"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
exports.ChatOpenAI = exports._convertMessagesToOpenAIParams = exports.messageToOpenAIRole = void 0;
const openai_1 = require("openai");
const messages_1 = require("@langchain/core/messages");
const outputs_1 = require("@langchain/core/outputs");
const env_1 = require("@langchain/core/utils/env");
const chat_models_1 = require("@langchain/core/language_models/chat_models");
const base_1 = require("@langchain/core/language_models/base");
const runnables_1 = require("@langchain/core/runnables");
const output_parsers_1 = require("@langchain/core/output_parsers");
const openai_tools_1 = require("@langchain/core/output_parsers/openai_tools");
const zod_to_json_schema_1 = require("zod-to-json-schema");
const zod_1 = require("openai/helpers/zod");
const azure_js_1 = require("./utils/azure.cjs");
const openai_js_1 = require("./utils/openai.cjs");
const openai_format_fndef_js_1 = require("./utils/openai-format-fndef.cjs");
const tools_js_1 = require("./utils/tools.cjs");
function extractGenericMessageCustomRole(message) {
if (message.role !== "system" &&
message.role !== "assistant" &&
message.role !== "user" &&
message.role !== "function" &&
message.role !== "tool") {
console.warn(`Unknown message role: ${message.role}`);
}
return message.role;
}
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 (!messages_1.ChatMessage.isInstance(message))
throw new Error("Invalid generic chat message");
return extractGenericMessageCustomRole(message);
}
default:
throw new Error(`Unknown message type: ${type}`);
}
}
exports.messageToOpenAIRole = messageToOpenAIRole;
function openAIResponseToChatMessage(message, rawResponse, includeRawResponse) {
const rawToolCalls = message.tool_calls;
switch (message.role) {
case "assistant": {
const toolCalls = [];
const invalidToolCalls = [];
for (const rawToolCall of rawToolCalls ?? []) {
try {
toolCalls.push((0, openai_tools_1.parseToolCall)(rawToolCall, { returnId: true }));
// eslint-disable-next-line @typescript-eslint/no-explicit-any
}
catch (e) {
invalidToolCalls.push((0, openai_tools_1.makeInvalidToolCall)(rawToolCall, e.message));
}
}
const additional_kwargs = {
function_call: message.function_call,
tool_calls: rawToolCalls,
};
if (includeRawResponse !== undefined) {
additional_kwargs.__raw_response = rawResponse;
}
let response_metadata;
if (rawResponse.system_fingerprint) {
response_metadata = {
usage: { ...rawResponse.usage },
system_fingerprint: rawResponse.system_fingerprint,
};
}
return new messages_1.AIMessage({
content: message.content || "",
tool_calls: toolCalls,
invalid_tool_calls: invalidToolCalls,
additional_kwargs,
response_metadata,
id: rawResponse.id,
});
}
default:
return new messages_1.ChatMessage(message.content || "", message.role ?? "unknown");
}
}
function _convertDeltaToMessageChunk(
// eslint-disable-next-line @typescript-eslint/no-explicit-any
delta, rawResponse, defaultRole, includeRawResponse) {
const role = delta.role ?? defaultRole;
const content = delta.content ?? "";
let additional_kwargs;
if (delta.function_call) {
additional_kwargs = {
function_call: delta.function_call,
};
}
else if (delta.tool_calls) {
additional_kwargs = {
tool_calls: delta.tool_calls,
};
}
else {
additional_kwargs = {};
}
if (includeRawResponse) {
additional_kwargs.__raw_response = rawResponse;
}
const response_metadata = { usage: { ...rawResponse.usage } };
if (role === "user") {
return new messages_1.HumanMessageChunk({ content, response_metadata });
}
else if (role === "assistant") {
const toolCallChunks = [];
if (Array.isArray(delta.tool_calls)) {
for (const rawToolCall of delta.tool_calls) {
toolCallChunks.push({
name: rawToolCall.function?.name,
args: rawToolCall.function?.arguments,
id: rawToolCall.id,
index: rawToolCall.index,
type: "tool_call_chunk",
});
}
}
return new messages_1.AIMessageChunk({
content,
tool_call_chunks: toolCallChunks,
additional_kwargs,
id: rawResponse.id,
response_metadata,
});
}
else if (role === "system") {
