AI

Cloud Development provides AI access capabilities, enabling quick access to large models and Agent.

Initialization

After initializing and logging in with js-sdk, obtain the AI instance via .ai().

import cloudbase from "@cloudbase/js-sdk";

const app = cloudbase.init({
    env: "your-env-id"
});
const auth = app.auth();
await auth.signInAnonymously();

const ai = app.ai();

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 the newly created AI instance.

AI

A class for creating AI models.

createModel()

Create the specified AI model.

Usage Example

const model = ai.createModel("hunyuan-exp");

Type Declaration

function createModel(model: string): ChatModel;

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

bot

Provides an instance of the Bot class that includes a series of methods for interacting with the Agent. For details, refer to the Bot class documentation.

Usage Example

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

registerFunctionTool()

Register function tools. When invoking a large model, you can inform it of the available function tools. When the model's response is parsed as a tool invocation, the corresponding function tool is automatically invoked.

Usage Example

// Omit the initialization operations of the AI sdk...

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

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

// 3. While sending a message to the Large Model, inform it that a weather retrieval tool is available
const model = ai.createModel("hunyuan-exp");
const result = await model.generateText({
  model: "hunyuan-turbo",
  tools: [getWeatherTool], // Here we pass in the weather retrieval tool
  messages: [
    {
      role: "user",
      content: "Please tell me the weather conditions in Beijing",
    },
  ],
});

console.log(result.text);

Type Declaration

function registerFunctionTool(functionTool: FunctionTool);

Parameters

Parameter Name	Required	Type	Description
functionTool	Required	FunctionTool	See FunctionTool

Return Value

undefined

ChatModel

This abstract class describes the interface provided by the AI text generation model class.

generateText()

Invoke large models to generate text.

Usage Example

const hy = ai.createModel("hunyuan-exp"); // Create a model
const res = await hy.generateText({
  model: "hunyuan-lite",
  messages: [{ role: "user", content: "Hello, could you please introduce Li Bai?" }],
});
console.log(res.text); // Print the generated text

Type Declaration

function generateText(data: BaseChatModelInput): Promise<{
  rawResponses: Array<unknown>;
  text: string;
  messages: Array<ChatModelMessage>;
  usage: Usage;
  error?: unknown;
}>;

Parameters

Parameter Name	Required	Type	Example	Description
data	Yes	BaseChatModelInput	{model: "hunyuan-lite", messages: [{ role: "user", content: "Hello, could you please introduce Li Bai?" }]}	The parameter type is defined as BaseChatModelInput, serving as the basic input parameter definition. In practice, different large models have their own unique input parameters. Developers can pass additional parameters not defined in this type according to the official documentation of the actual large model used, fully leveraging the capabilities provided by large models. Other parameters will be passed through to the large model interface, and the SDK does not perform additional processing on them.

Return Value

Property Name	Type	Example	Description
text	string	"Li Bai was a poet of the Tang Dynasty."	Text generated by the large model.
rawResponses	unknown[]	[{"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}}]	The complete return value from the large model, containing more detailed data such as message creation time, etc. Since return values vary among different large models, please use them according to the actual situation.
res.messages	ChatModelMessage[]	[{role: 'user', content: 'Hello'},{role: 'assistant', content: 'Hello! I am glad to communicate with you. May I ask what I can help you with? Whether it is about life, work, study, or any other aspect, I will do my best to assist you.'}]	The complete message list for this call.
usage	Usage	{"completion_tokens":33,"prompt_tokens":3,"total_tokens":36}	Tokens consumed by this call.
error	unknown		Error generated during invocation.

streamText()

To generate text by streaming invocation of large models. During streaming invocation, the generated text and other response data are returned via SSE. The return value of this interface encapsulates SSE to varying degrees, enabling developers to obtain text streams and complete data streams according to their actual needs.

Usage Example

const hy = ai.createModel("hunyuan-exp"); // Create a model
const res = await hy.streamText({
  model: "hunyuan-lite",
  messages: [{ role: "user", content: "Hello, could you 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 returned each time
}

Type Declaration

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

Parameters

Parameter Name	Required	Type	Example	Description
data	Yes	BaseChatModelInput	{model: "hunyuan-lite", messages: [{ role: "user", content: "Hello, could you please introduce Li Bai?" }]}	The parameter type is defined as BaseChatModelInput, serving as the basic input parameter definition. In practice, different large models have their own unique input parameters. Developers can pass additional parameters not defined in this type according to the official documentation of the actual large model used, fully leveraging the capabilities provided by large models. Other parameters will be passed through to the large model interface, and the SDK does not perform additional processing on them.

