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feat: add endpoint type selection to channel testing functionality

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This commit is contained in:
CaIon
2025-09-30 16:52:14 +08:00
parent f92d783496
commit 63bbd9e3c3
5 changed files with 235 additions and 86 deletions
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1
common/endpoint_defaults.go
View File

@@ -23,6 +23,7 @@ var defaultEndpointInfoMap = map[constant.EndpointType]EndpointInfo{
constant.EndpointTypeGemini: {Path: "/v1beta/models/{model}:generateContent", Method: "POST"},
constant.EndpointTypeJinaRerank: {Path: "/rerank", Method: "POST"},
constant.EndpointTypeImageGeneration: {Path: "/v1/images/generations", Method: "POST"},
constant.EndpointTypeEmbeddings: {Path: "/v1/embeddings", Method: "POST"},
}
// GetDefaultEndpointInfo 返回指定端点类型的默认信息以及是否存在

1
constant/endpoint_type.go
View File

@@ -9,6 +9,7 @@ const (
EndpointTypeGemini EndpointType = "gemini"
EndpointTypeJinaRerank EndpointType = "jina-rerank"
EndpointTypeImageGeneration EndpointType = "image-generation"
EndpointTypeEmbeddings EndpointType = "embeddings"
//EndpointTypeMidjourney EndpointType = "midjourney-proxy"
//EndpointTypeSuno EndpointType = "suno-proxy"
//EndpointTypeKling EndpointType = "kling"

