🎨 Support Baichuan2

common/constants.go

@@ -189,6 +189,7 @@ const (
 	ChannelTypeTencent        = 23
 	ChannelTypeAzureSpeech    = 24
 	ChannelTypeGemini         = 25
+	ChannelTypeBaichuan       = 26
 )
 
 var ChannelBaseURLs = []string{
@@ -218,6 +219,7 @@ var ChannelBaseURLs = []string{
 	"https://hunyuan.cloud.tencent.com", //23
 	"",                                  //24
 	"",                                  //25
+	"https://api.baichuan-ai.com",       //26
 }
 
 const (

common/model-ratio.go

@@ -101,6 +101,10 @@ var ModelRatio = map[string]float64{
 	"embedding_s1_v1":           0.0715, // ¥0.001 / 1k tokens
 	"semantic_similarity_s1_v1": 0.0715, // ¥0.001 / 1k tokens
 	"hunyuan":                   7.143,  // ¥0.1 / 1k tokens  // https://cloud.tencent.com/document/product/1729/97731#e0e6be58-60c8-469f-bdeb-6c264ce3b4d0
+	"Baichuan2-Turbo":           0.5715, // ¥0.008 / 1k tokens
+	"Baichuan2-Turbo-192k":      1.143,  // ¥0.016 / 1k tokens
+	"Baichuan2-53B":             1.4286, // ¥0.02 / 1k tokens
+	"Baichuan-Text-Embedding":   0.0357, // ¥0.0005 / 1k tokens
 }
 
 func ModelRatio2JSONString() string {

common/token.go

@@ -190,13 +190,13 @@ func countImageTokens(url string, detail string) (_ int, err error) {
 func CountTokenInput(input any, model string) int {
 	switch v := input.(type) {
 	case string:
-		return CountTokenInput(v, model)
+		return CountTokenText(v, model)
 	case []string:
 		text := ""
 		for _, s := range v {
 			text += s
 		}
-		return CountTokenInput(text, model)
+		return CountTokenText(text, model)
 	}
 	return 0
 }