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ragflow/internal/entity/models/baichuan.go

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//
// Copyright 2026 The InfiniFlow Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//
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package models
import (
"bytes"
"context"
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"encoding/json"
"fmt"
"io"
"net/http"
"ragflow/internal/common"
"strings"
)
type BaichuanModel struct {
baseModel BaseModel
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}
func NewBaichuanModel(baseURL map[string]string, urlSuffix URLSuffix) *BaichuanModel {
return &BaichuanModel{
baseModel: BaseModel{
BaseURL: baseURL,
URLSuffix: urlSuffix,
httpClient: NewDriverHTTPClient(),
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},
}
}
func (b *BaichuanModel) NewInstance(baseURL map[string]string) ModelDriver {
return NewBaichuanModel(baseURL, b.baseModel.URLSuffix)
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}
func (b *BaichuanModel) Name() string {
return "baichuan"
}
func (b *BaichuanModel) ChatWithMessages(modelName string, messages []Message, apiConfig *APIConfig, chatModelConfig *ChatConfig) (*ChatResponse, error) {
if err := b.baseModel.APIConfigCheck(apiConfig); err != nil {
return nil, err
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}
if len(messages) == 0 {
return nil, fmt.Errorf("messages is empty")
}
resolvedBaseURL, err := b.baseModel.GetBaseURL(apiConfig)
if err != nil {
return nil, err
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}
url := fmt.Sprintf("%s/%s", resolvedBaseURL, b.baseModel.URLSuffix.Chat)
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// Convert messages to API format
apiMessages := make([]map[string]interface{}, len(messages))
for i, msg := range messages {
apiMessages[i] = map[string]interface{}{
"role": msg.Role,
"content": msg.Content,
}
}
// Build request body
reqBody := map[string]interface{}{
"model": modelName,
"messages": apiMessages,
"stream": false,
"temperature": 1,
}
if chatModelConfig != nil {
if chatModelConfig.Temperature != nil {
reqBody["temperature"] = *chatModelConfig.Temperature
}
if chatModelConfig.MaxTokens != nil {
reqBody["max_tokens"] = *chatModelConfig.MaxTokens
}
if chatModelConfig.Stream != nil {
reqBody["stream"] = *chatModelConfig.Stream
}
if chatModelConfig.TopP != nil {
reqBody["top_p"] = *chatModelConfig.TopP
}
}
jsonData, err := json.Marshal(reqBody)
if err != nil {
return nil, fmt.Errorf("failed to marshal request: %w", err)
}
ctx, cancel := context.WithTimeout(context.Background(), nonStreamCallTimeout)
defer cancel()
req, err := http.NewRequestWithContext(ctx, "POST", url, bytes.NewBuffer(jsonData))
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if err != nil {
return nil, fmt.Errorf("failed to create request: %w", err)
}
req.Header.Add("Content-Type", "application/json")
req.Header.Add("Authorization", fmt.Sprintf("Bearer %s", *apiConfig.ApiKey))
resp, err := b.baseModel.httpClient.Do(req)
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if err != nil {
return nil, fmt.Errorf("failed to send request: %w", err)
}
defer resp.Body.Close()
body, err := io.ReadAll(resp.Body)
if err != nil {
return nil, fmt.Errorf("failed to read response body: %w", err)
}
if resp.StatusCode != http.StatusOK {
return nil, fmt.Errorf("failed to send request: %d %s", resp.StatusCode, string(body))
}
// Parse response
var result map[string]interface{}
if err := json.Unmarshal(body, &result); err != nil {
return nil, fmt.Errorf("failed to unmarshal response: %w", err)
}
choices, ok := result["choices"].([]interface{})
if !ok {
return nil, fmt.Errorf("no choices in response")
}
firstChoice, ok := choices[0].(map[string]interface{})
if !ok {
return nil, fmt.Errorf("no choices in response")
}
messageMap, ok := firstChoice["message"].(map[string]interface{})
if !ok {
return nil, fmt.Errorf("no message in response")
}
content, ok := messageMap["content"].(string)
if !ok {
return nil, fmt.Errorf("no message in response")
}
// baichuan not support think
