a non-Responses API message
id: functionCallIds?.[toolCall.id],
arguments: toolCall.function.arguments,
})));
}
const toolOutputs = lcMsg.response_metadata.output?.length
? lcMsg.response_metadata.output
: lcMsg.additional_kwargs.tool_outputs;
if (toolOutputs != null) {
const castToolOutputs = toolOutputs;
const reasoningCalls = castToolOutputs?.filter((item) => item.type === "reasoning");
const computerCalls = castToolOutputs?.filter((item) => item.type === "computer_call");
// NOTE: Reasoning outputs must be passed to the model BEFORE computer calls.
if (reasoningCalls.length > 0 && computerCalls.length > 0) {
input.push(...reasoningCalls);
}
if (computerCalls.length > 0)
input.push(...computerCalls);
}
return input;
}
if (role === "user") {
return {
type: "message",
role: "user",
content: typeof lcMsg.content === "string"
? lcMsg.content
: lcMsg.content.flatMap((item) => {
if (item.type === "text") {
return { type: "input_text", text: item.text };
}
if (item.type === "image_url") {
const image_url = typeof item.image_url === "string"
? item.image_url
: item.image_url.url;
const detail = typeof item.image_url === "string"
? "auto"
: item.image_url.detail;
return { type: "input_image", image_url, detail };
}
if (item.type === "input_text" ||
item.type === "input_image" ||
item.type === "input_file") {
return item;
}
return [];
}),
};
}
return [];
});
}
function _convertOpenAIResponsesMessageToBaseMessage(response) {
if (response.error) {
// TODO: add support for `addLangChainErrorFields`
const error = new Error(response.error.message);
error.name = response.error.code;
throw error;
}
const content = [];
const tool_calls = [];
const invalid_tool_calls = [];
const response_metadata = {
model: response.model,
created_at: response.created_at,
id: response.id,
incomplete_details: response.incomplete_details,
metadata: response.metadata,
object: response.object,
status: response.status,
user: response.user,
// for compatibility with chat completion calls.
model_name: response.model,
};
const additional_kwargs = {};
for (const item of response.output) {
if (item.type === "message") {
content.push(...item.content.flatMap((part) => {
if (part.type === "output_text") {
if ("parsed" in part && part.parsed != null) {
additional_kwargs.parsed = part.parsed;
}
return {
type: "text",
text: part.text,
annotations: part.annotations,
};
}
if (part.type === "refusal") {
additional_kwargs.refusal = part.refusal;
return [];
}
return part;
}));
}
else if (item.type === "function_call") {
const fnAdapter = {
function: { name: item.name, arguments: item.arguments },
id: item.call_id,
};
try {
tool_calls.push(parseToolCall(fnAdapter, { returnId: true }));
}
catch (e) {
let errMessage;
if (typeof e === "object" &&
e != null &&
"message" in e &&
typeof e.message === "string") {
errMessage = e.message;
}
invalid_tool_calls.push(makeInvalidToolCall(fnAdapter, errMessage));
}
additional_kwargs[_FUNCTION_CALL_IDS_MAP_KEY] ??= {};
additional_kwargs[_FUNCTION_CALL_IDS_MAP_KEY][item.call_id] = item.id;
}
else if (item.type === "reasoning") {
additional_kwargs.reasoning = item;
}
else {
additional_kwargs.tool_outputs ??= [];
additional_kwargs.tool_outputs.push(item);
}
}
return new AIMessage({
id: response.id,
content,
tool_calls,
invalid_tool_calls,
usage_metadata: response.usage,
additional_kwargs,
response_metadata,
});
}
function _convertOpenAIResponsesDeltaToBaseMessageChunk(chunk) {
const content = [];
let generationInfo = {};
let usage_metadata;
const tool_call_chunks = [];
const response_metadata = {};
const additional_kwargs = {};
let id;
if (chunk.type === "response.output_text.delta") {
content.push({
type: "text",
text: chunk.delta,
index: chunk.content_index,
});
}
