mirror of
https://github.com/QuantumNous/new-api.git
synced 2026-03-30 02:05:21 +00:00
Merge branch 'main-upstream' into fix/volcengine_default_baseurl
# Conflicts: # main.go
This commit is contained in:
@@ -2,9 +2,10 @@ package common
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import (
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"fmt"
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"github.com/gin-gonic/gin"
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"os"
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"time"
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"github.com/gin-gonic/gin"
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)
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func SysLog(s string) {
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@@ -22,3 +23,33 @@ func FatalLog(v ...any) {
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_, _ = fmt.Fprintf(gin.DefaultErrorWriter, "[FATAL] %v | %v \n", t.Format("2006/01/02 - 15:04:05"), v)
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os.Exit(1)
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}
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func LogStartupSuccess(startTime time.Time, port string) {
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duration := time.Since(startTime)
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durationMs := duration.Milliseconds()
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// Get network IPs
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networkIps := GetNetworkIps()
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// Print blank line for spacing
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fmt.Fprintf(gin.DefaultWriter, "\n")
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// Print the main success message
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fmt.Fprintf(gin.DefaultWriter, " \033[32m%s %s\033[0m ready in %d ms\n", SystemName, Version, durationMs)
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fmt.Fprintf(gin.DefaultWriter, "\n")
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// Skip fancy startup message in container environments
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if !IsRunningInContainer() {
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// Print local URL
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fmt.Fprintf(gin.DefaultWriter, " ➜ \033[1mLocal:\033[0m http://localhost:%s/\n", port)
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}
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// Print network URLs
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for _, ip := range networkIps {
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fmt.Fprintf(gin.DefaultWriter, " ➜ \033[1mNetwork:\033[0m http://%s:%s/\n", ip, port)
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}
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// Print blank line for spacing
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fmt.Fprintf(gin.DefaultWriter, "\n")
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}
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@@ -68,6 +68,78 @@ func GetIp() (ip string) {
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return
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}
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func GetNetworkIps() []string {
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var networkIps []string
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ips, err := net.InterfaceAddrs()
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if err != nil {
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log.Println(err)
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return networkIps
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}
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for _, a := range ips {
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if ipNet, ok := a.(*net.IPNet); ok && !ipNet.IP.IsLoopback() {
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if ipNet.IP.To4() != nil {
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ip := ipNet.IP.String()
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// Include common private network ranges
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if strings.HasPrefix(ip, "10.") ||
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strings.HasPrefix(ip, "172.") ||
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strings.HasPrefix(ip, "192.168.") {
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networkIps = append(networkIps, ip)
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}
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}
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}
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}
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return networkIps
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}
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// IsRunningInContainer detects if the application is running inside a container
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func IsRunningInContainer() bool {
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// Method 1: Check for .dockerenv file (Docker containers)
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if _, err := os.Stat("/.dockerenv"); err == nil {
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return true
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}
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// Method 2: Check cgroup for container indicators
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if data, err := os.ReadFile("/proc/1/cgroup"); err == nil {
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content := string(data)
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if strings.Contains(content, "docker") ||
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strings.Contains(content, "containerd") ||
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strings.Contains(content, "kubepods") ||
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strings.Contains(content, "/lxc/") {
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return true
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}
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}
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// Method 3: Check environment variables commonly set by container runtimes
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containerEnvVars := []string{
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"KUBERNETES_SERVICE_HOST",
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"DOCKER_CONTAINER",
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"container",
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}
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for _, envVar := range containerEnvVars {
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if os.Getenv(envVar) != "" {
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return true
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}
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}
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// Method 4: Check if init process is not the traditional init
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if data, err := os.ReadFile("/proc/1/comm"); err == nil {
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comm := strings.TrimSpace(string(data))
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// In containers, process 1 is often not "init" or "systemd"
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if comm != "init" && comm != "systemd" {
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// Additional check: if it's a common container entrypoint
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if strings.Contains(comm, "docker") ||
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strings.Contains(comm, "containerd") ||
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strings.Contains(comm, "runc") {
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return true
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}
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}
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}
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return false
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}
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var sizeKB = 1024
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var sizeMB = sizeKB * 1024
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var sizeGB = sizeMB * 1024
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@@ -19,4 +19,12 @@ const (
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type ChannelOtherSettings struct {
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AzureResponsesVersion string `json:"azure_responses_version,omitempty"`
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VertexKeyType VertexKeyType `json:"vertex_key_type,omitempty"` // "json" or "api_key"
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OpenRouterEnterprise *bool `json:"openrouter_enterprise,omitempty"`
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}
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func (s *ChannelOtherSettings) IsOpenRouterEnterprise() bool {
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if s == nil || s.OpenRouterEnterprise == nil {
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return false
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}
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return *s.OpenRouterEnterprise
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}
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8
main.go
8
main.go
@@ -18,6 +18,7 @@ import (
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"os"
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"strconv"
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"strings"
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"time"
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"github.com/bytedance/gopkg/util/gopool"
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"github.com/gin-contrib/sessions"
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@@ -35,6 +36,7 @@ var buildFS embed.FS
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var indexPage []byte
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func main() {
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startTime := time.Now()
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err := InitResources()
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if err != nil {
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@@ -168,6 +170,10 @@ func main() {
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if port == "" {
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port = strconv.Itoa(*common.Port)
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}
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// Log startup success message
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common.LogStartupSuccess(startTime, port)
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err = server.Run(":" + port)
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if err != nil {
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common.FatalLog("failed to start HTTP server: " + err.Error())
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@@ -222,4 +228,4 @@ func InitResources() error {
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return err
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}
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return nil
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}
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}
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@@ -265,6 +265,7 @@ func doRequest(c *gin.Context, req *http.Request, info *common.RelayInfo) (*http
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resp, err := client.Do(req)
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if err != nil {
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logger.LogError(c, "do request failed: "+err.Error())
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return nil, types.NewError(err, types.ErrorCodeDoRequestFailed, types.ErrOptionWithHideErrMsg("upstream error: do request failed"))
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}
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if resp == nil {
