package toolrouter

import (
	"context"
	"fmt"
	"strings"
	"time"

	searchagent "aichat/agents/search"
	sqlquery "aichat/agents/sql"
	"aichat/llm"
	"aichat/message"
	"aichat/stream"
	"aichat/utils"

	"github.com/volcengine/volcengine-go-sdk/service/arkruntime/model"
)

const maxAgentToolIterations = 6

func RunAgentToolLoop(ctx context.Context, state *State, profile *llm.Profile, chatMessages []message.ChatMessage, searchState *searchagent.State, sqlState *sqlquery.State, emit stream.EmitFunc) ([]*model.ChatCompletionMessage, error) {
	finalMessages, err := message.BuildArkMessages(chatMessages)
	if err != nil {
		return nil, err
	}
	routerProfile := profile
	if state != nil {
		routerProfile = state.RouterProfile(profile)
	}
	tools := availableAgentTools(state, routerProfile, searchState, sqlState, emit)
	if len(tools) == 0 {
		return finalMessages, nil
	}
	decisionMessages := append([]*model.ChatCompletionMessage(nil), finalMessages...)
	if message.HasImageMessage(chatMessages) {
		decisionMessages, err = message.BuildToolDecisionMessages(chatMessages)
		if err != nil {
			return nil, err
		}
		if emit != nil {
			emit(stream.Frame{Type: "trace", Tool: "agent_tools", Stage: "prepare", Status: "success", Message: "检测到图片输入，工具判断阶段将使用纯文本上下文"})
		}
	}
	toolByName := make(map[string]AgentTool, len(tools))
	definitions := make([]*model.Tool, 0, len(tools))
	availableNames := make([]string, 0, len(tools))
	toolDescriptions := make([]string, 0, len(tools))
	for _, tool := range tools {
		toolByName[tool.name] = tool
		definitions = append(definitions, tool.definition)
		availableNames = append(availableNames, tool.name)
		if tool.definition != nil && tool.definition.Function != nil {
			toolDescriptions = append(toolDescriptions, fmt.Sprintf("%s: %s", tool.name, tool.definition.Function.Description))
		}
	}
	if emit != nil {
		emit(stream.Frame{Type: "trace", Tool: "agent_tools", Stage: "prepare", Status: "success", Message: "已准备可用工具", Data: map[string]any{"tools": availableNames, "tool_descriptions": toolDescriptions}})
	}
	if state == nil || state.cfg == nil {
		return finalMessages, nil
	}
	if prompt := strings.TrimSpace(state.cfg.SystemPrompt); prompt != "" {
		systemMessage := &model.ChatCompletionMessage{Role: model.ChatMessageRoleSystem, Content: message.StringContent(prompt)}
		finalMessages = append([]*model.ChatCompletionMessage{systemMessage}, finalMessages...)
		decisionMessages = append([]*model.ChatCompletionMessage{systemMessage}, decisionMessages...)
	}
	for i := 0; i < maxAgentToolIterations; i++ {
		if emit != nil {
			emit(stream.Frame{Type: "trace", Tool: "agent_tools", Stage: "request", Status: "running", Message: fmt.Sprintf("正在进行第 %d 轮工具判断", i+1), Data: map[string]any{"iteration": i + 1, "max_iterations": maxAgentToolIterations, "tools": availableNames}})
		}
		resp, err := state.complete(ctx, routerProfile, model.CreateChatCompletionRequest{
			Model:             routerProfile.Config.Model,
			Messages:          decisionMessages,
			MaxTokens:         utils.IntPtr(state.cfg.MaxTokens),
			Tools:             definitions,
			ToolChoice:        model.ToolChoiceStringTypeAuto,
			ParallelToolCalls: utils.BoolPtr(false),
		}, time.Duration(state.cfg.Timeout)*time.Second)
		if err != nil {
			return finalMessages, err
		}
		if tracker := stream.TrackerFromContext(ctx); tracker != nil {
			tracker.AddTool(resp.Usage.PromptTokens, resp.Usage.CompletionTokens)
		}
		if len(resp.Choices) == 0 {
			return finalMessages, nil
		}
		choice := resp.Choices[0]
		decisionPreview := message.ChatMessageContentString(choice.Message.Content)
		if emit != nil {
			emit(stream.Frame{Type: "trace", Tool: "agent_tools", Stage: "decision", Status: "success", Message: "工具判断响应已返回", Data: map[string]any{"iteration": i + 1, "finish_reason": string(choice.FinishReason), "content_preview": utils.TruncateString(decisionPreview, 800)}})
		}
		calls := choice.Message.ToolCalls
		if len(calls) == 0 && choice.Message.FunctionCall != nil {
			calls = []*model.ToolCall{{ID: "legacy_function_call", Type: model.ToolTypeFunction, Function: *choice.Message.FunctionCall}}
		}
		if len(calls) == 0 {
			if emit != nil {
				emit(stream.Frame{Type: "trace", Tool: "agent_tools", Stage: "request", Status: "success", Message: "模型未请求工具，进入回答生成"})
			}
			return finalMessages, nil
		}
		callNames := make([]string, 0, len(calls))
		for _, call := range calls {
			if call != nil {
				callNames = append(callNames, call.Function.Name)
			}
		}
		if emit != nil {
			emit(stream.Frame{Type: "trace", Tool: "agent_tools", Stage: "tool_calls", Status: "running", Message: fmt.Sprintf("模型请求调用 %d 个工具", len(calls)), Data: map[string]any{"tools": callNames, "iteration": i + 1}})
		}
		assistantMessage := &model.ChatCompletionMessage{Role: model.ChatMessageRoleAssistant, ToolCalls: calls, Content: choice.Message.Content}
		finalMessages = append(finalMessages, assistantMessage)
		decisionMessages = append(decisionMessages, assistantMessage)
		for _, call := range calls {
			result := ExecuteAgentToolCall(ctx, call, toolByName, emit)
			toolMessage := &model.ChatCompletionMessage{Role: model.ChatMessageRoleTool, ToolCallID: call.ID, Content: message.StringContent(result)}
			finalMessages = append(finalMessages, toolMessage)
			decisionMessages = append(decisionMessages, toolMessage)
		}
	}
	limitMessage := &model.ChatCompletionMessage{Role: model.ChatMessageRoleSystem, Content: message.StringContent("工具调用轮数已达到上限。请基于已有工具结果回答，并说明可能未完成全部工具调用。")}
	finalMessages = append(finalMessages, limitMessage)
	return finalMessages, nil
}