else if (chunk.type === "response.output_text.annotation.added") {
content.push({
type: "text",
text: "",
annotations: [chunk.annotation],
index: chunk.content_index,
});
}
else if (chunk.type === "response.output_item.added" &&
chunk.item.type === "message") {
id = chunk.item.id;
}
else if (chunk.type === "response.output_item.added" &&
chunk.item.type === "function_call") {
tool_call_chunks.push({
type: "tool_call_chunk",
name: chunk.item.name,
args: chunk.item.arguments,
id: chunk.item.id,
index: chunk.output_index,
});
additional_kwargs[_FUNCTION_CALL_IDS_MAP_KEY] = {
[chunk.item.call_id]: chunk.item.id,
};
}
else if (chunk.type === "response.output_item.done" &&
(chunk.item.type === "web_search_call" ||
chunk.item.type === "file_search_call" ||
chunk.item.type === "computer_call")) {
additional_kwargs.tool_outputs = [chunk.item];
}
else if (chunk.type === "response.created") {
response_metadata.id = chunk.response.id;
response_metadata.model_name = chunk.response.model;
response_metadata.model = chunk.response.model;
}
else if (chunk.type === "response.completed") {
const msg = _convertOpenAIResponsesMessageToBaseMessage(chunk.response);
usage_metadata = chunk.response.usage;
if (chunk.response.text?.format?.type === "json_schema") {
additional_kwargs.parsed ??= JSON.parse(msg.text);
}
for (const [key, value] of Object.entries(chunk.response)) {
if (key !== "id")
response_metadata[key] = value;
}
}
else if (chunk.type === "response.function_call_arguments.delta") {
tool_call_chunks.push({
type: "tool_call_chunk",
args: chunk.delta,
index: chunk.output_index,
});
}
else if (chunk.type === "response.web_search_call.completed" ||
chunk.type === "response.file_search_call.completed") {
generationInfo = {
tool_outputs: {
id: chunk.item_id,
type: chunk.type.replace("response.", "").replace(".completed", ""),
status: "completed",
},
};
}
else if (chunk.type === "response.refusal.done") {
additional_kwargs.refusal = chunk.refusal;
}
else {
return null;
}
return new ChatGenerationChunk({
// Legacy reasons, `onLLMNewToken` should pulls this out
text: content.map((part) => part.text).join(""),
message: new AIMessageChunk({
id,
content,
tool_call_chunks,
usage_metadata,
additional_kwargs,
response_metadata,
}),
generationInfo,
});
}
function isBuiltInTool(tool) {
return "type" in tool && tool.type !== "function";
}
function isBuiltInToolChoice(tool_choice) {
return (tool_choice != null &&
typeof tool_choice === "object" &&
"type" in tool_choice &&
tool_choice.type !== "function");
}
function _convertChatOpenAIToolTypeToOpenAITool(tool, fields) {
if (isOpenAITool(tool)) {
if (fields?.strict !== undefined) {
return {
...tool,
function: {
...tool.function,
strict: fields.strict,
},
};
}
return tool;
}
return _convertToOpenAITool(tool, fields);
}
function isReasoningModel(model) {
return model?.startsWith("o1") || model?.startsWith("o3");
}
/**
* 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 `.bind`, or the second arg in `.bindTools`, like shown in the examples below:
*
* ```typescript
* // When calling `.bind`, call options should be passed via the first argument
* const llmWithArgsBound = llm.bind({
* stop: ["\n"],
* tools: [...],
* });
*
* // When calling `.bindTools`, call options should be passed via the second argument
* const llmWithTools = llm.bindTools(
* [...],
* {
* tool_choice: "auto",
* }
* );
* ```
*
* ## Examples
*
* <details open>
* <summary><strong>Instantiate</strong></summary>
*
* ```typescript
* import { ChatOpenAI } from '@langchain/openai';
*
* const llm = new ChatOpenAI({
* model: "gpt-4o",
* temperature: 0,
* maxTokens: undefined,
* timeout: undefined,
* maxRetries: 2,
* // apiKey: "...",
* // baseUrl: "...",
* // organization: "...",
* // other params...