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@@ -10,6 +10,7 @@ import (
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relaycommon "one-api/relay/common"
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relayconstant "one-api/relay/constant"
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"one-api/types"
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"strings"
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"github.com/gin-gonic/gin"
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)
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@@ -17,10 +18,7 @@ import (
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type Adaptor struct {
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}
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func (a *Adaptor) ConvertGeminiRequest(*gin.Context, *relaycommon.RelayInfo, *dto.GeminiChatRequest) (any, error) {
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//TODO implement me
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return nil, errors.New("not implemented")
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}
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func (a *Adaptor) ConvertGeminiRequest(*gin.Context, *relaycommon.RelayInfo, *dto.GeminiChatRequest) (any, error) { return nil, errors.New("not implemented") }
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func (a *Adaptor) ConvertClaudeRequest(c *gin.Context, info *relaycommon.RelayInfo, request *dto.ClaudeRequest) (any, error) {
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openaiAdaptor := openai.Adaptor{}
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@@ -31,32 +29,21 @@ func (a *Adaptor) ConvertClaudeRequest(c *gin.Context, info *relaycommon.RelayIn
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openaiRequest.(*dto.GeneralOpenAIRequest).StreamOptions = &dto.StreamOptions{
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IncludeUsage: true,
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}
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return requestOpenAI2Ollama(c, openaiRequest.(*dto.GeneralOpenAIRequest))
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// map to ollama chat request (Claude -> OpenAI -> Ollama chat)
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return openAIChatToOllamaChat(c, openaiRequest.(*dto.GeneralOpenAIRequest))
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}
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func (a *Adaptor) ConvertAudioRequest(c *gin.Context, info *relaycommon.RelayInfo, request dto.AudioRequest) (io.Reader, error) {
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||||
//TODO implement me
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return nil, errors.New("not implemented")
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}
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func (a *Adaptor) ConvertAudioRequest(c *gin.Context, info *relaycommon.RelayInfo, request dto.AudioRequest) (io.Reader, error) { return nil, errors.New("not implemented") }
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func (a *Adaptor) ConvertImageRequest(c *gin.Context, info *relaycommon.RelayInfo, request dto.ImageRequest) (any, error) {
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//TODO implement me
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return nil, errors.New("not implemented")
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}
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func (a *Adaptor) ConvertImageRequest(c *gin.Context, info *relaycommon.RelayInfo, request dto.ImageRequest) (any, error) { return nil, errors.New("not implemented") }
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func (a *Adaptor) Init(info *relaycommon.RelayInfo) {
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}
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func (a *Adaptor) GetRequestURL(info *relaycommon.RelayInfo) (string, error) {
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if info.RelayFormat == types.RelayFormatClaude {
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return info.ChannelBaseUrl + "/v1/chat/completions", nil
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}
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switch info.RelayMode {
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case relayconstant.RelayModeEmbeddings:
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return info.ChannelBaseUrl + "/api/embed", nil
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default:
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return relaycommon.GetFullRequestURL(info.ChannelBaseUrl, info.RequestURLPath, info.ChannelType), nil
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}
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if info.RelayMode == relayconstant.RelayModeEmbeddings { return info.ChannelBaseUrl + "/api/embed", nil }
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if strings.Contains(info.RequestURLPath, "/v1/completions") || info.RelayMode == relayconstant.RelayModeCompletions { return info.ChannelBaseUrl + "/api/generate", nil }
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return info.ChannelBaseUrl + "/api/chat", nil
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}
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func (a *Adaptor) SetupRequestHeader(c *gin.Context, req *http.Header, info *relaycommon.RelayInfo) error {
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@@ -66,10 +53,12 @@ func (a *Adaptor) SetupRequestHeader(c *gin.Context, req *http.Header, info *rel
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}
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func (a *Adaptor) ConvertOpenAIRequest(c *gin.Context, info *relaycommon.RelayInfo, request *dto.GeneralOpenAIRequest) (any, error) {
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if request == nil {
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return nil, errors.New("request is nil")
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if request == nil { return nil, errors.New("request is nil") }
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// decide generate or chat
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if strings.Contains(info.RequestURLPath, "/v1/completions") || info.RelayMode == relayconstant.RelayModeCompletions {
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return openAIToGenerate(c, request)
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}
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return requestOpenAI2Ollama(c, request)
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return openAIChatToOllamaChat(c, request)
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}
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|
||||
func (a *Adaptor) ConvertRerankRequest(c *gin.Context, relayMode int, request dto.RerankRequest) (any, error) {
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@@ -80,10 +69,7 @@ func (a *Adaptor) ConvertEmbeddingRequest(c *gin.Context, info *relaycommon.Rela
|
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return requestOpenAI2Embeddings(request), nil
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}
|
||||
|
||||
func (a *Adaptor) ConvertOpenAIResponsesRequest(c *gin.Context, info *relaycommon.RelayInfo, request dto.OpenAIResponsesRequest) (any, error) {
|
||||
// TODO implement me
|
||||
return nil, errors.New("not implemented")
|
||||
}
|
||||
func (a *Adaptor) ConvertOpenAIResponsesRequest(c *gin.Context, info *relaycommon.RelayInfo, request dto.OpenAIResponsesRequest) (any, error) { return nil, errors.New("not implemented") }
|
||||
|
||||
func (a *Adaptor) DoRequest(c *gin.Context, info *relaycommon.RelayInfo, requestBody io.Reader) (any, error) {
|
||||
return channel.DoApiRequest(a, c, info, requestBody)
|
||||
@@ -92,15 +78,13 @@ func (a *Adaptor) DoRequest(c *gin.Context, info *relaycommon.RelayInfo, request
|
||||
func (a *Adaptor) DoResponse(c *gin.Context, resp *http.Response, info *relaycommon.RelayInfo) (usage any, err *types.NewAPIError) {
|
||||
switch info.RelayMode {
|
||||
case relayconstant.RelayModeEmbeddings:
|
||||
usage, err = ollamaEmbeddingHandler(c, info, resp)
|
||||
return ollamaEmbeddingHandler(c, info, resp)
|
||||
default:
|
||||
if info.IsStream {
|
||||
usage, err = openai.OaiStreamHandler(c, info, resp)
|
||||
} else {
|
||||
usage, err = openai.OpenaiHandler(c, info, resp)
|
||||
return ollamaStreamHandler(c, info, resp)
|
||||
}
|
||||
return ollamaChatHandler(c, info, resp)
|
||||
}
|
||||
return
|
||||
}
|
||||
|
||||
func (a *Adaptor) GetModelList() []string {
|
||||
|
||||
@@ -2,48 +2,69 @@ package ollama
|
||||
|
||||
import (
|
||||
"encoding/json"
|
||||
"one-api/dto"
|
||||
)
|
||||
|
||||
type OllamaRequest struct {
|
||||
Model string `json:"model,omitempty"`
|
||||
Messages []dto.Message `json:"messages,omitempty"`
|
||||
Stream bool `json:"stream,omitempty"`
|
||||
Temperature *float64 `json:"temperature,omitempty"`
|
||||
Seed float64 `json:"seed,omitempty"`
|
||||
Topp float64 `json:"top_p,omitempty"`
|
||||
TopK int `json:"top_k,omitempty"`
|
||||
Stop any `json:"stop,omitempty"`
|
||||
MaxTokens uint `json:"max_tokens,omitempty"`
|
||||
Tools []dto.ToolCallRequest `json:"tools,omitempty"`
|
||||
ResponseFormat any `json:"response_format,omitempty"`
|
||||
FrequencyPenalty float64 `json:"frequency_penalty,omitempty"`
|
||||
PresencePenalty float64 `json:"presence_penalty,omitempty"`
|
||||
Suffix any `json:"suffix,omitempty"`
|
||||
StreamOptions *dto.StreamOptions `json:"stream_options,omitempty"`
|
||||
Prompt any `json:"prompt,omitempty"`
|
||||
Think json.RawMessage `json:"think,omitempty"`
|
||||
type OllamaChatMessage struct {
|
||||
Role string `json:"role"`
|
||||
Content string `json:"content,omitempty"`
|
||||
Images []string `json:"images,omitempty"`
|
||||
ToolCalls []OllamaToolCall `json:"tool_calls,omitempty"`
|
||||
ToolName string `json:"tool_name,omitempty"`
|
||||
Thinking json.RawMessage `json:"thinking,omitempty"`
|
||||
}
|
||||
|
||||
type Options struct {
|
||||
Seed int `json:"seed,omitempty"`
|
||||
Temperature *float64 `json:"temperature,omitempty"`
|
||||
TopK int `json:"top_k,omitempty"`
|
||||
TopP float64 `json:"top_p,omitempty"`
|
||||
FrequencyPenalty float64 `json:"frequency_penalty,omitempty"`
|
||||
PresencePenalty float64 `json:"presence_penalty,omitempty"`
|
||||
NumPredict int `json:"num_predict,omitempty"`
|
||||
NumCtx int `json:"num_ctx,omitempty"`
|
||||
type OllamaToolFunction struct {
|
||||
Name string `json:"name"`
|
||||
Description string `json:"description,omitempty"`
|
||||
Parameters interface{} `json:"parameters,omitempty"`
|
||||
}
|
||||
|
||||
type OllamaTool struct {
|
||||
Type string `json:"type"`
|
||||
Function OllamaToolFunction `json:"function"`
|
||||
}
|
||||
|
||||
type OllamaToolCall struct {
|
||||
Function struct {
|
||||
Name string `json:"name"`
|
||||
Arguments interface{} `json:"arguments"`
|
||||
} `json:"function"`
|
||||
}
|
||||
|
||||
type OllamaChatRequest struct {
|
||||
Model string `json:"model"`
|
||||
Messages []OllamaChatMessage `json:"messages"`
|
||||
Tools interface{} `json:"tools,omitempty"`
|
||||
Format interface{} `json:"format,omitempty"`
|
||||
Stream bool `json:"stream,omitempty"`
|
||||
Options map[string]any `json:"options,omitempty"`
|
||||
KeepAlive interface{} `json:"keep_alive,omitempty"`
|
||||
Think json.RawMessage `json:"think,omitempty"`
|
||||
}
|
||||
|
||||
type OllamaGenerateRequest struct {
|
||||
Model string `json:"model"`
|
||||
Prompt string `json:"prompt,omitempty"`
|
||||
Suffix string `json:"suffix,omitempty"`
|
||||
Images []string `json:"images,omitempty"`
|
||||
Format interface{} `json:"format,omitempty"`
|
||||
Stream bool `json:"stream,omitempty"`
|
||||
Options map[string]any `json:"options,omitempty"`
|
||||
KeepAlive interface{} `json:"keep_alive,omitempty"`
|
||||
Think json.RawMessage `json:"think,omitempty"`
|
||||
}
|
||||
|
||||
type OllamaEmbeddingRequest struct {
|
||||
Model string `json:"model,omitempty"`
|
||||
Input []string `json:"input"`
|
||||
Options *Options `json:"options,omitempty"`
|
||||
Model string `json:"model"`
|
||||
Input interface{} `json:"input"`
|
||||
Options map[string]any `json:"options,omitempty"`
|
||||
Dimensions int `json:"dimensions,omitempty"`
|
||||
}
|
||||
|
||||
type OllamaEmbeddingResponse struct {
|
||||
Error string `json:"error,omitempty"`
|
||||
Model string `json:"model"`
|
||||
Embedding [][]float64 `json:"embeddings,omitempty"`
|
||||