emptyReason := ""
chatResponse := &ChatResponse{
Answer: &content,
ReasonContent: &emptyReason,
}
return chatResponse, nil
}
func (b *BaichuanModel) ChatStreamlyWithSender(modelName string, messages []Message, apiConfig *APIConfig, modelConfig *ChatConfig, sender func(*string, *string) error) error {
if err := b.baseModel.APIConfigCheck(apiConfig); err != nil {
return err
}
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if len(messages) == 0 {
return fmt.Errorf("messages is empty")
}
resolvedBaseURL, err := b.baseModel.GetBaseURL(apiConfig)
if err != nil {
return err
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}
url := fmt.Sprintf("%s/%s", resolvedBaseURL, b.baseModel.URLSuffix.Chat)
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// Convert messages to API format
apiMessages := make([]map[string]interface{}, len(messages))
for i, msg := range messages {
apiMessages[i] = map[string]interface{}{
"role": msg.Role,
"content": msg.Content,
}
}
reqBody := map[string]interface{}{
"model": modelName,
"messages": apiMessages,
"stream": true,
"temperature": 1,
}
if modelConfig != nil {
if modelConfig.Stream != nil {
reqBody["stream"] = *modelConfig.Stream
}
if modelConfig.MaxTokens != nil {
reqBody["max_tokens"] = *modelConfig.MaxTokens
}
if modelConfig.Temperature != nil {
reqBody["temperature"] = *modelConfig.Temperature
}
if modelConfig.TopP != nil {
reqBody["top_p"] = *modelConfig.TopP
}
if modelConfig.Stop != nil {
reqBody["stop"] = *modelConfig.Stop
}
}
jsonData, err := json.Marshal(reqBody)
if err != nil {
return fmt.Errorf("failed to marshal request: %w", err)
}
ctx, cancel := context.WithTimeout(context.Background(), streamCallTimeout)
defer cancel()
req, err := http.NewRequestWithContext(ctx, "POST", url, bytes.NewBuffer(jsonData))
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if err != nil {
return fmt.Errorf("failed to create request: %w", err)
}
req.Header.Set("Content-Type", "application/json")
req.Header.Set("Authorization", fmt.Sprintf("Bearer %s", *apiConfig.ApiKey))
resp, err := b.baseModel.httpClient.Do(req)
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if err != nil {
return fmt.Errorf("failed to send request: %w", err)
}
defer resp.Body.Close()
if resp.StatusCode != http.StatusOK {
body, _ := io.ReadAll(resp.Body)
return fmt.Errorf("invalid status code: %d, body: %s", resp.StatusCode, string(body))
}
// SSE parsing: read line by line
sawTerminal := false
done, err := ParseSSEStream[map[string]interface{}](resp.Body, func(event map[string]interface{}) error {
common.Info(fmt.Sprintf("%v", event))
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choices, ok := event["choices"].([]interface{})
if !ok || len(choices) == 0 {
return nil
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}
firstChoice, ok := choices[0].(map[string]interface{})
if !ok {
return nil
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}
delta, ok := firstChoice["delta"].(map[string]interface{})
if !ok {
return nil
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}
content, ok := delta["content"].(string)
if ok && content != "" {
if err := sender(&content, nil); err != nil {
return err
}
}
finishReason, ok := firstChoice["finish_reason"].(string)
if ok && finishReason != "" {
sawTerminal = true
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}
return nil
})
if err != nil {
return fmt.Errorf("failed to scan response body: %w", err)
}
if !done && !sawTerminal {
return fmt.Errorf("baichuan: stream ended before [DONE] or finish_reason")
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}
// Send [DONE] marker for OpenAI compatibility
endOfStream := "[DONE]"
return sender(&endOfStream, nil)
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}
func (b *BaichuanModel) Embed(modelName *string, texts []string, apiConfig *APIConfig, embeddingConfig *EmbeddingConfig) ([]EmbeddingData, error) {
if err := b.baseModel.APIConfigCheck(apiConfig); err != nil {
return nil, err
}
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if len(texts) == 0 {
return []EmbeddingData{}, nil
