embedding

type Config struct {
	Source                    types.ModelSource `json:"source"`
	BaseURL                   string            `json:"base_url"`
	ModelName                 string            `json:"model_name"`
	APIKey                    string            `json:"api_key"`
	TruncatePromptTokens      int               `json:"truncate_prompt_tokens"`
	Dimensions                int               `json:"dimensions"`
	SupportsDimensionOverride bool              `json:"supports_dimension_override"`
	ModelID                   string            `json:"model_id"`
	Provider                  string            `json:"provider"`
	// MaxConcurrency caps concurrent background calls to this model; 0 falls
	// back to the process-wide default (see limiter.GateN).
	MaxConcurrency int                      `json:"max_concurrency"`
	Spec           *types.ModelSpecOverride `json:"spec,omitempty"`
	ExtraConfig    map[string]string        `json:"extra_config"`
	// CustomHeaders 允许在调用远程 API 时附加自定义 HTTP 请求头（类似 OpenAI Python SDK 的 extra_headers）。
	CustomHeaders map[string]string `json:"custom_headers"`
	AppID         string
	AppSecret     string // 加密值，工厂函数调用方传入，使用前已解密
}

type Embedder interface {
	// Embed converts text to vector
	Embed(ctx context.Context, text string) ([]float32, error)

	// BatchEmbed converts multiple texts to vectors in batch
	BatchEmbed(ctx context.Context, texts []string) ([][]float32, error)

	// GetModelName returns the model name
	GetModelName() string

	// GetDimensions returns the vector dimensions
	GetDimensions() int

	// GetModelID returns the model ID
	GetModelID() string

	EmbedderPooler
}

type EmbedderPooler interface {
	BatchEmbedWithPool(ctx context.Context, model Embedder, texts []string) ([][]float32, error)
}

type EmbedderType string

type Image = api.EmbedImage

type ImageEmbedder interface {
	// AcceptsImages reports whether both the model and its endpoint take
	// images.
	AcceptsImages() bool
	// ImageLimits are the vendor's documented per-image limits. The caller
	// shrinks or converts an image to fit; an image outside them is refused.
	ImageLimits() ImageLimits
	// BatchEmbedImages converts images to vectors comparable with Embed's.
	BatchEmbedImages(ctx context.Context, images []Image) ([][]float32, error)
}

type ImageLimits = imageprep.Limits

type OllamaEmbedRequest struct {
	Model                string `json:"model"`
	Prompt               string `json:"prompt"`
	TruncatePromptTokens int    `json:"truncate_prompt_tokens"`
}

type OllamaEmbedResponse struct {
	Embedding []float32 `json:"embedding"`
}

type OllamaEmbedder struct {
	EmbedderPooler
	// contains filtered or unexported fields
}