@ai-toolkit/openai
Version:
The **[OpenAI provider](https://sdk.khulnasoft.com/providers/ai-toolkit-providers/openai)** for the [AI TOOLKIT](https://sdk.khulnasoft.com/docs) contains language model support for the OpenAI chat and completion APIs and embedding model support for the O
1,420 lines (1,404 loc) • 56 kB
JavaScript
"use strict";
var __defProp = Object.defineProperty;
var __getOwnPropDesc = Object.getOwnPropertyDescriptor;
var __getOwnPropNames = Object.getOwnPropertyNames;
var __hasOwnProp = Object.prototype.hasOwnProperty;
var __export = (target, all) => {
for (var name in all)
__defProp(target, name, { get: all[name], enumerable: true });
};
var __copyProps = (to, from, except, desc) => {
if (from && typeof from === "object" || typeof from === "function") {
for (let key of __getOwnPropNames(from))
if (!__hasOwnProp.call(to, key) && key !== except)
__defProp(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc(from, key)) || desc.enumerable });
}
return to;
};
var __toCommonJS = (mod) => __copyProps(__defProp({}, "__esModule", { value: true }), mod);
// src/internal/index.ts
var internal_exports = {};
__export(internal_exports, {
OpenAIChatLanguageModel: () => OpenAIChatLanguageModel,
OpenAICompletionLanguageModel: () => OpenAICompletionLanguageModel,
OpenAIEmbeddingModel: () => OpenAIEmbeddingModel,
OpenAIImageModel: () => OpenAIImageModel,
modelMaxImagesPerCall: () => modelMaxImagesPerCall
});
module.exports = __toCommonJS(internal_exports);
// src/openai-chat-language-model.ts
var import_provider3 = require("@ai-toolkit/provider");
var import_provider_utils3 = require("@ai-toolkit/provider-utils");
var import_zod2 = require("zod");
// src/convert-to-openai-chat-messages.ts
var import_provider = require("@ai-toolkit/provider");
var import_provider_utils = require("@ai-toolkit/provider-utils");
function convertToOpenAIChatMessages({
prompt,
useLegacyFunctionCalling = false,
systemMessageMode = "system"
}) {
const messages = [];
for (const { role, content } of prompt) {
switch (role) {
case "system": {
switch (systemMessageMode) {
case "system": {
messages.push({ role: "system", content });
break;
}
case "developer": {
messages.push({ role: "developer", content });
break;
}
case "remove": {
break;
}
default: {
const _exhaustiveCheck = systemMessageMode;
throw new Error(
`Unsupported system message mode: ${_exhaustiveCheck}`
);
}
}
break;
}
case "user": {
if (content.length === 1 && content[0].type === "text") {
messages.push({ role: "user", content: content[0].text });
break;
}
messages.push({
role: "user",
content: content.map((part) => {
var _a, _b, _c;
switch (part.type) {
case "text": {
return { type: "text", text: part.text };
}
case "image": {
return {
type: "image_url",
image_url: {
url: part.image instanceof URL ? part.image.toString() : `data:${(_a = part.mimeType) != null ? _a : "image/jpeg"};base64,${(0, import_provider_utils.convertUint8ArrayToBase64)(part.image)}`,
// OpenAI specific extension: image detail
detail: (_c = (_b = part.providerMetadata) == null ? void 0 : _b.openai) == null ? void 0 : _c.imageDetail
}
};
}
case "file": {
if (part.data instanceof URL) {
throw new import_provider.UnsupportedFunctionalityError({
functionality: "'File content parts with URL data' functionality not supported."
});
}
switch (part.mimeType) {
case "audio/wav": {
return {
type: "input_audio",
input_audio: { data: part.data, format: "wav" }
};
}
case "audio/mp3":
case "audio/mpeg": {
return {
type: "input_audio",
input_audio: { data: part.data, format: "mp3" }
};
}
default: {
throw new import_provider.UnsupportedFunctionalityError({
functionality: `File content part type ${part.mimeType} in user messages`
});
}
}
}
}
})
});
break;
}
case "assistant": {
let text = "";
const toolCalls = [];
for (const part of content) {
switch (part.type) {
case "text": {
text += part.text;
break;
}
case "redacted-reasoning":
case "reasoning": {
break;
}
case "tool-call": {
toolCalls.push({
id: part.toolCallId,
type: "function",
function: {
name: part.toolName,
arguments: JSON.stringify(part.args)
}
});
break;
}
default: {
const _exhaustiveCheck = part;
throw new Error(`Unsupported part: ${_exhaustiveCheck}`);
}
}
}
if (useLegacyFunctionCalling) {
if (toolCalls.length > 1) {
throw new import_provider.UnsupportedFunctionalityError({
functionality: "useLegacyFunctionCalling with multiple tool calls in one message"
});
}
messages.push({
role: "assistant",
content: text,
function_call: toolCalls.length > 0 ? toolCalls[0].function : void 0
});
} else {
messages.push({
role: "assistant",
content: text,
tool_calls: toolCalls.length > 0 ? toolCalls : void 0
});
}
break;
}
case "tool": {
for (const toolResponse of content) {
if (useLegacyFunctionCalling) {
