增加vertex embedding的支持，修改vertex的模型adapter匹配逻辑

relay/adaptor/vertexai/embedding/adapter.go

@@ -0,0 +1,107 @@
+package vertexai
+
+import (
+	"encoding/json"
+	"io"
+	"net/http"
+	"strings"
+
+	"github.com/songquanpeng/one-api/relay/adaptor/gemini"
+	"github.com/songquanpeng/one-api/relay/adaptor/openai"
+	model2 "github.com/songquanpeng/one-api/relay/adaptor/vertexai/model"
+
+	"github.com/gin-gonic/gin"
+	"github.com/pkg/errors"
+
+	"github.com/songquanpeng/one-api/relay/meta"
+	"github.com/songquanpeng/one-api/relay/model"
+)
+
+var ModelList = []string{
+	"textembedding-gecko-multilingual@001", "text-multilingual-embedding-002",
+}
+
+type Adaptor struct {
+	model string
+	task  EmbeddingTaskType
+}
+
+var _ model2.InnerAIAdapter = (*Adaptor)(nil)
+
+func (a *Adaptor) ConvertRequest(c *gin.Context, relayMode int, request *model.GeneralOpenAIRequest) (any, error) {
+	if request == nil {
+		return nil, errors.New("request is nil")
+	}
+	inputs := request.ParseInput()
+	if len(inputs) == 0 {
+		return nil, errors.New("request is nil")
+	}
+	parts := strings.Split(request.Model, "|")
+	if len(parts) >= 2 {
+		a.task = EmbeddingTaskType(parts[1])
+	} else {
+		a.task = EmbeddingTaskTypeSemanticSimilarity
+	}
+	a.model = parts[0]
+	instances := make([]EmbeddingInstance, len(inputs))
+	for i, input := range inputs {
+		instances[i] = EmbeddingInstance{
+			Content:  input,
+			TaskType: a.task,
+		}
+	}
+
+	embeddingRequest := EmbeddingRequest{
+		Instances: instances,
+		Parameters: EmbeddingParams{
+			OutputDimensionality: request.Dimensions,
+		},
+	}
+
+	return embeddingRequest, nil
+}
+
+func (a *Adaptor) DoResponse(c *gin.Context, resp *http.Response, meta *meta.Meta) (usage *model.Usage, err *model.ErrorWithStatusCode) {
+	err, usage = EmbeddingHandler(c, a.model, resp)
+	return
+}
+
+func EmbeddingHandler(c *gin.Context, modelName string, resp *http.Response) (*model.ErrorWithStatusCode, *model.Usage) {
+	var vertexEmbeddingResponse EmbeddingResponse
+	responseBody, err := io.ReadAll(resp.Body)
+	if resp.StatusCode != http.StatusOK {
+		return openai.ErrorWrapper(err, "read_response_body_failed", http.StatusInternalServerError), nil
+	}
+	if err != nil {
+		return openai.ErrorWrapper(err, "read_response_body_failed", http.StatusInternalServerError), nil
+	}
+	err = resp.Body.Close()
+	if err != nil {
+		return openai.ErrorWrapper(err, "close_response_body_failed", http.StatusInternalServerError), nil
+	}
+	err = json.Unmarshal(responseBody, &vertexEmbeddingResponse)
+	if err != nil {
+		return openai.ErrorWrapper(err, "unmarshal_response_body_failed", http.StatusInternalServerError), nil
+	}
+
+	openaiResp := &openai.EmbeddingResponse{
+		Model: modelName,
+		Data:  make([]openai.EmbeddingResponseItem, 0, len(vertexEmbeddingResponse.Predictions)),
+		Usage: model.Usage{
+			TotalTokens: 0,
+		},
+	}
+
+	for i, pred := range vertexEmbeddingResponse.Predictions {
+		openaiResp.Data = append(openaiResp.Data, openai.EmbeddingResponseItem{
+			Index:     i,
+			Embedding: pred.Embeddings.Values,
+		})
+	}
+
+	for _, pred := range vertexEmbeddingResponse.Predictions {
+		openaiResp.Usage.TotalTokens += pred.Embeddings.Statistics.TokenCount
+	}
+
+	return gemini.EmbeddingResponseHandler(c, resp.StatusCode, openaiResp)
+}

relay/adaptor/vertexai/embedding/model.go

@@ -0,0 +1,45 @@
+package vertexai
+
+type EmbeddingTaskType string
+
+const (
+	EmbeddingTaskTypeRetrievalQuery     EmbeddingTaskType = "RETRIEVAL_QUERY"
+	EmbeddingTaskTypeRetrievalDocument  EmbeddingTaskType = "RETRIEVAL_DOCUMENT"
+	EmbeddingTaskTypeSemanticSimilarity EmbeddingTaskType = "SEMANTIC_SIMILARITY"
+	EmbeddingTaskTypeClassification     EmbeddingTaskType = "CLASSIFICATION"
+	EmbeddingTaskTypeClustering         EmbeddingTaskType = "CLUSTERING"
+	EmbeddingTaskTypeQuestionAnswering  EmbeddingTaskType = "QUESTION_ANSWERING"
+	EmbeddingTaskTypeFactVerification   EmbeddingTaskType = "FACT_VERIFICATION"
+	EmbeddingTaskTypeCodeRetrievalQuery EmbeddingTaskType = "CODE_RETRIEVAL_QUERY"
+)
+
+type EmbeddingRequest struct {
+	Instances  []EmbeddingInstance `json:"instances"`
+	Parameters EmbeddingParams     `json:"parameters"`
+}
+
+type EmbeddingInstance struct {
+	Content  string            `json:"content"`
+	TaskType EmbeddingTaskType `json:"task_type,omitempty"`
+	Title    string            `json:"title,omitempty"`
+}
+
+type EmbeddingParams struct {
+	AutoTruncate         bool `json:"autoTruncate,omitempty"`
+	OutputDimensionality int  `json:"outputDimensionality,omitempty"`
+	// Texts                []string `json:"texts,omitempty"`
+}
+
+type EmbeddingResponse struct {
+	Predictions []struct {
+		Embeddings EmbeddingData `json:"embeddings"`
+	} `json:"predictions"`
+}
+
+type EmbeddingData struct {
+	Statistics struct {
+		Truncated  bool `json:"truncated"`
+		TokenCount int  `json:"token_count"`
+	} `json:"statistics"`
+	Values []float64 `json:"values"`
+}