return new messages_1.SystemMessageChunk({ content, response_metadata });
}
else if (role === "function") {
return new messages_1.FunctionMessageChunk({
content,
additional_kwargs,
name: delta.name,
response_metadata,
});
}
else if (role === "tool") {
return new messages_1.ToolMessageChunk({
content,
additional_kwargs,
tool_call_id: delta.tool_call_id,
response_metadata,
});
}
else {
return new messages_1.ChatMessageChunk({ content, role, response_metadata });
}
}
// Used in LangSmith, export is important here
function _convertMessagesToOpenAIParams(messages) {
// TODO: Function messages do not support array content, fix cast
return messages.map((message) => {
// eslint-disable-next-line @typescript-eslint/no-explicit-any
const completionParam = {
role: messageToOpenAIRole(message),
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 = null;
}
if ((0, messages_1.isAIMessage)(message) && !!message.tool_calls?.length) {
completionParam.tool_calls = message.tool_calls.map(openai_tools_1.convertLangChainToolCallToOpenAI);
completionParam.content = null;
}
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;
}
}
return completionParam;
});
}
exports._convertMessagesToOpenAIParams = _convertMessagesToOpenAIParams;
function _convertChatOpenAIToolTypeToOpenAITool(tool, fields) {
if ((0, base_1.isOpenAITool)(tool)) {
if (fields?.strict !== undefined) {
return {
...tool,
function: {
...tool.function,
strict: fields.strict,
},
};
}
return tool;
}
return (0, tools_js_1._convertToOpenAITool)(tool, fields);
}
/**
* OpenAI chat model integration.
*
* Setup:
* Install `@langchain/openai` and set an environment variable named `OPENAI_API_KEY`.
*
* ```bash
* npm install @langchain/openai
* export OPENAI_API_KEY="your-api-key"
* ```
*
* ## [Constructor args](https://api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html#constructor)
*
* ## [Runtime args](https://api.js.langchain.com/interfaces/langchain_openai.ChatOpenAICallOptions.html)
*
* Runtime args can be passed as the second argument to any of the base runnable methods `.invoke`. `.stream`, `.batch`, etc.
* They can also be passed via `.bind`, or the second arg in `.bindTools`, like shown in the examples below:
*
* ```typescript
* // When calling `.bind`, call options should be passed via the first argument
* const llmWithArgsBound = llm.bind({
* stop: ["\n"],
* tools: [...],
* });
*
* // When calling `.bindTools`, call options should be passed via the second argument
* const llmWithTools = llm.bindTools(
* [...],
* {
* tool_choice: "auto",
* }
* );
* ```
*
* ## Examples
*
*
* Instantiate
*
* ```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...
* });
* ```
*
*
*
*
*
* Invoking
*
* ```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
* }
* }
* ```
*
*
*
*
*
* Streaming Chunks
*
* ```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
* }
* }
* ```
*
*
*
*
*
* Aggregate Streamed Chunks
*
* ```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
* }
* }
* ```
*
*
*
*
*
* Bind tools
*
* ```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'
* }
* ]
* ```
*
*
*
*
*
* Structured Output
*
* ```typescript
* import { z } from 'zod';
*
* const Joke = z.object({
* setup: z.string().describe("The setup of the joke"),
* punchline: z.string().describe("The punchline to the joke"),
* rating: z.number().optional().describe("How funny the joke is, from 1 to 10")
* }).describe('Joke to tell user.');
*
* const structuredLlm = llm.withStructuredOutput(Joke, {
* name: "Joke",
* strict: true, // Optionally enable OpenAI structured outputs
* });
* const jokeResult = await structuredLlm.invoke("Tell me a joke about cats");
* console.log(jokeResult);
* ```
*
* ```txt
* {
* setup: 'Why was the cat sitting on the computer?',
* punchline: 'Because it wanted to keep an eye on the mouse!',
* rating: 7
* }
* ```
*
*
*
*
*
* JSON Object Response Format
*
* ```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]
* }
* ```
*
*
*
*
*
* Multimodal
*
* ```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.
