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x-chat-provider

Focus on implementing custom Chat Provider, helping to adapt any streaming interface to Ant Design X standard format

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SKILL.md
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🎯 Skill Positioning

This skill focuses on solving one problem: How to quickly adapt your streaming interface to Ant Design X's Chat Provider.

Not involved: useXChat usage tutorial (that's another skill).

Table of Contents

📦 Technology Stack Overview

LayerPackage NameCore Purpose
UI Layer@ant-design/xReact UI component library
Logic Layer@ant-design/x-sdkDevelopment toolkit
Render Layer@ant-design/x-markdownMarkdown renderer
// ✅ Correct import examples
import { Bubble } from '@ant-design/x';
import { AbstractChatProvider, OpenAIChatProvider } from '@ant-design/x-sdk';
import XRequest from '@ant-design/x-sdk';

🚀 Quick Start

🎯 Provider Selection Decision Tree

graph TD
    A[Start] --> B{Use standard OpenAI/DeepSeek API?}
    B -->|Yes| C[Use built-in Provider]
    B -->|No| D{Raw data format as message?}
    D -->|Yes| E[Use DefaultChatProvider]
    D -->|No| F[Custom Provider]
    C --> G[OpenAIChatProvider / DeepSeekChatProvider]
    E --> H[Pass-through, no conversion needed]
    F --> I[Four-step custom Provider]

🏭 Built-in Provider Overview

Provider TypeApplicable ScenarioImport
OpenAIChatProviderStandard OpenAI API formatimport { OpenAIChatProvider } from '@ant-design/x-sdk'
DeepSeekChatProviderStandard DeepSeek API formatimport { DeepSeekChatProvider } from '@ant-design/x-sdk'
DefaultChatProviderPass-through raw response, no format conversionimport { DefaultChatProvider } from '@ant-design/x-sdk'

⚠️ Export names are OpenAIChatProvider / DeepSeekChatProvider / DefaultChatProvider, watch spelling

DefaultChatProvider Use Case

DefaultChatProvider passes through raw response data without any conversion. Suitable for:

  • The interface response format is already what you want to display
  • You want full control over Bubble.List's contentRender to render messages
import { DefaultChatProvider, XRequest } from '@ant-design/x-sdk';

interface ChatInput {
  query: string;
  stream?: boolean;
}

interface ChatOutput {
  choices: Array<{ message: { content: string; role: string } }>;
}

// DefaultChatProvider generic: <ChatMessage, Input, Output>
// ChatMessage is your Output type (passed through directly)
const provider = new DefaultChatProvider<ChatOutput | ChatInput, ChatInput, ChatOutput>({
  request: XRequest('https://your-api.com/chat', {
    manual: true,
    params: { stream: false },
  }),
});

// Render using contentRender in Bubble.List's role config
// role={{ assistant: { contentRender(content) { return content?.choices?.[0]?.message?.content } } }}

⚠️ When using DefaultChatProvider, ChatMessage is typically your Output type or a union type; rendering requires contentRender

📋 Four Steps to Implement Custom Provider

Step 1: Analyze Interface Format ⏱️ 2 minutes

Information TypeExample Value
Interface URLhttps://your-api.com/chat
Request MethodJSON, POST
Response FormatServer-Sent Events
Auth MethodBearer Token

Step 2: Create Provider Class ⏱️ 5 minutes

// MyChatProvider.ts
import { AbstractChatProvider } from '@ant-design/x-sdk';
import type { TransformMessage } from '@ant-design/x-sdk';
import type { XRequestOptions } from '@ant-design/x-sdk';

interface MyInput {
  query: string;
  model?: string;
  stream?: boolean;
}

interface MyOutput {
  content: string;
  finish_reason?: string;
}

interface MyMessage {
  content: string;
  role: 'user' | 'assistant';
}

export class MyChatProvider extends AbstractChatProvider<MyMessage, MyInput, MyOutput> {
  // Parameter conversion: merge onRequest params + XRequest default params
  // options comes from XRequest(url, options), can access options.params etc.
  transformParams(
    requestParams: Partial<MyInput>,
    options: XRequestOptions<MyInput, MyOutput, MyMessage>,
  ): MyInput {
    return {
      ...(options?.params || {}),
      query: requestParams.query || '',
      model: 'gpt-3.5-turbo',
      stream: true,
    };
  }

