"""
Research Agent — 深度调研 Agent，部署在 KnowHub 服务器端。

通过 HTTP API 被 FastAPI server 调用，每次请求是一次 AgentRunner.run()。
续跑由 caller 显式传入 continue_from 指定。
"""

import logging
import sys
from pathlib import Path
from typing import Dict, Any, Optional, List

# 确保项目路径可用
sys.path.insert(0, str(Path(__file__).parent.parent.parent))

from agent.core.runner import AgentRunner, RunConfig
from agent.trace import FileSystemTraceStore
from agent.llm import create_qwen_llm_call
from agent.llm.prompts import SimplePrompt
from agent.tools.builtin.knowledge import KnowledgeConfig

logger = logging.getLogger("agents.research")

# Research 目前不支持调用方注入 skill（skills 走 config.skills 服务器端固定）
ALLOWED_SKILLS: List[str] = []


# ===== 单例 Runner =====

_runner: Optional[AgentRunner] = None
_prompt_messages = None
_initialized = False


def _ensure_initialized():
    """延迟初始化 Runner 和 Prompt（首次调用时执行）"""
    global _runner, _prompt_messages, _initialized
    if _initialized:
        return
    _initialized = True

    skills_dir = Path(__file__).parent / "skills"

    _runner = AgentRunner(
        trace_store=FileSystemTraceStore(base_path=".trace"),
        llm_call=create_qwen_llm_call(model="qwen3.5-plus"),
        skills_dir=str(skills_dir) if skills_dir.exists() else None,
        debug=True,
        logger_name="agents.research",
    )

    prompt_path = Path(__file__).parent / "research_agent.prompt"
    if prompt_path.exists():
        prompt = SimplePrompt(prompt_path)
        _prompt_messages = prompt.build_messages()

        if getattr(prompt, "meta", None) and prompt.meta.get("model"):
            model_name = prompt.meta["model"]
            _runner.llm_call = create_qwen_llm_call(model=model_name)
    else:
        _prompt_messages = []
        logger.warning(f"Research prompt 文件不存在: {prompt_path}")

    logger.info("✓ Research Agent 已初始化")


# ===== 核心方法 =====

async def research(
    query: str,
    continue_from: Optional[str] = None,
    skills: Optional[List[str]] = None,
) -> Dict[str, Any]:
    """
    同步执行深度调研。运行 Research Agent，返回标准 agent 结果。

    Args:
        query: 用户设定的研究主题或查询
        continue_from: 已有 sub_trace_id，传入则续跑该 trace
        skills: 保留参数（Research 当前无白名单 skill）；传入会被忽略

    Returns:
        {"status", "sub_trace_id", "summary", "stats", "error"?}
    """
    if skills:
        logger.warning(f"[Research] 忽略 skills 参数（Research 不接受动态 skill 注入）")
    _ensure_initialized()

    # 初始化云端无头浏览器（线上部署必须用云浏览器）
    try:
        from agent.tools.builtin.browser import init_browser_session
        await init_browser_session(browser_type="cloud")
    except Exception as e:
        logger.warning(f"Failed to init cloud browser: {e}")

    config = RunConfig(
        model="qwen3.5-plus",
        temperature=0.3,
        max_iterations=200,
        tool_groups=["core", "content", "browser"],
        exclude_tools=["agent", "evaluate"],  # 远端 Agent 禁止递归派生子 Agent
        skills=["planning", "research", "browser"],
        # 远端 Agent 关闭自动知识操作（否则 injection 会回调 remote_librarian 形成递归）
        knowledge=KnowledgeConfig(
            enable_extraction=False,
            enable_completion_extraction=False,
            enable_injection=False,
        ),
    )
    config.trace_id = continue_from  # None = 新建；有值 = 续跑

    content = f"[RESEARCH TASK] {query}"
    if continue_from is None:
        messages = _prompt_messages + [{"role": "user", "content": content}]
    else:
        messages = [{"role": "user", "content": content}]

    result = await _runner.run_result(messages=messages, config=config)

    return {
        "status": result.get("status", "unknown"),
        "sub_trace_id": result.get("trace_id"),
        "summary": result.get("summary", ""),
        "stats": result.get("stats", {}),
        "error": result.get("error"),
    }