messages.push({
role: "function",
name: toolResponse.toolName,
content: JSON.stringify(toolResponse.result)
});
} else {
messages.push({
role: "tool",
tool_call_id: toolResponse.toolCallId,
content: JSON.stringify(toolResponse.result)
});
}
}
break;
}
default: {
const _exhaustiveCheck = role;
throw new Error(`Unsupported role: ${_exhaustiveCheck}`);
}
}
}
return messages;
}
// src/map-openai-chat-logprobs.ts
function mapOpenAIChatLogProbsOutput(logprobs) {
var _a, _b;
return (_b = (_a = logprobs == null ? void 0 : logprobs.content) == null ? void 0 : _a.map(({ token, logprob, top_logprobs }) => ({
token,
logprob,
topLogprobs: top_logprobs ? top_logprobs.map(({ token: token2, logprob: logprob2 }) => ({
token: token2,
logprob: logprob2
})) : []
}))) != null ? _b : void 0;
}
// src/map-openai-finish-reason.ts
function mapOpenAIFinishReason(finishReason) {
switch (finishReason) {
case "stop":
return "stop";
case "length":
return "length";
case "content_filter":
return "content-filter";
case "function_call":
case "tool_calls":
return "tool-calls";
default:
return "unknown";
}
}
// src/openai-error.ts
var import_zod = require("zod");
var import_provider_utils2 = require("@ai-toolkit/provider-utils");
var openaiErrorDataSchema = import_zod.z.object({
error: import_zod.z.object({
message: import_zod.z.string(),
// The additional information below is handled loosely to support
// OpenAI-compatible providers that have slightly different error
// responses:
type: import_zod.z.string().nullish(),
param: import_zod.z.any().nullish(),
code: import_zod.z.union([import_zod.z.string(), import_zod.z.number()]).nullish()
})
});
var openaiFailedResponseHandler = (0, import_provider_utils2.createJsonErrorResponseHandler)({
errorSchema: openaiErrorDataSchema,
errorToMessage: (data) => data.error.message
});
// src/get-response-metadata.ts
function getResponseMetadata({
id,
model,
created
}) {
return {
id: id != null ? id : void 0,
modelId: model != null ? model : void 0,
timestamp: created != null ? new Date(created * 1e3) : void 0
};
}
// src/openai-prepare-tools.ts
var import_provider2 = require("@ai-toolkit/provider");
function prepareTools({
mode,
useLegacyFunctionCalling = false,
structuredOutputs
}) {
var _a;
const tools = ((_a = mode.tools) == null ? void 0 : _a.length) ? mode.tools : void 0;
const toolWarnings = [];
if (tools == null) {
return { tools: void 0, tool_choice: void 0, toolWarnings };
}
const toolChoice = mode.toolChoice;
if (useLegacyFunctionCalling) {
const openaiFunctions = [];
for (const tool of tools) {
if (tool.type === "provider-defined") {
toolWarnings.push({ type: "unsupported-tool", tool });
} else {
openaiFunctions.push({
name: tool.name,
description: tool.description,
parameters: tool.parameters
});
}
}
if (toolChoice == null) {
return {
functions: openaiFunctions,
function_call: void 0,
toolWarnings
};
}
const type2 = toolChoice.type;
switch (type2) {
case "auto":
case "none":
case void 0:
return {
functions: openaiFunctions,
function_call: void 0,
toolWarnings
};
case "required":
throw new import_provider2.UnsupportedFunctionalityError({
functionality: "useLegacyFunctionCalling and toolChoice: required"
});
default:
return {
functions: openaiFunctions,
function_call: { name: toolChoice.toolName },
toolWarnings
};
}
}
const openaiTools = [];
for (const tool of tools) {
if (tool.type === "provider-defined") {
toolWarnings.push({ type: "unsupported-tool", tool });
} else {
openaiTools.push({
type: "function",
function: {
name: tool.name,
description: tool.description,
parameters: tool.parameters,
strict: structuredOutputs ? true : void 0
}
});
}
}
if (toolChoice == null) {
return { tools: openaiTools, tool_choice: void 0, toolWarnings };
}
const type = toolChoice.type;
switch (type) {
case "auto":
case "none":
case "required":
return { tools: openaiTools, tool_choice: type, toolWarnings };
case "tool":
return {
tools: openaiTools,
tool_choice: {
type: "function",
function: {
name: toolChoice.toolName
}
},
toolWarnings
};
default: {
const _exhaustiveCheck = type;
throw new import_provider2.UnsupportedFunctionalityError({
functionality: `Unsupported tool choice type: ${_exhaustiveCheck}`
});
}
}
}
// src/openai-chat-language-model.ts
var OpenAIChatLanguageModel = class {
constructor(modelId, settings, config) {
this.specificationVersion = "v1";
this.modelId = modelId;
this.settings = settings;
this.config = config;
}
get supportsStructuredOutputs() {
var _a;
return (_a = this.settings.structuredOutputs) != null ? _a : isReasoningModel(this.modelId);
}
get defaultObjectGenerationMode() {
if (isAudioModel(this.modelId)) {
return "tool";
}
return this.supportsStructuredOutputs ? "json" : "tool";
}
get provider() {