* ```
*
*
*
*
*
* Usage Metadata
*
* ```typescript
* const aiMsgForMetadata = await llm.invoke(input);
* console.log(aiMsgForMetadata.usage_metadata);
* ```
*
* ```txt
* { input_tokens: 28, output_tokens: 5, total_tokens: 33 }
* ```
*
*
*
*
*
* Logprobs
*
* ```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
* }
* ```
*
*
*
*
*
* Response Metadata
*
* ```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'
* }
* ```
*
*
*
*
*
* JSON Schema Structured Output
*
* ```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.'
* ]
* }
* ```
*
*
*
*/
class ChatOpenAI extends chat_models_1.BaseChatModel {
static lc_name() {
return "ChatOpenAI";
}
get callKeys() {
return [
...super.callKeys,
"options",
"function_call",
"functions",
"tools",
"tool_choice",
"promptIndex",
"response_format",
"seed",
];
}
get lc_secrets() {
return {
openAIApiKey: "OPENAI_API_KEY",
apiKey: "OPENAI_API_KEY",
azureOpenAIApiKey: "AZURE_OPENAI_API_KEY",
organization: "OPENAI_ORGANIZATION",
};
}
get lc_aliases() {
return {
modelName: "model",
openAIApiKey: "openai_api_key",
apiKey: "openai_api_key",
azureOpenAIApiVersion: "azure_openai_api_version",
azureOpenAIApiKey: "azure_openai_api_key",
azureOpenAIApiInstanceName: "azure_openai_api_instance_name",
azureOpenAIApiDeploymentName: "azure_openai_api_deployment_name",
};
}
constructor(fields,
/** @deprecated */
configuration) {
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: 1
});
Object.defineProperty(this, "topP", {
enumerable: true,
configurable: true,
writable: true,
value: 1
});
Object.defineProperty(this, "frequencyPenalty", {
enumerable: true,
configurable: true,
writable: true,
value: 0
});
Object.defineProperty(this, "presencePenalty", {
enumerable: true,
configurable: true,
writable: true,
value: 0
});
Object.defineProperty(this, "n", {
enumerable: true,
configurable: true,
writable: true,
value: 1
});
Object.defineProperty(this, "logitBias", {
enumerable: true,
configurable: true,
writable: true,
value: void 0
});
Object.defineProperty(this, "modelName", {
enumerable: true,
configurable: true,
writable: true,
value: "gpt-3.5-turbo"
});
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, "azureOpenAIApiVersion", {
enumerable: true,
configurable: true,
writable: true,
value: void 0
});
Object.defineProperty(this, "azureOpenAIApiKey", {
enumerable: true,
configurable: true,
writable: true,
value: void 0
});
Object.defineProperty(this, "azureADTokenProvider", {
enumerable: true,
configurable: true,
writable: true,
value: void 0
});
Object.defineProperty(this, "azureOpenAIApiInstanceName", {
enumerable: true,
configurable: true,
writable: true,
value: void 0
});
Object.defineProperty(this, "azureOpenAIApiDeploymentName", {
enumerable: true,
configurable: true,
writable: true,
value: void 0
});
Object.defineProperty(this, "azureOpenAIBasePath", {
enumerable: true,
configurable: true,
writable: true,
value: void 0
});
Object.defineProperty(this, "azureOpenAIEndpoint", {
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
});
this.openAIApiKey =
fields?.apiKey ??
fields?.openAIApiKey ??
fields?.configuration?.apiKey ??
(0, env_1.getEnvironmentVariable)("OPENAI_API_KEY");
this.apiKey = this.openAIApiKey;
this.azureOpenAIApiKey =
fields?.azureOpenAIApiKey ??
(0, env_1.getEnvironmentVariable)("AZURE_OPENAI_API_KEY");
this.azureADTokenProvider = fields?.azureADTokenProvider ?? undefined;
if (!this.azureOpenAIApiKey && !this.apiKey && !this.azureADTokenProvider) {
throw new Error("OpenAI or Azure OpenAI API key or Token Provider not found");
}
this.azureOpenAIApiInstanceName =
fields?.azureOpenAIApiInstanceName ??
