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AI SDK

Initialize SDK​

For initialization methods, please refer to Initialize SDK.

app.ai​

After initialization, you can use the ai method mounted on the cloudbase instance to create an AI instance for subsequent model creation.

Usage Example​

app = cloudbase.init({ env: "your-env" });
const ai = app.ai();

Type Declaration​

function ai(): AI;

Return Value​

AI

Returns a newly created AI instance.

AI​

A class for creating AI models.

createModel()​

Creates a specified AI model.

Usage Example​

const model = ai.createModel("cloudbase");

Type Declaration​

function createModel(model: string): ChatModel;

Returns a model instance that implements the ChatModel abstract class, providing AI text generation capabilities.

createImageModel()​

Creates a specified image generation model.

Usage Example​

const imageModel = ai.createImageModel("hunyuan-image");

Type Declaration​

function createImageModel(provider: string): ImageModel;

Parameters​

ParameterRequiredTypeDescription
providerYesstringModel provider name, e.g., "hunyuan-image"

Return Value​

Returns an ImageModel instance that provides AI image generation capabilities.

bot​

An instance of the Bot class mounted here, which includes a series of methods for interacting with Agents. For details, refer to the Bot class documentation.

Usage Example​

const agentList = await ai.bot.list({ pageNumber: 1, pageSize: 10 });

registerFunctionTool()​

Registers a function tool. When calling the LLM, you can inform it of available function tools. When the LLM's response is parsed as a tool call, the corresponding function tool will be automatically invoked.

Usage Example​

// Omitting AI SDK initialization...

// 1. Define the weather tool, see FunctionTool type for details
const getWeatherTool = {
  name: "get_weather",
  description: "Returns weather information for a city. Example call: get_weather({city: 'Beijing'})",
  fn: ({ city }) => `The weather in ${city} is: Clear autumn skies!!!`, // Define the tool execution content here
  parameters: {
    type: "object",
    properties: {
      city: {
        type: "string",
        description: "The city to query",
      },
    },
    required: ["city"],
  },
};

// 2. Register the tool we just defined
ai.registerFunctionTool(getWeatherTool);

// 3. Send a message to the LLM and inform it that a weather tool is available
const model = ai.createModel("cloudbase");
const result = await model.generateText({
  model: "hy3",
  tools: [getWeatherTool], // Here we pass in the weather tool
  messages: [
    {
      role: "user",
      content: "Please tell me the weather in Beijing",
    },
  ],
});

console.log(result.text);

Type Declaration​

function registerFunctionTool(functionTool: FunctionTool);

Parameters​

ParameterRequiredTypeDescription
functionToolYesFunctionToolSee FunctionTool for details

Return Value​

undefined

Parameters​

ParameterRequiredTypeExampleDescription
dataYesBaseChatModelInput{model: "deepseek-v4-flash", messages: [{ role: "user", content: "Hello, please introduce Li Bai" }]}Parameter type is defined as BaseChatModelInput as the basic input definition. In practice, different LLM providers have their own unique input parameters. Developers can pass additional parameters not defined in this type based on the LLM's official documentation to fully utilize the LLM's capabilities. Other parameters will be passed through to the LLM interface, and the SDK does not perform additional processing on them.

Return Value​

PropertyTypeExampleDescription
textstring"Li Bai was a Tang Dynasty poet."Text generated by the LLM.
rawResponsesunknown[][{"choices": [{"finish_reason": "stop","message": {"role": "assistant", "content": "Hello, is there anything I can help you with?"}}], "usage": {"prompt_tokens": 14, "completion_tokens": 9, "total_tokens": 23}}]Complete response from the LLM, containing more detailed data such as message creation time. Since different LLMs have varying return values, please use according to your situation.
res.messagesChatModelMessage[][{role: 'user', content: 'Hello'},{role: 'assistant', content: 'Hello! Nice to chat with you. Is there anything I can help you with? Whether it\'s about life, work, study, or other areas, I\'ll do my best to assist you.'}]Complete message list for this call.
usageUsage{"completion_tokens":33,"prompt_tokens":3,"total_tokens":36}Tokens consumed in this call.
errorunknownErrors that occurred during the call.

streamText()​

Calls the LLM to generate text in streaming mode. During streaming calls, the generated text and other response data are returned via SSE. The return value of this interface provides different levels of encapsulation for SSE, allowing developers to obtain text streams and complete data streams according to their needs.

