hjjjj
5730b2a798
refactor: 重构 Gemini 适配器以支持图片编辑和生成 feat(relay): 添加图片编辑模式支持 feat(controller): 实现 UsageAPIURL 用于获取真实 token 用量 feat(web): 在渠道测试中添加模型选择功能 perf(token): 优化多模态 token 计算逻辑 fix(web): 修复日志分页组件显示问题 docs: 更新渠道配置中的 UsageAPIURL 说明 style: 清理调试日志和注释 feat(gemini): 支持 Imagen 3+ 图片生成模型 feat(openai): 添加生成 ID 捕获和元数据获取功能
280 lines
9.1 KiB
Go
280 lines
9.1 KiB
Go
package openai
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import (
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"errors"
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"fmt"
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"math"
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"strings"
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"github.com/pkoukk/tiktoken-go"
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"github.com/songquanpeng/one-api/common/config"
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"github.com/songquanpeng/one-api/common/image"
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"github.com/songquanpeng/one-api/common/logger"
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billingratio "github.com/songquanpeng/one-api/relay/billing/ratio"
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"github.com/songquanpeng/one-api/relay/model"
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)
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// tokenEncoderMap won't grow after initialization
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var tokenEncoderMap = map[string]*tiktoken.Tiktoken{}
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var defaultTokenEncoder *tiktoken.Tiktoken
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func InitTokenEncoders() {
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logger.SysLog("initializing token encoders")
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gpt35TokenEncoder, err := tiktoken.EncodingForModel("gpt-3.5-turbo")
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if err != nil {
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logger.FatalLog(fmt.Sprintf("failed to get gpt-3.5-turbo token encoder: %s, "+
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"if you are using in offline environment, please set TIKTOKEN_CACHE_DIR to use exsited files, check this link for more information: https://stackoverflow.com/questions/76106366/how-to-use-tiktoken-in-offline-mode-computer ", err.Error()))
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}
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defaultTokenEncoder = gpt35TokenEncoder
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gpt4oTokenEncoder, err := tiktoken.EncodingForModel("gpt-4o")
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if err != nil {
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logger.FatalLog(fmt.Sprintf("failed to get gpt-4o token encoder: %s", err.Error()))
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}
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gpt4TokenEncoder, err := tiktoken.EncodingForModel("gpt-4")
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if err != nil {
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logger.FatalLog(fmt.Sprintf("failed to get gpt-4 token encoder: %s", err.Error()))
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}
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for model := range billingratio.ModelRatio {
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if strings.HasPrefix(model, "gpt-3.5") {
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tokenEncoderMap[model] = gpt35TokenEncoder
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} else if strings.HasPrefix(model, "gpt-4o") {
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tokenEncoderMap[model] = gpt4oTokenEncoder
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} else if strings.HasPrefix(model, "gpt-4") {
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tokenEncoderMap[model] = gpt4TokenEncoder
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} else {
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tokenEncoderMap[model] = nil
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}
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}
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logger.SysLog("token encoders initialized")
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}
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func getTokenEncoder(model string) *tiktoken.Tiktoken {
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tokenEncoder, ok := tokenEncoderMap[model]
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if ok && tokenEncoder != nil {
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return tokenEncoder
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}
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if ok {
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tokenEncoder, err := tiktoken.EncodingForModel(model)
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if err != nil {
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logger.SysError(fmt.Sprintf("failed to get token encoder for model %s: %s, using encoder for gpt-3.5-turbo", model, err.Error()))
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tokenEncoder = defaultTokenEncoder
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}
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tokenEncoderMap[model] = tokenEncoder
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return tokenEncoder
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}
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return defaultTokenEncoder
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}
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func getTokenNum(tokenEncoder *tiktoken.Tiktoken, text string) int {
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if config.ApproximateTokenEnabled {
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return int(float64(len(text)) * 0.38)
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}
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return len(tokenEncoder.Encode(text, nil, nil))
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}
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// stripBase64Payloads removes the base64 payload from data URIs so that huge
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// image/video/audio blobs do not inflate the local token estimate.
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// Example: "data:image/jpeg;base64,/9j/4AAQ..." → "data:image/jpeg;base64,"
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func stripBase64Payloads(s string) string {
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const marker = ";base64,"
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out := s
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for {
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idx := strings.Index(out, marker)
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if idx < 0 {
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break
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}
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payloadStart := idx + len(marker)
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end := payloadStart
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for end < len(out) {
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c := out[end]
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if (c >= 'A' && c <= 'Z') || (c >= 'a' && c <= 'z') ||
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(c >= '0' && c <= '9') || c == '+' || c == '/' || c == '=' {
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end++
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} else {
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break
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}
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}
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out = out[:payloadStart] + out[end:]
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}
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return out
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}
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func CountTokenMessages(messages []model.Message, model string) int {
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tokenEncoder := getTokenEncoder(model)
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// Reference:
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// https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb
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// https://github.com/pkoukk/tiktoken-go/issues/6
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//
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// Every message follows <|start|>{role/name}\n{content}<|end|>\n
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var tokensPerMessage int
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var tokensPerName int
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if model == "gpt-3.5-turbo-0301" {
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tokensPerMessage = 4
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tokensPerName = -1 // If there's a name, the role is omitted
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} else {
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tokensPerMessage = 3
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tokensPerName = 1
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}
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tokenNum := 0
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for _, message := range messages {
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tokenNum += tokensPerMessage
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switch v := message.Content.(type) {
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case string:
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tokenNum += getTokenNum(tokenEncoder, stripBase64Payloads(v))
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case []any:
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for _, it := range v {
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m := it.(map[string]any)
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switch m["type"] {
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case "text":
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if textValue, ok := m["text"]; ok {
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if textString, ok := textValue.(string); ok {
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tokenNum += getTokenNum(tokenEncoder, stripBase64Payloads(textString))
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}
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}
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case "image_url":
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imageUrl, ok := m["image_url"].(map[string]any)
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if ok {
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url := imageUrl["url"].(string)
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detail := ""
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if imageUrl["detail"] != nil {
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detail = imageUrl["detail"].(string)
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}
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imageTokens, err := countImageTokens(url, detail, model)
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if err != nil || imageTokens == 0 {
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// Fallback for base64 images (payload stripped, dimensions
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// unavailable) or any other failure: use a conservative
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// fixed estimate (~768x768 high-detail image).