return this.config.provider;
}
get supportsImageUrls() {
return !this.settings.downloadImages;
}
getArgs({
mode,
prompt,
maxTokens,
temperature,
topP,
topK,
frequencyPenalty,
presencePenalty,
stopSequences,
responseFormat,
seed,
providerMetadata
}) {
var _a, _b, _c, _d, _e, _f, _g, _h;
const type = mode.type;
const warnings = [];
if (topK != null) {
warnings.push({
type: "unsupported-setting",
setting: "topK"
});
}
if ((responseFormat == null ? void 0 : responseFormat.type) === "json" && responseFormat.schema != null && !this.supportsStructuredOutputs) {
warnings.push({
type: "unsupported-setting",
setting: "responseFormat",
details: "JSON response format schema is only supported with structuredOutputs"
});
}
const useLegacyFunctionCalling = this.settings.useLegacyFunctionCalling;
if (useLegacyFunctionCalling && this.settings.parallelToolCalls === true) {
throw new import_provider3.UnsupportedFunctionalityError({
functionality: "useLegacyFunctionCalling with parallelToolCalls"
});
}
if (useLegacyFunctionCalling && this.supportsStructuredOutputs) {
throw new import_provider3.UnsupportedFunctionalityError({
functionality: "structuredOutputs with useLegacyFunctionCalling"
});
}
if (getSystemMessageMode(this.modelId) === "remove" && prompt.some((message) => message.role === "system")) {
warnings.push({
type: "other",
message: "system messages are removed for this model"
});
}
const baseArgs = {
// model id:
model: this.modelId,
// model specific settings:
logit_bias: this.settings.logitBias,
logprobs: this.settings.logprobs === true || typeof this.settings.logprobs === "number" ? true : void 0,
top_logprobs: typeof this.settings.logprobs === "number" ? this.settings.logprobs : typeof this.settings.logprobs === "boolean" ? this.settings.logprobs ? 0 : void 0 : void 0,
user: this.settings.user,
parallel_tool_calls: this.settings.parallelToolCalls,
// standardized settings:
max_tokens: maxTokens,
temperature,
top_p: topP,
frequency_penalty: frequencyPenalty,
presence_penalty: presencePenalty,
response_format: (responseFormat == null ? void 0 : responseFormat.type) === "json" ? this.supportsStructuredOutputs && responseFormat.schema != null ? {
type: "json_schema",
json_schema: {
schema: responseFormat.schema,
strict: true,
name: (_a = responseFormat.name) != null ? _a : "response",
description: responseFormat.description
}
} : { type: "json_object" } : void 0,
stop: stopSequences,
seed,
// openai specific settings:
// TODO remove in next major version; we auto-map maxTokens now
max_completion_tokens: (_b = providerMetadata == null ? void 0 : providerMetadata.openai) == null ? void 0 : _b.maxCompletionTokens,
store: (_c = providerMetadata == null ? void 0 : providerMetadata.openai) == null ? void 0 : _c.store,
metadata: (_d = providerMetadata == null ? void 0 : providerMetadata.openai) == null ? void 0 : _d.metadata,
prediction: (_e = providerMetadata == null ? void 0 : providerMetadata.openai) == null ? void 0 : _e.prediction,
reasoning_effort: (_g = (_f = providerMetadata == null ? void 0 : providerMetadata.openai) == null ? void 0 : _f.reasoningEffort) != null ? _g : this.settings.reasoningEffort,
// messages:
messages: convertToOpenAIChatMessages({
prompt,
useLegacyFunctionCalling,
systemMessageMode: getSystemMessageMode(this.modelId)
})
};
if (isReasoningModel(this.modelId)) {
if (baseArgs.temperature != null) {
baseArgs.temperature = void 0;
warnings.push({
type: "unsupported-setting",
setting: "temperature",
details: "temperature is not supported for reasoning models"
});
}
if (baseArgs.top_p != null) {
baseArgs.top_p = void 0;
warnings.push({
type: "unsupported-setting",
setting: "topP",
details: "topP is not supported for reasoning models"
});
}
if (baseArgs.frequency_penalty != null) {
baseArgs.frequency_penalty = void 0;
warnings.push({
type: "unsupported-setting",
setting: "frequencyPenalty",
details: "frequencyPenalty is not supported for reasoning models"
});
}
if (baseArgs.presence_penalty != null) {
baseArgs.presence_penalty = void 0;
warnings.push({
type: "unsupported-setting",
setting: "presencePenalty",
details: "presencePenalty is not supported for reasoning models"
});
}
if (baseArgs.logit_bias != null) {
baseArgs.logit_bias = void 0;
warnings.push({
type: "other",
message: "logitBias is not supported for reasoning models"
});
}
if (baseArgs.logprobs != null) {
baseArgs.logprobs = void 0;
warnings.push({
type: "other",
message: "logprobs is not supported for reasoning models"
});
}
if (baseArgs.top_logprobs != null) {
baseArgs.top_logprobs = void 0;
warnings.push({
type: "other",
message: "topLogprobs is not supported for reasoning models"
});
}
if (baseArgs.max_tokens != null) {
if (baseArgs.max_completion_tokens == null) {