* });
* ```
* </details>
*
* <br />
*
* <details>
* <summary><strong>Invoking</strong></summary>
*
* ```typescript
* const input = `Translate "I love programming" into French.`;
*
* // Models also accept a list of chat messages or a formatted prompt
* const result = await llm.invoke(input);
* console.log(result);
* ```
*
* ```txt
* AIMessage {
* "id": "chatcmpl-9u4Mpu44CbPjwYFkTbeoZgvzB00Tz",
* "content": "J'adore la programmation.",
* "response_metadata": {
* "tokenUsage": {
* "completionTokens": 5,
* "promptTokens": 28,
* "totalTokens": 33
* },
* "finish_reason": "stop",
* "system_fingerprint": "fp_3aa7262c27"
* },
* "usage_metadata": {
* "input_tokens": 28,
* "output_tokens": 5,
* "total_tokens": 33
* }
* }
* ```
* </details>
*
* <br />
*
* <details>
* <summary><strong>Streaming Chunks</strong></summary>
*
* ```typescript
* for await (const chunk of await llm.stream(input)) {
* console.log(chunk);
* }
* ```
*
* ```txt
* AIMessageChunk {
* "id": "chatcmpl-9u4NWB7yUeHCKdLr6jP3HpaOYHTqs",
* "content": ""
* }
* AIMessageChunk {
* "content": "J"
* }
* AIMessageChunk {
* "content": "'adore"
* }
* AIMessageChunk {
* "content": " la"
* }
* AIMessageChunk {
* "content": " programmation",,
* }
* AIMessageChunk {
* "content": ".",,
* }
* AIMessageChunk {
* "content": "",
* "response_metadata": {
* "finish_reason": "stop",
* "system_fingerprint": "fp_c9aa9c0491"
* },
* }
* AIMessageChunk {
* "content": "",
* "usage_metadata": {
* "input_tokens": 28,
* "output_tokens": 5,
* "total_tokens": 33
* }
* }
* ```
* </details>
*
* <br />
*
* <details>
* <summary><strong>Aggregate Streamed Chunks</strong></summary>
*
* ```typescript
* import { AIMessageChunk } from '@langchain/core/messages';
* import { concat } from '@langchain/core/utils/stream';
*
* const stream = await llm.stream(input);
* let full: AIMessageChunk | undefined;
* for await (const chunk of stream) {
* full = !full ? chunk : concat(full, chunk);
* }
* console.log(full);
* ```
*
* ```txt
* AIMessageChunk {
* "id": "chatcmpl-9u4PnX6Fy7OmK46DASy0bH6cxn5Xu",
* "content": "J'adore la programmation.",
* "response_metadata": {
* "prompt": 0,
* "completion": 0,
* "finish_reason": "stop",
* },
* "usage_metadata": {
* "input_tokens": 28,
* "output_tokens": 5,
* "total_tokens": 33
* }
* }
* ```
* </details>
*
* <br />
*
* <details>
* <summary><strong>Bind tools</strong></summary>
*
* ```typescript
* import { z } from 'zod';
*
* const GetWeather = {
* name: "GetWeather",
* description: "Get the current weather in a given location",
* schema: z.object({
* location: z.string().describe("The city and state, e.g. San Francisco, CA")
* }),
* }
*
* const GetPopulation = {
* name: "GetPopulation",
* description: "Get the current population in a given location",
* schema: z.object({
* location: z.string().describe("The city and state, e.g. San Francisco, CA")
* }),
* }
*
* const llmWithTools = llm.bindTools(
* [GetWeather, GetPopulation],
* {
* // strict: true // enforce tool args schema is respected
* }
* );
* const aiMsg = await llmWithTools.invoke(
* "Which city is hotter today and which is bigger: LA or NY?"
* );
* console.log(aiMsg.tool_calls);
* ```
*
* ```txt
* [
* {
* name: 'GetWeather',
* args: { location: 'Los Angeles, CA' },
* type: 'tool_call',
* id: 'call_uPU4FiFzoKAtMxfmPnfQL6UK'
* },
* {
* name: 'GetWeather',
* args: { location: 'New York, NY' },
* type: 'tool_call',
* id: 'call_UNkEwuQsHrGYqgDQuH9nPAtX'
* },
* {
* name: 'GetPopulation',
* args: { location: 'Los Angeles, CA' },
* type: 'tool_call',
* id: 'call_kL3OXxaq9OjIKqRTpvjaCH14'
* },
* {
* name: 'GetPopulation',
* args: { location: 'New York, NY' },
* type: 'tool_call',
* id: 'call_s9KQB1UWj45LLGaEnjz0179q'
* }
* ]
* ```
* </details>
*
* <br />
*
* <details>
* <summary><strong>Structured Output</strong></summary>
*
* ```typescript
* import { z } from 'zod';
*
* const Joke = z.object({
* setup: z.string().describe("The setup of the joke"),
* punchline: z.string().describe("The punchline to the joke"),
* rating: z.number().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.bind({ response_format: { type: "json_object" } });
* const jsonLlmAiMsg = await jsonLlm.invoke(
* "Return a JSON object with key 'randomInts' and a value of 10 random ints in [0-99]"
* );
* console.log(jsonLlmAiMsg.content);
* ```
*
* ```txt
* {
* "randomInts": [23, 87, 45, 12, 78, 34, 56, 90, 11, 67]
* }
* ```
* </details>
*
* <br />
*
* <details>
* <summary><strong>Multimodal</strong></summary>
*
* ```typescript
* import { HumanMessage } from '@langchain/core/messages';
*
* const imageUrl = "https://example.com/image.jpg";
* const imageData = await fetch(imageUrl).then(res => res.arrayBuffer());
* const base64Image = Buffer.from(imageData).toString('base64');
*
* const message = new HumanMessage({
* content: [
* { type: "text", text: "describe the weather in this image" },
* {
* type: "image_url",
* image_url: { url: `data:image/jpeg;base64,${base64Image}` },
* },
* ]
* });
*
* const imageDescriptionAiMsg = await llm.invoke([message]);
* console.log(imageDescriptionAiMsg.content);
* ```
*
* ```txt
* The weather in the image appears to be clear and sunny. The sky is mostly blue with a few scattered white clouds, indicating fair weather. The bright sunlight is casting shadows on the green, grassy hill, suggesting it is a pleasant day with good visibility. There are no signs of rain or stormy conditions.