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if err != nil {
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logger.SysError("error counting image tokens, using fallback: " + err.Error())
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}
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imageTokens = 765
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}
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tokenNum += imageTokens
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}
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case "video_url":
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// Videos cannot be tokenised locally; conservative fixed estimate.
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// Final billing always uses the upstream's real usage.
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tokenNum += 2000
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case "input_audio", "audio":
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// Audio cannot be tokenised locally; conservative fixed estimate.
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tokenNum += 500
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}
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}
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}
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tokenNum += getTokenNum(tokenEncoder, message.Role)
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if message.Name != nil {
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tokenNum += tokensPerName
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tokenNum += getTokenNum(tokenEncoder, *message.Name)
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}
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}
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tokenNum += 3 // Every reply is primed with <|start|>assistant<|message|>
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return tokenNum
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}
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const (
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lowDetailCost = 85
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highDetailCostPerTile = 170
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additionalCost = 85
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// gpt-4o-mini cost higher than other model
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gpt4oMiniLowDetailCost = 2833
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gpt4oMiniHighDetailCost = 5667
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gpt4oMiniAdditionalCost = 2833
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)
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// https://platform.openai.com/docs/guides/vision/calculating-costs
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// https://github.com/openai/openai-cookbook/blob/05e3f9be4c7a2ae7ecf029a7c32065b024730ebe/examples/How_to_count_tokens_with_tiktoken.ipynb
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func countImageTokens(url string, detail string, model string) (_ int, err error) {
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// Skip token counting for non-image data URLs (video, audio, etc.)
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// These cannot be decoded as images and will cause errors.
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if strings.HasPrefix(url, "data:") && !strings.HasPrefix(url, "data:image/") {
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return 0, nil
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}
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var fetchSize = true
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var width, height int
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// Reference: https://platform.openai.com/docs/guides/vision/low-or-high-fidelity-image-understanding
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// detail == "auto" is undocumented on how it works, it just said the model will use the auto setting which will look at the image input size and decide if it should use the low or high setting.
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// According to the official guide, "low" disable the high-res model,
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// and only receive low-res 512px x 512px version of the image, indicating
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// that image is treated as low-res when size is smaller than 512px x 512px,
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// then we can assume that image size larger than 512px x 512px is treated
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// as high-res. Then we have the following logic:
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// if detail == "" || detail == "auto" {
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// width, height, err = image.GetImageSize(url)
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// if err != nil {
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// return 0, err
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// }
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// fetchSize = false
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// // not sure if this is correct
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// if width > 512 || height > 512 {
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// detail = "high"
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// } else {
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// detail = "low"
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// }
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// }
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// However, in my test, it seems to be always the same as "high".
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// The following image, which is 125x50, is still treated as high-res, taken
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// 255 tokens in the response of non-stream chat completion api.
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// https://upload.wikimedia.org/wikipedia/commons/1/10/18_Infantry_Division_Messina.jpg
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if detail == "" || detail == "auto" {
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// assume by test, not sure if this is correct
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detail = "high"
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}
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switch detail {
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case "low":
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if strings.HasPrefix(model, "gpt-4o-mini") {
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return gpt4oMiniLowDetailCost, nil
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}
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return lowDetailCost, nil
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case "high":
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if fetchSize {
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width, height, err = image.GetImageSize(url)
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if err != nil {
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return 0, err
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}
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}
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if width > 2048 || height > 2048 { // max(width, height) > 2048
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ratio := float64(2048) / math.Max(float64(width), float64(height))
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width = int(float64(width) * ratio)
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height = int(float64(height) * ratio)
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}
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if width > 768 && height > 768 { // min(width, height) > 768
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ratio := float64(768) / math.Min(float64(width), float64(height))
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width = int(float64(width) * ratio)
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height = int(float64(height) * ratio)
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}
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numSquares := int(math.Ceil(float64(width)/512) * math.Ceil(float64(height)/512))
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if strings.HasPrefix(model, "gpt-4o-mini") {
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return numSquares*gpt4oMiniHighDetailCost + gpt4oMiniAdditionalCost, nil
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}
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result := numSquares*highDetailCostPerTile + additionalCost
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return result, nil
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default:
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return 0, errors.New("invalid detail option")
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}
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}
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func CountTokenInput(input any, model string) int {
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switch v := input.(type) {
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case string:
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return CountTokenText(v, model)
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case []string:
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text := ""
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for _, s := range v {
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text += s
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}
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return CountTokenText(text, model)
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}
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return 0
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}
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func CountTokenText(text string, model string) int {
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tokenEncoder := getTokenEncoder(model)
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return getTokenNum(tokenEncoder, text)
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}
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func CountToken(text string) int {
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return CountTokenInput(text, "gpt-3.5-turbo")
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}
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