baseArgs.max_completion_tokens = baseArgs.max_tokens;
}
baseArgs.max_tokens = void 0;
}
}
switch (type) {
case "regular": {
const { tools, tool_choice, functions, function_call, toolWarnings } = prepareTools({
mode,
useLegacyFunctionCalling,
structuredOutputs: this.supportsStructuredOutputs
});
return {
args: {
...baseArgs,
tools,
tool_choice,
functions,
function_call
},
warnings: [...warnings, ...toolWarnings]
};
}
case "object-json": {
return {
args: {
...baseArgs,
response_format: this.supportsStructuredOutputs && mode.schema != null ? {
type: "json_schema",
json_schema: {
schema: mode.schema,
strict: true,
name: (_h = mode.name) != null ? _h : "response",
description: mode.description
}
} : { type: "json_object" }
},
warnings
};
}
case "object-tool": {
return {
args: useLegacyFunctionCalling ? {
...baseArgs,
function_call: {
name: mode.tool.name
},
functions: [
{
name: mode.tool.name,
description: mode.tool.description,
parameters: mode.tool.parameters
}
]
} : {
...baseArgs,
tool_choice: {
type: "function",
function: { name: mode.tool.name }
},
tools: [
{
type: "function",
function: {
name: mode.tool.name,
description: mode.tool.description,
parameters: mode.tool.parameters,
strict: this.supportsStructuredOutputs ? true : void 0
}
}
]
},
warnings
};
}
default: {
const _exhaustiveCheck = type;
throw new Error(`Unsupported type: ${_exhaustiveCheck}`);
}
}
}
async doGenerate(options) {
var _a, _b, _c, _d, _e, _f, _g, _h;
const { args: body, warnings } = this.getArgs(options);
const {
responseHeaders,
value: response,
rawValue: rawResponse
} = await (0, import_provider_utils3.postJsonToApi)({
url: this.config.url({
path: "/chat/completions",
modelId: this.modelId
}),
headers: (0, import_provider_utils3.combineHeaders)(this.config.headers(), options.headers),
body,
failedResponseHandler: openaiFailedResponseHandler,
successfulResponseHandler: (0, import_provider_utils3.createJsonResponseHandler)(
openaiChatResponseSchema
),
abortSignal: options.abortSignal,
fetch: this.config.fetch
});
const { messages: rawPrompt, ...rawSettings } = body;
const choice = response.choices[0];
const completionTokenDetails = (_a = response.usage) == null ? void 0 : _a.completion_tokens_details;
const promptTokenDetails = (_b = response.usage) == null ? void 0 : _b.prompt_tokens_details;
const providerMetadata = { openai: {} };
if ((completionTokenDetails == null ? void 0 : completionTokenDetails.reasoning_tokens) != null) {
providerMetadata.openai.reasoningTokens = completionTokenDetails == null ? void 0 : completionTokenDetails.reasoning_tokens;
}
if ((completionTokenDetails == null ? void 0 : completionTokenDetails.accepted_prediction_tokens) != null) {
providerMetadata.openai.acceptedPredictionTokens = completionTokenDetails == null ? void 0 : completionTokenDetails.accepted_prediction_tokens;
}
if ((completionTokenDetails == null ? void 0 : completionTokenDetails.rejected_prediction_tokens) != null) {
providerMetadata.openai.rejectedPredictionTokens = completionTokenDetails == null ? void 0 : completionTokenDetails.rejected_prediction_tokens;
}
if ((promptTokenDetails == null ? void 0 : promptTokenDetails.cached_tokens) != null) {
providerMetadata.openai.cachedPromptTokens = promptTokenDetails == null ? void 0 : promptTokenDetails.cached_tokens;
}
return {
text: (_c = choice.message.content) != null ? _c : void 0,
toolCalls: this.settings.useLegacyFunctionCalling && choice.message.function_call ? [
{
toolCallType: "function",
toolCallId: (0, import_provider_utils3.generateId)(),
toolName: choice.message.function_call.name,
args: choice.message.function_call.arguments
}
] : (_d = choice.message.tool_calls) == null ? void 0 : _d.map((toolCall) => {
var _a2;
return {
toolCallType: "function",
toolCallId: (_a2 = toolCall.id) != null ? _a2 : (0, import_provider_utils3.generateId)(),
toolName: toolCall.function.name,
args: toolCall.function.arguments
};
}),
finishReason: mapOpenAIFinishReason(choice.finish_reason),
usage: {
promptTokens: (_f = (_e = response.usage) == null ? void 0 : _e.prompt_tokens) != null ? _f : NaN,
completionTokens: (_h = (_g = response.usage) == null ? void 0 : _g.completion_tokens) != null ? _h : NaN
},
rawCall: { rawPrompt, rawSettings },
rawResponse: { headers: responseHeaders, body: rawResponse },
request: { body: JSON.stringify(body) },
response: getResponseMetadata(response),
warnings,
logprobs: mapOpenAIChatLogProbsOutput(choice.logprobs),
providerMetadata
};
}
async doStream(options) {
if (this.settings.simulateStreaming) {
const result = await this.doGenerate(options);
const simulatedStream = new ReadableStream({
start(controller) {
controller.enqueue({ type: "response-metadata", ...result.response });