Usage Example​

const hy = ai.createModel("cloudbase"); // Create model
const res = await hy.streamText({
  model: "hy3",
  messages: [{ role: "user", content: "Hello, please introduce Li Bai" }],
});

for await (let str of res.textStream) {
  console.log(str); // Print the generated text
}
for await (let data of res.dataStream) {
  console.log(data); // Print the complete data for each response
}

Type Declaration​

function streamText(data: BaseChatModelInput): Promise<StreamTextResult>;

Parameters​

ParameterRequiredTypeExampleDescription
dataYesBaseChatModelInput{model: "deepseek-v4-flash", messages: [{ role: "user", content: "Hello, please introduce Li Bai" }]}Parameter type is defined as BaseChatModelInput as the basic input definition. In practice, different LLM providers have their own unique input parameters. Developers can pass additional parameters not defined in this type based on the LLM's official documentation to fully utilize the LLM's capabilities. Other parameters will be passed through to the LLM interface, and the SDK does not perform additional processing on them.

Return Value​

StreamTextResult PropertyTypeDescription
textStreamReadableStream<string>LLM-generated text returned in streaming mode. Refer to the usage example to get the incremental generated text.
dataStreamReadableStream<DataChunk>LLM response data returned in streaming mode. Refer to the usage example to get the incremental generated data. Since different LLMs have varying response values, please use according to your situation.
messagesPromise<ChatModelMessage[]>Complete message list for this call.
usagePromise<Usage>Tokens consumed in this call.
errorunknownErrors that occurred during this call.
DataChunk PropertyTypeDescription
choicesArray<object>
choices[n].finish_reasonstringReason for model inference termination.
choices[n].deltaChatModelMessageMessage for this request.
usageUsageTokens consumed in this request.
rawResponseunknownRaw response from the LLM.

Example​

const hy = ai.createModel("cloudbase");
const res = await hy.streamText({
  model: "hy3",
  messages: [{ role: "user", content: "What is 1+1" }],
});

// Text stream
for await (let str of res.textStream) {
  console.log(str);
}
// 1
// plus
// 1
// equals
// 2
// .

// Data stream
for await (let str of res.dataStream) {
  console.log(str);
}

// {created: 1723013866, id: "a95a54b5c5d2144eb700e60d0dfa5c98", model: "hy3", version: "202404011000", choices: Array(1), …}
// {created: 1723013866, id: "a95a54b5c5d2144eb700e60d0dfa5c98", model: "hy3", version: "202404011000", choices: Array(1), …}
// {created: 1723013866, id: "a95a54b5c5d2144eb700e60d0dfa5c98", model: "hy3", version: "202404011000", choices: Array(1), …}
// {created: 1723013866, id: "a95a54b5c5d2144eb700e60d0dfa5c98", model: "hy3", version: "202404011000", choices: Array(1), …}
// {created: 1723013866, id: "a95a54b5c5d2144eb700e60d0dfa5c98", model: "hy3", version: "202404011000", choices: Array(1), …}
// {created: 1723013866, id: "a95a54b5c5d2144eb700e60d0dfa5c98", model: "hy3", version: "202404011000", choices: Array(1), …}
// {created: 1723013866, id: "a95a54b5c5d2144eb700e60d0dfa5c98", model: "hy3", version: "202404011000", choices: Array(1), …}
// {created: 1723013866, id: "a95a54b5c5d2144eb700e60d0dfa5c98", model: "hy3", version: "202404011000", choices: Array(1), …}

ImageModel​

This class describes the interface provided by AI image generation model classes.

generateImageSubUrlConfig​

Used to configure the API sub-path for different provider and model combinations. When calling generateImage(), the SDK looks up the corresponding sub-path based on input.model; if not found, it falls back to the default path images/generations.