277
controller/channel-test.go
View File

@@ -38,7 +38,7 @@ type testResult struct {
newAPIError *types.NewAPIError
}
func testChannel(channel *model.Channel, testModel string) testResult {
func testChannel(channel *model.Channel, testModel string, endpointType string) testResult {
tik := time.Now()
if channel.Type == constant.ChannelTypeMidjourney {
return testResult{
@@ -81,18 +81,26 @@ func testChannel(channel *model.Channel, testModel string) testResult {
requestPath := "/v1/chat/completions"
// 先判断是否为 Embedding 模型
if strings.Contains(strings.ToLower(testModel), "embedding") ||
strings.HasPrefix(testModel, "m3e") || // m3e 系列模型
strings.Contains(testModel, "bge-") || // bge 系列模型
strings.Contains(testModel, "embed") ||
channel.Type == constant.ChannelTypeMokaAI { // 其他 embedding 模型
requestPath = "/v1/embeddings" // 修改请求路径
}
// 如果指定了端点类型，使用指定的端点类型
if endpointType != "" {
if endpointInfo, ok := common.GetDefaultEndpointInfo(constant.EndpointType(endpointType)); ok {
requestPath = endpointInfo.Path
}
} else {
// 如果没有指定端点类型，使用原有的自动检测逻辑
// 先判断是否为 Embedding 模型
if strings.Contains(strings.ToLower(testModel), "embedding") ||
strings.HasPrefix(testModel, "m3e") || // m3e 系列模型
strings.Contains(testModel, "bge-") || // bge 系列模型
strings.Contains(testModel, "embed") ||
channel.Type == constant.ChannelTypeMokaAI { // 其他 embedding 模型
requestPath = "/v1/embeddings" // 修改请求路径
}
// VolcEngine 图像生成模型
if channel.Type == constant.ChannelTypeVolcEngine && strings.Contains(testModel, "seedream") {
requestPath = "/v1/images/generations"
// VolcEngine 图像生成模型
if channel.Type == constant.ChannelTypeVolcEngine && strings.Contains(testModel, "seedream") {
requestPath = "/v1/images/generations"
}
}
c.Request = &http.Request{
@@ -114,21 +122,6 @@ func testChannel(channel *model.Channel, testModel string) testResult {
}
}
// 重新检查模型类型并更新请求路径
if strings.Contains(strings.ToLower(testModel), "embedding") ||
strings.HasPrefix(testModel, "m3e") ||
strings.Contains(testModel, "bge-") ||
strings.Contains(testModel, "embed") ||
channel.Type == constant.ChannelTypeMokaAI {
requestPath = "/v1/embeddings"
c.Request.URL.Path = requestPath
}
if channel.Type == constant.ChannelTypeVolcEngine && strings.Contains(testModel, "seedream") {
requestPath = "/v1/images/generations"
c.Request.URL.Path = requestPath
}
cache, err := model.GetUserCache(1)
if err != nil {
return testResult{
@@ -153,17 +146,54 @@ func testChannel(channel *model.Channel, testModel string) testResult {
newAPIError: newAPIError,
}
}
request := buildTestRequest(testModel)
// Determine relay format based on request path
relayFormat := types.RelayFormatOpenAI
if c.Request.URL.Path == "/v1/embeddings" {
relayFormat = types.RelayFormatEmbedding
}
if c.Request.URL.Path == "/v1/images/generations" {
relayFormat = types.RelayFormatOpenAIImage
// Determine relay format based on endpoint type or request path
var relayFormat types.RelayFormat
if endpointType != "" {
// 根据指定的端点类型设置 relayFormat
switch constant.EndpointType(endpointType) {
case constant.EndpointTypeOpenAI:
relayFormat = types.RelayFormatOpenAI
case constant.EndpointTypeOpenAIResponse:
relayFormat = types.RelayFormatOpenAIResponses
case constant.EndpointTypeAnthropic:
relayFormat = types.RelayFormatClaude
case constant.EndpointTypeGemini:
relayFormat = types.RelayFormatGemini
case constant.EndpointTypeJinaRerank:
relayFormat = types.RelayFormatRerank
case constant.EndpointTypeImageGeneration:
relayFormat = types.RelayFormatOpenAIImage
case constant.EndpointTypeEmbeddings:
relayFormat = types.RelayFormatEmbedding
default:
relayFormat = types.RelayFormatOpenAI
}
} else {
// 根据请求路径自动检测
relayFormat = types.RelayFormatOpenAI
if c.Request.URL.Path == "/v1/embeddings" {
relayFormat = types.RelayFormatEmbedding
}
if c.Request.URL.Path == "/v1/images/generations" {
relayFormat = types.RelayFormatOpenAIImage
}
if c.Request.URL.Path == "/v1/messages" {
relayFormat = types.RelayFormatClaude
}
if strings.Contains(c.Request.URL.Path, "/v1beta/models") {