if (result.text) {
controller.enqueue({
type: "text-delta",
textDelta: result.text
});
}
if (result.toolCalls) {
for (const toolCall of result.toolCalls) {
controller.enqueue({
type: "tool-call-delta",
toolCallType: "function",
toolCallId: toolCall.toolCallId,
toolName: toolCall.toolName,
argsTextDelta: toolCall.args
});
controller.enqueue({
type: "tool-call",
...toolCall
});
}
}
controller.enqueue({
type: "finish",
finishReason: result.finishReason,
usage: result.usage,
logprobs: result.logprobs,
providerMetadata: result.providerMetadata
});
controller.close();
}
});
return {
stream: simulatedStream,
rawCall: result.rawCall,
rawResponse: result.rawResponse,
warnings: result.warnings
};
}
const { args, warnings } = this.getArgs(options);
const body = {
...args,
stream: true,
// only include stream_options when in strict compatibility mode:
stream_options: this.config.compatibility === "strict" ? { include_usage: true } : void 0
};
const { responseHeaders, value: response } = await (0, import_provider_utils3.postJsonToApi)({
url: this.config.url({
path: "/chat/completions",
modelId: this.modelId
}),
headers: (0, import_provider_utils3.combineHeaders)(this.config.headers(), options.headers),
body,
failedResponseHandler: openaiFailedResponseHandler,
successfulResponseHandler: (0, import_provider_utils3.createEventSourceResponseHandler)(
openaiChatChunkSchema
),
abortSignal: options.abortSignal,
fetch: this.config.fetch
});
const { messages: rawPrompt, ...rawSettings } = args;
const toolCalls = [];
let finishReason = "unknown";
let usage = {
promptTokens: void 0,
completionTokens: void 0
};
let logprobs;
let isFirstChunk = true;
const { useLegacyFunctionCalling } = this.settings;
const providerMetadata = { openai: {} };
return {
stream: response.pipeThrough(
new TransformStream({
transform(chunk, controller) {
var _a, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l;
if (!chunk.success) {
finishReason = "error";
controller.enqueue({ type: "error", error: chunk.error });
return;
}
const value = chunk.value;
if ("error" in value) {
finishReason = "error";
controller.enqueue({ type: "error", error: value.error });
return;
}
if (isFirstChunk) {
isFirstChunk = false;
controller.enqueue({
type: "response-metadata",
...getResponseMetadata(value)
});
}
if (value.usage != null) {
const {
prompt_tokens,
completion_tokens,
prompt_tokens_details,
completion_tokens_details
} = value.usage;
usage = {
promptTokens: prompt_tokens != null ? prompt_tokens : void 0,
completionTokens: completion_tokens != null ? completion_tokens : void 0
};
if ((completion_tokens_details == null ? void 0 : completion_tokens_details.reasoning_tokens) != null) {
providerMetadata.openai.reasoningTokens = completion_tokens_details == null ? void 0 : completion_tokens_details.reasoning_tokens;
}
if ((completion_tokens_details == null ? void 0 : completion_tokens_details.accepted_prediction_tokens) != null) {
providerMetadata.openai.acceptedPredictionTokens = completion_tokens_details == null ? void 0 : completion_tokens_details.accepted_prediction_tokens;
}
if ((completion_tokens_details == null ? void 0 : completion_tokens_details.rejected_prediction_tokens) != null) {
providerMetadata.openai.rejectedPredictionTokens = completion_tokens_details == null ? void 0 : completion_tokens_details.rejected_prediction_tokens;
}
if ((prompt_tokens_details == null ? void 0 : prompt_tokens_details.cached_tokens) != null) {
providerMetadata.openai.cachedPromptTokens = prompt_tokens_details == null ? void 0 : prompt_tokens_details.cached_tokens;
}
}
const choice = value.choices[0];
if ((choice == null ? void 0 : choice.finish_reason) != null) {
finishReason = mapOpenAIFinishReason(choice.finish_reason);
}
if ((choice == null ? void 0 : choice.delta) == null) {
return;
}
const delta = choice.delta;
if (delta.content != null) {
controller.enqueue({
type: "text-delta",
textDelta: delta.content
});
}
const mappedLogprobs = mapOpenAIChatLogProbsOutput(
choice == null ? void 0 : choice.logprobs
);
if (mappedLogprobs == null ? void 0 : mappedLogprobs.length) {
if (logprobs === void 0) logprobs = [];
logprobs.push(...mappedLogprobs);
}
const mappedToolCalls = useLegacyFunctionCalling && delta.function_call != null ? [
{
type: "function",
id: (0, import_provider_utils3.generateId)(),
function: delta.function_call,
index: 0
}
] : delta.tool_calls;
if (mappedToolCalls != null) {
for (const toolCallDelta of mappedToolCalls) {
const index = toolCallDelta.index;
if (toolCalls[index] == null) {
if (toolCallDelta.type !== "function") {
throw new import_provider3.InvalidResponseDataError({
data: toolCallDelta,
message: `Expected 'function' type.`
});
}
if (toolCallDelta.id == null) {