Error string `json:"error,omitempty"`
|
||||
Model string `json:"model"`
|
||||
Embeddings [][]float64 `json:"embeddings"`
|
||||
PromptEvalCount int `json:"prompt_eval_count,omitempty"`
|
||||
}
|
||||
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
package ollama
|
||||
|
||||
import (
|
||||
"encoding/json"
|
||||
"fmt"
|
||||
"io"
|
||||
"net/http"
|
||||
@@ -14,121 +15,176 @@ import (
|
||||
"github.com/gin-gonic/gin"
|
||||
)
|
||||
|
||||
func requestOpenAI2Ollama(c *gin.Context, request *dto.GeneralOpenAIRequest) (*OllamaRequest, error) {
|
||||
messages := make([]dto.Message, 0, len(request.Messages))
|
||||
for _, message := range request.Messages {
|
||||
if !message.IsStringContent() {
|
||||
mediaMessages := message.ParseContent()
|
||||
for j, mediaMessage := range mediaMessages {
|
||||
if mediaMessage.Type == dto.ContentTypeImageURL {
|
||||
imageUrl := mediaMessage.GetImageMedia()
|
||||
// check if not base64
|
||||
if strings.HasPrefix(imageUrl.Url, "http") {
|
||||
fileData, err := service.GetFileBase64FromUrl(c, imageUrl.Url, "formatting image for Ollama")
|
||||
if err != nil {
|
||||
return nil, err
|
||||
func openAIChatToOllamaChat(c *gin.Context, r *dto.GeneralOpenAIRequest) (*OllamaChatRequest, error) {
|
||||
chatReq := &OllamaChatRequest{
|
||||
Model: r.Model,
|
||||
Stream: r.Stream,
|
||||
Options: map[string]any{},
|
||||
Think: r.Think,
|
||||
}
|
||||
if r.ResponseFormat != nil {
|
||||
if r.ResponseFormat.Type == "json" {
|
||||
chatReq.Format = "json"
|
||||
} else if r.ResponseFormat.Type == "json_schema" {
|
||||
if len(r.ResponseFormat.JsonSchema) > 0 {
|
||||
var schema any
|
||||
_ = json.Unmarshal(r.ResponseFormat.JsonSchema, &schema)
|
||||
chatReq.Format = schema
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// options mapping
|
||||
if r.Temperature != nil { chatReq.Options["temperature"] = r.Temperature }
|
||||
if r.TopP != 0 { chatReq.Options["top_p"] = r.TopP }
|
||||
if r.TopK != 0 { chatReq.Options["top_k"] = r.TopK }
|
||||
if r.FrequencyPenalty != 0 { chatReq.Options["frequency_penalty"] = r.FrequencyPenalty }
|
||||
if r.PresencePenalty != 0 { chatReq.Options["presence_penalty"] = r.PresencePenalty }
|
||||
if r.Seed != 0 { chatReq.Options["seed"] = int(r.Seed) }
|
||||
if mt := r.GetMaxTokens(); mt != 0 { chatReq.Options["num_predict"] = int(mt) }
|
||||
|
||||
if r.Stop != nil {
|
||||
switch v := r.Stop.(type) {
|
||||
case string:
|
||||
chatReq.Options["stop"] = []string{v}
|
||||
case []string:
|
||||
chatReq.Options["stop"] = v
|
||||
case []any:
|
||||
arr := make([]string,0,len(v))
|
||||
for _, i := range v { if s,ok:=i.(string); ok { arr = append(arr,s) } }
|
||||
if len(arr)>0 { chatReq.Options["stop"] = arr }
|
||||
}
|
||||
}
|
||||
|
||||
if len(r.Tools) > 0 {
|
||||
tools := make([]OllamaTool,0,len(r.Tools))
|
||||
for _, t := range r.Tools {
|
||||
tools = append(tools, OllamaTool{Type: "function", Function: OllamaToolFunction{Name: t.Function.Name, Description: t.Function.Description, Parameters: t.Function.Parameters}})
|
||||
}
|
||||
chatReq.Tools = tools
|
||||
}
|
||||
|
||||
chatReq.Messages = make([]OllamaChatMessage,0,len(r.Messages))
|
||||
for _, m := range r.Messages {
|
||||
var textBuilder strings.Builder
|
||||
var images []string
|
||||
if m.IsStringContent() {
|
||||
textBuilder.WriteString(m.StringContent())
|
||||
} else {
|
||||
parts := m.ParseContent()
|
||||
for _, part := range parts {
|
||||
if part.Type == dto.ContentTypeImageURL {
|
||||
img := part.GetImageMedia()
|
||||
if img != nil && img.Url != "" {
|
||||
var base64Data string
|
||||
if strings.HasPrefix(img.Url, "http") {
|
||||
fileData, err := service.GetFileBase64FromUrl(c, img.Url, "fetch image for ollama chat")
|
||||
if err != nil { return nil, err }
|
||||
base64Data = fileData.Base64Data
|
||||
} else if strings.HasPrefix(img.Url, "data:") {
|
||||
if idx := strings.Index(img.Url, ","); idx != -1 && idx+1 < len(img.Url) { base64Data = img.Url[idx+1:] }
|
||||
} else {
|
||||
base64Data = img.Url
|
||||
}
|
||||
imageUrl.Url = fmt.Sprintf("data:%s;base64,%s", fileData.MimeType, fileData.Base64Data)
|
||||
if base64Data != "" { images = append(images, base64Data) }
|
||||
}
|
||||
mediaMessage.ImageUrl = imageUrl
|
||||
mediaMessages[j] = mediaMessage
|
||||
} else if part.Type == dto.ContentTypeText {
|
||||
textBuilder.WriteString(part.Text)
|
||||
}
|
||||
}
|
||||
message.SetMediaContent(mediaMessages)
|
||||
}
|
||||
messages = append(messages, dto.Message{
|
||||
Role: message.Role,
|
||||
Content: message.Content,
|
||||
ToolCalls: message.ToolCalls,
|
||||
ToolCallId: message.ToolCallId,
|
||||
})
|
||||
cm := OllamaChatMessage{Role: m.Role, Content: textBuilder.String()}
|
||||
if len(images)>0 { cm.Images = images }
|
||||
if m.Role == "tool" && m.Name != nil { cm.ToolName = *m.Name }
|
||||
if m.ToolCalls != nil && len(m.ToolCalls) > 0 {
|
||||
parsed := m.ParseToolCalls()
|
||||
if len(parsed) > 0 {
|
||||
calls := make([]OllamaToolCall,0,len(parsed))
|
||||
for _, tc := range parsed {
|
||||
var args interface{}
|
||||
if tc.Function.Arguments != "" { _ = json.Unmarshal([]byte(tc.Function.Arguments), &args) }
|
||||
if args==nil { args = map[string]any{} }
|
||||
oc := OllamaToolCall{}
|
||||
oc.Function.Name = tc.Function.Name
|
||||
oc.Function.Arguments = args
|
||||
calls = append(calls, oc)
|
||||
}
|
||||
cm.ToolCalls = calls
|
||||
}
|
||||
}
|
||||
chatReq.Messages = append(chatReq.Messages, cm)
|
||||
}
|
||||
str, ok := request.Stop.(string)
|
||||
var Stop []string
|
||||
if ok {
|
||||
Stop = []string{str}
|
||||
} else {
|
||||
Stop, _ = request.Stop.([]string)
|
||||
}
|
||||
ollamaRequest := &OllamaRequest{
|
||||
Model: request.Model,
|
||||
Messages: messages,
|
||||
Stream: request.Stream,
|
||||
Temperature: request.Temperature,
|
||||
Seed: request.Seed,
|
||||
Topp: request.TopP,
|
||||
TopK: request.TopK,
|
||||
Stop: Stop,
|
||||
Tools: request.Tools,
|
||||
MaxTokens: request.GetMaxTokens(),
|
||||
ResponseFormat: request.ResponseFormat,
|
||||
FrequencyPenalty: request.FrequencyPenalty,
|
||||
PresencePenalty: request.PresencePenalty,
|
||||
Prompt: request.Prompt,
|
||||
StreamOptions: request.StreamOptions,
|
||||
Suffix: request.Suffix,
|
||||
}
|
||||
ollamaRequest.Think = request.Think
|
||||
return ollamaRequest, nil
|
||||
return chatReq, nil
|
||||
}
|
||||
|
||||
func requestOpenAI2Embeddings(request dto.EmbeddingRequest) *OllamaEmbeddingRequest {
|
||||
return &OllamaEmbeddingRequest{
|
||||
Model: request.Model,
|
||||
Input: request.ParseInput(),
|
||||
Options: &Options{
|
||||
Seed: int(request.Seed),
|
||||
Temperature: request.Temperature,
|
||||
TopP: request.TopP,
|
||||
FrequencyPenalty: request.FrequencyPenalty,
|
||||
PresencePenalty: request.PresencePenalty,
|
||||
},
|
||||
// openAIToGenerate converts OpenAI completions request to Ollama generate
|
||||
func openAIToGenerate(c *gin.Context, r *dto.GeneralOpenAIRequest) (*OllamaGenerateRequest, error) {
|
||||
gen := &OllamaGenerateRequest{
|
||||
Model: r.Model,
|
||||
Stream: r.Stream,
|
||||
Options: map[string]any{},
|
||||
Think: r.Think,
|
||||
}
|
||||
// Prompt may be in r.Prompt (string or []any)
|
||||
if r.Prompt != nil {
|
||||
switch v := r.Prompt.(type) {
|
||||
case string:
|
||||
gen.Prompt = v
|
||||
case []any:
|
||||
var sb strings.Builder
|
||||
for _, it := range v { if s,ok:=it.(string); ok { sb.WriteString(s) } }
|
||||
gen.Prompt = sb.String()
|
||||
default:
|
||||
gen.Prompt = fmt.Sprintf("%v", r.Prompt)
|
||||
}
|
||||
}
|
||||
if r.Suffix != nil { if s,ok:=r.Suffix.(string); ok { gen.Suffix = s } }
|
||||
if r.ResponseFormat != nil {
|
||||
if r.ResponseFormat.Type == "json" { gen.Format = "json" } else if r.ResponseFormat.Type == "json_schema" { var schema any; _ = json.Unmarshal(r.ResponseFormat.JsonSchema,&schema); gen.Format=schema }
|
||||
}
|
||||
if r.Temperature != nil { gen.Options["temperature"] = r.Temperature }
|
||||
if r.TopP != 0 { gen.Options["top_p"] = r.TopP }
|
||||
if r.TopK != 0 { gen.Options["top_k"] = r.TopK }
|
||||
if r.FrequencyPenalty != 0 { gen.Options["frequency_penalty"] = r.FrequencyPenalty }
|
||||
if r.PresencePenalty != 0 { gen.Options["presence_penalty"] = r.PresencePenalty }
|
||||
if r.Seed != 0 { gen.Options["seed"] = int(r.Seed) }
|
||||
if mt := r.GetMaxTokens(); mt != 0 { gen.Options["num_predict"] = int(mt) }
|
||||
if r.Stop != nil {
|
||||
switch v := r.Stop.(type) {
|
||||
case string: gen.Options["stop"] = []string{v}
|
||||
case []string: gen.Options["stop"] = v
|
||||
case []any: arr:=make([]string,0,len(v)); for _,i:= range v { if s,ok:=i.(string); ok { arr=append(arr,s) } }; if len(arr)>0 { gen.Options["stop"]=arr }
|
||||
}
|
||||
}
|
||||
return gen, nil
|
||||
}
|
||||
|
||||
func requestOpenAI2Embeddings(r dto.EmbeddingRequest) *OllamaEmbeddingRequest {
|
||||
opts := map[string]any{}
|
||||
if r.Temperature != nil { opts["temperature"] = r.Temperature }
|
||||
if r.TopP != 0 { opts["top_p"] = r.TopP }
|
||||
if r.FrequencyPenalty != 0 { opts["frequency_penalty"] = r.FrequencyPenalty }
|
||||
if r.PresencePenalty != 0 { opts["presence_penalty"] = r.PresencePenalty }
|
||||
if r.Seed != 0 { opts["seed"] = int(r.Seed) }
|
||||
if r.Dimensions != 0 { opts["dimensions"] = r.Dimensions }
|
||||
input := r.ParseInput()
|
||||
if len(input)==1 { return &OllamaEmbeddingRequest{Model:r.Model, Input: input[0], Options: opts, Dimensions:r.Dimensions} }
|
||||
return &OllamaEmbeddingRequest{Model:r.Model, Input: input, Options: opts, Dimensions:r.Dimensions}
|
||||
}
|
||||
|
||||
func ollamaEmbeddingHandler(c *gin.Context, info *relaycommon.RelayInfo, resp *http.Response) (*dto.Usage, *types.NewAPIError) {
|
||||
var ollamaEmbeddingResponse OllamaEmbeddingResponse
|
||||
responseBody, err := io.ReadAll(resp.Body)
|
||||
if err != nil {
|
||||
return nil, types.NewOpenAIError(err, types.ErrorCodeBadResponseBody, http.StatusInternalServerError)
|
||||
}
|
||||
var oResp OllamaEmbeddingResponse
|
||||
body, err := io.ReadAll(resp.Body)
|
||||
if err != nil { return nil, types.NewOpenAIError(err, types.ErrorCodeBadResponseBody, http.StatusInternalServerError) }
|
||||
service.CloseResponseBodyGracefully(resp)
|
||||
err = common.Unmarshal(responseBody, &ollamaEmbeddingResponse)
|
||||
if err != nil {
|
||||
return nil, types.NewOpenAIError(err, types.ErrorCodeBadResponseBody, http.StatusInternalServerError)
|
||||
}
|
||||
if ollamaEmbeddingResponse.Error != "" {
|
||||
return nil, types.NewOpenAIError(fmt.Errorf("ollama error: %s", ollamaEmbeddingResponse.Error), types.ErrorCodeBadResponseBody, http.StatusInternalServerError)
|
||||
}
|
||||
flattenedEmbeddings := flattenEmbeddings(ollamaEmbeddingResponse.Embedding)
|
||||
data := make([]dto.OpenAIEmbeddingResponseItem, 0, 1)
|
||||
data = append(data, dto.OpenAIEmbeddingResponseItem{
|
||||
Embedding: flattenedEmbeddings,
|
||||
Object: "embedding",
|
||||
})
|
||||
usage := &dto.Usage{
|
||||
TotalTokens: info.PromptTokens,
|
||||
CompletionTokens: 0,
|
||||
PromptTokens: info.PromptTokens,
|
||||
}
|
||||
embeddingResponse := &dto.OpenAIEmbeddingResponse{
|
||||
Object: "list",
|
||||
Data: data,
|
||||
Model: info.UpstreamModelName,
|
||||
Usage: *usage,
|
||||
}
|
||||
doResponseBody, err := common.Marshal(embeddingResponse)
|
||||
if err != nil {
|
||||
return nil, types.NewOpenAIError(err, types.ErrorCodeBadResponseBody, http.StatusInternalServerError)
|
||||
}
|
||||
service.IOCopyBytesGracefully(c, resp, doResponseBody)
|
||||
if err = common.Unmarshal(body, &oResp); err != nil { return nil, types.NewOpenAIError(err, types.ErrorCodeBadResponseBody, http.StatusInternalServerError) }
|
||||
if oResp.Error != "" { return nil, types.NewOpenAIError(fmt.Errorf("ollama error: %s", oResp.Error), types.ErrorCodeBadResponseBody, http.StatusInternalServerError) }
|
||||
data := make([]dto.OpenAIEmbeddingResponseItem,0,len(oResp.Embeddings))
|
||||
for i, emb := range oResp.Embeddings { data = append(data, dto.OpenAIEmbeddingResponseItem{Index:i,Object:"embedding",Embedding:emb}) }
|
||||
usage := &dto.Usage{PromptTokens: oResp.PromptEvalCount, CompletionTokens:0, TotalTokens: oResp.PromptEvalCount}
|
||||
embResp := &dto.OpenAIEmbeddingResponse{Object:"list", Data:data, Model: info.UpstreamModelName, Usage:*usage}
|
||||
out, _ := common.Marshal(embResp)
|
||||
service.IOCopyBytesGracefully(c, resp, out)
|
||||
return usage, nil
|
||||
}
|
||||
|
||||
func flattenEmbeddings(embeddings [][]float64) []float64 {
|
||||
flattened := []float64{}
|
||||
for _, row := range embeddings {
|
||||
flattened = append(flattened, row...)