Return Value

StreamTextResult Property Name	Type	Description
textStream	ReadableStream<string>	Large model-generated text returned in streaming mode. Refer to the usage sample to obtain incrementally generated text.
dataStream	ReadableStream<DataChunk>	Large model response data returned in streaming mode. Refer to the usage sample to obtain incrementally generated data. As response values vary across different large models, please use them appropriately based on actual conditions.
messages	Promise<ChatModelMessage[]>	The complete message list for this call.
usage	Promise<Usage>	Tokens consumed by this call.
error	unknown	Error generated during this call.

DataChunk Property Name	Type	Description
choices	Array<object>
choices[n].finish_reason	string	Reason for model inference termination
choices[n].delta	ChatModelMessage	The message for this request.
usage	Usage	Tokens consumed by this request.
rawResponse	unknown	Raw response returned by the large model.

Example

const hy = ai.createModel("hunyuan-exp");
const res = await hy.streamText({
  model: "hunyuan-lite",
  messages: [{ role: "user", content: "What is the result of 1+1?" }],
});

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

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

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

Bot

A class for interacting with Agent.

get()

Get 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

Parameter Name	Required	Type	Description
props.botId	Yes	string	The id of the Agent for which to retrieve information

Return Value

Property Name	Type	Example	Description
botId	string	"bot-27973647"	Agent ID
name	string	"Example Agent Name"	Agent name
introduction	string		Agent introduction
welcomeMessage	string		Agent welcome message
avatar	string		Agent avatar link
background	string		Agent chat background image link
isNeedRecommend	boolean		Whether to recommend questions after the Agent answers
type	string		Agent type

list()

Retrieve information for 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

Parameter Name	Required	Type	Description
props.pageNumber	Yes	number	Page index
props.pageSize	Yes	number	Page size
props.enable	Yes	boolean	Whether the Agent is enabled
props.name	Yes	string	Agent name, used for fuzzy query
props.information	Yes	string	Agent information, used for fuzzy query
props.introduction	Yes	string	Agent introduction, used for fuzzy query

Return Value

Property Name	Type	Example	Description
total	number	---	Total Agents
botList	Array<object>		Agent list
botList[n].botId	string	"bot-27973647"	Agent ID
botList[n].name	string	"Xindaya Translation"	Agent name
botList[n].introduction	string		Agent introduction
botList[n].welcomeMessage	string		Agent welcome message
botList[n].avatar	string		Agent avatar link
botList[n].background	string		Agent chat background image link
botList[n].isNeedRecommend	boolean		Whether to recommend questions after the Agent answers
botList[n].type	string		Agent type

sendMessage()

Converse with the Agent. The response is returned via SSE. The return value of this interface encapsulates SSE to varying degrees, enabling developers to obtain text streams and complete data streams according to their actual 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

Parameter Name	Required	Type	Description
props.botId	Yes	string	Agent id
props.msg	Yes	string	Message to send in this dialog
props.history	Yes	[]	Chat history before this conversation
props.history[n].role	Yes	string	The sender role of this chat message
props.history[n].content	Yes	string	The content of this chat message

Return Value

Promise<StreamResult>

StreamResult Property Name	Type	Description
textStream	AsyncIterable<string>	Agent-generated text returned in streaming mode. Refer to the usage example to obtain incrementally generated text.
dataStream	AsyncIterable<AgentStreamChunk>	Agent-generated text returned in streaming mode. Refer to the usage example to obtain incrementally generated text.

AgentStreamChunk Property Name	Type	Description
created	number	Conversation timestamp
record_id	string	conversation record ID
model	string	Large model type
version	string	large model version
type	string	Response type: text: main answer content, thinking: thinking process, search: search results, knowledge: knowledge base
role	string	Dialogue role, always 'assistant' in responses.
content	string	Conversation content
finish_reasion	string	Conversation end flag: 'continue' indicates the conversation is ongoing, 'stop' indicates the conversation has ended.
reasoning_content	string	Deep reasoning content (only non-empty for deepseek-r1)
usage	object	token usage
usage.prompt_tokens	number	Indicates the number of tokens in the prompt, remains unchanged across multiple responses.
usage.completion_tokens	number	Total number of tokens in the completion. In streaming responses, it represents the cumulative total of tokens for all completions so far and continues to accumulate across multiple responses.
usage.total_tokens	number	represents the sum of prompt_tokens and completion_tokens
knowledge_base	string[]	knowledge bases used in the conversation
search_info	object	Search result information, requires enabling web search
search_info.search_results	object[]	search citation information
search_info.search_results[n].index	string	citation index
search_info.search_results[n].title	string	search citation title
search_info.search_results[n].url	string	citation URL

getChatRecords()

Get chat history.