throw new import_provider3.InvalidResponseDataError({
data: toolCallDelta,
message: `Expected 'id' to be a string.`
});
}
if (((_a = toolCallDelta.function) == null ? void 0 : _a.name) == null) {
throw new import_provider3.InvalidResponseDataError({
data: toolCallDelta,
message: `Expected 'function.name' to be a string.`
});
}
toolCalls[index] = {
id: toolCallDelta.id,
type: "function",
function: {
name: toolCallDelta.function.name,
arguments: (_b = toolCallDelta.function.arguments) != null ? _b : ""
},
hasFinished: false
};
const toolCall2 = toolCalls[index];
if (((_c = toolCall2.function) == null ? void 0 : _c.name) != null && ((_d = toolCall2.function) == null ? void 0 : _d.arguments) != null) {
if (toolCall2.function.arguments.length > 0) {
controller.enqueue({
type: "tool-call-delta",
toolCallType: "function",
toolCallId: toolCall2.id,
toolName: toolCall2.function.name,
argsTextDelta: toolCall2.function.arguments
});
}
if ((0, import_provider_utils3.isParsableJson)(toolCall2.function.arguments)) {
controller.enqueue({
type: "tool-call",
toolCallType: "function",
toolCallId: (_e = toolCall2.id) != null ? _e : (0, import_provider_utils3.generateId)(),
toolName: toolCall2.function.name,
args: toolCall2.function.arguments
});
toolCall2.hasFinished = true;
}
}
continue;
}
const toolCall = toolCalls[index];
if (toolCall.hasFinished) {
continue;
}
if (((_f = toolCallDelta.function) == null ? void 0 : _f.arguments) != null) {
toolCall.function.arguments += (_h = (_g = toolCallDelta.function) == null ? void 0 : _g.arguments) != null ? _h : "";
}
controller.enqueue({
type: "tool-call-delta",
toolCallType: "function",
toolCallId: toolCall.id,
toolName: toolCall.function.name,
argsTextDelta: (_i = toolCallDelta.function.arguments) != null ? _i : ""
});
if (((_j = toolCall.function) == null ? void 0 : _j.name) != null && ((_k = toolCall.function) == null ? void 0 : _k.arguments) != null && (0, import_provider_utils3.isParsableJson)(toolCall.function.arguments)) {
controller.enqueue({
type: "tool-call",
toolCallType: "function",
toolCallId: (_l = toolCall.id) != null ? _l : (0, import_provider_utils3.generateId)(),
toolName: toolCall.function.name,
args: toolCall.function.arguments
});
toolCall.hasFinished = true;
}
}
}
},
flush(controller) {
var _a, _b;
controller.enqueue({
type: "finish",
finishReason,
logprobs,
usage: {
promptTokens: (_a = usage.promptTokens) != null ? _a : NaN,
completionTokens: (_b = usage.completionTokens) != null ? _b : NaN
},
...providerMetadata != null ? { providerMetadata } : {}
});
}
})
),
rawCall: { rawPrompt, rawSettings },
rawResponse: { headers: responseHeaders },
request: { body: JSON.stringify(body) },
warnings
};
}
};
var openaiTokenUsageSchema = import_zod2.z.object({
prompt_tokens: import_zod2.z.number().nullish(),
completion_tokens: import_zod2.z.number().nullish(),
prompt_tokens_details: import_zod2.z.object({
cached_tokens: import_zod2.z.number().nullish()
}).nullish(),
completion_tokens_details: import_zod2.z.object({
reasoning_tokens: import_zod2.z.number().nullish(),
accepted_prediction_tokens: import_zod2.z.number().nullish(),
rejected_prediction_tokens: import_zod2.z.number().nullish()
}).nullish()
}).nullish();
var openaiChatResponseSchema = import_zod2.z.object({
id: import_zod2.z.string().nullish(),
created: import_zod2.z.number().nullish(),
model: import_zod2.z.string().nullish(),
choices: import_zod2.z.array(
import_zod2.z.object({
message: import_zod2.z.object({
role: import_zod2.z.literal("assistant").nullish(),
content: import_zod2.z.string().nullish(),
function_call: import_zod2.z.object({
arguments: import_zod2.z.string(),
name: import_zod2.z.string()
}).nullish(),
tool_calls: import_zod2.z.array(
import_zod2.z.object({
id: import_zod2.z.string().nullish(),
type: import_zod2.z.literal("function"),
function: import_zod2.z.object({
name: import_zod2.z.string(),
arguments: import_zod2.z.string()
})
})
).nullish()
}),
index: import_zod2.z.number(),
logprobs: import_zod2.z.object({
content: import_zod2.z.array(
import_zod2.z.object({
token: import_zod2.z.string(),
logprob: import_zod2.z.number(),
top_logprobs: import_zod2.z.array(
import_zod2.z.object({
token: import_zod2.z.string(),
logprob: import_zod2.z.number()
})
)
})
).nullable()
}).nullish(),
finish_reason: import_zod2.z.string().nullish()
})
),
usage: openaiTokenUsageSchema
});
var openaiChatChunkSchema = import_zod2.z.union([
import_zod2.z.object({
id: import_zod2.z.string().nullish(),
created: import_zod2.z.number().nullish(),
model: import_zod2.z.string().nullish(),
choices: import_zod2.z.array(
import_zod2.z.object({
delta: import_zod2.z.object({
role: import_zod2.z.enum(["assistant"]).nullish(),
content: import_zod2.z.string().nullish(),
