func()

in openai/embeddings.go [26:73]

• 43 lines of code
• 8 McCabe index (conditional complexity)

func (h *handlers) EmbeddingsHandler(w http.ResponseWriter, r *http.Request) {
	if r.Method != http.MethodPost {
		internal.ErrorHandler(w, r, http.StatusMethodNotAllowed, "method not allowed")
		return
	}

	body, err := io.ReadAll(r.Body)
	if err != nil {
		internal.ErrorHandler(w, r, http.StatusInternalServerError, "failed to read request body: %v", err)
		return
	}
	defer r.Body.Close()

	var embeddingsReq EmbeddingsRequest
	if err := json.Unmarshal(body, &embeddingsReq); err != nil {
		internal.ErrorHandler(w, r, http.StatusInternalServerError, "failed to unmarshal request body: %v", err)
		return
	}

	model := h.geminiClient.EmbeddingModel(embeddingsReq.Model)
	batch := model.NewBatch()
	for _, content := range embeddingsReq.Input {
		batch.AddContent(genai.Text(content))
	}

	geminiResp, err := model.BatchEmbedContents(r.Context(), batch)
	if err != nil {
		internal.ErrorHandler(w, r, http.StatusInternalServerError, "failed to make embeddings request: %v", err)
		return
	}

	embeddingsResp := &EmbeddingsResponse{
		Object: "list",
		Model:  embeddingsReq.Model,
		Data:   make([]EmbeddingData, 0, len(geminiResp.Embeddings)),
	}
	for i, contentEmbedding := range geminiResp.Embeddings {
		embeddingsResp.Data = append(embeddingsResp.Data, EmbeddingData{
			Index:     i,
			Object:    "embedding",
			Embedding: contentEmbedding.Values,
		})
	}
	if err := json.NewEncoder(w).Encode(embeddingsResp); err != nil {
		internal.ErrorHandler(w, r, http.StatusInternalServerError, "failed to encode embeddings response: %v", err)
		return
	}
}