|
||||
}
|
||||
return flattened
|
||||
}
|
||||
|
||||
210
relay/channel/ollama/stream.go
Normal file
210
relay/channel/ollama/stream.go
Normal file
@@ -0,0 +1,210 @@
|
||||
package ollama
|
||||
|
||||
import (
|
||||
"bufio"
|
||||
"encoding/json"
|
||||
"fmt"
|
||||
"io"
|
||||
"net/http"
|
||||
"one-api/common"
|
||||
"one-api/dto"
|
||||
"one-api/logger"
|
||||
relaycommon "one-api/relay/common"
|
||||
"one-api/relay/helper"
|
||||
"one-api/service"
|
||||
"one-api/types"
|
||||
"strings"
|
||||
"time"
|
||||
|
||||
"github.com/gin-gonic/gin"
|
||||
)
|
||||
|
||||
type ollamaChatStreamChunk struct {
|
||||
Model string `json:"model"`
|
||||
CreatedAt string `json:"created_at"`
|
||||
// chat
|
||||
Message *struct {
|
||||
Role string `json:"role"`
|
||||
Content string `json:"content"`
|
||||
Thinking json.RawMessage `json:"thinking"`
|
||||
ToolCalls []struct {
|
||||
Function struct {
|
||||
Name string `json:"name"`
|
||||
Arguments interface{} `json:"arguments"`
|
||||
} `json:"function"`
|
||||
} `json:"tool_calls"`
|
||||
} `json:"message"`
|
||||
// generate
|
||||
Response string `json:"response"`
|
||||
Done bool `json:"done"`
|
||||
DoneReason string `json:"done_reason"`
|
||||
TotalDuration int64 `json:"total_duration"`
|
||||
LoadDuration int64 `json:"load_duration"`
|
||||
PromptEvalCount int `json:"prompt_eval_count"`
|
||||
EvalCount int `json:"eval_count"`
|
||||
PromptEvalDuration int64 `json:"prompt_eval_duration"`
|
||||
EvalDuration int64 `json:"eval_duration"`
|
||||
}
|
||||
|
||||
func toUnix(ts string) int64 {
|
||||
if ts == "" { return time.Now().Unix() }
|
||||
// try time.RFC3339 or with nanoseconds
|
||||
t, err := time.Parse(time.RFC3339Nano, ts)
|
||||
if err != nil { t2, err2 := time.Parse(time.RFC3339, ts); if err2==nil { return t2.Unix() }; return time.Now().Unix() }
|
||||
return t.Unix()
|
||||
}
|
||||
|
||||
func ollamaStreamHandler(c *gin.Context, info *relaycommon.RelayInfo, resp *http.Response) (*dto.Usage, *types.NewAPIError) {
|
||||
if resp == nil || resp.Body == nil { return nil, types.NewOpenAIError(fmt.Errorf("empty response"), types.ErrorCodeBadResponse, http.StatusBadRequest) }
|
||||
defer service.CloseResponseBodyGracefully(resp)
|
||||
|
||||
helper.SetEventStreamHeaders(c)
|
||||
scanner := bufio.NewScanner(resp.Body)
|
||||
usage := &dto.Usage{}
|
||||
var model = info.UpstreamModelName
|
||||
var responseId = common.GetUUID()
|
||||
var created = time.Now().Unix()
|
||||
var toolCallIndex int
|
||||
start := helper.GenerateStartEmptyResponse(responseId, created, model, nil)
|
||||
if data, err := common.Marshal(start); err == nil { _ = helper.StringData(c, string(data)) }
|
||||
|
||||
for scanner.Scan() {
|
||||
line := scanner.Text()
|
||||
line = strings.TrimSpace(line)
|
||||
if line == "" { continue }
|
||||
var chunk ollamaChatStreamChunk
|
||||
if err := json.Unmarshal([]byte(line), &chunk); err != nil {
|
||||
logger.LogError(c, "ollama stream json decode error: "+err.Error()+" line="+line)
|
||||
return usage, types.NewOpenAIError(err, types.ErrorCodeBadResponseBody, http.StatusInternalServerError)
|
||||
}
|
||||
if chunk.Model != "" { model = chunk.Model }
|
||||
created = toUnix(chunk.CreatedAt)
|
||||
|
||||
if !chunk.Done {
|
||||
// delta content
|
||||
var content string
|
||||
if chunk.Message != nil { content = chunk.Message.Content } else { content = chunk.Response }
|
||||
delta := dto.ChatCompletionsStreamResponse{
|
||||
Id: responseId,
|
||||
Object: "chat.completion.chunk",
|
||||
Created: created,
|
||||
Model: model,
|
||||
Choices: []dto.ChatCompletionsStreamResponseChoice{ {
|
||||
Index: 0,
|
||||
Delta: dto.ChatCompletionsStreamResponseChoiceDelta{ Role: "assistant" },
|
||||
} },
|
||||
}
|
||||
if content != "" { delta.Choices[0].Delta.SetContentString(content) }
|
||||
if chunk.Message != nil && len(chunk.Message.Thinking) > 0 {
|
||||
raw := strings.TrimSpace(string(chunk.Message.Thinking))
|
||||
if raw != "" && raw != "null" { delta.Choices[0].Delta.SetReasoningContent(raw) }
|
||||
}
|
||||
// tool calls
|
||||
if chunk.Message != nil && len(chunk.Message.ToolCalls) > 0 {
|
||||
delta.Choices[0].Delta.ToolCalls = make([]dto.ToolCallResponse,0,len(chunk.Message.ToolCalls))
|
||||
for _, tc := range chunk.Message.ToolCalls {
|
||||
// arguments -> string
|
||||
argBytes, _ := json.Marshal(tc.Function.Arguments)
|
||||
toolId := fmt.Sprintf("call_%d", toolCallIndex)
|
||||
tr := dto.ToolCallResponse{ID:toolId, Type:"function", Function: dto.FunctionResponse{Name: tc.Function.Name, Arguments: string(argBytes)}}
|
||||
tr.SetIndex(toolCallIndex)
|
||||
toolCallIndex++
|
||||
delta.Choices[0].Delta.ToolCalls = append(delta.Choices[0].Delta.ToolCalls, tr)
|
||||
}
|
||||
}
|
||||
if data, err := common.Marshal(delta); err == nil { _ = helper.StringData(c, string(data)) }
|
||||
continue
|
||||
}
|
||||
// done frame
|
||||
// finalize once and break loop
|
||||
usage.PromptTokens = chunk.PromptEvalCount
|
||||
usage.CompletionTokens = chunk.EvalCount
|
||||
usage.TotalTokens = usage.PromptTokens + usage.CompletionTokens
|
||||
finishReason := chunk.DoneReason
|
||||
if finishReason == "" { finishReason = "stop" }
|
||||
// emit stop delta
|
||||
if stop := helper.GenerateStopResponse(responseId, created, model, finishReason); stop != nil {
|
||||
if data, err := common.Marshal(stop); err == nil { _ = helper.StringData(c, string(data)) }
|
||||
}
|
||||
// emit usage frame
|
||||
if final := helper.GenerateFinalUsageResponse(responseId, created, model, *usage); final != nil {
|
||||
if data, err := common.Marshal(final); err == nil { _ = helper.StringData(c, string(data)) }
|
||||
}
|
||||
// send [DONE]
|
||||
helper.Done(c)
|
||||
break
|
||||
}
|
||||
if err := scanner.Err(); err != nil && err != io.EOF { logger.LogError(c, "ollama stream scan error: "+err.Error()) }
|
||||
return usage, nil
|
||||
}
|
||||
|
||||
// non-stream handler for chat/generate
|
||||
func ollamaChatHandler(c *gin.Context, info *relaycommon.RelayInfo, resp *http.Response) (*dto.Usage, *types.NewAPIError) {
|
||||
body, err := io.ReadAll(resp.Body)
|
||||
if err != nil { return nil, types.NewOpenAIError(err, types.ErrorCodeReadResponseBodyFailed, http.StatusInternalServerError) }
|
||||
service.CloseResponseBodyGracefully(resp)
|
||||
raw := string(body)
|
||||
if common.DebugEnabled { println("ollama non-stream raw resp:", raw) }
|
||||
|
||||
lines := strings.Split(raw, "\n")
|
||||
var (
|
||||
aggContent strings.Builder
|
||||
reasoningBuilder strings.Builder
|
||||
lastChunk ollamaChatStreamChunk
|
||||
parsedAny bool
|
||||
)
|
||||
for _, ln := range lines {
|
||||
ln = strings.TrimSpace(ln)
|
||||
if ln == "" { continue }
|
||||
var ck ollamaChatStreamChunk
|
||||
if err := json.Unmarshal([]byte(ln), &ck); err != nil {
|
||||
if len(lines) == 1 { return nil, types.NewOpenAIError(err, types.ErrorCodeBadResponseBody, http.StatusInternalServerError) }
|
||||
continue
|
||||
}
|
||||
parsedAny = true
|
||||
lastChunk = ck
|
||||
if ck.Message != nil && len(ck.Message.Thinking) > 0 {
|
||||
raw := strings.TrimSpace(string(ck.Message.Thinking))
|
||||
if raw != "" && raw != "null" { reasoningBuilder.WriteString(raw) }
|
||||
}
|
||||
if ck.Message != nil && ck.Message.Content != "" { aggContent.WriteString(ck.Message.Content) } else if ck.Response != "" { aggContent.WriteString(ck.Response) }
|
||||
}
|
||||
|
||||
if !parsedAny {
|
||||
var single ollamaChatStreamChunk
|
||||
if err := json.Unmarshal(body, &single); err != nil { return nil, types.NewOpenAIError(err, types.ErrorCodeBadResponseBody, http.StatusInternalServerError) }