* ```
* </details>
*
* <br />
*
* <details>
* <summary><strong>Usage Metadata</strong></summary>
*
* ```typescript
* const aiMsgForMetadata = await llm.invoke(input);
* console.log(aiMsgForMetadata.usage_metadata);
* ```
*
* ```txt
* { input_tokens: 28, output_tokens: 5, total_tokens: 33 }
* ```
* </details>
*
* <br />
*
* <details>
* <summary><strong>Logprobs</strong></summary>
*
* ```typescript
* const logprobsLlm = new ChatOpenAI({ logprobs: true });
* const aiMsgForLogprobs = await logprobsLlm.invoke(input);
* console.log(aiMsgForLogprobs.response_metadata.logprobs);
* ```
*
* ```txt
* {
* content: [
* {
* token: 'J',
* logprob: -0.000050616763,
* bytes: [Array],
* top_logprobs: []
* },
* {
* token: "'",
* logprob: -0.01868736,
* bytes: [Array],
* top_logprobs: []
* },
* {
* token: 'ad',
* logprob: -0.0000030545007,
* bytes: [Array],
* top_logprobs: []
* },
* { token: 'ore', logprob: 0, bytes: [Array], top_logprobs: [] },
* {
* token: ' la',
* logprob: -0.515404,
* bytes: [Array],
* top_logprobs: []
* },
* {
* token: ' programm',
* logprob: -0.0000118755715,
* bytes: [Array],
* top_logprobs: []
* },
* { token: 'ation', logprob: 0, bytes: [Array], top_logprobs: [] },
* {
* token: '.',
* logprob: -0.0000037697225,
* bytes: [Array],
* top_logprobs: []
* }
* ],
* refusal: null
* }
* ```
* </details>
*
* <br />
*
* <details>
* <summary><strong>Response Metadata</strong></summary>
*
* ```typescript
* const aiMsgForResponseMetadata = await llm.invoke(input);
* console.log(aiMsgForResponseMetadata.response_metadata);
* ```
*
* ```txt
* {
* tokenUsage: { completionTokens: 5, promptTokens: 28, totalTokens: 33 },
* finish_reason: 'stop',
* system_fingerprint: 'fp_3aa7262c27'
* }
* ```
* </details>
*
* <br />
*
* <details>
* <summary><strong>JSON Schema Structured Output</strong></summary>
*
* ```typescript
* const llmForJsonSchema = new ChatOpenAI({
* model: "gpt-4o-2024-08-06",
* }).withStructuredOutput(
* z.object({
* command: z.string().describe("The command to execute"),
* expectedOutput: z.string().describe("The expected output of the command"),
* options: z
* .array(z.string())
* .describe("The options you can pass to the command"),
* }),
* {
* method: "jsonSchema",
* strict: true, // Optional when using the `jsonSchema` method
* }
* );
*
* const jsonSchemaRes = await llmForJsonSchema.invoke(
* "What is the command to list files in a directory?"