Type Declaration​

generateImageSubUrlConfig: Record<string, Array([RegExp, string])>

Default Value​

{
  'hunyuan-image': [[xxx,xxx]],
}

Usage Example​

const imageModel = ai.createImageModel("custom-provider");

// Custom sub-path configuration
imageModel.generateImageSubUrlConfig['custom-provider'] = [[/custom-model/, 'images/custom/generations']];

generateImage()​

Calls the LLM to generate images.

Usage Example​

const imageModel = ai.createImageModel("hunyuan-image");
const res = await imageModel.generateImage({
  model: "HY-Image-3.0-Plus-4090-Tob-v1.0",
  prompt: "A cute cat playing on the grass",
});
console.log(res.data[0].url); // Print the generated image URL

Type Declaration​

function generateImage(input: HunyuanARGenerateImageInput): Promise<HunyuanARGenerateImageOutput>;

Parameters​

ParameterRequiredTypeDescription
inputYesHunyuanARGenerateImageInputImage generation parameters, see HunyuanARGenerateImageInput for details

Return Value​

Promise<HunyuanARGenerateImageOutput>

PropertyTypeDescription
idstringID of this request
creatednumberUnix timestamp
dataArray<object>Returned image generation content
data[n].urlstringGenerated image URL, valid for 24 hours
data[n].revised_promptstringText after prompt revision (if revise is false, it's the original prompt)

Bot​

A class for interacting with Agents.

get()​

Gets information about a specific Agent.

Usage Example​

const res = await ai.bot.get({ botId: "botId-xxx" });
console.log(res);

Type Declaration​

function get(props: { botId: string });

Parameters​

ParameterRequiredTypeDescription
props.botIdYesstringID of the Agent to get info for

Return Value​

PropertyTypeExampleDescription
botIdstring"bot-27973647"Agent ID
namestring"Translator"Agent name
introductionstringAgent introduction
welcomeMessagestringAgent welcome message
avatarstringAgent avatar URL
backgroundstringAgent chat background image URL
isNeedRecommendbooleanWhether to recommend questions after Agent responds
typestringAgent type

list()​

Gets information about multiple Agents in batch.

Usage Example​

await ai.bot.list({
  pageNumber: 1,
  pageSize: 10,
  name: "",
  enable: true,
  information: "",
  introduction: "",
});

Type Declaration​

function list(props: {
  name: string;
  introduction: string;
  information: string;
  enable: boolean;
  pageSize: number;
  pageNumber: number;
});

Parameters​

ParameterRequiredTypeDescription
props.pageNumberYesnumberPage index
props.pageSizeYesnumberPage size
props.enableYesbooleanWhether the Agent is enabled
props.nameYesstringAgent name, for fuzzy search
props.informationYesstringAgent information, for fuzzy search
props.introductionYesstringAgent introduction, for fuzzy search

Return Value​

PropertyTypeExampleDescription
totalnumber---Total number of Agents
botListArray<object>Agent list
botList[n].botIdstring"bot-27973647"Agent ID
botList[n].namestring"Translator"Agent name
botList[n].introductionstringAgent introduction
botList[n].welcomeMessagestringAgent welcome message
botList[n].avatarstringAgent avatar URL
botList[n].backgroundstringAgent chat background image URL
botList[n].isNeedRecommendbooleanWhether to recommend questions after Agent responds
botList[n].typestringAgent type

sendMessage()​

Sends a message to an Agent for conversation. The response is returned via SSE, and this interface's return value provides different levels of encapsulation for SSE, allowing developers to obtain text streams and complete data streams according to their needs.