|
||||
lastChunk = single
|
||||
if single.Message != nil {
|
||||
if len(single.Message.Thinking) > 0 { raw := strings.TrimSpace(string(single.Message.Thinking)); if raw != "" && raw != "null" { reasoningBuilder.WriteString(raw) } }
|
||||
aggContent.WriteString(single.Message.Content)
|
||||
} else { aggContent.WriteString(single.Response) }
|
||||
}
|
||||
|
||||
model := lastChunk.Model
|
||||
if model == "" { model = info.UpstreamModelName }
|
||||
created := toUnix(lastChunk.CreatedAt)
|
||||
usage := &dto.Usage{PromptTokens: lastChunk.PromptEvalCount, CompletionTokens: lastChunk.EvalCount, TotalTokens: lastChunk.PromptEvalCount + lastChunk.EvalCount}
|
||||
content := aggContent.String()
|
||||
finishReason := lastChunk.DoneReason
|
||||
if finishReason == "" { finishReason = "stop" }
|
||||
|
||||
msg := dto.Message{Role: "assistant", Content: contentPtr(content)}
|
||||
if rc := reasoningBuilder.String(); rc != "" { msg.ReasoningContent = rc }
|
||||
full := dto.OpenAITextResponse{
|
||||
Id: common.GetUUID(),
|
||||
Model: model,
|
||||
Object: "chat.completion",
|
||||
Created: created,
|
||||
Choices: []dto.OpenAITextResponseChoice{ {
|
||||
Index: 0,
|
||||
Message: msg,
|
||||
FinishReason: finishReason,
|
||||
} },
|
||||
Usage: *usage,
|
||||
}
|
||||
out, _ := common.Marshal(full)
|
||||
service.IOCopyBytesGracefully(c, resp, out)
|
||||
return usage, nil
|
||||
}
|
||||
|
||||
func contentPtr(s string) *string { if s=="" { return nil }; return &s }
|
||||
@@ -12,6 +12,7 @@ import (
|
||||
"one-api/constant"
|
||||
"one-api/dto"
|
||||
"one-api/logger"
|
||||
"one-api/relay/channel/openrouter"
|
||||
relaycommon "one-api/relay/common"
|
||||
"one-api/relay/helper"
|
||||
"one-api/service"
|
||||
@@ -185,10 +186,27 @@ func OpenaiHandler(c *gin.Context, info *relaycommon.RelayInfo, resp *http.Respo
|
||||
if common.DebugEnabled {
|
||||
println("upstream response body:", string(responseBody))
|
||||
}
|
||||
// Unmarshal to simpleResponse
|
||||
if info.ChannelType == constant.ChannelTypeOpenRouter && info.ChannelOtherSettings.IsOpenRouterEnterprise() {
|
||||
// 尝试解析为 openrouter enterprise
|
||||
var enterpriseResponse openrouter.OpenRouterEnterpriseResponse
|
||||
err = common.Unmarshal(responseBody, &enterpriseResponse)
|
||||
if err != nil {
|
||||
return nil, types.NewOpenAIError(err, types.ErrorCodeBadResponseBody, http.StatusInternalServerError)
|
||||
}
|
||||
if enterpriseResponse.Success {
|
||||
responseBody = enterpriseResponse.Data
|
||||
} else {
|
||||
logger.LogError(c, fmt.Sprintf("openrouter enterprise response success=false, data: %s", enterpriseResponse.Data))
|
||||
return nil, types.NewOpenAIError(fmt.Errorf("openrouter response success=false"), types.ErrorCodeBadResponseBody, http.StatusInternalServerError)
|
||||
}
|
||||
}
|
||||
|
||||
err = common.Unmarshal(responseBody, &simpleResponse)
|
||||
if err != nil {
|
||||
return nil, types.NewOpenAIError(err, types.ErrorCodeBadResponseBody, http.StatusInternalServerError)
|
||||
}
|
||||
|
||||
if oaiError := simpleResponse.GetOpenAIError(); oaiError != nil && oaiError.Type != "" {
|
||||
return nil, types.WithOpenAIError(*oaiError, resp.StatusCode)
|
||||
}
|
||||
|
||||
@@ -1,5 +1,7 @@
|
||||
package openrouter
|
||||
|
||||
import "encoding/json"
|
||||
|
||||
type RequestReasoning struct {
|
||||
// One of the following (not both):
|
||||
Effort string `json:"effort,omitempty"` // Can be "high", "medium", or "low" (OpenAI-style)
|
||||
@@ -7,3 +9,8 @@ type RequestReasoning struct {
|
||||
// Optional: Default is false. All models support this.
|
||||
Exclude bool `json:"exclude,omitempty"` // Set to true to exclude reasoning tokens from response
|
||||
}
|
||||
|
||||
type OpenRouterEnterpriseResponse struct {
|
||||
Data json.RawMessage `json:"data"`
|
||||
Success bool `json:"success"`
|
||||
}
|
||||
|
||||
@@ -9,6 +9,7 @@ import (
|
||||
"mime/multipart"
|
||||
"net/http"
|
||||
"net/textproto"
|
||||
channelconstant "one-api/constant"
|
||||
"one-api/dto"
|
||||
"one-api/relay/channel"
|
||||
"one-api/relay/channel/openai"
|
||||
@@ -188,20 +189,26 @@ func (a *Adaptor) Init(info *relaycommon.RelayInfo) {
|
||||
}
|
||||
|
||||
func (a *Adaptor) GetRequestURL(info *relaycommon.RelayInfo) (string, error) {
|
||||
// 支持自定义域名,如果未设置则使用默认域名
|
||||
baseUrl := info.ChannelBaseUrl
|
||||
if baseUrl == "" {
|
||||
baseUrl = channelconstant.ChannelBaseURLs[channelconstant.ChannelTypeVolcEngine]
|
||||
}
|
||||
|
||||
switch info.RelayMode {
|
||||
case constant.RelayModeChatCompletions:
|
||||
if strings.HasPrefix(info.UpstreamModelName, "bot") {
|
||||
return fmt.Sprintf("%s/api/v3/bots/chat/completions", info.ChannelBaseUrl), nil
|
||||
return fmt.Sprintf("%s/api/v3/bots/chat/completions", baseUrl), nil
|
||||
}
|
||||
return fmt.Sprintf("%s/api/v3/chat/completions", info.ChannelBaseUrl), nil
|
||||
return fmt.Sprintf("%s/api/v3/chat/completions", baseUrl), nil
|
||||
case constant.RelayModeEmbeddings:
|
||||
return fmt.Sprintf("%s/api/v3/embeddings", info.ChannelBaseUrl), nil
|
||||
return fmt.Sprintf("%s/api/v3/embeddings", baseUrl), nil
|
||||
case constant.RelayModeImagesGenerations:
|
||||
return fmt.Sprintf("%s/api/v3/images/generations", info.ChannelBaseUrl), nil
|
||||
return fmt.Sprintf("%s/api/v3/images/generations", baseUrl), nil
|
||||
case constant.RelayModeImagesEdits:
|
||||
return fmt.Sprintf("%s/api/v3/images/edits", info.ChannelBaseUrl), nil
|
||||
return fmt.Sprintf("%s/api/v3/images/edits", baseUrl), nil
|
||||
case constant.RelayModeRerank:
|
||||
return fmt.Sprintf("%s/api/v3/rerank", info.ChannelBaseUrl), nil
|
||||
return fmt.Sprintf("%s/api/v3/rerank", baseUrl), nil
|
||||
default:
|
||||
}
|
||||
return "", fmt.Errorf("unsupported relay mode: %d", info.RelayMode)
|
||||
|
||||
@@ -9,6 +9,11 @@ var ModelList = []string{
|
||||
"Doubao-lite-4k",
|
||||
"Doubao-embedding",
|
||||
"doubao-seedream-4-0-250828",
|
||||
"seedream-4-0-250828",
|
||||
"doubao-seedance-1-0-pro-250528",
|
||||
"seedance-1-0-pro-250528",
|
||||
"doubao-seed-1-6-thinking-250715",
|
||||
"seed-1-6-thinking-250715",
|
||||
}
|
||||
|
||||
var ChannelName = "volcengine"
|
||||
|
||||
@@ -207,10 +207,6 @@ func xunfeiMakeRequest(textRequest dto.GeneralOpenAIRequest, domain, authUrl, ap
|
||||
return nil, nil, err
|
||||
}
|
||||
|
||||
defer func() {
|
||||
conn.Close()
|
||||
}()
|
||||
|
||||
data := requestOpenAI2Xunfei(textRequest, appId, domain)
|
||||
err = conn.WriteJSON(data)
|
||||
if err != nil {
|
||||
@@ -220,6 +216,9 @@ func xunfeiMakeRequest(textRequest dto.GeneralOpenAIRequest, domain, authUrl, ap
|
||||
dataChan := make(chan XunfeiChatResponse)
|
||||
stopChan := make(chan bool)
|
||||
go func() {
|
||||
defer func() {
|
||||
conn.Close()
|
||||
}()
|
||||
for {
|
||||
_, msg, err := conn.ReadMessage()
|
||||
if err != nil {
|
||||
|
||||
@@ -164,6 +164,8 @@ const EditChannelModal = (props) => {
|
||||
settings: '',
|
||||
// 仅 Vertex: 密钥格式(存入 settings.vertex_key_type)
|
||||
vertex_key_type: 'json',
|
||||
// 企业账户设置
|
||||
is_enterprise_account: false,
|
||||
};
|
||||
const [batch, setBatch] = useState(false);
|
||||
const [multiToSingle, setMultiToSingle] = useState(false);
|
||||
@@ -189,6 +191,7 @@ const EditChannelModal = (props) => {
|
||||
const [channelSearchValue, setChannelSearchValue] = useState('');
|
||||
const [useManualInput, setUseManualInput] = useState(false); // 是否使用手动输入模式
|
||||
const [keyMode, setKeyMode] = useState('append'); // 密钥模式:replace(覆盖)或 append(追加)
|
||||