  // Local message: convert onRequest params to the user-side display message (can return array)
  transformLocalMessage(requestParams: Partial<MyInput>): MyMessage {
    return {
      content: requestParams.query || '',
      role: 'user',
    };
  }

  // Response conversion:
  // info.originMessage: previous content of this message (for stream accumulation)
  // info.chunk: current streaming chunk
  // info.chunks: all received chunks (used in onSuccess)
  // info.status: current status
  // ⚠️ Return only MyMessage type; do NOT add a status field
  transformMessage(info: TransformMessage<MyMessage, MyOutput>): MyMessage {
    const { originMessage, chunk } = info;

    if (!chunk?.content || chunk.content === '[DONE]') {
      return { ...(originMessage || { content: '', role: 'assistant' }) };
    }

    return {
      content: `${originMessage?.content || ''}${chunk.content}`,
      role: 'assistant',
    };
  }
}

Step 3: Verify ⏱️ 1 minute

Check ItemDescription
Only 3 methodstransformParams, transformLocalMessage, transformMessage
transformParams signatureMust include second parameter options: XRequestOptions<...>
No status in returntransformMessage return value has no status field
No request methodConfirm no request method implemented
Type check passestsc --noEmit no errors

Step 4: Use Provider ⏱️ 1 minute

import { MyChatProvider } from './MyChatProvider';
import XRequest from '@ant-design/x-sdk';

// ⚠️ Must pass manual: true, otherwise AbstractChatProvider constructor will throw
const provider = new MyChatProvider({
  request: XRequest('https://your-api.com/chat', {
    manual: true,
    headers: {
      Authorization: 'Bearer your-token',
      'Content-Type': 'application/json',
    },
    params: {
      model: 'gpt-3.5-turbo',
      stream: true,
    },
  }),
});

export { provider };

🔑 Core Types and Exports

Key types exported from @ant-design/x-sdk:

import type {
  // OpenAI standard message format
  XModelMessage, // { role: string; content: string | { text: string; type: string } }
  XModelParams, // Full OpenAI request params type (model, messages, stream, temperature, etc.)
  XModelResponse, // Full OpenAI response type (choices, usage, etc.)

  // SSE stream field types
  SSEFields, // 'data' | 'event' | 'id' | 'retry'
  SSEOutput, // Partial<Record<SSEFields, any>>

  // Provider related
  TransformMessage, // { originMessage, chunk, chunks, status, responseHeaders }

  // XRequest related
  XRequestOptions, // Full request config
  XRequestCallbacks, // { onUpdate, onSuccess, onError }

  // Message related
  MessageInfo, // { id, message, status, extraInfo }
} from '@ant-design/x-sdk';

XModelMessage Structure (OpenAI message format)

// XModelMessage is the standard OpenAI message format
// Used for OpenAIChatProvider / DeepSeekChatProvider ChatMessage generic
const userMessage: XModelMessage = { role: 'user', content: 'Hello' };
const systemMessage: XModelMessage = { role: 'system', content: 'You are an assistant' };
const developerMessage: XModelMessage = { role: 'developer', content: 'System prompt' };

SSEOutput and SSEFields

// SSEOutput is the type for raw SSE stream data
// { data?: string; event?: string; id?: string; retry?: number }
// DeepSeekChatProvider uses Partial<Record<SSEFields, XModelResponse>>

import { DeepSeekChatProvider, XRequest } from '@ant-design/x-sdk';
import type { SSEFields, XModelParams, XModelResponse } from '@ant-design/x-sdk';

const provider = new DeepSeekChatProvider({
  request: XRequest<XModelParams, Partial<Record<SSEFields, XModelResponse>>>(
    'https://api.deepseek.com/v1/chat/completions',
    {
      manual: true,
      params: { model: 'deepseek-chat', stream: true },
    },
  ),
});

