package memory

import (
	"context"
	"encoding/json"
	"fmt"
	"strings"
	"time"

	"git.yeij.top/AskaEth/Cyrene_v2/services/ai-core/model"
)

// LLMClient is the interface for calling an LLM.
type LLMClient interface {
	Chat(ctx context.Context, messages []model.LLMMessage) (*model.LLMResponse, error)
}

// Extractor uses LLM to extract memories from conversations.
type Extractor struct {
	svc     *Service
	llm     LLMClient
	enabled bool
}

// NewExtractor creates a memory extractor.
func NewExtractor(svc *Service, llm LLMClient) *Extractor {
	return &Extractor{svc: svc, llm: llm, enabled: llm != nil}
}

// ExtractedMemory represents a single memory extracted by the LLM.
type ExtractedMemory struct {
	Content  string  `json:"content"`
	Category string  `json:"category"`
	Priority float64 `json:"priority"`
}

// ExtractFromConversation analyzes a conversation and extracts memories asynchronously.
// Does not block — runs in a goroutine.
func (e *Extractor) ExtractFromConversation(ctx context.Context, userID string, conversation []model.Message) {
	if !e.enabled || e.llm == nil || len(conversation) == 0 {
		return
	}

	go func() {
		bgCtx, cancel := context.WithTimeout(context.Background(), 30*time.Second)
		defer cancel()

		memories, err := e.extract(bgCtx, conversation)
		if err != nil {
			return // Silent fail — don't block main flow
		}

		for _, m := range memories {
			if m.Content == "" { continue }
			if m.Priority <= 0 { m.Priority = 0.7 }
			if m.Category == "" { m.Category = CatFact }
			e.svc.Add(bgCtx, userID, "", m.Content, m.Category, m.Priority)
		}
	}()
}

func (e *Extractor) extract(ctx context.Context, messages []model.Message) ([]ExtractedMemory, error) {
	// Build conversation text
	var convText strings.Builder
	for _, msg := range messages {
		role := "用户"
		if msg.Role == "assistant" || msg.Role == "action" {
			role = "AI"
		}
		convText.WriteString(fmt.Sprintf("%s: %s\n", role, msg.Content))
	}

	prompt := []model.LLMMessage{
		{Role: "system", Content: `你是一个记忆提取助手。从对话中提取关于用户的重要信息。
每条记忆用一行 JSON 表示，格式：{"content":"记忆内容","category":"category","priority":0.8}

类别可选: fact(事实), preference(偏好), event(事件), plan(计划)
优先级 0-1，重要信息用高优先级。
只提取新信息，不重复已有内容。
只输出 JSON 行，不要其他文字。`},
		{Role: "user", Content: fmt.Sprintf("从以下对话中提取记忆：\n%s", convText.String())},
	}

	resp, err := e.llm.Chat(ctx, prompt)
	if err != nil {
		return nil, fmt.Errorf("llm extract: %w", err)
	}

	return parseExtractedMemories(resp.Content), nil
}

func parseExtractedMemories(text string) []ExtractedMemory {
	var memories []ExtractedMemory
	lines := strings.Split(text, "\n")
	for _, line := range lines {
		line = strings.TrimSpace(line)
		if line == "" || !strings.HasPrefix(line, "{") {
			continue
		}
		var m ExtractedMemory
		if err := json.Unmarshal([]byte(line), &m); err != nil {
			// Try to extract just the JSON part
			start := strings.Index(line, "{")
			end := strings.LastIndex(line, "}")
			if start >= 0 && end > start {
				if err := json.Unmarshal([]byte(line[start:end+1]), &m); err == nil {
					if m.Content != "" { memories = append(memories, m) }
				}
			}
			continue
		}
		if m.Content != "" {
			memories = append(memories, m)
		}
	}
	return memories
}