* );
* console.log(jsonSchemaRes);
* ```
*
* ```txt
* {
* command: 'ls',
* expectedOutput: 'A list of files and subdirectories within the specified directory.',
* options: [
* '-a: include directory entries whose names begin with a dot (.).',
* '-l: use a long listing format.',
* '-h: with -l, print sizes in human readable format (e.g., 1K, 234M, 2G).',
* '-t: sort by time, newest first.',
* '-r: reverse order while sorting.',
* '-S: sort by file size, largest first.',
* '-R: list subdirectories recursively.'
* ]
* }
* ```
* </details>
*
* <br />
*
* <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 `.bind` 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 `.bind` 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 />
*/
export class ChatOpenAI extends BaseChatModel {
static lc_name() {
return "ChatOpenAI";
}
get callKeys() {
return [
...super.callKeys,
"options",
"function_call",
"functions",
"tools",
"tool_choice",
"promptIndex",
"response_format",
"seed",
"reasoning_effort",
];
}
get lc_secrets() {
return {
openAIApiKey: "OPENAI_API_KEY",
apiKey: "OPENAI_API_KEY",
organization: "OPENAI_ORGANIZATION",
};
}
get lc_aliases() {
return {
modelName: "model",
openAIApiKey: "openai_api_key",
apiKey: "openai_api_key",
};
}
get lc_serializable_keys() {
return [
"configuration",
"logprobs",
"topLogprobs",
"prefixMessages",
"supportsStrictToolCalling",
"modalities",
"audio",
"reasoningEffort",
"temperature",
"maxTokens",
"topP",
"frequencyPenalty",
"presencePenalty",
"n",
"logitBias",
"user",
"streaming",
"streamUsage",
"modelName",
"model",
"modelKwargs",
"stop",
"stopSequences",
"timeout",
"openAIApiKey",
"apiKey",
"cache",
"maxConcurrency",
"maxRetries",
"verbose",
"callbacks",
"tags",
"metadata",
"disableStreaming",
];
}
constructor(fields) {
super(fields ?? {});
Object.defineProperty(this, "lc_serializable", {
enumerable: true,
configurable: true,
writable: true,
value: true
});
Object.defineProperty(this, "temperature", {
enumerable: true,
configurable: true,
writable: true,
value: void 0
});
Object.defineProperty(this, "topP", {
enumerable: true,
configurable: true,
writable: true,
value: void 0
});
Object.defineProperty(this, "frequencyPenalty", {
enumerable: true,
configurable: true,
writable: true,
value: void 0
});
Object.defineProperty(this, "presencePenalty", {
enumerable: true,
configurable: true,
writable: true,
value: void 0
});
Object.defineProperty(this, "n", {
enumerable: true,
configurable: true,
writable: true,
value: void 0
});
Object.defineProperty(this, "logitBias", {
enumerable: true,
configurable: true,
writable: true,
value: void 0
});
/** @deprecated Use "model" instead */
Object.defineProperty(this, "modelName", {
enumerable: true,
configurable: true,
writable: true,
value: void 0
});
Object.defineProperty(this, "model", {
enumerable: true,
configurable: true,
writable: true,
value: "gpt-3.5-turbo"
});
Object.defineProperty(this, "modelKwargs", {
enumerable: true,
configurable: true,
writable: true,
value: void 0
});
Object.defineProperty(this, "stop", {
enumerable: true,
configurable: true,
writable: true,
value: void 0
});
Object.defineProperty(this, "stopSequences", {
enumerable: true,
configurable: true,
writable: true,
value: void 0
});
Object.defineProperty(this, "user", {
enumerable: true,
configurable: true,
writable: true,
value: void 0
});
Object.defineProperty(this, "timeout", {
enumerable: true,
configurable: true,
writable: true,
value: void 0
});
Object.defineProperty(this, "streaming", {
enumerable: true,
configurable: true,
writable: true,
value: false
});
Object.defineProperty(this, "streamUsage", {
enumerable: true,
configurable: true,
writable: true,
value: true
});
Object.defineProperty(this, "maxTokens", {
enumerable: true,
configurable: true,
writable: true,
value: void 0
});
Object.defineProperty(this, "logprobs", {
enumerable: true,
configurable: true,
writable: true,
value: void 0
});
Object.defineProperty(this, "topLogprobs", {
enumerable: true,
configurable: true,
writable: true,
value: void 0
});
Object.defineProperty(this, "openAIApiKey", {
enumerable: true,
configurable: true,
writable: true,
value: void 0
});
Object.defineProperty(this, "apiKey", {
enumerable: true,
configurable: true,
writable: true,
value: void 0
});
Object.defineProperty(this, "organization", {
enumerable: true,
configurable: true,
writable: true,
value: void 0
});
Object.defineProperty(this, "__includeRawResponse", {
enumerable: true,
configurable: true,
writable: true,
value: void 0
});
Object.defineProperty(this, "client", {
enumerable: true,
configurable: true,
writable: true,
value: void 0
});
Object.defineProperty(this, "clientConfig", {
enumerable: true,
configurable: true,
writable: true,
value: void 0
});
/**
* Whether the model supports the `strict` argument when passing in tools.
