"""
Agent 工具管理器：包装已有 ToolRegistry，提供 Agent 需要的工具格式转换和执行。
"""
from __future__ import annotations

import hashlib
import json
import logging
from typing import Any, Dict, List, Optional

from app.services.tool_registry import tool_registry

logger = logging.getLogger(__name__)

# 默认确定性工具（结果可缓存）
_DETERMINISTIC_TOOLS = {
    "file_read", "math_calculate", "database_query",
    "json", "text", "csv", "excel", "pdf", "image",
}


class AgentToolManager:
    """
    为 Agent Runtime 管理工具：
    - 将 ToolRegistry 的工具 schema 转为 OpenAI Function Calling 格式
    - 按 Agent 配置过滤（白名单/黑名单）
    - 执行工具调用并返回结果字符串
    - 工具结果缓存（Redis / 内存 fallback）
    """

    def __init__(self, include_tools: Optional[List[str]] = None,
                 exclude_tools: Optional[List[str]] = None,
                 cache_enabled: bool = True,
                 cache_tool_whitelist: Optional[List[str]] = None,
                 cache_ttl_ms: int = 3600000):
        self._include_tools: set = set(include_tools or [])
        self._exclude_tools: set = set(exclude_tools or [])
        self._cache_enabled = cache_enabled
        self._cache_whitelist: set = set(cache_tool_whitelist or [])
        self._cache_ttl_s = max(1, int(cache_ttl_ms / 1000))
        self._cache_store: Dict[str, str] = {}  # 内存 fallback

    def _is_cacheable(self, tool_name: str) -> bool:
        """判断工具结果是否可缓存。"""
        if not self._cache_enabled:
            return False
        if self._cache_whitelist:
            return tool_name in self._cache_whitelist
        return tool_name in _DETERMINISTIC_TOOLS

    @staticmethod
    def _cache_key(name: str, args: Dict[str, Any]) -> str:
        raw = json.dumps([name, args], sort_keys=True, ensure_ascii=False)
        return f"tool:{name}:{hashlib.sha256(raw.encode()).hexdigest()[:16]}"

    async def _cache_get(self, key: str) -> Optional[str]:
        try:
            from app.core.redis_client import get_redis_client
            redis = get_redis_client()
            if redis:
                return await redis.get(key)
        except Exception:
            pass
        return self._cache_store.get(key)

    async def _cache_set(self, key: str, value: str):
        try:
            from app.core.redis_client import get_redis_client
            redis = get_redis_client()
            if redis:
                await redis.setex(key, self._cache_ttl_s, value)
                return
        except Exception:
            pass
        self._cache_store[key] = value

    def get_tool_schemas(self) -> List[Dict[str, Any]]:
        """获取 Agent 可用的工具定义列表（OpenAI Function Calling 格式）。"""
        all_schemas = tool_registry.get_all_tool_schemas()
        if not self._include_tools and not self._exclude_tools:
            return all_schemas

        filtered = []
        for schema in all_schemas:
            name = self._extract_tool_name(schema)
            if not name:
                continue
            if self._include_tools and name not in self._include_tools:
                continue
            if name in self._exclude_tools:
                continue
            filtered.append(schema)
        return filtered

    def has_tools(self) -> bool:
        """是否有可用工具。"""
        return len(self.get_tool_schemas()) > 0

    def tool_names(self) -> List[str]:
        """可用工具名称列表。"""
        return [
            self._extract_tool_name(s) or "?"
            for s in self.get_tool_schemas()
        ]

    async def execute(self, name: str, args: Dict[str, Any]) -> str:
        """
        执行工具调用（带缓存）。

        优先查找内置工具，其次查找数据库自定义工具（HTTP / Code）。

        Args:
            name: 工具名称
            args: 工具参数字典

        Returns:
            工具执行结果的字符串表示
        """
        # 缓存检查
        if self._is_cacheable(name):
            ck = self._cache_key(name, args)
            cached = await self._cache_get(ck)
            if cached is not None:
                logger.info("Agent 工具命中缓存: %s", name)
                return cached

        logger.info("Agent 执行工具: %s", name)
        result = await tool_registry.execute_tool(name, args)

        # 缓存写入
        if self._is_cacheable(name):
            ck = self._cache_key(name, args)
            await self._cache_set(ck, result)
            logger.debug("Agent 工具结果已缓存: %s", name)

        return result

    @staticmethod
    def _extract_tool_name(schema: Dict[str, Any]) -> Optional[str]:
        """从工具 schema 中提取工具名称。"""
        fn = schema.get("function") or schema
        return fn.get("name") if isinstance(fn, dict) else None