function_call: import_zod2.z.object({
name: import_zod2.z.string().optional(),
arguments: import_zod2.z.string().optional()
}).nullish(),
tool_calls: import_zod2.z.array(
import_zod2.z.object({
index: import_zod2.z.number(),
id: import_zod2.z.string().nullish(),
type: import_zod2.z.literal("function").optional(),
function: import_zod2.z.object({
name: import_zod2.z.string().nullish(),
arguments: import_zod2.z.string().nullish()
})
})
).nullish()
}).nullish(),
logprobs: import_zod2.z.object({
content: import_zod2.z.array(
import_zod2.z.object({
token: import_zod2.z.string(),
logprob: import_zod2.z.number(),
top_logprobs: import_zod2.z.array(
import_zod2.z.object({
token: import_zod2.z.string(),
logprob: import_zod2.z.number()
})
)
})
).nullable()
}).nullish(),
finish_reason: import_zod2.z.string().nullable().optional(),
index: import_zod2.z.number()
})
),
usage: openaiTokenUsageSchema
}),
openaiErrorDataSchema
]);
function isReasoningModel(modelId) {
return modelId === "o1" || modelId.startsWith("o1-") || modelId === "o3" || modelId.startsWith("o3-");
}
function isAudioModel(modelId) {
return modelId.startsWith("gpt-4o-audio-preview");
}
function getSystemMessageMode(modelId) {
var _a, _b;
if (!isReasoningModel(modelId)) {
return "system";
}
return (_b = (_a = reasoningModels[modelId]) == null ? void 0 : _a.systemMessageMode) != null ? _b : "developer";
}
var reasoningModels = {
"o1-mini": {
systemMessageMode: "remove"
},
"o1-mini-2024-09-12": {
systemMessageMode: "remove"
},
"o1-preview": {
systemMessageMode: "remove"
},
"o1-preview-2024-09-12": {
systemMessageMode: "remove"
},
"o3-mini": {
systemMessageMode: "developer"
},
"o3-mini-2025-01-31": {
systemMessageMode: "developer"
}
};
// src/openai-completion-language-model.ts
var import_provider5 = require("@ai-toolkit/provider");
var import_provider_utils4 = require("@ai-toolkit/provider-utils");
var import_zod3 = require("zod");
// src/convert-to-openai-completion-prompt.ts
var import_provider4 = require("@ai-toolkit/provider");
function convertToOpenAICompletionPrompt({
prompt,
inputFormat,
user = "user",
assistant = "assistant"
}) {
if (inputFormat === "prompt" && prompt.length === 1 && prompt[0].role === "user" && prompt[0].content.length === 1 && prompt[0].content[0].type === "text") {
return { prompt: prompt[0].content[0].text };
}
let text = "";
if (prompt[0].role === "system") {
text += `${prompt[0].content}
`;
prompt = prompt.slice(1);
}
for (const { role, content } of prompt) {
switch (role) {
case "system": {
throw new import_provider4.InvalidPromptError({
message: "Unexpected system message in prompt: ${content}",
prompt
});
}
case "user": {
const userMessage = content.map((part) => {
switch (part.type) {
case "text": {
return part.text;
}
case "image": {
throw new import_provider4.UnsupportedFunctionalityError({
functionality: "images"
});
}
}
}).join("");
text += `${user}:
${userMessage}
`;
break;
}
case "assistant": {
const assistantMessage = content.map((part) => {
switch (part.type) {
case "text": {
return part.text;
}
case "tool-call": {
throw new import_provider4.UnsupportedFunctionalityError({
functionality: "tool-call messages"
});
}
}
}).join("");
text += `${assistant}:
${assistantMessage}
`;
break;
}
case "tool": {
throw new import_provider4.UnsupportedFunctionalityError({
functionality: "tool messages"
});
}
default: {
const _exhaustiveCheck = role;
throw new Error(`Unsupported role: ${_exhaustiveCheck}`);
}
}
}
text += `${assistant}:
`;
return {
prompt: text,
stopSequences: [`
${user}:`]
};
}
// src/map-openai-completion-logprobs.ts
function mapOpenAICompletionLogProbs(logprobs) {
return logprobs == null ? void 0 : logprobs.tokens.map((token, index) => ({
token,
logprob: logprobs.token_logprobs[index],
topLogprobs: logprobs.top_logprobs ? Object.entries(logprobs.top_logprobs[index]).map(
([token2, logprob]) => ({
token: token2,
logprob
})
) : []
}));
}
// src/openai-completion-language-model.ts
var OpenAICompletionLanguageModel = class {
constructor(modelId, settings, config) {
this.specificationVersion = "v1";
this.defaultObjectGenerationMode = void 0;
this.modelId = modelId;
this.settings = settings;
this.config = config;
}
get provider() {
return this.config.provider;
}
getArgs({
mode,
inputFormat,
prompt,
maxTokens,
temperature,
topP,
topK,
frequencyPenalty,
presencePenalty,
stopSequences: userStopSequences,
responseFormat,
seed
}) {
var _a;
const type = mode.type;
const warnings = [];
if (topK != null) {
warnings.push({
type: "unsupported-setting",
setting: "topK"
});
}
if (responseFormat != null && responseFormat.type !== "text") {
warnings.push({
type: "unsupported-setting",
setting: "responseFormat",
details: "JSON response format is not supported."
