types

type ChatRequest struct {
	Messages               []Message
	Tools                  []ToolDefinition
	ThinkingEffort         string
	PreviousResponseID     string // OpenAI specific
	WebSearch              bool
	StructuredOutputs      bool
	StructuredOutputSchema json.RawMessage
}

type ChatResponse struct {
	ID                 string
	Text               string
	Model              string
	ToolCalls          []ToolCall
	PreviousResponseID string
	TokenUsage         TokenUsage
}

type LLMProvider interface {
	Chat(ctx context.Context, model string, request *ChatRequest) (ChatResponse, error)
	ValidateModel(ctx context.Context, model string) error
	CalculateCost(model string, inputTokens, outputTokens, cachedTokens int64) int64
	EstimateInputTokens(model string, messages []Message) (int, error)
	CheckContextWindow(model string, totalInputTokens int, compactionPercentage int) error
}

type Message struct {
	Role       MessageRole // Who sent the message
	Content    string      // The text or payload of the message
	Name       string      // Optional: tool name or function name
	ToolCallID string      // Optional: maps back to the provider's call identifier
	ToolCall   *ToolCall   // Optional: captures assistant-issued tool calls
}

type MessageRole string

type ReasoningEffort string

type TokenUsage struct {
	InputTokens           int64 `json:"input_tokens"`
	OutputTokens          int64 `json:"output_tokens"`
	CacheReadInputTokens  int64 `json:"cache_read_input_tokens,omitempty"`
	CacheWriteInputTokens int64 `json:"cache_write_input_tokens,omitempty"`
}

type ToolCall struct {
	Name          string          // tool name the model asked for
	CallID        string          // call id to echo back when submitting results
	Arguments     string          // raw JSON string the model returned
	JSONArguments json.RawMessage // same as arguments but handy for re-marshaling
}

type ToolDefinition struct {
	Name        string
	Description string
	JSONSchema  map[string]any `bun:"-"`
}