}
resolvedBaseURL, err := b.baseModel.GetBaseURL(apiConfig)
if err != nil {
return nil, err
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}
url := fmt.Sprintf("%s/%s", resolvedBaseURL, b.baseModel.URLSuffix.Embedding)
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reqBody := map[string]interface{}{
"model": *modelName,
"input": texts,
}
jsonData, err := json.Marshal(reqBody)
if err != nil {
return nil, fmt.Errorf("failed to marshal request: %w", err)
}
ctx, cancel := context.WithTimeout(context.Background(), nonStreamCallTimeout)
defer cancel()
req, err := http.NewRequestWithContext(ctx, "POST", url, bytes.NewBuffer(jsonData))
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if err != nil {
return nil, fmt.Errorf("failed to create request: %w", err)
}
req.Header.Set("Content-Type", "application/json")
req.Header.Set("Authorization", fmt.Sprintf("Bearer %s", strings.TrimSpace(*apiConfig.ApiKey)))
resp, err := b.baseModel.httpClient.Do(req)
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if err != nil {
return nil, fmt.Errorf("failed to send request: %w", err)
}
defer resp.Body.Close()
body, err := io.ReadAll(resp.Body)
if err != nil {
return nil, fmt.Errorf("failed to read response: %w", err)
}
if resp.StatusCode != http.StatusOK {
return nil, fmt.Errorf("Baichuan embedding API error: status %d, body: %s", resp.StatusCode, string(body))
}
var parsedResponse struct {
Data []struct {
Embedding []float64 `json:"embedding"`
Index int `json:"index"`
} `json:"data"`
}
if err = json.Unmarshal(body, &parsedResponse); err != nil {
return nil, fmt.Errorf("failed to decode response: %w", err)
}
if len(parsedResponse.Data) == 0 {
return nil, fmt.Errorf("Baichuan embedding response contains no data: %s", string(body))
}
var embeddings []EmbeddingData
for _, dataElem := range parsedResponse.Data {
embeddings = append(embeddings, EmbeddingData{
Embedding: dataElem.Embedding,
Index: dataElem.Index,
})
}
return embeddings, nil
}
func (b *BaichuanModel) Rerank(modelName *string, query string, documents []string, apiConfig *APIConfig, rerankConfig *RerankConfig) (*RerankResponse, error) {
return nil, fmt.Errorf("no such method")
}
// TranscribeAudio transcribe audio
func (b *BaichuanModel) TranscribeAudio(modelName *string, file *string, apiConfig *APIConfig, asrConfig *ASRConfig) (*ASRResponse, error) {
return nil, fmt.Errorf("%s, no such method", b.Name())
}
func (b *BaichuanModel) TranscribeAudioWithSender(modelName *string, file *string, apiConfig *APIConfig, asrConfig *ASRConfig, sender func(*string, *string) error) error {
return fmt.Errorf("%s, no such method", b.Name())
}
Go: implement PaddleOCR provider and implement ASR for CoHere (#14954) ### What problem does this PR solve? This PR implement implement OCR for Baidu and Mistral, implement PaddleOCR provider and implement ASR for CoHere **Verified examples from the CLI:** ``` RAGFlow(user)> ocr with 'mistral-ocr-2512@test@mistral' file './internal/text.jpg' +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | text | +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | Parallel to these organizational innovations there were significant complementary technical innovations (e.g., improved methods of manufacturing cast-iron pipe and of coating interiors for pressure maintenance, and newer paving and construction material... | +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ RAGFlow(user)> ocr with 'paddleocr-vl-0.9b@test@baidu' file './internal/text.jpg' +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | text | +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | Parallel to these organizational innovations there were significant complementary technical innovations (e.g., improved methods of manufacturing cast-iron pipe and of coating interiors for pressure maintenance, and newer paving and construction material... | +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ # PaddleOCR RAGFlow(user)> ocr with 'PaddleOCR-VL-1.5@test@paddleocr' file './internal/test.pdf' +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | text | +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | # Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation Bingxin Ke Nando Metzger Photogra Anton Obukhov Rodrigo Caye Daudt netry and Remote Sensing, Shengyu Huang Konrad Schindler ETH Zürich <div style="text-align: c... | +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ # Cohere RAGFlow(user)> asr with 'cohere-transcribe-03-2026@test@cohere' audio './internal/test.wav' param '{"language": "en"}' +-----------------------------------------------------------------------------------------------------------------------+ | text | +-----------------------------------------------------------------------------------------------------------------------+ | The examination and testimony of the experts enabled the Commission to conclude that five shots may have been fired. | +-----------------------------------------------------------------------------------------------------------------------+ ``` ### Type of change - [x] New Feature (non-breaking change which adds functionality) - [x] Refactoring
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// AudioSpeech convert text to audio
func (b *BaichuanModel) AudioSpeech(modelName *string, audioContent *string, apiConfig *APIConfig, ttsConfig *TTSConfig) (*TTSResponse, error) {
return nil, fmt.Errorf("%s, no such method", b.Name())
}
func (b *BaichuanModel) AudioSpeechWithSender(modelName *string, audioContent *string, apiConfig *APIConfig, ttsConfig *TTSConfig, sender func(*string, *string) error) error {
return fmt.Errorf("%s, no such method", b.Name())
}
// OCRFile OCR file
func (b *BaichuanModel) OCRFile(modelName *string, content []byte, url *string, apiConfig *APIConfig, ocrConfig *OCRConfig) (*OCRFileResponse, error) {
return nil, fmt.Errorf("%s, no such method", b.Name())
Go: add file parse command (#14892) ### What problem does this PR solve? ``` RAGFlow(user)> ocr with 'hunyuanocr@test@gitee' file './picture.png' +----------------------------------------------------------+ | text | +----------------------------------------------------------+ | 生活不是等待风暴过去,而是学会在雨中翩翩起舞。 ——佚名 | +----------------------------------------------------------+ RAGFlow(user)> list 'test@gitee' tasks; +---------+----------------------------------+ | status | task_id | +---------+----------------------------------+ | success | C3FX4MQNKY5MGC6ZFMIXIAMJKHCEBQB5 | +---------+----------------------------------+ RAGFlow(user)> show 'test@gitee' task 'C3FX4MQNKY5MGC6ZFMIXIAMJKHCEBQB5'; +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+ | content | index | +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+ | # PDF 1: Purpose of RAGFlow RAGFlow is an open source Retrieval-Augmented Generation (RAG) engine designed to turn raw documents into reliable context for large language models.Its purpose is to make it practical to build an Al assistant that can ans... | 1 | +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+ ``` ### Type of change - [x] New Feature (non-breaking change which adds functionality) --------- Signed-off-by: Jin Hai <haijin.chn@gmail.com>
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}
// ParseFile parse file
func (b *BaichuanModel) ParseFile(modelName *string, content []byte, url *string, apiConfig *APIConfig, parseFileConfig *ParseFileConfig) (*ParseFileResponse, error) {
return nil, fmt.Errorf("%s, no such method", b.Name())
}
func (b *BaichuanModel) ListModels(apiConfig *APIConfig) ([]ListModelResponse, error) {
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return nil, fmt.Errorf("no such method")
}
func (b *BaichuanModel) Balance(apiConfig *APIConfig) (map[string]interface{}, error) {
return nil, fmt.Errorf("no such method")
}
func (b *BaichuanModel) CheckConnection(apiConfig *APIConfig) error {
return fmt.Errorf("no such method")
}