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

Parameter Name	Required	Type	Description
props.botId	Yes	string	Agent id
props.sort	Yes	string	Sort method
props.pageSize	Yes	number	Page size
props.pageNumber	Yes	number	Page index

Return Value

Property Name	Type	Description
total	number	Total conversations
recordList	Array<object>	Total conversations
recordList[n].botId	string	Agent ID
recordList[n].recordId	string	Conversation ID, system-generated
recordList[n].role	string	Role in conversation
recordList[n].content	string	Conversation content
recordList[n].conversation	string	User identifier
recordList[n].type	string	Conversation data type
recordList[n].image	string	Conversation-generated image URL
recordList[n].triggerSrc	string	Conversation initiation source
recordList[n].replyTo	string	Replied-to record ID
recordList[n].createTime	string	Conversation time

sendFeedback()

Send feedback on a specific chat history.

Usage Example

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

Type Declaration

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

Parameters

Parameter Name	Required	Type	Description
props.userFeedback	Yes	IUserFeedback	User feedback. See IUserFeedback type definition
props.botId	Yes	string	Agent id to be provided for feedback

getFeedback()

Get 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

Parameter Name	Required	Type	Description
props.botId	Yes	string	Agent id
props.type	Yes	string	User feedback type: upvote (like) or downvote (dislike)
props.sender	Yes	string	User who created the comment
props.senderFilter	Yes	string	Filter relationship for comment creator: include: include, exclude: exclude, equal: equal, unequal: not equal, prefix: prefix
props.minRating	Yes	number	Minimum rating
props.maxRating	Yes	number	Maximum rating
props.from	Yes	number	Start timestamp
props.to	Yes	number	End timestamp
props.pageSize	Yes	number	Page size
props.pageNumber	Yes	number	Page index

Return Value

Property Name	Type	Description
feedbackList	object[]	Feedback query results
feedbackList[n].recordId	string	Conversation record ID
feedbackList[n].type	string	User feedback type: upvote (like) or downvote (dislike)
feedbackList[n].botId	string	Agent ID
feedbackList[n].comment	string	User comment
feedbackList[n].rating	number	User rating
feedbackList[n].tags	string[]	Array of user feedback tags
feedbackList[n].input	string	User input question
feedbackList[n].aiAnswer	string	Agent's answer
total	number	Total feedback

uploadFiles()

Upload files from cloud storage to the Agent for document-based chatting.

Usage Example

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

// Document-Based Chatting
const res = await ai.bot.sendMessage({
  botId: "your-bot-id",
  msg: "What is the content of this file",
  files: ["xxx.docx"], // File fileId array
});

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

Parameter Name	Required	Type	Description
props.botId	Yes	string	Agent id
props.fileList	Yes	string	File list
props.fileList[n].fileId	Yes	string	Cloud storage file id
props.fileList[n].fileName	Yes	string	File name
props.fileList[n].type	Yes	string	Currently only supports "file"

getRecommendQuestions()

Get 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

Parameter Name	Required	Type	Description
props.botId	Yes	string	Agent id
props.name	Yes	string	Agent Name
props.introduction	Yes	string	Agent introduction
props.agentSetting	Yes	string	Agent Setting
props.msg	Yes	string	User message
props.history	Yes	Array	Historical conversation information
props.history[n].role	Yes	string	Historical message role
props.history[n].content	Yes	string	Historical message content

Return Value

Promise<StreamResult>

StreamResult Property Name	Type	Description
textStream	AsyncIterable<string>	Agent-generated text returned in streaming mode. Refer to the usage example to obtain incrementally generated text.
dataStream	AsyncIterable<AgentStreamChunk>	Agent-generated text returned in streaming mode. Refer to the usage example to obtain incrementally generated text.