* If `undefined` the `strict` argument will not be passed to OpenAI.
*/
Object.defineProperty(this, "supportsStrictToolCalling", {
enumerable: true,
configurable: true,
writable: true,
value: void 0
});
Object.defineProperty(this, "audio", {
enumerable: true,
configurable: true,
writable: true,
value: void 0
});
Object.defineProperty(this, "modalities", {
enumerable: true,
configurable: true,
writable: true,
value: void 0
});
Object.defineProperty(this, "reasoningEffort", {
enumerable: true,
configurable: true,
writable: true,
value: void 0
});
Object.defineProperty(this, "useResponsesApi", {
enumerable: true,
configurable: true,
writable: true,
value: false
});
this.openAIApiKey =
fields?.apiKey ??
fields?.openAIApiKey ??
fields?.configuration?.apiKey ??
getEnvironmentVariable("OPENAI_API_KEY");
this.apiKey = this.openAIApiKey;
this.organization =
fields?.configuration?.organization ??
getEnvironmentVariable("OPENAI_ORGANIZATION");
this.model = fields?.model ?? fields?.modelName ?? this.model;
this.modelName = this.model;
this.modelKwargs = fields?.modelKwargs ?? {};
this.timeout = fields?.timeout;
this.temperature = fields?.temperature ?? this.temperature;
this.topP = fields?.topP ?? this.topP;
this.frequencyPenalty = fields?.frequencyPenalty ?? this.frequencyPenalty;
this.presencePenalty = fields?.presencePenalty ?? this.presencePenalty;
this.logprobs = fields?.logprobs;
this.topLogprobs = fields?.topLogprobs;
this.n = fields?.n ?? this.n;
this.logitBias = fields?.logitBias;
this.stop = fields?.stopSequences ?? fields?.stop;
this.stopSequences = this.stop;
this.user = fields?.user;
this.__includeRawResponse = fields?.__includeRawResponse;
this.audio = fields?.audio;
this.modalities = fields?.modalities;
this.reasoningEffort = fields?.reasoningEffort;
this.maxTokens = fields?.maxCompletionTokens ?? fields?.maxTokens;
this.useResponsesApi = fields?.useResponsesApi ?? this.useResponsesApi;
if (this.model === "o1") {
this.disableStreaming = true;
}
this.streaming = fields?.streaming ?? false;
this.streamUsage = fields?.streamUsage ?? this.streamUsage;
this.clientConfig = {
apiKey: this.apiKey,
organization: this.organization,
dangerouslyAllowBrowser: true,
...fields?.configuration,
};
// If `supportsStrictToolCalling` is explicitly set, use that value.
// Else leave undefined so it's not passed to OpenAI.
if (fields?.supportsStrictToolCalling !== undefined) {
this.supportsStrictToolCalling = fields.supportsStrictToolCalling;
}
}
getLsParams(options) {
const params = this.invocationParams(options);
return {
ls_provider: "openai",
ls_model_name: this.model,
ls_model_type: "chat",
ls_temperature: params.temperature ?? undefined,
ls_max_tokens: params.max_tokens ?? undefined,
ls_stop: options.stop,
};
}
bindTools(tools, kwargs) {
let strict;
if (kwargs?.strict !== undefined) {
strict = kwargs.strict;
}
else if (this.supportsStrictToolCalling !== undefined) {
strict = this.supportsStrictToolCalling;
}
return this.bind({
tools: tools.map((tool) => isBuiltInTool(tool)
? tool
: _convertChatOpenAIToolTypeToOpenAITool(tool, { strict })),
...kwargs,
});
}
createResponseFormat(resFormat) {
if (resFormat &&
resFormat.type === "json_schema" &&
resFormat.json_schema.schema &&
isZodSchema(resFormat.json_schema.schema)) {
return zodResponseFormat(resFormat.json_schema.schema, resFormat.json_schema.name, {
description: resFormat.json_schema.description,
});
}
return resFormat;
}
/**
* Get the parameters used to invoke the model
*/
invocationParams(options, extra) {
let strict;
if (options?.strict !== undefined) {
strict = options.strict;
}
else if (this.supportsStrictToolCalling !== undefined) {
strict = this.supportsStrictToolCalling;
}
if (this._useResponseApi(options)) {
const params = {
model: this.model,
temperature: this.temperature,
top_p: this.topP,
user: this.user,
// if include_usage is set or streamUsage then stream must be set to true.