Usage Example​

const res = await ai.bot.sendMessage({
  botId: "botId-xxx",
  history: [{ content: "You are Li Bai.", role: "user" }],
  msg: "Hello",
});
for await (let str of res.textStream) {
  console.log(str);
}
for await (let data of res.dataStream) {
  console.log(data);
}

Type Declaration​

function sendMessage(props: {
  botId: string;
  msg: string;
  history: Array<{
    role: string;
    content: string;
  }>;
}): Promise<StreamResult>;

Parameters​

ParameterRequiredTypeDescription
props.botIdYesstringAgent ID
props.msgYesstringMessage to send in this conversation
props.historyYes[]Chat history before this conversation
props.history[n].roleYesstringRole of the message sender
props.history[n].contentYesstringContent of the message

Return Value​

Promise<StreamResult>

StreamResult PropertyTypeDescription
textStreamAsyncIterable<string>Agent-generated text returned in streaming mode. Refer to the usage example to get the incremental generated text.
dataStreamAsyncIterable<AgentStreamChunk>Agent-generated data returned in streaming mode. Refer to the usage example to get the incremental generated data.
AgentStreamChunk PropertyTypeDescription
creatednumberConversation timestamp
record_idstringConversation record ID
modelstringLLM type
versionstringLLM version
typestringReply type: text: main answer content, thinking: thinking process, search: search results, knowledge: knowledge base
rolestringConversation role, fixed as assistant in responses
contentstringConversation content
finish_reasonstringConversation end flag, continue means conversation is ongoing, stop means conversation ended
reasoning_contentstringDeep thinking content (only non-empty for hy3)
usageobjectToken usage
usage.prompt_tokensnumberNumber of prompt tokens, remains unchanged across multiple returns
usage.completion_tokensnumberTotal completion tokens. In streaming returns, represents the cumulative total of all completion tokens so far
usage.total_tokensnumberSum of prompt_tokens and completion_tokens
knowledge_basestring[]Knowledge bases used in the conversation
search_infoobjectSearch result information, requires web search to be enabled
search_info.search_resultsobject[]Search citation information
search_info.search_results[n].indexstringSearch citation index
search_info.search_results[n].titlestringSearch citation title
search_info.search_results[n].urlstringSearch citation URL

getChatRecords()​

Gets chat records.

Usage Example​

await ai.bot.getChatRecords({
  botId: "botId-xxx",
  pageNumber: 1,
  pageSize: 10,
  sort: "asc",
});

Type Declaration​

function getChatRecords(props: {
  botId: string;
  sort: string;
  pageSize: number;
  pageNumber: number;
});

Parameters​

ParameterRequiredTypeDescription
props.botIdYesstringAgent ID
props.sortYesstringSort order
props.pageSizeYesnumberPage size
props.pageNumberYesnumberPage index

Return Value​

PropertyTypeDescription
totalnumberTotal number of conversations
recordListArray<object>Conversation list
recordList[n].botIdstringAgent ID
recordList[n].recordIdstringConversation ID, system-generated
recordList[n].rolestringRole in conversation
recordList[n].contentstringConversation content
recordList[n].conversationstringUser identifier
recordList[n].typestringConversation data type
recordList[n].imagestringImage URL generated in conversation
recordList[n].triggerSrcstringConversation trigger source
recordList[n].replyTostringRecord ID being replied to
recordList[n].createTimestringConversation time

sendFeedback()​

Sends feedback for a specific chat record.

Usage Example​

const res = await ai.bot.sendFeedback({
  userFeedback: {
    botId: "botId-xxx",
    recordId: "recordId-xxx",
    comment: "Excellent",
    rating: 5,
    tags: ["Beautiful"],
    aiAnswer: "falling petals",
    input: "Give me an idiom",
    type: "upvote",
  },
  botId: "botId-xxx",
});

Type Declaration​

function sendFeedback(props: { userFeedback: IUserFeedback; botId: string });

Parameters​

ParameterRequiredTypeDescription
props.userFeedbackYesIUserFeedbackUser feedback, see IUserFeedback type definition
props.botIdYesstringID of the Agent to provide feedback for

getFeedback()​

Gets existing feedback information.