});
}
const { prompt: completionPrompt, stopSequences } = convertToOpenAICompletionPrompt({ prompt, inputFormat });
const stop = [...stopSequences != null ? stopSequences : [], ...userStopSequences != null ? userStopSequences : []];
const baseArgs = {
// model id:
model: this.modelId,
// model specific settings:
echo: this.settings.echo,
logit_bias: this.settings.logitBias,
logprobs: typeof this.settings.logprobs === "number" ? this.settings.logprobs : typeof this.settings.logprobs === "boolean" ? this.settings.logprobs ? 0 : void 0 : void 0,
suffix: this.settings.suffix,
user: this.settings.user,
// standardized settings:
max_tokens: maxTokens,
temperature,
top_p: topP,
frequency_penalty: frequencyPenalty,
presence_penalty: presencePenalty,
seed,
// prompt:
prompt: completionPrompt,
// stop sequences:
stop: stop.length > 0 ? stop : void 0
};
switch (type) {
case "regular": {
if ((_a = mode.tools) == null ? void 0 : _a.length) {
throw new import_provider5.UnsupportedFunctionalityError({
functionality: "tools"
});
}
if (mode.toolChoice) {
throw new import_provider5.UnsupportedFunctionalityError({
functionality: "toolChoice"
});
}
return { args: baseArgs, warnings };
}
case "object-json": {
throw new import_provider5.UnsupportedFunctionalityError({
functionality: "object-json mode"
});
}
case "object-tool": {
throw new import_provider5.UnsupportedFunctionalityError({
functionality: "object-tool mode"
});
}
default: {
const _exhaustiveCheck = type;
throw new Error(`Unsupported type: ${_exhaustiveCheck}`);
}
}
}
async doGenerate(options) {
const { args, warnings } = this.getArgs(options);
const {
responseHeaders,
value: response,
rawValue: rawResponse
} = await (0, import_provider_utils4.postJsonToApi)({
url: this.config.url({
path: "/completions",
modelId: this.modelId
}),
headers: (0, import_provider_utils4.combineHeaders)(this.config.headers(), options.headers),
body: args,
failedResponseHandler: openaiFailedResponseHandler,
successfulResponseHandler: (0, import_provider_utils4.createJsonResponseHandler)(
openaiCompletionResponseSchema
),
abortSignal: options.abortSignal,
fetch: this.config.fetch
});
const { prompt: rawPrompt, ...rawSettings } = args;
const choice = response.choices[0];
return {
text: choice.text,
usage: {
promptTokens: response.usage.prompt_tokens,
completionTokens: response.usage.completion_tokens
},
finishReason: mapOpenAIFinishReason(choice.finish_reason),
logprobs: mapOpenAICompletionLogProbs(choice.logprobs),
rawCall: { rawPrompt, rawSettings },
rawResponse: { headers: responseHeaders, body: rawResponse },
response: getResponseMetadata(response),
warnings,
request: { body: JSON.stringify(args) }
};
}
async doStream(options) {
const { args, warnings } = this.getArgs(options);
const body = {
...args,
stream: true,
// only include stream_options when in strict compatibility mode:
stream_options: this.config.compatibility === "strict" ? { include_usage: true } : void 0
};
const { responseHeaders, value: response } = await (0, import_provider_utils4.postJsonToApi)({
url: this.config.url({
path: "/completions",
modelId: this.modelId
}),
headers: (0, import_provider_utils4.combineHeaders)(this.config.headers(), options.headers),
body,
failedResponseHandler: openaiFailedResponseHandler,
successfulResponseHandler: (0, import_provider_utils4.createEventSourceResponseHandler)(
openaiCompletionChunkSchema
),
abortSignal: options.abortSignal,
fetch: this.config.fetch
});
const { prompt: rawPrompt, ...rawSettings } = args;
let finishReason = "unknown";
let usage = {
promptTokens: Number.NaN,
completionTokens: Number.NaN
};
let logprobs;
let isFirstChunk = true;
return {
stream: response.pipeThrough(
new TransformStream({
transform(chunk, controller) {
if (!chunk.success) {
finishReason = "error";
controller.enqueue({ type: "error", error: chunk.error });
return;
}
const value = chunk.value;
if ("error" in value) {
finishReason = "error";
controller.enqueue({ type: "error", error: value.error });
return;
}
if (isFirstChunk) {
isFirstChunk = false;
controller.enqueue({
type: "response-metadata",
...getResponseMetadata(value)
});
}
if (value.usage != null) {
usage = {
promptTokens: value.usage.prompt_tokens,
completionTokens: value.usage.completion_tokens
};
}
const choice = value.choices[0];
if ((choice == null ? void 0 : choice.finish_reason) != null) {
finishReason = mapOpenAIFinishReason(choice.finish_reason);
}
if ((choice == null ? void 0 : choice.text) != null) {
controller.enqueue({
type: "text-delta",
textDelta: choice.text
});
}
const mappedLogprobs = mapOpenAICompletionLogProbs(
choice == null ? void 0 : choice.logprobs
);
if (mappedLogprobs == null ? void 0 : mappedLogprobs.length) {
if (logprobs === void 0) logprobs = [];
logprobs.push(...mappedLogprobs);
}
},
flush(controller) {
controller.enqueue({
type: "finish",
finishReason,
logprobs,
usage
});
}
})
),
rawCall: { rawPrompt, rawSettings },
rawResponse: { headers: responseHeaders },
warnings,
request: { body: JSON.stringify(body) }
};
}
};
var openaiCompletionResponseSchema = import_zod3.z.object({
id: import_zod3.z.string().nullish(),
created: import_zod3.z.number().nullish(),
model: import_zod3.z.string().nullish(),
choices: import_zod3.z.array(
import_zod3.z.object({
text: import_zod3.z.string(),
finish_reason: import_zod3.z.string(),
logprobs: import_zod3.z.object({
tokens: import_zod3.z.array(import_zod3.z.string()),
token_logprobs: import_zod3.z.array(import_zod3.z.number()),
top_logprobs: import_zod3.z.array(import_zod3.z.record(import_zod3.z.string(), import_zod3.z.number())).nullable()
}).nullish()
})
),
usage: import_zod3.z.object({
prompt_