(0, env_1.getEnvironmentVariable)("AZURE_OPENAI_API_INSTANCE_NAME");
this.azureOpenAIApiDeploymentName =
fields?.azureOpenAIApiDeploymentName ??
(0, env_1.getEnvironmentVariable)("AZURE_OPENAI_API_DEPLOYMENT_NAME");
this.azureOpenAIApiVersion =
fields?.azureOpenAIApiVersion ??
(0, env_1.getEnvironmentVariable)("AZURE_OPENAI_API_VERSION");
this.azureOpenAIBasePath =
fields?.azureOpenAIBasePath ??
(0, env_1.getEnvironmentVariable)("AZURE_OPENAI_BASE_PATH");
this.organization =
fields?.configuration?.organization ??
(0, env_1.getEnvironmentVariable)("OPENAI_ORGANIZATION");
this.azureOpenAIEndpoint =
fields?.azureOpenAIEndpoint ??
(0, env_1.getEnvironmentVariable)("AZURE_OPENAI_ENDPOINT");
this.modelName = fields?.model ?? fields?.modelName ?? this.model;
this.model = this.modelName;
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.maxTokens = fields?.maxTokens;
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;
if (this.azureOpenAIApiKey || this.azureADTokenProvider) {
if (!this.azureOpenAIApiInstanceName &&
!this.azureOpenAIBasePath &&
!this.azureOpenAIEndpoint) {
throw new Error("Azure OpenAI API instance name not found");
}
if (!this.azureOpenAIApiDeploymentName && this.azureOpenAIBasePath) {
const parts = this.azureOpenAIBasePath.split("/openai/deployments/");
if (parts.length === 2) {
const [, deployment] = parts;
this.azureOpenAIApiDeploymentName = deployment;
}
}
if (!this.azureOpenAIApiDeploymentName) {
throw new Error("Azure OpenAI API deployment name not found");
}
if (!this.azureOpenAIApiVersion) {
throw new Error("Azure OpenAI API version not found");
}
this.apiKey = this.apiKey ?? "";
// Streaming usage is not supported by Azure deployments, so default to false
this.streamUsage = false;
}
this.streaming = fields?.streaming ?? false;
this.streamUsage = fields?.streamUsage ?? this.streamUsage;
this.clientConfig = {
apiKey: this.apiKey,
organization: this.organization,
baseURL: configuration?.basePath ?? fields?.configuration?.basePath,
dangerouslyAllowBrowser: true,
defaultHeaders: configuration?.baseOptions?.headers ??
fields?.configuration?.baseOptions?.headers,
defaultQuery: configuration?.baseOptions?.params ??
fields?.configuration?.baseOptions?.params,
...configuration,
...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) => _convertChatOpenAIToolTypeToOpenAITool(tool, { strict })),
...kwargs,
});
}
createResponseFormat(resFormat) {
if (resFormat &&
resFormat.type === "json_schema" &&
resFormat.json_schema.schema &&
isZodSchema(resFormat.json_schema.schema)) {
return (0, zod_1.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;
}
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,
max_tokens: this.maxTokens === -1 ? undefined : this.maxTokens,
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: (0, openai_js_1.formatToOpenAIToolChoice)(options?.tool_choice),
response_format: this.createResponseFormat(options?.response_format),
seed: options?.seed,
...streamOptionsConfig,
parallel_tool_calls: options?.parallel_tool_calls,
...this.modelKwargs,
};
return params;
}
/** @ignore */
_identifyingParams() {
return {
model_name: this.model,
...this.invocationParams(),
...this.clientConfig,
};
}
async *_streamResponseChunks(messages, options, runManager) {
if (this.model.includes("o1-")) {
console.warn("[WARNING]: OpenAI o1 models do not yet support token-level streaming. Streaming will yield single chunk.");