Usage Example​

const res = await ai.bot.getFeedback({
  botId: "botId-xxx",
  from: 0,
  to: 0,
  maxRating: 4,
  minRating: 3,
  pageNumber: 1,
  pageSize: 10,
  sender: "user-a",
  senderFilter: "include",
  type: "upvote",
});

Type Declaration​

function sendFeedback(props: {
  botId: string;
  type: string;
  sender: string;
  senderFilter: string;
  minRating: number;
  maxRating: number;
  from: number;
  to: number;
  pageSize: number;
  pageNumber: number;
});

Parameters​

ParameterRequiredTypeDescription
props.botIdYesstringAgent ID
props.typeYesstringUser feedback type, upvote or downvote
props.senderYesstringFeedback creator user
props.senderFilterYesstringFeedback creator user filter: include, exclude, equal, unequal, prefix
props.minRatingYesnumberMinimum rating
props.maxRatingYesnumberMaximum rating
props.fromYesnumberStart timestamp
props.toYesnumberEnd timestamp
props.pageSizeYesnumberPage size
props.pageNumberYesnumberPage index

Return Value​

PropertyTypeDescription
feedbackListobject[]Feedback query results
feedbackList[n].recordIdstringConversation record ID
feedbackList[n].typestringUser feedback type, upvote or downvote
feedbackList[n].botIdstringAgent ID
feedbackList[n].commentstringUser comment
feedbackList[n].ratingnumberUser rating
feedbackList[n].tagsstring[]User feedback tags array
feedbackList[n].inputstringUser input question
feedbackList[n].aiAnswerstringAgent's answer
totalnumberTotal number of feedbacks

uploadFiles()​

Uploads files from cloud storage to an Agent for document-based chat.

Usage Example​

// Upload files
await ai.bot.uploadFiles({
  botId: "botId-xxx",
  fileList: [
    {
      fileId: "cloud://xxx.docx",
      fileName: "xxx.docx",
      type: "file",
    },
  ],
});

// Conduct document-based chat
const res = await ai.bot.sendMessage({
  botId: "your-bot-id",
  msg: "What is the content of this file",
  files: ["xxx.docx"], // Array of file fileIds
});

for await (let text of res.textStream) {
  console.let(text);
}

Type Declaration​

function uploadFiles(props: {
  botId: string;
  fileList: Array<{
    fileId: "string";
    fileName: "string";
    type: "file";
  }>;
});

Parameters​

ParameterRequiredTypeDescription
props.botIdYesstringAgent ID
props.fileListYesstringFile list
props.fileList[n].fileIdYesstringCloud storage file ID
props.fileList[n].fileNameYesstringFile name
props.fileList[n].typeYesstringCurrently only supports "file"

getRecommendQuestions()​

Gets recommended questions.

Usage Example​

const res = ai.bot.getRecommendQuestions({
  botId: "botId-xxx",
  history: [{ content: "Who are you", role: "user" }],
  msg: "Hello",
  agentSetting: "",
  introduction: "",
  name: "",
});

for await (let str of res.textStream) {
  console.log(str);
}

Type Declaration​

function getRecommendQuestions(props: {
  botId: string;
  name: string;
  introduction: string;
  agentSetting: string;
  msg: string;
  history: Array<{
    role: string;
    content: string;
  }>;
}): Promise<StreamResult>;

Parameters​

ParameterRequiredTypeDescription
props.botIdYesstringAgent ID
props.nameYesstringAgent name
props.introductionYesstringAgent introduction
props.agentSettingYesstringAgent settings
props.msgYesstringUser message
props.historyYesArrayHistory messages
props.history[n].roleYesstringHistory message role
props.history[n].contentYesstringHistory message content