Go: add file parse command (#14892) ### What problem does this PR solve? ``` RAGFlow(user)> ocr with 'hunyuanocr@test@gitee' file './picture.png' +----------------------------------------------------------+ | text | +----------------------------------------------------------+ | 生活不是等待风暴过去,而是学会在雨中翩翩起舞。 ——佚名 | +----------------------------------------------------------+ RAGFlow(user)> list 'test@gitee' tasks; +---------+----------------------------------+ | status | task_id | +---------+----------------------------------+ | success | C3FX4MQNKY5MGC6ZFMIXIAMJKHCEBQB5 | +---------+----------------------------------+ RAGFlow(user)> show 'test@gitee' task 'C3FX4MQNKY5MGC6ZFMIXIAMJKHCEBQB5'; +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+ | content | index | +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+ | # PDF 1: Purpose of RAGFlow RAGFlow is an open source Retrieval-Augmented Generation (RAG) engine designed to turn raw documents into reliable context for large language models.Its purpose is to make it practical to build an Al assistant that can ans... | 1 | +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+ ``` ### Type of change - [x] New Feature (non-breaking change which adds functionality) --------- Signed-off-by: Jin Hai <haijin.chn@gmail.com>
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func (b *BaichuanModel) ListTasks(apiConfig *APIConfig) ([]ListTaskStatus, error) {
return nil, fmt.Errorf("%s, no such method", b.Name())
Go: add file parse command (#14892) ### What problem does this PR solve? ``` RAGFlow(user)> ocr with 'hunyuanocr@test@gitee' file './picture.png' +----------------------------------------------------------+ | text | +----------------------------------------------------------+ | 生活不是等待风暴过去,而是学会在雨中翩翩起舞。 ——佚名 | +----------------------------------------------------------+ RAGFlow(user)> list 'test@gitee' tasks; +---------+----------------------------------+ | status | task_id | +---------+----------------------------------+ | success | C3FX4MQNKY5MGC6ZFMIXIAMJKHCEBQB5 | +---------+----------------------------------+ RAGFlow(user)> show 'test@gitee' task 'C3FX4MQNKY5MGC6ZFMIXIAMJKHCEBQB5'; +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+ | content | index | +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+ | # PDF 1: Purpose of RAGFlow RAGFlow is an open source Retrieval-Augmented Generation (RAG) engine designed to turn raw documents into reliable context for large language models.Its purpose is to make it practical to build an Al assistant that can ans... | 1 | +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+ ``` ### Type of change - [x] New Feature (non-breaking change which adds functionality) --------- Signed-off-by: Jin Hai <haijin.chn@gmail.com>
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}
func (b *BaichuanModel) ShowTask(taskID string, apiConfig *APIConfig) (*TaskResponse, error) {
return nil, fmt.Errorf("%s, no such method", b.Name())
Go: add file parse command (#14892) ### What problem does this PR solve? ``` RAGFlow(user)> ocr with 'hunyuanocr@test@gitee' file './picture.png' +----------------------------------------------------------+ | text | +----------------------------------------------------------+ | 生活不是等待风暴过去,而是学会在雨中翩翩起舞。 ——佚名 | +----------------------------------------------------------+ RAGFlow(user)> list 'test@gitee' tasks; +---------+----------------------------------+ | status | task_id | +---------+----------------------------------+ | success | C3FX4MQNKY5MGC6ZFMIXIAMJKHCEBQB5 | +---------+----------------------------------+ RAGFlow(user)> show 'test@gitee' task 'C3FX4MQNKY5MGC6ZFMIXIAMJKHCEBQB5'; +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+ | content | index | +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+ | # PDF 1: Purpose of RAGFlow RAGFlow is an open source Retrieval-Augmented Generation (RAG) engine designed to turn raw documents into reliable context for large language models.Its purpose is to make it practical to build an Al assistant that can ans... | 1 | +------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------+ ``` ### Type of change - [x] New Feature (non-breaking change which adds functionality) --------- Signed-off-by: Jin Hai <haijin.chn@gmail.com>
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}