const result = await this._generate(messages, options, runManager);
const messageChunk = (0, messages_1.convertToChunk)(result.generations[0].message);
yield new outputs_1.ChatGenerationChunk({
message: messageChunk,
text: typeof messageChunk.content === "string" ? messageChunk.content : "",
});
return;
}
const messagesMapped = _convertMessagesToOpenAIParams(messages);
const params = {
...this.invocationParams(options, {
streaming: true,
}),
messages: messagesMapped,
stream: true,
};
let defaultRole;
const streamIterable = await this.completionWithRetry(params, options);
let usage;
for await (const data of streamIterable) {
const choice = data?.choices[0];
if (data.usage) {
usage = data.usage;
}
if (!choice) {
continue;
}
const { delta } = choice;
if (!delta) {
continue;
}
const chunk = _convertDeltaToMessageChunk(delta, data, defaultRole, this.__includeRawResponse);
defaultRole = delta.role ?? defaultRole;
const newTokenIndices = {
prompt: options.promptIndex ?? 0,
completion: choice.index ?? 0,
};
if (typeof chunk.content !== "string") {
console.log("[WARNING]: Received non-string content from OpenAI. This is currently not supported.");
continue;
}
// eslint-disable-next-line @typescript-eslint/no-explicit-any
const generationInfo = { ...newTokenIndices };
if (choice.finish_reason != null) {
generationInfo.finish_reason = choice.finish_reason;
// Only include system fingerprint in the last chunk for now
// to avoid concatenation issues
generationInfo.system_fingerprint = data.system_fingerprint;
}
if (this.logprobs) {
generationInfo.logprobs = choice.logprobs;
}
const generationChunk = new outputs_1.ChatGenerationChunk({
message: chunk,
text: chunk.content,
generationInfo,
});
yield generationChunk;
await runManager?.handleLLMNewToken(generationChunk.text ?? "", newTokenIndices, undefined, undefined, undefined, { chunk: generationChunk });
}
if (usage) {
const generationChunk = new outputs_1.ChatGenerationChunk({
message: new messages_1.AIMessageChunk({
content: "",
response_metadata: {
usage: { ...usage },
},
usage_metadata: {
input_tokens: usage.prompt_tokens,
output_tokens: usage.completion_tokens,
total_tokens: usage.total_tokens,
},
}),
text: "",
});
yield generationChunk;
}
if (options.signal?.aborted) {
throw new Error("AbortError");
}
}
/**
* Get the identifying parameters for the model
*
*/
identifyingParams() {
return this._identifyingParams();
}
/** @ignore */
async _generate(messages, options, runManager) {
const tokenUsage = {};
const params = this.invocationParams(options);
const messagesMapped = _convertMessagesToOpenAIParams(messages);
if (params.stream) {
const stream = this._streamResponseChunks(messages, options, runManager);
const finalChunks = {};
for await (const chunk of stream) {
chunk.message.response_metadata = {
...chunk.generationInfo,
...chunk.message.response_metadata,
};
const index = chunk.generationInfo?.completion ?? 0;
if (finalChunks[index] === undefined) {
finalChunks[index] = chunk;
}
else {
finalChunks[index] = finalChunks[index].concat(chunk);
}
}
const generations = Object.entries(finalChunks)
.sort(([aKey], [bKey]) => parseInt(aKey, 10) - parseInt(bKey, 10))
.map(([_, value]) => value);
const { functions, function_call } = this.invocationParams(options);
// OpenAI does not support token usage report under stream mode,
// fallback to estimation.
