from typing import List, Dict, Any, Optional
from langchain_core.tools import tool
from app.core.memory.memory_manager import get_memory_manager
from app.core.review.review_manager import get_review_manager
from app.models.project import SeriesProject, Episode, Memory
from app.models.review import ReviewConfig

# ============================================================================
# Memory Tools - 赋予 Agent 记忆能力
# ============================================================================

@tool
async def query_project_memory(project_id: str, query: str) -> str:
    """
    查询项目记忆库。
    当你需要了解过去发生的事件、角色当前状态、未解决的伏笔或任何背景信息时使用此工具。
    
    Args:
        project_id: 项目 ID
        query: 查询内容，例如 "主角现在的心理状态" 或 "第三集的关键转折"
        
    Returns:
        相关的记忆信息摘要
    """
    memory_manager = get_memory_manager()
    # 注意：这里假设 MemoryManager 需要实现一个 semantic_search 或类似的查询方法
    # 目前可以用简单的规则匹配或 LLM 总结来模拟
    # 示例实现：
    # results = await memory_manager.search(project_id, query)
    # return format_results(results)
    return f"Memory query results for '{query}' (Not implemented yet)"

@tool
async def update_episode_memory(project_id: str, episode_number: int, content: str) -> Dict[str, Any]:
    """
    更新剧集记忆。
    在一集创作完成后调用此工具，它会自动分析内容，提取关键事件、伏笔、角色状态变化，
    并更新到项目的长期记忆库中。
    
    Args:
        project_id: 项目 ID
        episode_number: 集数
        content: 剧集完整内容
        
    Returns:
        提取结果摘要（包含提取的事件数、伏笔数等）
    """
    memory_manager = get_memory_manager()
    
    # 需要获取 Project 对象，这里简化处理，实际需要 ProjectService
    # project = await project_service.get_project(project_id)
    
    # 模拟 Episode 对象
    episode = Episode(
        projectId=project_id,
        number=episode_number,
        content=content,
        status="completed"
    )
    
    # result = await memory_manager.update_memory_from_episode(project, episode)
    # return result.dict()
    return {"status": "success", "message": "Memory updated (Simulation)"}

# ============================================================================
# Review Tools - 赋予 Agent 自我审查能力
# ============================================================================

@tool
async def review_content_consistency(project_id: str, episode_number: int, content: str) -> Dict[str, Any]:
    """
    审查内容一致性。
    在完成创作后调用，检查内容是否与设定、历史剧情、人物性格一致。
    
    Args:
        project_id: 项目 ID
        episode_number: 集数
        content: 待审查的内容
        
    Returns:
        审查结果，包含分数和发现的问题列表
    """
    review_manager = get_review_manager()
    
    # 同样需要获取 Project 和 Config
    # config = await review_service.get_config(project_id)
    
    # 模拟调用
    # result = await review_manager.review_episode(project, episode, config, dimensions=[DimensionType.consistency])
    
    return {
        "score": 85,
        "passed": True,
        "issues": [
            {"severity": "low", "description": "Simulation: Character tone slight mismatch"}
        ]
    }

# ============================================================================
# Context Tools - 赋予 Agent 设定管理能力
# ============================================================================

@tool
async def get_world_setting(project_id: str) -> str:
    """获取项目的世界观设定"""
    # return project.globalContext.worldSetting
    return "Cyberpunk future city (Simulation)"

@tool
async def get_character_profile(project_id: str, character_name: str) -> str:
    """获取特定角色的详细设定"""
    # return project.globalContext.characterProfiles.get(character_name)
    return f"Profile for {character_name} (Simulation)"