const [isEnterpriseAccount, setIsEnterpriseAccount] = useState(false); // 是否为企业账户
|
||||
|
||||
// 2FA验证查看密钥相关状态
|
||||
const [twoFAState, setTwoFAState] = useState({
|
||||
@@ -235,7 +238,7 @@ const EditChannelModal = (props) => {
|
||||
pass_through_body_enabled: false,
|
||||
system_prompt: '',
|
||||
});
|
||||
const showApiConfigCard = inputs.type !== 45; // 控制是否显示 API 配置卡片(仅当渠道类型不是 豆包 时显示)
|
||||
const showApiConfigCard = true; // 控制是否显示 API 配置卡片
|
||||
const getInitValues = () => ({ ...originInputs });
|
||||
|
||||
// 处理渠道额外设置的更新
|
||||
@@ -342,6 +345,10 @@ const EditChannelModal = (props) => {
|
||||
case 36:
|
||||
localModels = ['suno_music', 'suno_lyrics'];
|
||||
break;
|
||||
case 45:
|
||||
localModels = getChannelModels(value);
|
||||
setInputs((prevInputs) => ({ ...prevInputs, base_url: 'https://ark.cn-beijing.volces.com' }));
|
||||
break;
|
||||
default:
|
||||
localModels = getChannelModels(value);
|
||||
break;
|
||||
@@ -433,15 +440,19 @@ const EditChannelModal = (props) => {
|
||||
parsedSettings.azure_responses_version || '';
|
||||
// 读取 Vertex 密钥格式
|
||||
data.vertex_key_type = parsedSettings.vertex_key_type || 'json';
|
||||
// 读取企业账户设置
|
||||
data.is_enterprise_account = parsedSettings.openrouter_enterprise === true;
|
||||
} catch (error) {
|
||||
console.error('解析其他设置失败:', error);
|
||||
data.azure_responses_version = '';
|
||||
data.region = '';
|
||||
data.vertex_key_type = 'json';
|
||||
data.is_enterprise_account = false;
|
||||
}
|
||||
} else {
|
||||
// 兼容历史数据:老渠道没有 settings 时,默认按 json 展示
|
||||
data.vertex_key_type = 'json';
|
||||
data.is_enterprise_account = false;
|
||||
}
|
||||
|
||||
setInputs(data);
|
||||
@@ -453,6 +464,8 @@ const EditChannelModal = (props) => {
|
||||
} else {
|
||||
setAutoBan(true);
|
||||
}
|
||||
// 同步企业账户状态
|
||||
setIsEnterpriseAccount(data.is_enterprise_account || false);
|
||||
setBasicModels(getChannelModels(data.type));
|
||||
// 同步更新channelSettings状态显示
|
||||
setChannelSettings({
|
||||
@@ -712,6 +725,8 @@ const EditChannelModal = (props) => {
|
||||
});
|
||||
// 重置密钥模式状态
|
||||
setKeyMode('append');
|
||||
// 重置企业账户状态
|
||||
setIsEnterpriseAccount(false);
|
||||
// 清空表单中的key_mode字段
|
||||
if (formApiRef.current) {
|
||||
formApiRef.current.setValue('key_mode', undefined);
|
||||
@@ -844,6 +859,10 @@ const EditChannelModal = (props) => {
|
||||
showInfo(t('请至少选择一个模型!'));
|
||||
return;
|
||||
}
|
||||
if (localInputs.type === 45 && (!localInputs.base_url || localInputs.base_url.trim() === '')) {
|
||||
showInfo(t('请输入API地址!'));
|
||||
return;
|
||||
}
|
||||
if (
|
||||
localInputs.model_mapping &&
|
||||
localInputs.model_mapping !== '' &&
|
||||
@@ -873,6 +892,21 @@ const EditChannelModal = (props) => {
|
||||
};
|
||||
localInputs.setting = JSON.stringify(channelExtraSettings);
|
||||
|
||||
// 处理type === 20的企业账户设置
|
||||
if (localInputs.type === 20) {
|
||||
let settings = {};
|
||||
if (localInputs.settings) {
|
||||
try {
|
||||
settings = JSON.parse(localInputs.settings);
|
||||
} catch (error) {
|
||||
console.error('解析settings失败:', error);
|
||||
}
|
||||
}
|
||||
// 设置企业账户标识,无论是true还是false都要传到后端
|
||||
settings.openrouter_enterprise = localInputs.is_enterprise_account === true;
|
||||
localInputs.settings = JSON.stringify(settings);
|
||||
}
|
||||
|
||||
// 清理不需要发送到后端的字段
|
||||
delete localInputs.force_format;
|
||||
delete localInputs.thinking_to_content;
|
||||
@@ -880,6 +914,7 @@ const EditChannelModal = (props) => {
|
||||
delete localInputs.pass_through_body_enabled;
|
||||
delete localInputs.system_prompt;
|
||||
delete localInputs.system_prompt_override;
|
||||
delete localInputs.is_enterprise_account;
|
||||
// 顶层的 vertex_key_type 不应发送给后端
|
||||
delete localInputs.vertex_key_type;
|
||||
|
||||
@@ -1264,6 +1299,21 @@ const EditChannelModal = (props) => {
|
||||
onChange={(value) => handleInputChange('type', value)}
|
||||
/>
|
||||
|
||||
{inputs.type === 20 && (
|
||||
<Form.Switch
|
||||
field='is_enterprise_account'
|
||||
label={t('是否为企业账户')}
|
||||
checkedText={t('是')}
|
||||
uncheckedText={t('否')}
|
||||
onChange={(value) => {
|
||||
setIsEnterpriseAccount(value);
|
||||
handleInputChange('is_enterprise_account', value);
|
||||
}}
|
||||
extraText={t('企业账户为特殊返回格式,需要特殊处理,如果非企业账户,请勿勾选')}
|
||||
initValue={inputs.is_enterprise_account}
|
||||
/>
|
||||
)}
|
||||
|
||||
<Form.Input
|
||||
field='name'
|
||||
label={t('名称')}
|
||||
@@ -1883,6 +1933,30 @@ const EditChannelModal = (props) => {
|
||||
/>
|
||||
</div>
|
||||
)}
|
||||
|
||||
{inputs.type === 45 && (
|
||||
<div>
|
||||
<Form.Select
|
||||
field='base_url'
|
||||
label={t('API地址')}
|
||||
placeholder={t('请选择API地址')}
|
||||
onChange={(value) =>
|
||||
handleInputChange('base_url', value)
|
||||
}
|
||||
optionList={[
|
||||
{
|
||||
value: 'https://ark.cn-beijing.volces.com',
|
||||
label: 'https://ark.cn-beijing.volces.com'
|
||||
},
|
||||
{
|
||||
value: 'https://ark.ap-southeast.bytepluses.com',
|
||||
label: 'https://ark.ap-southeast.bytepluses.com'
|
||||
}
|
||||
]}
|
||||
defaultValue='https://ark.cn-beijing.volces.com'
|
||||
/>
|
||||
</div>
|
||||
)}
|
||||
</Card>
|
||||
)}
|
||||
|
||||
|
||||
@@ -25,13 +25,9 @@ import {
|
||||
showInfo,
|
||||
showSuccess,
|
||||
loadChannelModels,
|
||||
copy,
|
||||
copy
|
||||
} from '../../helpers';
|
||||
import {
|
||||
CHANNEL_OPTIONS,
|
||||
ITEMS_PER_PAGE,
|
||||
MODEL_TABLE_PAGE_SIZE,
|
||||
} from '../../constants';
|
||||
import { CHANNEL_OPTIONS, ITEMS_PER_PAGE, MODEL_TABLE_PAGE_SIZE } from '../../constants';
|
||||
import { useIsMobile } from '../common/useIsMobile';
|
||||
import { useTableCompactMode } from '../common/useTableCompactMode';
|
||||
import { Modal } from '@douyinfe/semi-ui';
|
||||
@@ -68,7 +64,7 @@ export const useChannelsData = () => {
|
||||
|
||||
// Status filter
|
||||
const [statusFilter, setStatusFilter] = useState(
|
||||
localStorage.getItem('channel-status-filter') || 'all',
|
||||
localStorage.getItem('channel-status-filter') || 'all'
|
||||
);
|
||||
|
||||
// Type tabs states
|
||||
@@ -83,9 +79,10 @@ export const useChannelsData = () => {
|
||||
const [testingModels, setTestingModels] = useState(new Set());
|
||||
const [selectedModelKeys, setSelectedModelKeys] = useState([]);
|
||||
const [isBatchTesting, setIsBatchTesting] = useState(false);
|
||||
const [testQueue, setTestQueue] = useState([]);
|
||||
const [isProcessingQueue, setIsProcessingQueue] = useState(false);
|
||||
const [modelTablePage, setModelTablePage] = useState(1);
|
||||
|
||||
// 使用 ref 来避免闭包问题,类似旧版实现
|
||||
const shouldStopBatchTestingRef = useRef(false);
|
||||
|
||||
// Multi-key management states
|
||||
const [showMultiKeyManageModal, setShowMultiKeyManageModal] = useState(false);
|
||||
@@ -119,12 +116,9 @@ export const useChannelsData = () => {
|
||||
// Initialize from localStorage
|
||||
useEffect(() => {
|
||||
const localIdSort = localStorage.getItem('id-sort') === 'true';
|
||||
const localPageSize =
|
||||
parseInt(localStorage.getItem('page-size')) || ITEMS_PER_PAGE;
|
||||
const localEnableTagMode =
|
||||
localStorage.getItem('enable-tag-mode') === 'true';
|
||||
const localEnableBatchDelete =
|
||||
localStorage.getItem('enable-batch-delete') === 'true';
|
||||
const localPageSize = parseInt(localStorage.getItem('page-size')) || ITEMS_PER_PAGE;
|
||||
const localEnableTagMode = localStorage.getItem('enable-tag-mode') === 'true';
|
||||
const localEnableBatchDelete = localStorage.getItem('enable-batch-delete') === 'true';
|
||||
|
||||
setIdSort(localIdSort);
|
||||
setPageSize(localPageSize);
|
||||
@@ -182,10 +176,7 @@ export const useChannelsData = () => {
