e2037ad756
This commit introduces a major architectural refactoring to improve quota management, centralize logging, and streamline the relay handling logic. Key changes: - **Pre-consume Quota:** Implements a new mechanism to check and reserve user quota *before* making the request to the upstream provider. This ensures more accurate quota deduction and prevents users from exceeding their limits due to concurrent requests. - **Unified Relay Handlers:** Refactors the relay logic to use generic handlers (e.g., `ChatHandler`, `ImageHandler`) instead of provider-specific implementations. This significantly reduces code duplication and simplifies adding new channels. - **Centralized Logger:** A new dedicated `logger` package is introduced, and all system logging calls are migrated to use it, moving this responsibility out of the `common` package. - **Code Reorganization:** DTOs are generalized (e.g., `dalle.go` -> `openai_image.go`) and utility code is moved to more appropriate packages (e.g., `common/http.go` -> `service/http.go`) for better code structure.
82 lines
2.2 KiB
Go
82 lines
2.2 KiB
Go
package dto
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import (
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"one-api/types"
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"strings"
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"github.com/gin-gonic/gin"
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)
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type EmbeddingOptions struct {
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Seed int `json:"seed,omitempty"`
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Temperature *float64 `json:"temperature,omitempty"`
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TopK int `json:"top_k,omitempty"`
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TopP *float64 `json:"top_p,omitempty"`
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FrequencyPenalty *float64 `json:"frequency_penalty,omitempty"`
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PresencePenalty *float64 `json:"presence_penalty,omitempty"`
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NumPredict int `json:"num_predict,omitempty"`
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NumCtx int `json:"num_ctx,omitempty"`
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}
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type EmbeddingRequest struct {
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Model string `json:"model"`
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Input any `json:"input"`
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EncodingFormat string `json:"encoding_format,omitempty"`
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Dimensions int `json:"dimensions,omitempty"`
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User string `json:"user,omitempty"`
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Seed float64 `json:"seed,omitempty"`
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Temperature *float64 `json:"temperature,omitempty"`
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TopP float64 `json:"top_p,omitempty"`
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FrequencyPenalty float64 `json:"frequency_penalty,omitempty"`
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PresencePenalty float64 `json:"presence_penalty,omitempty"`
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}
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func (r *EmbeddingRequest) GetTokenCountMeta() *types.TokenCountMeta {
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var texts = make([]string, 0)
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inputs := r.ParseInput()
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for _, input := range inputs {
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texts = append(texts, input)
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}
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return &types.TokenCountMeta{
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CombineText: strings.Join(texts, "\n"),
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}
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}
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func (r *EmbeddingRequest) IsStream(c *gin.Context) bool {
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return false
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}
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func (r *EmbeddingRequest) ParseInput() []string {
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if r.Input == nil {
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return make([]string, 0)
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}
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var input []string
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switch r.Input.(type) {
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case string:
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input = []string{r.Input.(string)}
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case []any:
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input = make([]string, 0, len(r.Input.([]any)))
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for _, item := range r.Input.([]any) {
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if str, ok := item.(string); ok {
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input = append(input, str)
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}
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}
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}
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return input
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}
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type EmbeddingResponseItem struct {
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Object string `json:"object"`
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Index int `json:"index"`
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Embedding []float64 `json:"embedding"`
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}
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type EmbeddingResponse struct {
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Object string `json:"object"`
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Data []EmbeddingResponseItem `json:"data"`
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Model string `json:"model"`
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Usage `json:"usage"`
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}
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