relayFormat = types.RelayFormatGemini
}
if c.Request.URL.Path == "/v1/rerank" || c.Request.URL.Path == "/rerank" {
relayFormat = types.RelayFormatRerank
}
if c.Request.URL.Path == "/v1/responses" {
relayFormat = types.RelayFormatOpenAIResponses
}
}
request := buildTestRequest(testModel, endpointType)
info, err := relaycommon.GenRelayInfo(c, relayFormat, request, nil)
if err != nil {
@@ -186,7 +216,8 @@ func testChannel(channel *model.Channel, testModel string) testResult {
}
testModel = info.UpstreamModelName
request.Model = testModel
// 更新请求中的模型名称
request.SetModelName(testModel)
apiType, _ := common.ChannelType2APIType(channel.Type)
adaptor := relay.GetAdaptor(apiType)
@@ -216,33 +247,62 @@ func testChannel(channel *model.Channel, testModel string) testResult {
var convertedRequest any
// 根据 RelayMode 选择正确的转换函数
if info.RelayMode == relayconstant.RelayModeEmbeddings {
// 创建一个 EmbeddingRequest
embeddingRequest := dto.EmbeddingRequest{
Input: request.Input,
Model: request.Model,
}
// 调用专门用于 Embedding 的转换函数
convertedRequest, err = adaptor.ConvertEmbeddingRequest(c, info, embeddingRequest)
} else if info.RelayMode == relayconstant.RelayModeImagesGenerations {
// 创建一个 ImageRequest
prompt := "cat"
if request.Prompt != nil {
if promptStr, ok := request.Prompt.(string); ok && promptStr != "" {
prompt = promptStr
switch info.RelayMode {
case relayconstant.RelayModeEmbeddings:
// Embedding 请求 - request 已经是正确的类型
if embeddingReq, ok := request.(*dto.EmbeddingRequest); ok {
convertedRequest, err = adaptor.ConvertEmbeddingRequest(c, info, *embeddingReq)
} else {
return testResult{
context: c,
localErr: errors.New("invalid embedding request type"),
newAPIError: types.NewError(errors.New("invalid embedding request type"), types.ErrorCodeConvertRequestFailed),
}
}
imageRequest := dto.ImageRequest{
Prompt: prompt,
Model: request.Model,
N: uint(request.N),
Size: request.Size,
case relayconstant.RelayModeImagesGenerations:
// 图像生成请求 - request 已经是正确的类型
if imageReq, ok := request.(*dto.ImageRequest); ok {
convertedRequest, err = adaptor.ConvertImageRequest(c, info, *imageReq)
} else {
return testResult{
context: c,
localErr: errors.New("invalid image request type"),
newAPIError: types.NewError(errors.New("invalid image request type"), types.ErrorCodeConvertRequestFailed),
}
}
case relayconstant.RelayModeRerank:
// Rerank 请求 - request 已经是正确的类型
if rerankReq, ok := request.(*dto.RerankRequest); ok {
convertedRequest, err = adaptor.ConvertRerankRequest(c, info.RelayMode, *rerankReq)
} else {
return testResult{
context: c,
localErr: errors.New("invalid rerank request type"),
newAPIError: types.NewError(errors.New("invalid rerank request type"), types.ErrorCodeConvertRequestFailed),
}
}
case relayconstant.RelayModeResponses:
// Response 请求 - request 已经是正确的类型
if responseReq, ok := request.(*dto.OpenAIResponsesRequest); ok {
convertedRequest, err = adaptor.ConvertOpenAIResponsesRequest(c, info, *responseReq)
} else {
return testResult{
context: c,
localErr: errors.New("invalid response request type"),
newAPIError: types.NewError(errors.New("invalid response request type"), types.ErrorCodeConvertRequestFailed),
}
}
default:
// Chat/Completion 等其他请求类型
if generalReq, ok := request.(*dto.GeneralOpenAIRequest); ok {
convertedRequest, err = adaptor.ConvertOpenAIRequest(c, info, generalReq)
} else {
return testResult{
context: c,
localErr: errors.New("invalid general request type"),
newAPIError: types.NewError(errors.New("invalid general request type"), types.ErrorCodeConvertRequestFailed),
}
}
// 调用专门用于图像生成的转换函数
convertedRequest, err = adaptor.ConvertImageRequest(c, info, imageRequest)
} else {
// 对其他所有请求类型（如 Chat），保持原有逻辑
convertedRequest, err = adaptor.ConvertOpenAIRequest(c, info, request)
}
if err != nil {
@@ -345,22 +405,82 @@ func testChannel(channel *model.Channel, testModel string) testResult {
}
}
func buildTestRequest(model string) *dto.GeneralOpenAIRequest {
testRequest := &dto.GeneralOpenAIRequest{