|
||||
// Save column preferences
|
||||
useEffect(() => {
|
||||
if (Object.keys(visibleColumns).length > 0) {
|
||||
localStorage.setItem(
|
||||
'channels-table-columns',
|
||||
JSON.stringify(visibleColumns),
|
||||
);
|
||||
localStorage.setItem('channels-table-columns', JSON.stringify(visibleColumns));
|
||||
}
|
||||
}, [visibleColumns]);
|
||||
|
||||
@@ -299,21 +290,14 @@ export const useChannelsData = () => {
|
||||
const { searchKeyword, searchGroup, searchModel } = getFormValues();
|
||||
if (searchKeyword !== '' || searchGroup !== '' || searchModel !== '') {
|
||||
setLoading(true);
|
||||
await searchChannels(
|
||||
enableTagMode,
|
||||
typeKey,
|
||||
statusF,
|
||||
page,
|
||||
pageSize,
|
||||
idSort,
|
||||
);
|
||||
await searchChannels(enableTagMode, typeKey, statusF, page, pageSize, idSort);
|
||||
setLoading(false);
|
||||
return;
|
||||
}
|
||||
|
||||
const reqId = ++requestCounter.current;
|
||||
setLoading(true);
|
||||
const typeParam = typeKey !== 'all' ? `&type=${typeKey}` : '';
|
||||
const typeParam = (typeKey !== 'all') ? `&type=${typeKey}` : '';
|
||||
const statusParam = statusF !== 'all' ? `&status=${statusF}` : '';
|
||||
const res = await API.get(
|
||||
`/api/channel/?p=${page}&page_size=${pageSize}&id_sort=${idSort}&tag_mode=${enableTagMode}${typeParam}${statusParam}`,
|
||||
@@ -327,10 +311,7 @@ export const useChannelsData = () => {
|
||||
if (success) {
|
||||
const { items, total, type_counts } = data;
|
||||
if (type_counts) {
|
||||
const sumAll = Object.values(type_counts).reduce(
|
||||
(acc, v) => acc + v,
|
||||
0,
|
||||
);
|
||||
const sumAll = Object.values(type_counts).reduce((acc, v) => acc + v, 0);
|
||||
setTypeCounts({ ...type_counts, all: sumAll });
|
||||
}
|
||||
setChannelFormat(items, enableTagMode);
|
||||
@@ -354,18 +335,11 @@ export const useChannelsData = () => {
|
||||
setSearching(true);
|
||||
try {
|
||||
if (searchKeyword === '' && searchGroup === '' && searchModel === '') {
|
||||
await loadChannels(
|
||||
page,
|
||||
pageSz,
|
||||
sortFlag,
|
||||
enableTagMode,
|
||||
typeKey,
|
||||
statusF,
|
||||
);
|
||||
await loadChannels(page, pageSz, sortFlag, enableTagMode, typeKey, statusF);
|
||||
return;
|
||||
}
|
||||
|
||||
const typeParam = typeKey !== 'all' ? `&type=${typeKey}` : '';
|
||||
const typeParam = (typeKey !== 'all') ? `&type=${typeKey}` : '';
|
||||
const statusParam = statusF !== 'all' ? `&status=${statusF}` : '';
|
||||
const res = await API.get(
|
||||
`/api/channel/search?keyword=${searchKeyword}&group=${searchGroup}&model=${searchModel}&id_sort=${sortFlag}&tag_mode=${enableTagMode}&p=${page}&page_size=${pageSz}${typeParam}${statusParam}`,
|
||||
@@ -373,10 +347,7 @@ export const useChannelsData = () => {
|
||||
const { success, message, data } = res.data;
|
||||
if (success) {
|
||||
const { items = [], total = 0, type_counts = {} } = data;
|
||||
const sumAll = Object.values(type_counts).reduce(
|
||||
(acc, v) => acc + v,
|
||||
0,
|
||||
);
|
||||
const sumAll = Object.values(type_counts).reduce((acc, v) => acc + v, 0);
|
||||
setTypeCounts({ ...type_counts, all: sumAll });
|
||||
setChannelFormat(items, enableTagMode);
|
||||
setChannelCount(total);
|
||||
@@ -395,14 +366,7 @@ export const useChannelsData = () => {
|
||||
if (searchKeyword === '' && searchGroup === '' && searchModel === '') {
|
||||
await loadChannels(page, pageSize, idSort, enableTagMode);
|
||||
} else {
|
||||
await searchChannels(
|
||||
enableTagMode,
|
||||
activeTypeKey,
|
||||
statusFilter,
|
||||
page,
|
||||
pageSize,
|
||||
idSort,
|
||||
);
|
||||
await searchChannels(enableTagMode, activeTypeKey, statusFilter, page, pageSize, idSort);
|
||||
}
|
||||
};
|
||||
|
||||
@@ -488,16 +452,9 @@ export const useChannelsData = () => {
|
||||
const { searchKeyword, searchGroup, searchModel } = getFormValues();
|
||||
setActivePage(page);
|
||||
if (searchKeyword === '' && searchGroup === '' && searchModel === '') {
|
||||
loadChannels(page, pageSize, idSort, enableTagMode).then(() => {});
|
||||
loadChannels(page, pageSize, idSort, enableTagMode).then(() => { });
|
||||
} else {
|
||||
searchChannels(
|
||||
enableTagMode,
|
||||
activeTypeKey,
|
||||
statusFilter,
|
||||
page,
|
||||
pageSize,
|
||||
idSort,
|
||||
);
|
||||
searchChannels(enableTagMode, activeTypeKey, statusFilter, page, pageSize, idSort);
|
||||
}
|
||||
};
|
||||
|
||||
@@ -513,14 +470,7 @@ export const useChannelsData = () => {
|
||||
showError(reason);
|
||||
});
|
||||
} else {
|
||||
searchChannels(
|
||||
enableTagMode,
|
||||
activeTypeKey,
|
||||
statusFilter,
|
||||
1,
|
||||
size,
|
||||
idSort,
|
||||
);
|
||||
searchChannels(enableTagMode, activeTypeKey, statusFilter, 1, size, idSort);
|
||||
}
|
||||
};
|
||||
|
||||
@@ -551,10 +501,7 @@ export const useChannelsData = () => {
|
||||
showError(res?.data?.message || t('渠道复制失败'));
|
||||
}
|
||||
} catch (error) {
|
||||
showError(
|
||||
t('渠道复制失败: ') +
|
||||
(error?.response?.data?.message || error?.message || error),
|
||||
);
|
||||
showError(t('渠道复制失败: ') + (error?.response?.data?.message || error?.message || error));
|
||||
}
|
||||
};
|
||||
|
||||
@@ -593,11 +540,7 @@ export const useChannelsData = () => {
|
||||
data.priority = parseInt(data.priority);
|
||||
break;
|
||||
case 'weight':
|
||||
if (
|
||||
data.weight === undefined ||
|
||||
data.weight < 0 ||
|
||||
data.weight === ''
|
||||
) {
|
||||
if (data.weight === undefined || data.weight < 0 || data.weight === '') {
|
||||
showInfo('权重必须是非负整数!');
|
||||
return;
|
||||
}
|
||||
@@ -740,136 +683,226 @@ export const useChannelsData = () => {
|
||||
const res = await API.post(`/api/channel/fix`);
|
||||
const { success, message, data } = res.data;
|
||||
if (success) {
|
||||
showSuccess(
|
||||
t('已修复 ${success} 个通道,失败 ${fails} 个通道。')
|
||||
.replace('${success}', data.success)
|
||||
.replace('${fails}', data.fails),
|
||||
);
|
||||
showSuccess(t('已修复 ${success} 个通道,失败 ${fails} 个通道。').replace('${success}', data.success).replace('${fails}', data.fails));
|
||||
await refresh();
|
||||
} else {
|
||||
showError(message);
|
||||
}
|
||||
};
|
||||
|
||||
// Test channel
|
||||
// Test channel - 单个模型测试,参考旧版实现
|
||||
const testChannel = async (record, model) => {
|
||||
setTestQueue((prev) => [...prev, { channel: record, model }]);
|
||||
if (!isProcessingQueue) {
|
||||
setIsProcessingQueue(true);
|
||||
const testKey = `${record.id}-${model}`;
|
||||
|
||||
// 检查是否应该停止批量测试
|
||||
if (shouldStopBatchTestingRef.current && isBatchTesting) {
|
||||
return Promise.resolve();
|
||||
}
|
||||
};
|
||||
|
||||
// Process test queue
|
||||
const processTestQueue = async () => {
|
||||
if (!isProcessingQueue || testQueue.length === 0) return;
|
||||
|
||||
const { channel, model, indexInFiltered } = testQueue[0];
|
||||
|
||||
if (currentTestChannel && currentTestChannel.id === channel.id) {
|
||||
let pageNo;
|
||||
if (indexInFiltered !== undefined) {
|
||||
pageNo = Math.floor(indexInFiltered / MODEL_TABLE_PAGE_SIZE) + 1;
|
||||
} else {
|
||||
const filteredModelsList = currentTestChannel.models
|
||||
.split(',')
|
||||
.filter((m) =>
|
||||
m.toLowerCase().includes(modelSearchKeyword.toLowerCase()),
|
||||
);
|
||||
const modelIdx = filteredModelsList.indexOf(model);
|
||||
pageNo =
|
||||
modelIdx !== -1
|
||||
? Math.floor(modelIdx / MODEL_TABLE_PAGE_SIZE) + 1
|
||||
: 1;
|
||||
}
|
||||
setModelTablePage(pageNo);
|
||||
}
|
||||
// 添加到正在测试的模型集合
|
||||
setTestingModels(prev => new Set([...prev, model]));
|
||||
|
||||
try {
|
||||
setTestingModels((prev) => new Set([...prev, model]));