Return Value​

Promise<StreamResult>

StreamResult PropertyTypeDescription
textStreamAsyncIterable<string>Agent-generated text returned in streaming mode. Refer to the usage example to get the incremental generated text.
dataStreamAsyncIterable<AgentStreamChunk>Agent-generated data returned in streaming mode. Refer to the usage example to get the incremental generated data.
AgentStreamChunk PropertyTypeDescription
creatednumberConversation timestamp
record_idstringConversation record ID
modelstringLLM type
versionstringLLM version
typestringReply type: text: main answer content, thinking: thinking process, search: search results, knowledge: knowledge base
rolestringConversation role, fixed as assistant in responses
contentstringConversation content
finish_reasonstringConversation end flag, continue means conversation is ongoing, stop means conversation ended
reasoning_contentstringDeep thinking content (only non-empty for hy3)
usageobjectToken usage
usage.prompt_tokensnumberNumber of prompt tokens, remains unchanged across multiple returns
usage.completion_tokensnumberTotal completion tokens. In streaming returns, represents the cumulative total of all completion tokens so far
usage.total_tokensnumberSum of prompt_tokens and completion_tokens
knowledge_basestring[]Knowledge bases used in the conversation
search_infoobjectSearch result information, requires web search to be enabled
search_info.search_resultsobject[]Search citation information
search_info.search_results[n].indexstringSearch citation index
search_info.search_results[n].titlestringSearch citation title
search_info.search_results[n].urlstringSearch citation URL

createConversation()​

Creates a new conversation with an Agent.

Usage Example​

const res = await ai.bot.createConversation({
  botId: "botId-xxx",
  title: "My Conversation",
}): Promise<IConversation>;

Type Declaration​

function createConversation(props: IBotCreateConversation);

Parameters​

ParameterRequiredTypeDescription
props.botIdYesstringAgent ID
props.titleNostringConversation title

Return Value​

Promise<IConversation>

See related type: IConversation

getConversation()​

Gets the conversation list.

Usage Example​

const res = await ai.bot.getConversation({
  botId: "botId-xxx",
  pageSize: 10,
  pageNumber: 1,
  isDefault: false,
});

Type Declaration​

function getConversation(props: IBotGetConversation);

Parameters​

ParameterRequiredTypeDescription
props.botIdYesstringAgent ID
props.pageSizeNonumberPage size, default 10
props.pageNumberNonumberPage index, default 1
props.isDefaultNobooleanWhether to only get default conversation

deleteConversation()​

Deletes a specified conversation.

Usage Example​

await ai.bot.deleteConversation({
  botId: "botId-xxx",
  conversationId: "conv-123",
});

Type Declaration​

function deleteConversation(props: IBotDeleteConversation);

Parameters​

ParameterRequiredTypeDescription
props.botIdYesstringAgent ID
props.conversationIdYesstringID of conversation to delete

speechToText()​

Converts speech to text.

Usage Example​

const res = await ai.bot.speechToText({
  botId: "botId-xxx",
  engSerViceType: "16k_zh",
  voiceFormat: "mp3",
  url: "https://example.com/audio.mp3",
});

Type Declaration​

function speechToText(props: IBotSpeechToText);

Parameters​

ParameterRequiredTypeDescription
props.botIdYesstringAgent ID
props.engSerViceTypeYesstringEngine type, e.g., "16k_zh"
props.voiceFormatYesstringAudio format, e.g., "mp3"
props.urlYesstringAudio file URL
props.isPreviewNobooleanWhether in preview mode

textToSpeech()​

Converts text to speech.

Usage Example​

const res = await ai.bot.textToSpeech({
  botId: "botId-xxx",
  voiceType: 1,
  text: "Hello, I am an AI assistant",
});

Type Declaration​

function textToSpeech(props: IBotTextToSpeech);

Parameters​

ParameterRequiredTypeDescription
props.botIdYesstringAgent ID
props.voiceTypeYesnumberVoice type
props.textYesstringText to convert
props.isPreviewNobooleanWhether in preview mode

getTextToSpeechResult()​

Gets the text-to-speech result.

