one-api/providers/minimax/embeddings.go
2024-01-22 12:25:55 +08:00

94 lines
2.6 KiB
Go

package minimax
import (
"net/http"
"one-api/common"
"one-api/types"
)
func (p *MiniMaxProvider) CreateEmbeddings(request *types.EmbeddingRequest) (*types.EmbeddingResponse, *types.OpenAIErrorWithStatusCode) {
url, errWithCode := p.GetSupportedAPIUri(common.RelayModeEmbeddings)
if errWithCode != nil {
return nil, errWithCode
}
// 获取请求地址
fullRequestURL := p.GetFullRequestURL(url, request.Model)
if fullRequestURL == "" {
return nil, common.ErrorWrapper(nil, "invalid_minimax_config", http.StatusInternalServerError)
}
// 获取请求头
headers := p.GetRequestHeaders()
minimaxRequest := convertFromEmbeddingOpenai(request)
// 创建请求
req, err := p.Requester.NewRequest(http.MethodPost, fullRequestURL, p.Requester.WithBody(minimaxRequest), p.Requester.WithHeader(headers))
if err != nil {
return nil, common.ErrorWrapper(err, "new_request_failed", http.StatusInternalServerError)
}
defer req.Body.Close()
minimaxResponse := &MiniMaxEmbeddingResponse{}
// 发送请求
_, errWithCode = p.Requester.SendRequest(req, minimaxResponse, false)
if errWithCode != nil {
return nil, errWithCode
}
return p.convertToEmbeddingOpenai(minimaxResponse, request)
}
func convertFromEmbeddingOpenai(request *types.EmbeddingRequest) *MiniMaxEmbeddingRequest {
minimaxRequest := &MiniMaxEmbeddingRequest{
Model: request.Model,
Type: "db",
}
if input, ok := request.Input.(string); ok {
minimaxRequest.Texts = []string{input}
} else if inputs, ok := request.Input.([]any); ok {
for _, item := range inputs {
if input, ok := item.(string); ok {
minimaxRequest.Texts = append(minimaxRequest.Texts, input)
}
}
}
return minimaxRequest
}
func (p *MiniMaxProvider) convertToEmbeddingOpenai(response *MiniMaxEmbeddingResponse, request *types.EmbeddingRequest) (openaiResponse *types.EmbeddingResponse, errWithCode *types.OpenAIErrorWithStatusCode) {
error := errorHandle(&response.BaseResp)
if error != nil {
errWithCode = &types.OpenAIErrorWithStatusCode{
OpenAIError: *error,
StatusCode: http.StatusBadRequest,
}
return
}
openaiResponse = &types.EmbeddingResponse{
Object: "list",
Model: request.Model,
}
for _, item := range response.Vectors {
openaiResponse.Data = append(openaiResponse.Data, types.Embedding{
Object: "embedding",
Embedding: item,
})
}
if response.TotalTokens < p.Usage.PromptTokens {
p.Usage.PromptTokens = response.TotalTokens
}
p.Usage.TotalTokens = response.TotalTokens
p.Usage.CompletionTokens = response.TotalTokens - p.Usage.PromptTokens
openaiResponse.Usage = p.Usage
return
}