|
||||
const res = await API.get(
|
||||
`/api/channel/test/${channel.id}?model=${model}`,
|
||||
);
|
||||
const res = await API.get(`/api/channel/test/${record.id}?model=${model}`);
|
||||
|
||||
// 检查是否在请求期间被停止
|
||||
if (shouldStopBatchTestingRef.current && isBatchTesting) {
|
||||
return Promise.resolve();
|
||||
}
|
||||
|
||||
const { success, message, time } = res.data;
|
||||
|
||||
setModelTestResults((prev) => ({
|
||||
// 更新测试结果
|
||||
setModelTestResults(prev => ({
|
||||
...prev,
|
||||
[`${channel.id}-${model}`]: { success, time },
|
||||
[testKey]: {
|
||||
success,
|
||||
message,
|
||||
time: time || 0,
|
||||
timestamp: Date.now()
|
||||
}
|
||||
}));
|
||||
|
||||
if (success) {
|
||||
updateChannelProperty(channel.id, (ch) => {
|
||||
ch.response_time = time * 1000;
|
||||
ch.test_time = Date.now() / 1000;
|
||||
// 更新渠道响应时间
|
||||
updateChannelProperty(record.id, (channel) => {
|
||||
channel.response_time = time * 1000;
|
||||
channel.test_time = Date.now() / 1000;
|
||||
});
|
||||
if (!model) {
|
||||
|
||||
if (!model || model === '') {
|
||||
showInfo(
|
||||
t('通道 ${name} 测试成功,耗时 ${time.toFixed(2)} 秒。')
|
||||
.replace('${name}', channel.name)
|
||||
.replace('${name}', record.name)
|
||||
.replace('${time.toFixed(2)}', time.toFixed(2)),
|
||||
);
|
||||
} else {
|
||||
showInfo(
|
||||
t('通道 ${name} 测试成功,模型 ${model} 耗时 ${time.toFixed(2)} 秒。')
|
||||
.replace('${name}', record.name)
|
||||
.replace('${model}', model)
|
||||
.replace('${time.toFixed(2)}', time.toFixed(2)),
|
||||
);
|
||||
}
|
||||
} else {
|
||||
showError(message);
|
||||
showError(`${t('模型')} ${model}: ${message}`);
|
||||
}
|
||||
} catch (error) {
|
||||
showError(error.message);
|
||||
// 处理网络错误
|
||||
const testKey = `${record.id}-${model}`;
|
||||
setModelTestResults(prev => ({
|
||||
...prev,
|
||||
[testKey]: {
|
||||
success: false,
|
||||
message: error.message || t('网络错误'),
|
||||
time: 0,
|
||||
timestamp: Date.now()
|
||||
}
|
||||
}));
|
||||
showError(`${t('模型')} ${model}: ${error.message || t('测试失败')}`);
|
||||
} finally {
|
||||
setTestingModels((prev) => {
|
||||
// 从正在测试的模型集合中移除
|
||||
setTestingModels(prev => {
|
||||
const newSet = new Set(prev);
|
||||
newSet.delete(model);
|
||||
return newSet;
|
||||
});
|
||||
}
|
||||
|
||||
setTestQueue((prev) => prev.slice(1));
|
||||
};
|
||||
|
||||
// Monitor queue changes
|
||||
useEffect(() => {
|
||||
if (testQueue.length > 0 && isProcessingQueue) {
|
||||
processTestQueue();
|
||||
} else if (testQueue.length === 0 && isProcessingQueue) {
|
||||
setIsProcessingQueue(false);
|
||||
setIsBatchTesting(false);
|
||||
}
|
||||
}, [testQueue, isProcessingQueue]);
|
||||
|
||||
// Batch test models
|
||||
// 批量测试单个渠道的所有模型,参考旧版实现
|
||||
const batchTestModels = async () => {
|
||||
if (!currentTestChannel) return;
|
||||
if (!currentTestChannel || !currentTestChannel.models) {
|
||||
showError(t('渠道模型信息不完整'));
|
||||
return;
|
||||
}
|
||||
|
||||
const models = currentTestChannel.models.split(',').filter(model =>
|
||||
model.toLowerCase().includes(modelSearchKeyword.toLowerCase())
|
||||
);
|
||||
|
||||
if (models.length === 0) {
|
||||
showError(t('没有找到匹配的模型'));
|
||||
return;
|
||||
}
|
||||
|
||||
setIsBatchTesting(true);
|
||||
setModelTablePage(1);
|
||||
shouldStopBatchTestingRef.current = false; // 重置停止标志
|
||||
|
||||
const filteredModels = currentTestChannel.models
|
||||
.split(',')
|
||||
.filter((model) =>
|
||||
model.toLowerCase().includes(modelSearchKeyword.toLowerCase()),
|
||||
);
|
||||
// 清空该渠道之前的测试结果
|
||||
setModelTestResults(prev => {
|
||||
const newResults = { ...prev };
|
||||
models.forEach(model => {
|
||||
const testKey = `${currentTestChannel.id}-${model}`;
|
||||
delete newResults[testKey];
|
||||
});
|
||||
return newResults;
|
||||
});
|
||||
|
||||
setTestQueue(
|
||||
filteredModels.map((model, idx) => ({
|
||||
channel: currentTestChannel,
|
||||
model,
|
||||
indexInFiltered: idx,
|
||||
})),
|
||||
);
|
||||
setIsProcessingQueue(true);
|
||||
try {
|
||||
showInfo(t('开始批量测试 ${count} 个模型,已清空上次结果...').replace('${count}', models.length));
|
||||
|
||||
// 提高并发数量以加快测试速度,参考旧版的并发限制
|
||||
const concurrencyLimit = 5;
|
||||
const results = [];
|
||||
|
||||
for (let i = 0; i < models.length; i += concurrencyLimit) {
|
||||
// 检查是否应该停止
|
||||
if (shouldStopBatchTestingRef.current) {
|
||||
showInfo(t('批量测试已停止'));
|
||||
break;
|
||||
}
|
||||
|
||||
const batch = models.slice(i, i + concurrencyLimit);
|
||||
showInfo(t('正在测试第 ${current} - ${end} 个模型 (共 ${total} 个)')
|
||||
.replace('${current}', i + 1)
|
||||
.replace('${end}', Math.min(i + concurrencyLimit, models.length))
|
||||
.replace('${total}', models.length)
|
||||
);
|
||||
|
||||
const batchPromises = batch.map(model => testChannel(currentTestChannel, model));
|
||||
const batchResults = await Promise.allSettled(batchPromises);
|
||||
results.push(...batchResults);
|
||||
|
||||
// 再次检查是否应该停止
|
||||
if (shouldStopBatchTestingRef.current) {
|
||||
showInfo(t('批量测试已停止'));
|
||||
break;
|
||||
}
|
||||
|
||||
// 短暂延迟避免过于频繁的请求
|
||||
if (i + concurrencyLimit < models.length) {
|
||||
await new Promise(resolve => setTimeout(resolve, 100));
|
||||
}
|
||||
}
|
||||
|
||||
if (!shouldStopBatchTestingRef.current) {
|
||||
// 等待一小段时间确保所有结果都已更新
|
||||
await new Promise(resolve => setTimeout(resolve, 300));
|
||||
|
||||
// 使用当前状态重新计算结果统计
|
||||
setModelTestResults(currentResults => {
|
||||
let successCount = 0;
|
||||
let failCount = 0;
|
||||
|
||||
models.forEach(model => {
|
||||
const testKey = `${currentTestChannel.id}-${model}`;
|
||||
const result = currentResults[testKey];
|
||||
if (result && result.success) {
|
||||
successCount++;
|
||||
} else {
|
||||
failCount++;
|
||||
}
|
||||
});
|
||||
|
||||
// 显示完成消息
|
||||
setTimeout(() => {
|
||||
showSuccess(t('批量测试完成!成功: ${success}, 失败: ${fail}, 总计: ${total}')
|
||||
.replace('${success}', successCount)
|
||||
.replace('${fail}', failCount)
|
||||
.replace('${total}', models.length)
|
||||
);
|
||||
}, 100);
|
||||
|
||||
return currentResults; // 不修改状态,只是为了获取最新值
|
||||
});
|
||||
}
|
||||
} catch (error) {
|
||||
showError(t('批量测试过程中发生错误: ') + error.message);
|
||||
} finally {
|
||||
setIsBatchTesting(false);
|
||||
}
|
||||
};
|
||||
|
||||
// 停止批量测试
|
||||
const stopBatchTesting = () => {
|
||||
shouldStopBatchTestingRef.current = true;
|
||||
setIsBatchTesting(false);
|
||||
setTestingModels(new Set());
|
||||
showInfo(t('已停止批量测试'));
|
||||
};
|
||||
|
||||
// 清空测试结果
|
||||
const clearTestResults = () => {
|
||||
setModelTestResults({});
|
||||
showInfo(t('已清空测试结果'));
|
||||
};
|
||||
|
||||
// Handle close modal
|
||||
const handleCloseModal = () => {
|
||||
// 如果正在批量测试,先停止测试
|
||||
if (isBatchTesting) {
|
||||
setTestQueue([]);
|
||||
setIsProcessingQueue(false);
|
||||
setIsBatchTesting(false);
|
||||
showSuccess(t('已停止测试'));
|
||||
} else {
|
||||
setShowModelTestModal(false);
|
||||
setModelSearchKeyword('');
|
||||
setSelectedModelKeys([]);
|
||||
setModelTablePage(1);
|
||||
shouldStopBatchTestingRef.current = true;
|
||||
showInfo(t('关闭弹窗,已停止批量测试'));
|
||||
}
|
||||
|
||||
setShowModelTestModal(false);
|
||||
setModelSearchKeyword('');
|
||||
setIsBatchTesting(false);
|
||||
setTestingModels(new Set());
|
||||
setSelectedModelKeys([]);
|
||||
setModelTablePage(1);
|
||||
// 可选择性保留测试结果,这里不清空以便用户查看
|
||||
};
|
||||
|
||||
// Type counts
|
||||
@@ -1012,4 +1045,4 @@ export const useChannelsData = () => {
|
||||
setCompactMode,
|
||||
setActivePage,
|
||||
};
|
||||
};
|
||||
};
|
||||
Reference in New Issue
Block a user