const promptTokenUsage = await this.getEstimatedTokenCountFromPrompt(messages, functions, function_call);
const completionTokenUsage = await this.getNumTokensFromGenerations(generations);
tokenUsage.promptTokens = promptTokenUsage;
tokenUsage.completionTokens = completionTokenUsage;
tokenUsage.totalTokens = promptTokenUsage + completionTokenUsage;
return { generations, llmOutput: { estimatedTokenUsage: tokenUsage } };
}
else {
let data;
if (options.response_format &&
options.response_format.type === "json_schema") {
data = await this.betaParsedCompletionWithRetry({
...params,
stream: false,
messages: messagesMapped,
}, {
signal: options?.signal,
...options?.options,
});
}
else {
data = await this.completionWithRetry({
...params,
stream: false,
messages: messagesMapped,
}, {
signal: options?.signal,
...options?.options,
});
}
const { completion_tokens: completionTokens, prompt_tokens: promptTokens, total_tokens: totalTokens, } = data?.usage ?? {};
if (completionTokens) {
tokenUsage.completionTokens =
(tokenUsage.completionTokens ?? 0) + completionTokens;
}
if (promptTokens) {
tokenUsage.promptTokens = (tokenUsage.promptTokens ?? 0) + promptTokens;
}
if (totalTokens) {
tokenUsage.totalTokens = (tokenUsage.totalTokens ?? 0) + totalTokens;
}
const generations = [];
for (const part of data?.choices ?? []) {
const text = part.message?.content ?? "";
const generation = {
text,
message: openAIResponseToChatMessage(part.message ?? { role: "assistant" }, data, this.__includeRawResponse),
};
generation.generationInfo = {
...(part.finish_reason ? { finish_reason: part.finish_reason } : {}),
...(part.logprobs ? { logprobs: part.logprobs } : {}),
};
if ((0, messages_1.isAIMessage)(generation.message)) {
generation.message.usage_metadata = {
input_tokens: tokenUsage.promptTokens ?? 0,
output_tokens: tokenUsage.completionTokens ?? 0,
total_tokens: tokenUsage.totalTokens ?? 0,
};
}
generations.push(generation);
}
return {
generations,
llmOutput: { tokenUsage },
};
}
}
/**
* Estimate the number of tokens a prompt will use.
* Modified from: https://github.com/hmarr/openai-chat-tokens/blob/main/src/index.ts
*/
async getEstimatedTokenCountFromPrompt(messages, functions, function_call) {
// It appears that if functions are present, the first system message is padded with a trailing newline. This
// was inferred by trying lots of combinations of messages and functions and seeing what the token counts were.
let tokens = (await this.getNumTokensFromMessages(messages)).totalCount;
// If there are functions, add the function definitions as they count towards token usage
if (functions && function_call !== "auto") {
const promptDefinitions = (0, openai_format_fndef_js_1.formatFunctionDefinitions)(functions);
tokens += await this.getNumTokens(promptDefinitions);
tokens += 9; // Add nine per completion
}
// If there's a system message _and_ functions are present, subtract four tokens. I assume this is because
// functions typically add a system message, but reuse the first one if it's already there. This offsets
// the extra 9 tokens added by the function definitions.
if (functions && messages.find((m) => m._getType() === "system")) {
tokens -= 4;
}
// If function_call is 'none', add one token.
// If it's a FunctionCall object, add 4 + the number of tokens in the function name.
// If it's undefined or 'auto', don't add anything.
if (function_call === "none") {
tokens += 1;
}
else if (typeof function_call === "object") {
tokens += (await this.getNumTokens(function_call.name)) + 4;
}
return tokens;
}
/**
* Estimate the number of tokens an array of generations have used.