Usage Example​

const res = await ai.bot.getTextToSpeechResult({
  botId: "botId-xxx",
  taskId: "task-123",
});

Type Declaration​

function getTextToSpeechResult(props: IBotGetTextToSpeechResult);

Parameters​

ParameterRequiredTypeDescription
props.botIdYesstringAgent ID
props.taskIdYesstringTask ID
props.isPreviewNobooleanWhether in preview mode

IBotCreateConversation​

interface IBotCreateConversation {
  botId: string;
  title?: string;
}

IBotGetConversation​

interface IBotGetConversation {
  botId: string;
  pageSize?: number;
  pageNumber?: number;
  isDefault?: boolean;
}

IBotDeleteConversation​

interface IBotDeleteConversation {
  botId: string;
  conversationId: string;
}

IBotSpeechToText​

interface IBotSpeechToText {
  botId: string;
  engSerViceType: string;
  voiceFormat: string;
  url: string;
  isPreview?: boolean;
}

IBotTextToSpeech​

interface IBotTextToSpeech {
  botId: string;
  voiceType: number;
  text: string;
  isPreview?: boolean;
}

IBotGetTextToSpeechResult​

interface IBotGetTextToSpeechResult {
  botId: string;
  taskId: string;
  isPreview?: boolean;
}

BaseChatModelInput​

interface BaseChatModelInput {
  model: string;
  messages: Array<ChatModelMessage>;
  temperature?: number;
  topP?: number;
  tools?: Array<FunctionTool>;
  toolChoice?: "none" | "auto" | "custom";
  maxSteps?: number;
  onStepFinish?: (prop: IOnStepFinish) => unknown;
}
BaseChatModelInput PropertyTypeDescription
modelstringModel name.
messagesArray<ChatModelMessage>Message list.
temperaturenumberSampling temperature, controls output randomness.
topPnumberTemperature sampling, model considers tokens with probability mass of top_p.
toolsArray<FunctionTool>List of tools available to the LLM.
toolChoicestringSpecifies how the LLM selects tools.
maxStepsnumberMaximum number of LLM requests.
onStepFinish(prop: IOnStepFinish) => unknownCallback function triggered when an LLM request completes.

BotInfo​

interface BotInfo {
  botId: string;
  name: string;
  introduction: string;
  agentSetting: string;
  welcomeMessage: string;
  avatar: string;
  background: string;
  tags: Array<string>;
  isNeedRecommend: boolean;
  knowledgeBase: Array<string>;
  type: string;
  initQuestions: Array<string>;
  enable: true;
}

IUserFeedback​

interface IUserFeedback {
  recordId: string;
  type: string;
  botId: string;
  comment: string;
  rating: number;
  tags: Array<string>;
  input: string;
  aiAnswer: string;
}

ChatModelMessage​

type ChatModelMessage =
  | UserMessage
  | SystemMessage
  | AssistantMessage
  | ToolMessage;

UserMessage​

type UserMessage = {
  role: "user";
  content: string;
};

SystemMessage​

type SystemMessage = {
  role: "system";
  content: string;
};

AssistantMessage​

type AssistantMessage = {
  role: "assistant";
  content?: string;
  tool_calls?: Array<ToolCall>;
};

ToolMessage​

type ToolMessage = {
  role: "tool";
  tool_call_id: string;
  content: string;
};

ToolCall​

export type ToolCall = {
  id: string;
  type: string;
  function: { name: string; arguments: string };
};

FunctionTool​

Tool definition type.

type FunctionTool = {
  name: string;
  description: string;
  fn: CallableFunction;
  parameters: object;
};
FunctionTool PropertyTypeDescription
namestringTool name.
descriptionstringTool description. A clear tool description helps the LLM understand the tool's purpose.
fnCallableFunctionTool execution function. When the AI SDK parses the LLM's response as requiring this tool call, it calls this function and returns the result to the LLM.
parametersobjectTool execution function parameters. Must be defined in JSON Schema format.

IOnStepFinish​

Parameter type for the callback function triggered after LLM response.

interface IOnStepFinish {
  messages: Array<ChatModelMessage>;
  text?: string;
  toolCall?: ToolCall;
  toolResult?: unknown;
  finishReason?: string;
  stepUsage?: Usage;
  totalUsage?: Usage;
}
IOnStepFinish PropertyTypeDescription
messagesArray<ChatModelMessage>Complete message list up to the current step.
textstringText from the current response.
toolCallToolCallTool called in the current response.
toolResultunknownResult of the corresponding tool call.
finishReasonstringReason for LLM inference completion.
stepUsageUsageTokens consumed in the current step.
totalUsageUsageTotal tokens consumed up to the current step.