Model: "", // this will be set later
Stream: false,
func buildTestRequest(model string, endpointType string) dto.Request {
// 根据端点类型构建不同的测试请求
if endpointType != "" {
switch constant.EndpointType(endpointType) {
case constant.EndpointTypeEmbeddings:
// 返回 EmbeddingRequest
return &dto.EmbeddingRequest{
Model: model,
Input: []any{"hello world"},
}
case constant.EndpointTypeImageGeneration:
// 返回 ImageRequest
return &dto.ImageRequest{
Model: model,
Prompt: "a cute cat",
N: 1,
Size: "1024x1024",
}
case constant.EndpointTypeJinaRerank:
// 返回 RerankRequest
return &dto.RerankRequest{
Model: model,
Query: "What is Deep Learning?",
Documents: []any{"Deep Learning is a subset of machine learning.", "Machine learning is a field of artificial intelligence."},
TopN: 2,
}
case constant.EndpointTypeOpenAIResponse:
// 返回 OpenAIResponsesRequest
return &dto.OpenAIResponsesRequest{
Model: model,
Input: json.RawMessage("\"hi\""),
}
case constant.EndpointTypeAnthropic, constant.EndpointTypeGemini, constant.EndpointTypeOpenAI:
// 返回 GeneralOpenAIRequest
maxTokens := uint(10)
if constant.EndpointType(endpointType) == constant.EndpointTypeGemini {
maxTokens = 3000
}
return &dto.GeneralOpenAIRequest{
Model: model,
Stream: false,
Messages: []dto.Message{
{
Role: "user",
Content: "hi",
},
},
MaxTokens: maxTokens,
}
}
}
// 自动检测逻辑（保持原有行为）
// 先判断是否为 Embedding 模型
if strings.Contains(strings.ToLower(model), "embedding") || // 其他 embedding 模型
strings.HasPrefix(model, "m3e") || // m3e 系列模型
if strings.Contains(strings.ToLower(model), "embedding") ||
strings.HasPrefix(model, "m3e") ||
strings.Contains(model, "bge-") {
testRequest.Model = model
// Embedding 请求
testRequest.Input = []any{"hello world"} // 修改为any，因为dto/openai_request.go 的ParseInput方法无法处理[]string类型
return testRequest
// 返回 EmbeddingRequest
return &dto.EmbeddingRequest{
Model: model,
Input: []any{"hello world"},
}
}
// 并非Embedding 模型
// Chat/Completion 请求 - 返回 GeneralOpenAIRequest
testRequest := &dto.GeneralOpenAIRequest{
Model: model,
Stream: false,
Messages: []dto.Message{
{
Role: "user",
Content: "hi",
},
},
}
if strings.HasPrefix(model, "o") {
testRequest.MaxCompletionTokens = 10
} else if strings.Contains(model, "thinking") {
@@ -373,12 +493,6 @@ func buildTestRequest(model string) *dto.GeneralOpenAIRequest {
testRequest.MaxTokens = 10
}
testMessage := dto.Message{
Role: "user",
Content: "hi",
}
testRequest.Model = model
testRequest.Messages = append(testRequest.Messages, testMessage)
return testRequest
}
@@ -402,8 +516,9 @@ func TestChannel(c *gin.Context) {
// }
//}()
testModel := c.Query("model")
endpointType := c.Query("endpoint_type")
tik := time.Now()
result := testChannel(channel, testModel)
result := testChannel(channel, testModel, endpointType)
if result.localErr != nil {
c.JSON(http.StatusOK, gin.H{
"success": false,
@@ -429,7 +544,6 @@ func TestChannel(c *gin.Context) {
"message": "",
"time": consumedTime,
})
return
}
var testAllChannelsLock sync.Mutex
@@ -463,7 +577,7 @@ func testAllChannels(notify bool) error {
for _, channel := range channels {
isChannelEnabled := channel.Status == common.ChannelStatusEnabled
tik := time.Now()
result := testChannel(channel, "")
result := testChannel(channel, "", "")
tok := time.Now()
milliseconds := tok.Sub(tik).Milliseconds()
@@ -477,7 +591,7 @@ func testAllChannels(notify bool) error {
// 当错误检查通过，才检查响应时间
if common.AutomaticDisableChannelEnabled && !shouldBanChannel {
if milliseconds > disableThreshold {
err := errors.New(fmt.Sprintf("响应时间 %.2fs 超过阈值 %.2fs", float64(milliseconds)/1000.0, float64(disableThreshold)/1000.0))
err := fmt.Errorf("响应时间 %.2fs 超过阈值 %.2fs", float64(milliseconds)/1000.0, float64(disableThreshold)/1000.0)
newAPIError = types.NewOpenAIError(err, types.ErrorCodeChannelResponseTimeExceeded, http.StatusRequestTimeout)
shouldBanChannel = true
}
@@ -514,7 +628,6 @@ func TestAllChannels(c *gin.Context) {
"success": true,
"message": "",
})
return
}
var autoTestChannelsOnce sync.Once