*/
async getNumTokensFromGenerations(generations) {
const generationUsages = await Promise.all(generations.map(async (generation) => {
if (generation.message.additional_kwargs?.function_call) {
return (await this.getNumTokensFromMessages([generation.message]))
.countPerMessage[0];
}
else {
return await this.getNumTokens(generation.message.content);
}
}));
return generationUsages.reduce((a, b) => a + b, 0);
}
async getNumTokensFromMessages(messages) {
let totalCount = 0;
let tokensPerMessage = 0;
let tokensPerName = 0;
// From: https://github.com/openai/openai-cookbook/blob/main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb
if (this.model === "gpt-3.5-turbo-0301") {
tokensPerMessage = 4;
tokensPerName = -1;
}
else {
tokensPerMessage = 3;
tokensPerName = 1;
}
const countPerMessage = await Promise.all(messages.map(async (message) => {
const textCount = await this.getNumTokens(message.content);
const roleCount = await this.getNumTokens(messageToOpenAIRole(message));
const nameCount = message.name !== undefined
? tokensPerName + (await this.getNumTokens(message.name))
: 0;
let count = textCount + tokensPerMessage + roleCount + nameCount;
// From: https://github.com/hmarr/openai-chat-tokens/blob/main/src/index.ts messageTokenEstimate
const openAIMessage = message;
if (openAIMessage._getType() === "function") {
count -= 2;
}
if (openAIMessage.additional_kwargs?.function_call) {
count += 3;
}
if (openAIMessage?.additional_kwargs.function_call?.name) {
count += await this.getNumTokens(openAIMessage.additional_kwargs.function_call?.name);
}
if (openAIMessage.additional_kwargs.function_call?.arguments) {
try {
count += await this.getNumTokens(
// Remove newlines and spaces
JSON.stringify(JSON.parse(openAIMessage.additional_kwargs.function_call?.arguments)));
}
catch (error) {
console.error("Error parsing function arguments", error, JSON.stringify(openAIMessage.additional_kwargs.function_call));
count += await this.getNumTokens(openAIMessage.additional_kwargs.function_call?.arguments);
}
}
totalCount += count;
return count;
}));
totalCount += 3; // every reply is primed with <|start|>assistant<|message|>
return { totalCount, countPerMessage };
}
async completionWithRetry(request, options) {
const requestOptions = this._getClientOptions(options);
return this.caller.call(async () => {
try {
const res = await this.client.chat.completions.create(request, requestOptions);
return res;
}
catch (e) {
const error = (0, openai_js_1.wrapOpenAIClientError)(e);
throw error;
}
});
}
/**
* Call the beta chat completions parse endpoint. This should only be called if
* response_format is set to "json_object".
* @param {OpenAIClient.Chat.ChatCompletionCreateParamsNonStreaming} request
* @param {OpenAICoreRequestOptions | undefined} options
*/
async betaParsedCompletionWithRetry(request, options
// Avoid relying importing a beta type with no official entrypoint
) {
const requestOptions = this._getClientOptions(options);
return this.caller.call(async () => {
try {
const res = await this.client.beta.chat.completions.parse(request, requestOptions);
return res;
}
catch (e) {
const error = (0, openai_js_1.wrapOpenAIClientError)(e);
throw error;
}
});
}
_getClientOptions(options) {
if (!this.client) {
const openAIEndpointConfig = {
azureOpenAIApiDeploymentName: this.azureOpenAIApiDeploymentName,
azureOpenAIApiInstanceName: this.azureOpenAIApiInstanceName,
azureOpenAIApiKey: this.azureOpenAIApiKey,
azureOpenAIBasePath: this.azureOpenAIBasePath,
baseURL: this.clientConfig.baseURL,
azureOpenAIEndpoint: this.azureOpenAIEndpoint,
};
const endpoint = (0, azure_js_1.getEndpoint)(openAIEndpointConfig);
const params = {
...this.clientConfig,
baseURL: endpoint,
timeout: this.timeout,
maxRetries: 0,
};
if (!params.baseURL) {
delete params.baseURL;
}
this.client = new openai_1.OpenAI(params);
}
const requestOptions = {
...this.clientConfig,
...options,
};
if (this.azureOpenAIApiKey) {
requestOptions.headers = {
"api-key": this.azureOpenAIApiKey,
...requestOptions.headers,
};
requestOptions.query = {
"api-version": this.azureOpenAIApiVersion,
...requestOptions.query,
};
}
return requestOptions;
}
_llmType() {
return "openai";