Usage​

type Usage = {
  completion_tokens: number;
  prompt_tokens: number;
  total_tokens: number;
};

IConversation​

Agent conversation.

interface IConversation {
  id: string;
  envId: string;
  ownerUin: string;
  userId: string;
  conversationId: string;
  title: string;
  startTime: string; // date-time format
  createTime: string;
  updateTime: string;
}

HunyuanARGenerateImageInput​

Hunyuan image generation v3.0 input parameters, supports custom aspect ratio. The image-to-image model HY-Image-v3.0-I2I-ToB-v1.0.1 additionally supports the image_urls and images reference image parameters.

interface HunyuanARGenerateImageInput {
  /** Model name: HY-Image-3.0-Plus-4090-Tob-v1.0 (text-to-image) or HY-Image-v3.0-I2I-ToB-v1.0.1 (image-to-image) */
  model: 'HY-Image-3.0-Plus-4090-Tob-v1.0' | 'HY-Image-v3.0-I2I-ToB-v1.0.1';
  /** Text for image generation, max 8192 characters */
  prompt: string;
  /**
   * Image size, format "${width}x${height}", default "1024x1024"
   * HY-Image-3.0-Plus-4090-Tob-v1.0: width/height range [512, 2048], area not exceeding 1024x1024
   */
  size?: string;
  /** Generation seed, only effective when generating 1 image, range [1, 4294967295] */
  seed?: number;
  /** Custom watermark content, max 16 characters, displayed at bottom-right */
  footnote?: string;
  /** Whether to rewrite prompt, enabled by default. Rewriting adds ~30s latency */
  revise?: { value: boolean };
  /** Reference image URL list, alternative to image, image takes precedence, max 10MB each, supports jpg/jpeg/png, max 1 image; only supported by HY-Image-v3.0-I2I-ToB-v1.0.1 */
  image_urls?: string[];
  /** Reference image base64 list, alternative to image_url, image takes precedence, max 10MB each, supports jpg/jpeg/png, max 1 image; only supported by HY-Image-v3.0-I2I-ToB-v1.0.1 */
  images?: string[];
}
HunyuanARGenerateImageInput PropertyTypeDescription
modelstringModel name, HY-Image-3.0-Plus-4090-Tob-v1.0 (text-to-image) or HY-Image-v3.0-I2I-ToB-v1.0.1 (image-to-image)
promptstringText for image generation, max 8192 characters
sizestringImage size, format "widthxheight", default "1024x1024", width/height range [512, 2048]
seednumberGeneration seed, only effective when generating 1 image, range [1, 4294967295]
footnotestringCustom watermark content, max 16 characters, displayed at bottom-right
revise{ value: boolean }Whether to rewrite prompt, enabled by default. Adds ~30s latency
image_urlsstring[]Reference image URL list, alternative to image, image takes precedence, max 10MB each, supports jpg/jpeg/png, max 1 image; only supported by HY-Image-v3.0-I2I-ToB-v1.0.1
imagesstring[]Reference image base64 list, alternative to image_url, image takes precedence, max 10MB each, supports jpg/jpeg/png, max 1 image; only supported by HY-Image-v3.0-I2I-ToB-v1.0.1

HunyuanARGenerateImageOutput​

Hunyuan image generation v3.0 output.

interface HunyuanARGenerateImageOutput {
  /** Request id */
  id: string;
  /** Unix timestamp */
  created: number;
  /** Generated image content */
  data: Array<{
    /** Generated image url, valid for 24 hours */
    url: string;
    /** Rewritten prompt */
    revised_prompt?: string;
  }>;
}
HunyuanARGenerateImageOutput PropertyTypeDescription
idstringRequest id
creatednumberUnix timestamp
dataArray<object>Generated image content
data[n].urlstringGenerated image url, valid for 24 hours
data[n].revised_promptstringRewritten prompt