stream: this.streaming,
previous_response_id: options?.previous_response_id,
truncation: options?.truncation,
include: options?.include,
tools: options?.tools?.length
? options.tools
.map((tool) => {
if (isBuiltInTool(tool)) {
return tool;
}
else if (isOpenAITool(tool)) {
return {
type: "function",
name: tool.function.name,
parameters: tool.function.parameters,
description: tool.function.description,
strict,
};
}
else {
return null;
}
})
.filter((tool) => tool !== null)
: undefined,
tool_choice: isBuiltInToolChoice(options?.tool_choice)
? options?.tool_choice
: (() => {
const formatted = formatToOpenAIToolChoice(options?.tool_choice);
if (typeof formatted === "object" && "type" in formatted) {
return { type: "function", name: formatted.function.name };
}
else {
return undefined;
}
})(),
text: (() => {
if (options?.text)
return options.text;
const format = this.createResponseFormat(options?.response_format);
if (format?.type === "json_schema") {
if (format.json_schema.schema != null) {
return {
format: {
type: "json_schema",
schema: format.json_schema.schema,
description: format.json_schema.description,
name: format.json_schema.name,
strict: format.json_schema.strict,
},
};
}
return undefined;
}
return { format };
})(),
parallel_tool_calls: options?.parallel_tool_calls,
max_output_tokens: this.maxTokens === -1 ? undefined : this.maxTokens,
...this.modelKwargs,
};
const reasoningEffort = options?.reasoning_effort ?? this.reasoningEffort;
if (reasoningEffort !== undefined) {
params.reasoning = { effort: reasoningEffort };
}
// eslint-disable-next-line @typescript-eslint/no-explicit-any
return params;
}
let streamOptionsConfig = {};
if (options?.stream_options !== undefined) {
streamOptionsConfig = { stream_options: options.stream_options };
}
else if (this.streamUsage && (this.streaming || extra?.streaming)) {
streamOptionsConfig = { stream_options: { include_usage: true } };
}
const params = {
model: this.model,
temperature: this.temperature,
top_p: this.topP,
frequency_penalty: this.frequencyPenalty,
presence_penalty: this.presencePenalty,
logprobs: this.logprobs,
top_logprobs: this.topLogprobs,
n: this.n,
logit_bias: this.logitBias,
stop: options?.stop ?? this.stopSequences,
user: this.user,
// if include_usage is set or streamUsage then stream must be set to true.
stream: this.streaming,
functions: options?.functions,
function_call: options?.function_call,
tools: options?.tools?.length
? options.tools.map((tool) => _convertChatOpenAIToolTypeToOpenAITool(tool, { strict }))
: undefined,
tool_choice: formatToOpenAIToolChoice(options?.tool_choice),
response_format: this.createResponseFormat(options?.response_format),
seed: options?.seed,
...streamOptionsConfig,
parallel_tool_calls: options?.parallel_tool_calls,
...(this.audio || options?.audio
? { audio: this.audio || options?.audio }
: {}),
...(this.modalities || options?.modalities
? { modalities: this.modalities || options?.modalities }
: {}),
...this.modelKwargs,
};
if (options?.prediction !== undefined) {
params.prediction = options.prediction;
}
const reasoningEffort = options?.reasoning_effort ?? this.reasoningEffort;
if (reasoningEffort !== undefined) {
params.reasoning_effort = reasoningEffort;
}
if (isReasoningModel(params.model)) {
params.max_completion_tokens =
this.maxTokens === -1 ? undefined : this.maxTokens;
}
else {
params.max_tokens = this.maxTokens === -1 ? undefined : this.maxTokens;
}
// eslint-disable-next-line @typescript-eslint/no-explicit-any
return params;
}
_convertOpenAIChatCompletionMessageToBaseMessage(message, rawResponse) {
const rawToolCalls = message.tool_calls;