AgentStreamChunk Property Name	Type	Description
created	number	Conversation timestamp
record_id	string	conversation record ID
model	string	Large model type
version	string	large model version
type	string	Response type: text: main answer content, thinking: thinking process, search: search results, knowledge: knowledge base
role	string	Dialogue role, always 'assistant' in responses.
content	string	Conversation content
finish_reasion	string	Conversation end flag: 'continue' indicates the conversation is ongoing, 'stop' indicates the conversation has ended.
reasoning_content	string	Deep reasoning content (only non-empty for deepseek-r1)
usage	object	token usage
usage.prompt_tokens	number	Indicates the number of tokens in the prompt, remains unchanged across multiple responses.
usage.completion_tokens	number	Total number of tokens in the completion. In streaming responses, it represents the cumulative total of tokens for all completions so far and continues to accumulate across multiple responses.
usage.total_tokens	number	represents the sum of prompt_tokens and completion_tokens
knowledge_base	string[]	knowledge bases used in the conversation
search_info	object	Search result information, requires enabling web search
search_info.search_results	object[]	search citation information
search_info.search_results[n].index	string	citation index
search_info.search_results[n].title	string	search citation title
search_info.search_results[n].url	string	citation URL

createConversation()

Create a new conversation with the Agent.

Usage Example

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

Type Declaration

function createConversation(props: IBotCreateConversation);

Parameters

Parameter Name	Required	Type	Description
props.botId	Yes	string	Agent ID
props.title	No	string	Conversation Title

Return Value

Promise<IConversation>

Related Type: IConversation

getConversation()

Get 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

Parameter Name	Required	Type	Description
props.botId	Yes	string	Agent ID
props.pageSize	No	number	Page size, default 10
props.pageNumber	No	number	Page index, default 1
props.isDefault	No	boolean	Whether to get only the default conversation

deleteConversation()

Delete specified conversation.

Usage Example

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

Type Declaration

function deleteConversation(props: IBotDeleteConversation);

Parameters

Parameter Name	Required	Type	Description
props.botId	Yes	string	Agent ID
props.conversationId	Yes	string	Conversation ID to be deleted

speechToText()

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

Parameter Name	Required	Type	Description
props.botId	Yes	string	Agent ID
props.engSerViceType	Yes	string	Engine type, e.g. "16k_zh"
props.voiceFormat	Yes	string	Audio format, e.g. "mp3"
props.url	Yes	string	Audio file URL
props.isPreview	No	boolean	Whether preview mode is enabled

textToSpeech()

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

Parameter Name	Required	Type	Description
props.botId	Yes	string	Agent ID
props.voiceType	Yes	number	Voice type
props.text	Yes	string	Text to be converted
props.isPreview	No	boolean	Whether preview mode is enabled

getTextToSpeechResult()

Get 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

Parameter Name	Required	Type	Description
props.botId	Yes	string	Agent ID
props.taskId	Yes	string	Task ID
props.isPreview	No	boolean	Whether preview mode is enabled

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 Property Name	Type	Description
model	string	Model name.
messages	Array<ChatModelMessage>	Message list.
temperature	number	Sampling temperature, controlling the randomness of the output.
topP	number	Nucleus sampling, where the model considers tokens with cumulative probability mass top_p.
tools	Array<FunctionTool>	List of tools available for the large language model
toolChoice	string	Specifies how the large language model selects tools.
maxSteps	number	Maximum number of requests to the large language model.
onStepFinish	(prop: IOnStepFinish) => unknown	Callback function triggered when a request to the large language model is completed.

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 Property Name	Type	Description
name	string	Tool name.
description	string	Description of the tool. A clear tool description helps the large language model understand the tool's purpose.
fn	CallableFunction	The execution function of the tool. When the AI SDK parses that the large language model's response requires this tool invocation, it will call this function and return the result to the large language model.
parameters	object	Input parameters for the tool execution function, which must be defined using the JSON Schema format.

IOnStepFinish

Type of input parameters for the callback function triggered after the large language model responds.

interface IOnStepFinish {
  messages: Array<ChatModelMessage>;
  text?: string;
  toolCall?: ToolCall;
  toolResult?: unknown;
  finishReason?: string;
  stepUsage?: Usage;
  totalUsage?: Usage;
}

IOnStepFinish Property Name	Type	Description
messages	Array<ChatModelMessage>	List of all messages up to the current step.
text	string	Text of the current response.
toolCall	ToolCall	Tool invoked in the current response.
toolResult	unknown	Corresponding tool call result.
finishReason	string	Reason for large language model inference termination
stepUsage	Usage	Tokens consumed by the current step.
totalUsage	Usage	Total tokens consumed up to the current step.

Usage

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

IConversation

Agent session.

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