}
/** @ignore */
_combineLLMOutput(...llmOutputs) {
return llmOutputs.reduce((acc, llmOutput) => {
if (llmOutput && llmOutput.tokenUsage) {
acc.tokenUsage.completionTokens +=
llmOutput.tokenUsage.completionTokens ?? 0;
acc.tokenUsage.promptTokens += llmOutput.tokenUsage.promptTokens ?? 0;
acc.tokenUsage.totalTokens += llmOutput.tokenUsage.totalTokens ?? 0;
}
return acc;
}, {
tokenUsage: {
completionTokens: 0,
promptTokens: 0,
totalTokens: 0,
},
});
}
withStructuredOutput(outputSchema, config) {
// eslint-disable-next-line @typescript-eslint/no-explicit-any
let schema;
let name;
let method;
let includeRaw;
if (isStructuredOutputMethodParams(outputSchema)) {
schema = outputSchema.schema;
name = outputSchema.name;
method = outputSchema.method;
includeRaw = outputSchema.includeRaw;
}
else {
schema = outputSchema;
name = config?.name;
method = config?.method;
includeRaw = config?.includeRaw;
}
let llm;
let outputParser;
if (config?.strict !== undefined && method === "jsonMode") {
throw new Error("Argument `strict` is only supported for `method` = 'function_calling'");
}
if (method === "jsonMode") {
llm = this.bind({
response_format: { type: "json_object" },
});
if (isZodSchema(schema)) {
outputParser = output_parsers_1.StructuredOutputParser.fromZodSchema(schema);
}
else {
outputParser = new output_parsers_1.JsonOutputParser();
}
}
else if (method === "jsonSchema") {
llm = this.bind({
response_format: {
type: "json_schema",
json_schema: {
name: name ?? "extract",
description: schema.description,
schema,
strict: config?.strict,
},
},
});
if (isZodSchema(schema)) {
outputParser = output_parsers_1.StructuredOutputParser.fromZodSchema(schema);
}
else {
outputParser = new output_parsers_1.JsonOutputParser();
}
}
else {
let functionName = name ?? "extract";
// Is function calling
if (isZodSchema(schema)) {
const asJsonSchema = (0, zod_to_json_schema_1.zodToJsonSchema)(schema);
llm = this.bind({
tools: [
{
type: "function",
function: {
name: functionName,
description: asJsonSchema.description,
parameters: asJsonSchema,
},
},
],
tool_choice: {
type: "function",
function: {
name: functionName,
},
},
// Do not pass `strict` argument to OpenAI if `config.strict` is undefined
...(config?.strict !== undefined ? { strict: config.strict } : {}),
});
outputParser = new openai_tools_1.JsonOutputKeyToolsParser({
returnSingle: true,
keyName: functionName,
zodSchema: schema,
});
}
else {
let openAIFunctionDefinition;
if (typeof schema.name === "string" &&
typeof schema.parameters === "object" &&
schema.parameters != null) {
openAIFunctionDefinition = schema;
functionName = schema.name;
}
else {
functionName = schema.title ?? functionName;
openAIFunctionDefinition = {
name: functionName,
description: schema.description ?? "",
parameters: schema,
};
}
llm = this.bind({
tools: [
{
type: "function",
function: openAIFunctionDefinition,
},
],
tool_choice: {
type: "function",
function: {
name: functionName,
},
},
// Do not pass `strict` argument to OpenAI if `config.strict` is undefined
...(config?.strict !== undefined ? { strict: config.strict } : {}),
});
outputParser = new openai_tools_1.JsonOutputKeyToolsParser({
returnSingle: true,
keyName: functionName,
});
}
}
if (!includeRaw) {
return llm.pipe(outputParser);
}
const parserAssign = runnables_1.RunnablePassthrough.assign({
// eslint-disable-next-line @typescript-eslint/no-explicit-any
parsed: (input, config) => outputParser.invoke(input.raw, config),
});
const parserNone = runnables_1.RunnablePassthrough.assign({
parsed: () => null,
});
const parsedWithFallback = parserAssign.withFallbacks({
fallbacks: [parserNone],
});
return runnables_1.RunnableSequence.from([
{
raw: llm,
},
parsedWithFallback,
]);
}
}
exports.ChatOpenAI = ChatOpenAI;
function isZodSchema(
// eslint-disable-next-line @typescript-eslint/no-explicit-any
input) {
// Check for a characteristic method of Zod schemas
return typeof input?.parse === "function";
}
function isStructuredOutputMethodParams(x
// eslint-disable-next-line @typescript-eslint/no-explicit-any
) {
return (x !== undefined &&
// eslint-disable-next-line @typescript-eslint/no-explicit-any
typeof x.schema ===
"object");
}