⚙️ XRequest Advanced Configuration

callbacks

callbacks allows monitoring request events at the Provider level. The third parameter in callbacks is the MessageInfo processed by transformMessage:

const provider = new OpenAIChatProvider({
  request: XRequest<XModelParams, XModelResponse, XModelMessage>(BASE_URL, {
    manual: true,
    callbacks: {
      // onUpdate: triggered on each streaming chunk arrival
      // chunk: current chunk; responseHeaders: response headers; message: current MessageInfo
      onUpdate: (chunk, responseHeaders, message) => {
        console.log('Stream update:', message?.message?.content);
      },
      // onSuccess: triggered when all chunks are received
      // chunks: all chunks array; message: final MessageInfo
      onSuccess: (chunks, responseHeaders, message) => {
        console.log('Request complete:', message?.message?.content);
        // Good place for analytics, logging, etc.
      },
      // onError: triggered on request failure (including AbortError)
      // error: error object; errorInfo: extra error info; message: MessageInfo at failure
      onError: (error, errorInfo, responseHeaders, message) => {
        console.error('Request failed:', error.message);
      },
    },
    params: { model: 'gpt-4o', stream: true },
  }),
});

⚠️ callbacks and useXChat's requestFallback do not conflict — both execute. callbacks is better for logging/reporting; requestFallback controls UI display.

retryInterval Retry

const request = XRequest('https://your-api.com/chat', {
  manual: true,
  // Retry interval after failure (ms)
  retryInterval: 3000,
  // Max retry count (unlimited if not set)
  retryTimes: 3,
  // onError can also return a number to dynamically set retry interval
  callbacks: {
    onError: (error) => {
      if (error.name === 'AbortError') return; // Don't retry on user cancel
      return 5000; // Return number = retry after 5s (higher priority than retryInterval)
    },
  },
});

transformStream Custom Stream

Use when the server returns a non-standard SSE stream format:

const request = XRequest('https://your-api.com/chat', {
  manual: true,
  // Fixed TransformStream
  transformStream: new TransformStream({
    transform(chunk, controller) {
      controller.enqueue(JSON.parse(chunk));
    },
  }),
  // Or decide dynamically based on URL and response headers
  transformStream: (baseURL, responseHeaders) => {
    if (responseHeaders.get('x-stream-type') === 'ndjson') {
      return new TransformStream({
        /* ... */
      });
    }
    return undefined; // Use default SSE parsing
  },
});

🔧 Common Scenario Adaptation

📖 Complete Examples: EXAMPLES.md

Scenario TypeDifficultyDescription
Standard OpenAI🟢Use built-in OpenAIChatProvider directly
Standard DeepSeek🟢Use built-in DeepSeekChatProvider directly
Pass-through raw data🟢Use DefaultChatProvider
Private SSE API🟡Four-step custom Provider
Multi-field response🟡Custom Provider + complex ChatMessage
Non-SSE stream🔴Custom Provider + transformStream

⚠️ Important Reminders

🚨 Mandatory Rule: Never write a request method!

// ❌ Serious error
class MyProvider extends AbstractChatProvider {
  async request(params: any) {
    /* Forbidden! */
  }
}

// ✅ Only correct approach: implement only the three conversion methods
class MyProvider extends AbstractChatProvider {
  transformParams(params, options) {
    /* ... */
  }
  transformLocalMessage(params) {
    /* ... */
  }
  transformMessage(info) {
    /* ... */
  }
}

⚠️ transformMessage must not return status

// ❌ Wrong
transformMessage(info) {
  return { content: '...', status: 'error' }; // ❌ status is managed by the framework
}

// ✅ Correct
transformMessage(info) {
  return { content: '...' }; // ✅
}

⚠️ Provider instantiation notes

// ✅ In React components, use useState to ensure only created once
const [provider] = React.useState(
  new MyChatProvider({
    request: XRequest(URL, { manual: true }),
  }),
);

// ❌ Don't create directly in render function (creates new instance on every render)
// const provider = new MyChatProvider(...); // inside component body causes issues

⚡ Quick Checklist

Before creating Provider:

  • Have interface docs and response format
  • Confirmed whether custom is needed (or if built-in Provider suffices)
  • Defined Input, Output, ChatMessage types

After completion:

  • Only implemented the three required methods
  • transformParams includes second parameter options
  • transformMessage return value has no status field
  • XRequest configured with manual: true
  • Absolutely no request method implemented
  • Provider wrapped with useState in React component
  • Type check passes (tsc --noEmit)

🚨 Development Rules

  • If the user does not explicitly need test cases, do not add test files
  • After completion, must check types: Run tsc --noEmit to ensure no type errors
  • Keep code clean: Remove all unused variables and imports

🔗 Reference Resources

📚 Core Reference Documentation

🌐 SDK Official Documentation

💻 Example Code

Repository
modelscope/ms-agent
Last updated
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