28
web/src/components/table/channels/modals/ModelTestModal.jsx
View File

@@ -25,6 +25,7 @@ import {
Table,
Tag,
Typography,
Select,
} from '@douyinfe/semi-ui';
import { IconSearch } from '@douyinfe/semi-icons';
import { copy, showError, showInfo, showSuccess } from '../../../../helpers';
@@ -45,6 +46,8 @@ const ModelTestModal = ({
testChannel,
modelTablePage,
setModelTablePage,
selectedEndpointType,
setSelectedEndpointType,
allSelectingRef,
isMobile,
t,
@@ -59,6 +62,17 @@ const ModelTestModal = ({
)
: [];
const endpointTypeOptions = [
{ value: '', label: t('自动检测') },
{ value: 'openai', label: 'OpenAI (/v1/chat/completions)' },
{ value: 'openai-response', label: 'OpenAI Response (/v1/responses)' },
{ value: 'anthropic', label: 'Anthropic (/v1/messages)' },
{ value: 'gemini', label: 'Gemini (/v1beta/models/{model}:generateContent)' },
{ value: 'jina-rerank', label: 'Jina Rerank (/rerank)' },
{ value: 'image-generation', label: t('图像生成') + ' (/v1/images/generations)' },
{ value: 'embeddings', label: 'Embeddings (/v1/embeddings)' },
];
const handleCopySelected = () => {
if (selectedModelKeys.length === 0) {
showError(t('请先选择模型！'));
@@ -152,7 +166,7 @@ const ModelTestModal = ({
return (
<Button
type='tertiary'
onClick={() => testChannel(currentTestChannel, record.model)}
onClick={() => testChannel(currentTestChannel, record.model, selectedEndpointType)}
loading={isTesting}
size='small'
>
@@ -228,6 +242,18 @@ const ModelTestModal = ({
>
{hasChannel && (
<div className='model-test-scroll'>
{/* 端点类型选择器 */}
<div className='flex items-center gap-2 w-full mb-2'>
<Typography.Text strong>{t('端点类型')}:</Typography.Text>
<Select
value={selectedEndpointType}
onChange={setSelectedEndpointType}
optionList={endpointTypeOptions}
className='!w-full'
placeholder={t('选择端点类型')}
/>
</div>
{/* 搜索与操作按钮 */}
<div className='flex items-center justify-end gap-2 w-full mb-2'>
<Input

14
web/src/hooks/channels/useChannelsData.jsx
View File

@@ -80,6 +80,7 @@ export const useChannelsData = () => {
const [selectedModelKeys, setSelectedModelKeys] = useState([]);
const [isBatchTesting, setIsBatchTesting] = useState(false);
const [modelTablePage, setModelTablePage] = useState(1);
const [selectedEndpointType, setSelectedEndpointType] = useState('');
// 使用 ref 来避免闭包问题，类似旧版实现
const shouldStopBatchTestingRef = useRef(false);
@@ -691,7 +692,7 @@ export const useChannelsData = () => {
};
// Test channel - 单个模型测试，参考旧版实现
const testChannel = async (record, model) => {
const testChannel = async (record, model, endpointType = '') => {
const testKey = `${record.id}-${model}`;
// 检查是否应该停止批量测试
@@ -703,7 +704,11 @@ export const useChannelsData = () => {
setTestingModels(prev => new Set([...prev, model]));
try {
const res = await API.get(`/api/channel/test/${record.id}?model=${model}`);
let url = `/api/channel/test/${record.id}?model=${model}`;
if (endpointType) {
url += `&endpoint_type=${endpointType}`;
}
const res = await API.get(url);
// 检查是否在请求期间被停止
if (shouldStopBatchTestingRef.current && isBatchTesting) {
@@ -820,7 +825,7 @@ export const useChannelsData = () => {
.replace('${total}', models.length)
);
const batchPromises = batch.map(model => testChannel(currentTestChannel, model));
const batchPromises = batch.map(model => testChannel(currentTestChannel, model, selectedEndpointType));
const batchResults = await Promise.allSettled(batchPromises);
results.push(...batchResults);
@@ -902,6 +907,7 @@ export const useChannelsData = () => {
setTestingModels(new Set());
setSelectedModelKeys([]);
setModelTablePage(1);
setSelectedEndpointType('');
// 可选择性保留测试结果，这里不清空以便用户查看
};
@@ -989,6 +995,8 @@ export const useChannelsData = () => {
isBatchTesting,
modelTablePage,
setModelTablePage,
selectedEndpointType,
setSelectedEndpointType,
allSelectingRef,
// Multi-key management states