232 lines
6.8 KiB
Go
232 lines
6.8 KiB
Go
package gemini
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import (
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"encoding/json"
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"errors"
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"fmt"
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"io"
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"net/http"
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"one-api/common"
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"one-api/dto"
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"one-api/relay/channel"
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relaycommon "one-api/relay/common"
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"one-api/service"
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"one-api/setting/model_setting"
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"strings"
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"github.com/gin-gonic/gin"
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)
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type Adaptor struct {
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}
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func (a *Adaptor) ConvertClaudeRequest(*gin.Context, *relaycommon.RelayInfo, *dto.ClaudeRequest) (any, error) {
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//TODO implement me
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panic("implement me")
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return nil, nil
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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) ConvertImageRequest(c *gin.Context, info *relaycommon.RelayInfo, request dto.ImageRequest) (any, error) {
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if !strings.HasPrefix(info.UpstreamModelName, "imagen") {
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return nil, errors.New("not supported model for image generation")
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}
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// convert size to aspect ratio
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aspectRatio := "1:1" // default aspect ratio
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switch request.Size {
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case "1024x1024":
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aspectRatio = "1:1"
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case "1024x1792":
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aspectRatio = "9:16"
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case "1792x1024":
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aspectRatio = "16:9"
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}
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// build gemini imagen request
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geminiRequest := GeminiImageRequest{
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Instances: []GeminiImageInstance{
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{
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Prompt: request.Prompt,
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},
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},
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Parameters: GeminiImageParameters{
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SampleCount: request.N,
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AspectRatio: aspectRatio,
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PersonGeneration: "allow_adult", // default allow adult
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},
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}
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return geminiRequest, nil
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}
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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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version := model_setting.GetGeminiVersionSetting(info.UpstreamModelName)
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if strings.HasPrefix(info.UpstreamModelName, "imagen") {
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return fmt.Sprintf("%s/%s/models/%s:predict", info.BaseUrl, version, info.UpstreamModelName), nil
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}
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if strings.HasPrefix(info.UpstreamModelName, "text-embedding") ||
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strings.HasPrefix(info.UpstreamModelName, "embedding") ||
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strings.HasPrefix(info.UpstreamModelName, "gemini-embedding") {
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return fmt.Sprintf("%s/%s/models/%s:embedContent", info.BaseUrl, version, info.UpstreamModelName), nil
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}
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action := "generateContent"
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if info.IsStream {
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action = "streamGenerateContent?alt=sse"
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}
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return fmt.Sprintf("%s/%s/models/%s:%s", info.BaseUrl, version, info.UpstreamModelName, action), 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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channel.SetupApiRequestHeader(info, c, req)
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req.Set("x-goog-api-key", info.ApiKey)
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return nil
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}
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func (a *Adaptor) ConvertRequest(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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}
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ai, err := CovertGemini2OpenAI(*request)
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if err != nil {
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return nil, err
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}
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return ai, nil
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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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return nil, nil
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}
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func (a *Adaptor) ConvertEmbeddingRequest(c *gin.Context, info *relaycommon.RelayInfo, request dto.EmbeddingRequest) (any, error) {
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if request.Input == nil {
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return nil, errors.New("input is required")
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}
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inputs := request.ParseInput()
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if len(inputs) == 0 {
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return nil, errors.New("input is empty")
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}
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// only process the first input
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geminiRequest := GeminiEmbeddingRequest{
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Content: GeminiChatContent{
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Parts: []GeminiPart{
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{
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Text: inputs[0],
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},
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},
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},
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}
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// set specific parameters for different models
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// https://ai.google.dev/api/embeddings?hl=zh-cn#method:-models.embedcontent
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switch info.UpstreamModelName {
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case "text-embedding-004":
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// except embedding-001 supports setting `OutputDimensionality`
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if request.Dimensions > 0 {
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geminiRequest.OutputDimensionality = request.Dimensions
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}
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}
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return geminiRequest, nil
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}
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func (a *Adaptor) DoRequest(c *gin.Context, info *relaycommon.RelayInfo, requestBody io.Reader) (any, error) {
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return channel.DoApiRequest(a, c, info, requestBody)
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}
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func (a *Adaptor) DoResponse(c *gin.Context, resp *http.Response, info *relaycommon.RelayInfo) (usage any, err *dto.OpenAIErrorWithStatusCode) {
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if strings.HasPrefix(info.UpstreamModelName, "imagen") {
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return GeminiImageHandler(c, resp, info)
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}
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// check if the model is an embedding model
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if strings.HasPrefix(info.UpstreamModelName, "text-embedding") ||
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strings.HasPrefix(info.UpstreamModelName, "embedding") ||
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strings.HasPrefix(info.UpstreamModelName, "gemini-embedding") {
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return GeminiEmbeddingHandler(c, resp, info)
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}
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if info.IsStream {
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err, usage = GeminiChatStreamHandler(c, resp, info)
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} else {
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err, usage = GeminiChatHandler(c, resp, info)
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}
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return
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}
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func GeminiImageHandler(c *gin.Context, resp *http.Response, info *relaycommon.RelayInfo) (usage any, err *dto.OpenAIErrorWithStatusCode) {
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responseBody, readErr := io.ReadAll(resp.Body)
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if readErr != nil {
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return nil, service.OpenAIErrorWrapper(readErr, "read_response_body_failed", http.StatusInternalServerError)
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}
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_ = resp.Body.Close()
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var geminiResponse GeminiImageResponse
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if jsonErr := json.Unmarshal(responseBody, &geminiResponse); jsonErr != nil {
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return nil, service.OpenAIErrorWrapper(jsonErr, "unmarshal_response_body_failed", http.StatusInternalServerError)
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}
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if len(geminiResponse.Predictions) == 0 {
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return nil, service.OpenAIErrorWrapper(errors.New("no images generated"), "no_images", http.StatusBadRequest)
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}
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// convert to openai format response
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openAIResponse := dto.ImageResponse{
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Created: common.GetTimestamp(),
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Data: make([]dto.ImageData, 0, len(geminiResponse.Predictions)),
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}
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for _, prediction := range geminiResponse.Predictions {
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if prediction.RaiFilteredReason != "" {
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continue // skip filtered image
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}
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openAIResponse.Data = append(openAIResponse.Data, dto.ImageData{
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B64Json: prediction.BytesBase64Encoded,
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})
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}
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jsonResponse, jsonErr := json.Marshal(openAIResponse)
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if jsonErr != nil {
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return nil, service.OpenAIErrorWrapper(jsonErr, "marshal_response_failed", http.StatusInternalServerError)
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}
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c.Writer.Header().Set("Content-Type", "application/json")
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c.Writer.WriteHeader(resp.StatusCode)
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_, _ = c.Writer.Write(jsonResponse)
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// https://github.com/google-gemini/cookbook/blob/719a27d752aac33f39de18a8d3cb42a70874917e/quickstarts/Counting_Tokens.ipynb
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// each image has fixed 258 tokens
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const imageTokens = 258
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generatedImages := len(openAIResponse.Data)
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usage = &dto.Usage{
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PromptTokens: imageTokens * generatedImages, // each generated image has fixed 258 tokens
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CompletionTokens: 0, // image generation does not calculate completion tokens
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TotalTokens: imageTokens * generatedImages,
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}
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return usage, nil
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}
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func (a *Adaptor) GetModelList() []string {
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return ModelList
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}
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func (a *Adaptor) GetChannelName() string {
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return ChannelName
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}
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