import json
import logging
import os
import sqlite3
from typing import Any, Dict, Optional, List
from langchain.chat_models import init_chat_model
# from deepagents import create_deep_agent
from langchain.agents import create_agent
from langchain.agents.middleware import SummarizationMiddleware
from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.checkpoint.memory import MemorySaver
from utils.fastapi_utils import detect_provider

from .guideline_middleware import GuidelineMiddleware
from .tool_output_length_middleware import ToolOutputLengthMiddleware
from utils.settings import SUMMARIZATION_MAX_TOKENS, TOOL_OUTPUT_MAX_LENGTH
from agent.agent_config import AgentConfig


# Utility functions
def read_system_prompt():
    """读取通用的无状态系统prompt"""
    with open("./prompt/system_prompt_default.md", "r", encoding="utf-8") as f:
        return f.read().strip()


def read_mcp_settings():
    """读取MCP工具配置"""
    with open("./mcp/mcp_settings.json", "r") as f:
        mcp_settings_json = json.load(f)
    return mcp_settings_json


async def get_tools_from_mcp(mcp):
    """从MCP配置中提取工具"""
    # 防御式处理：确保 mcp 是列表且长度大于 0，且包含 mcpServers
    if not isinstance(mcp, list) or len(mcp) == 0 or "mcpServers" not in mcp[0]:
        return []

    # 修改 mcp[0]["mcpServers"] 列表，把 type 字段改成 transport
    # 如果没有 transport，则根据是否存在 url 默认 transport 为 http 或 stdio
    for cfg in mcp[0]["mcpServers"].values():
        if "type" in cfg:
            cfg.pop("type")
        if "transport" not in cfg:
            cfg["transport"] = "http" if "url" in cfg else "stdio"

    # 确保 mcp[0]["mcpServers"] 是字典类型
    if not isinstance(mcp[0]["mcpServers"], dict):
        return []

    try:
        mcp_client = MultiServerMCPClient(mcp[0]["mcpServers"])
        mcp_tools = await mcp_client.get_tools()
        return mcp_tools
    except Exception:
        # 发生异常时返回空列表，避免上层调用报错
        return []


async def init_agent(config: AgentConfig):
    """
    初始化 Agent，支持持久化内存和对话摘要

    Args:
        config: AgentConfig 对象，包含所有初始化参数
        mcp: MCP配置（如果为None则使用配置中的mcp_settings）
    """
    # 如果没有提供mcp，使用config中的mcp_settings
    mcp_settings = config.mcp_settings if config.mcp_settings else read_mcp_settings()
    system_prompt = config.system_prompt if config.system_prompt else read_system_prompt()
    mcp_tools = await get_tools_from_mcp(mcp_settings)

    # 检测或使用指定的提供商
    model_provider,base_url = detect_provider(config.model_name, config.model_server)

    # 构建模型参数
    model_kwargs = {
        "model": config.model_name,
        "model_provider": model_provider,
        "temperature": 0.8,
        "base_url": base_url,
        "api_key": config.api_key
    }
    if config.generate_cfg:
        model_kwargs.update(config.generate_cfg)
    llm_instance = init_chat_model(**model_kwargs)

    # 构建中间件列表
    middleware = []
    # 只有在 enable_thinking 为 True 时才添加 GuidelineMiddleware
    if config.enable_thinking:
        middleware.append(GuidelineMiddleware(llm_instance, config, system_prompt))

    # 添加工具输出长度控制中间件
    tool_output_middleware = ToolOutputLengthMiddleware(
        max_length=getattr(config.generate_cfg, 'tool_output_max_length', None) if config.generate_cfg else None or TOOL_OUTPUT_MAX_LENGTH,
        truncation_strategy=getattr(config.generate_cfg, 'tool_output_truncation_strategy', 'smart') if config.generate_cfg else 'smart',
        tool_filters=getattr(config.generate_cfg, 'tool_output_filters', None) if config.generate_cfg else None,  # 可配置特定工具
        exclude_tools=getattr(config.generate_cfg, 'tool_output_exclude', []) if config.generate_cfg else [],  # 排除的工具
        preserve_code_blocks=getattr(config.generate_cfg, 'preserve_code_blocks', True) if config.generate_cfg else True,
        preserve_json=getattr(config.generate_cfg, 'preserve_json', True) if config.generate_cfg else True
    )
    middleware.append(tool_output_middleware)

    # 初始化 checkpointer 和中间件
    checkpointer = None

    if config.session_id:
        checkpointer = MemorySaver()
        summarization_middleware = SummarizationMiddleware(
            model=llm_instance,
            max_tokens_before_summary=SUMMARIZATION_MAX_TOKENS,
            messages_to_keep=20,  # 摘要后保留最近 20 条消息
            summary_prompt="请简洁地总结以上对话的要点，包括重要的用户信息、讨论过的话题和关键结论。"
        )
        middleware.append(summarization_middleware)

    agent = create_agent(
        model=llm_instance,
        system_prompt=system_prompt,
        tools=mcp_tools,
        middleware=middleware,
        checkpointer=checkpointer  # 传入 checkpointer 以启用持久化
    )

    return agent